Global-Female-Employment-Analysis-2015_to_2019.ipynb

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        "<a href=\"https://colab.research.google.com/github/Karincheong/August-2020-WaiLEARN-Female-Employment-Analysis/blob/main/Global-Female-Employment-Analysis-2015_to_2019.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Allowing intellisence in pandas\n",
        "%config IPCompleter.greedy=True\n",
        "\n",
        "# Import the relevant libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "# !pip install seaborn\n",
        "import seaborn as sns\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CWKipTr0VBe"
      },
      "source": [
        "# **Reading the Females in Industries Dataset**\n",
        "This dataset is our principal dataset downloaded from gapminder at https://www.gapminder.org/data/ . It can be identified as female industry workers(% of female employment)\n"
      ]
    },
    {
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      },
      "source": [
        "dataset=pd.read_csv(r\"female_industry_workers.csv\")\n",
        "dataset.head(5)"
      ],
      "execution_count": null,
      "outputs": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0.0676</td>\n",
              "      <td>0.0429</td>\n",
              "      <td>0.0425</td>\n",
              "      <td>0.0400</td>\n",
              "      <td>0.0296</td>\n",
              "      <td>0.0351</td>\n",
              "      <td>0.0397</td>\n",
              "      <td>0.0457</td>\n",
              "      <td>0.0541</td>\n",
              "      <td>0.0663</td>\n",
              "      <td>0.0790</td>\n",
              "      <td>0.0626</td>\n",
              "      <td>0.0614</td>\n",
              "      <td>0.0723</td>\n",
              "      <td>0.0676</td>\n",
              "      <td>0.0684</td>\n",
              "      <td>0.0738</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>0.0555</td>\n",
              "      <td>0.0553</td>\n",
              "      <td>0.0598</td>\n",
              "      <td>0.0553</td>\n",
              "      <td>0.0544</td>\n",
              "      <td>0.0549</td>\n",
              "      <td>0.0566</td>\n",
              "      <td>0.0556</td>\n",
              "      <td>0.0552</td>\n",
              "      <td>0.0549</td>\n",
              "      <td>0.0546</td>\n",
              "      <td>0.0541</td>\n",
              "      <td>0.0535</td>\n",
              "      <td>0.0529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>0.1030</td>\n",
              "      <td>0.0970</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.1190</td>\n",
              "      <td>0.1040</td>\n",
              "      <td>0.0993</td>\n",
              "      <td>0.1040</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.1070</td>\n",
              "      <td>0.1150</td>\n",
              "      <td>0.1180</td>\n",
              "      <td>0.1220</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.1090</td>\n",
              "      <td>0.1090</td>\n",
              "      <td>0.1030</td>\n",
              "      <td>0.1090</td>\n",
              "      <td>0.1140</td>\n",
              "      <td>0.0874</td>\n",
              "      <td>0.1020</td>\n",
              "      <td>0.1170</td>\n",
              "      <td>0.1390</td>\n",
              "      <td>0.1370</td>\n",
              "      <td>0.1380</td>\n",
              "      <td>0.1380</td>\n",
              "      <td>0.1370</td>\n",
              "      <td>0.1370</td>\n",
              "      <td>0.1360</td>\n",
              "      <td>0.1350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>0.3510</td>\n",
              "      <td>0.3550</td>\n",
              "      <td>0.3630</td>\n",
              "      <td>0.3690</td>\n",
              "      <td>0.3610</td>\n",
              "      <td>0.3300</td>\n",
              "      <td>0.3230</td>\n",
              "      <td>0.3340</td>\n",
              "      <td>0.3250</td>\n",
              "      <td>0.3100</td>\n",
              "      <td>0.3130</td>\n",
              "      <td>0.3010</td>\n",
              "      <td>0.2720</td>\n",
              "      <td>0.3100</td>\n",
              "      <td>0.3600</td>\n",
              "      <td>0.3900</td>\n",
              "      <td>0.4250</td>\n",
              "      <td>0.4530</td>\n",
              "      <td>0.4930</td>\n",
              "      <td>0.5030</td>\n",
              "      <td>0.5020</td>\n",
              "      <td>0.5170</td>\n",
              "      <td>0.5460</td>\n",
              "      <td>0.5550</td>\n",
              "      <td>0.5580</td>\n",
              "      <td>0.5540</td>\n",
              "      <td>0.5580</td>\n",
              "      <td>0.5570</td>\n",
              "      <td>0.5570</td>\n",
              "      <td>0.5590</td>\n",
              "      <td>0.5610</td>\n",
              "      <td>0.5610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>0.0168</td>\n",
              "      <td>0.0152</td>\n",
              "      <td>0.0181</td>\n",
              "      <td>0.0184</td>\n",
              "      <td>0.0179</td>\n",
              "      <td>0.0192</td>\n",
              "      <td>0.0184</td>\n",
              "      <td>0.0199</td>\n",
              "      <td>0.0184</td>\n",
              "      <td>0.0171</td>\n",
              "      <td>0.0166</td>\n",
              "      <td>0.0181</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>0.0179</td>\n",
              "      <td>0.0155</td>\n",
              "      <td>0.0152</td>\n",
              "      <td>0.0139</td>\n",
              "      <td>0.0129</td>\n",
              "      <td>0.0145</td>\n",
              "      <td>0.0123</td>\n",
              "      <td>0.0112</td>\n",
              "      <td>0.0121</td>\n",
              "      <td>0.0125</td>\n",
              "      <td>0.0130</td>\n",
              "      <td>0.0125</td>\n",
              "      <td>0.0127</td>\n",
              "      <td>0.0127</td>\n",
              "      <td>0.0129</td>\n",
              "      <td>0.0130</td>\n",
              "      <td>0.0131</td>\n",
              "      <td>0.0133</td>\n",
              "      <td>0.0134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.1880</td>\n",
              "      <td>0.1610</td>\n",
              "      <td>0.1440</td>\n",
              "      <td>0.1490</td>\n",
              "      <td>0.1230</td>\n",
              "      <td>0.1150</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.0912</td>\n",
              "      <td>0.1110</td>\n",
              "      <td>0.1080</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.1040</td>\n",
              "      <td>0.1020</td>\n",
              "      <td>0.0962</td>\n",
              "      <td>0.0931</td>\n",
              "      <td>0.0995</td>\n",
              "      <td>0.0943</td>\n",
              "      <td>0.0888</td>\n",
              "      <td>0.0832</td>\n",
              "      <td>0.0808</td>\n",
              "      <td>0.0802</td>\n",
              "      <td>0.0782</td>\n",
              "      <td>0.0766</td>\n",
              "      <td>0.0750</td>\n",
              "      <td>0.0735</td>\n",
              "      <td>0.0723</td>\n",
              "      <td>0.0713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       country    1991    1992    1993  ...    2019    2020    2021    2022\n",
              "0  Afghanistan  0.0676  0.0429  0.0425  ...  0.0546  0.0541  0.0535  0.0529\n",
              "1      Albania  0.1210  0.1030  0.0970  ...  0.1370  0.1370  0.1360  0.1350\n",
              "2      Algeria  0.3510  0.3550  0.3630  ...  0.5570  0.5590  0.5610  0.5610\n",
              "3       Angola  0.0168  0.0152  0.0181  ...  0.0130  0.0131  0.0133  0.0134\n",
              "4    Argentina  0.1980  0.1880  0.1610  ...  0.0750  0.0735  0.0723  0.0713\n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset.shape"
      ],
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        {
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          "data": {
            "text/plain": [
              "(179, 33)"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsBzfYcZot3n"
      },
      "source": [
        "**Converting the Decimal Values to Percentage values multiplying by 100**\n",
        "\n",
        "This section also finds the maximum and minimum values of each year\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5teRself4d0"
      },
      "source": [
        "for i in range (1991,2023, 1):\n",
        " dataset[str(i)] = dataset[str(i)] * 100;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv61wVdQxI_Y",
        "outputId": "0a0917fb-1c6a-4d70-f932-c7204a1630f1",
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          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>6.76</td>\n",
              "      <td>4.29</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2.96</td>\n",
              "      <td>3.51</td>\n",
              "      <td>3.97</td>\n",
              "      <td>4.57</td>\n",
              "      <td>5.41</td>\n",
              "      <td>6.63</td>\n",
              "      <td>7.90</td>\n",
              "      <td>6.26</td>\n",
              "      <td>6.14</td>\n",
              "      <td>7.23</td>\n",
              "      <td>6.76</td>\n",
              "      <td>6.84</td>\n",
              "      <td>7.38</td>\n",
              "      <td>7.49</td>\n",
              "      <td>5.55</td>\n",
              "      <td>5.53</td>\n",
              "      <td>5.98</td>\n",
              "      <td>5.53</td>\n",
              "      <td>5.44</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.56</td>\n",
              "      <td>5.52</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.46</td>\n",
              "      <td>5.41</td>\n",
              "      <td>5.35</td>\n",
              "      <td>5.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>12.10</td>\n",
              "      <td>10.30</td>\n",
              "      <td>9.70</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.60</td>\n",
              "      <td>11.90</td>\n",
              "      <td>10.40</td>\n",
              "      <td>9.93</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.70</td>\n",
              "      <td>11.50</td>\n",
              "      <td>11.80</td>\n",
              "      <td>12.20</td>\n",
              "      <td>12.50</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.30</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.40</td>\n",
              "      <td>8.74</td>\n",
              "      <td>10.20</td>\n",
              "      <td>11.70</td>\n",
              "      <td>13.90</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.60</td>\n",
              "      <td>13.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>35.10</td>\n",
              "      <td>35.50</td>\n",
              "      <td>36.30</td>\n",
              "      <td>36.90</td>\n",
              "      <td>36.10</td>\n",
              "      <td>33.00</td>\n",
              "      <td>32.30</td>\n",
              "      <td>33.40</td>\n",
              "      <td>32.50</td>\n",
              "      <td>31.00</td>\n",
              "      <td>31.30</td>\n",
              "      <td>30.10</td>\n",
              "      <td>27.20</td>\n",
              "      <td>31.00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>42.50</td>\n",
              "      <td>45.30</td>\n",
              "      <td>49.30</td>\n",
              "      <td>50.30</td>\n",
              "      <td>50.20</td>\n",
              "      <td>51.70</td>\n",
              "      <td>54.60</td>\n",
              "      <td>55.50</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.40</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.90</td>\n",
              "      <td>56.10</td>\n",
              "      <td>56.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.68</td>\n",
              "      <td>1.52</td>\n",
              "      <td>1.81</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.99</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.71</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.81</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.55</td>\n",
              "      <td>1.52</td>\n",
              "      <td>1.39</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.45</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.31</td>\n",
              "      <td>1.33</td>\n",
              "      <td>1.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>19.80</td>\n",
              "      <td>18.80</td>\n",
              "      <td>16.10</td>\n",
              "      <td>14.40</td>\n",
              "      <td>14.90</td>\n",
              "      <td>12.30</td>\n",
              "      <td>11.50</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.12</td>\n",
              "      <td>11.10</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.20</td>\n",
              "      <td>9.62</td>\n",
              "      <td>9.31</td>\n",
              "      <td>9.95</td>\n",
              "      <td>9.43</td>\n",
              "      <td>8.88</td>\n",
              "      <td>8.32</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.66</td>\n",
              "      <td>7.50</td>\n",
              "      <td>7.35</td>\n",
              "      <td>7.23</td>\n",
              "      <td>7.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   1991   1992   1993   1994  ...   2018   2019   2020   2021   2022\n",
              "0  Afghanistan   6.76   4.29   4.25   4.00  ...   5.49   5.46   5.41   5.35   5.29\n",
              "1      Albania  12.10  10.30   9.70  10.10  ...  13.80  13.70  13.70  13.60  13.50\n",
              "2      Algeria  35.10  35.50  36.30  36.90  ...  55.70  55.70  55.90  56.10  56.10\n",
              "3       Angola   1.68   1.52   1.81   1.84  ...   1.29   1.30   1.31   1.33   1.34\n",
              "4    Argentina  19.80  18.80  16.10  14.40  ...   7.66   7.50   7.35   7.23   7.13\n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Dc9Q5_HL73r"
      },
      "source": [
        "# **What are the top 10 countries globally with the highest percentage of females in Industries?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1g_EuftLu1F"
      },
      "source": [
        "**Taking a section of the dataset from 2015 to 2019 for the analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gkc9Jz9fJCBU"
      },
      "source": [
        "#picking a subset of the dataset from 2015 to 2019\n",
        "sectioned_dataset2 = dataset[[\"country\",\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPqboUfcJCBt"
      },
      "source": [
        "#finding the median of all the dataset in the percentage of females"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdntH9G3JCB5",
        "outputId": "05ee11e8-8fbe-4d69-dcbf-08d5fa935f44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "sectioned_dataset2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.56</td>\n",
              "      <td>5.52</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.90</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.40</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.66</td>\n",
              "      <td>7.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>12.60</td>\n",
              "      <td>12.60</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>19.30</td>\n",
              "      <td>20.80</td>\n",
              "      <td>21.00</td>\n",
              "      <td>21.20</td>\n",
              "      <td>21.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Yemen</td>\n",
              "      <td>14.60</td>\n",
              "      <td>15.70</td>\n",
              "      <td>12.70</td>\n",
              "      <td>11.30</td>\n",
              "      <td>13.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>3.96</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.12</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2.19</td>\n",
              "      <td>2.27</td>\n",
              "      <td>2.37</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>179 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         country   2015   2016   2017   2018   2019\n",
              "0    Afghanistan   5.66   5.56   5.52   5.49   5.46\n",
              "1        Albania  13.90  13.70  13.80  13.80  13.70\n",
              "2        Algeria  55.80  55.40  55.80  55.70  55.70\n",
              "3         Angola   1.25   1.27   1.27   1.29   1.30\n",
              "4      Argentina   8.08   8.02   7.82   7.66   7.50\n",
              "..           ...    ...    ...    ...    ...    ...\n",
              "174    Venezuela  12.60  12.60  13.40  13.70  13.80\n",
              "175      Vietnam  19.30  20.80  21.00  21.20  21.10\n",
              "176        Yemen  14.60  15.70  12.70  11.30  13.60\n",
              "177       Zambia   3.96   4.09   4.12   4.14   4.10\n",
              "178     Zimbabwe   2.19   2.27   2.37   2.36   2.33\n",
              "\n",
              "[179 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXuDXEZ5QG2k"
      },
      "source": [
        "**Distribution of Data for the the sectioned years 2015 to 2019**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uK0RG6bJCCN",
        "outputId": "23b3ffc7-7004-45d6-dd15-5615821c3991",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        }
      },
      "source": [
        "num_cols = ['2015','2016', '2017', '2018', '2019']\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "sectioned_dataset2 [num_cols].boxplot()\n",
        "plt.title(\"Distribution of data over the period from 2015 to 2019\", fontsize=20)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Quz8r23LI6qI"
      },
      "source": [
        "**Analysis**\n",
        "From the boxplot it can be seen that the variability of data accross the years 2015 to 2019 are approximately the same with marjority of the data points between the 0 and 30 mark in addition to notable outliers which is visible accross the years mostly clustered around the 30 to 40 mark as well as the 55 to 60 mark. Also, 50% of the data lies between the 8 to 15 mark."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4054pZhJCCf"
      },
      "source": [
        "#Using the a sectioned data set 2 with years btn 2015 to 2019 and finding the median across the years\n",
        "pd.set_option('mode.chained_assignment', None)   #for turning of the \"SettingWithCopyWarning\"\n",
        "sectioned_dataset2['median'] = sectioned_dataset2.median(axis=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgkQj1hRJCCm"
      },
      "source": [
        "sectioned_dataset2_desc_top10 = sectioned_dataset2.sort_values(by=['median'], ascending=False)\n",
        "sectioned_dataset2_desc_top10 = sectioned_dataset2_desc_top10.head(10)\n",
        "sectioned_dataset2_desc_top10_asc =sectioned_dataset2_desc_top10.head(10)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAbhgQGlJCCt",
        "outputId": "352c5ab1-5009-47cb-94c8-f021fb60a915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "sectioned_dataset2_desc_top10_asc =sectioned_dataset2_desc_top10_asc.sort_values(by=['median'], ascending=True)\n",
        "sectioned_dataset2_desc_top10_asc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>26.3</td>\n",
              "      <td>26.2</td>\n",
              "      <td>25.9</td>\n",
              "      <td>25.5</td>\n",
              "      <td>25.1</td>\n",
              "      <td>25.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>25.9</td>\n",
              "      <td>26.9</td>\n",
              "      <td>26.6</td>\n",
              "      <td>26.1</td>\n",
              "      <td>25.6</td>\n",
              "      <td>26.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>27.2</td>\n",
              "      <td>26.8</td>\n",
              "      <td>26.7</td>\n",
              "      <td>26.4</td>\n",
              "      <td>26.2</td>\n",
              "      <td>26.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Libya</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.8</td>\n",
              "      <td>25.8</td>\n",
              "      <td>24.5</td>\n",
              "      <td>27.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.2</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>33.1</td>\n",
              "      <td>32.6</td>\n",
              "      <td>32.4</td>\n",
              "      <td>32.2</td>\n",
              "      <td>31.9</td>\n",
              "      <td>32.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>37.2</td>\n",
              "      <td>37.4</td>\n",
              "      <td>37.2</td>\n",
              "      <td>37.1</td>\n",
              "      <td>37.1</td>\n",
              "      <td>37.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>42.1</td>\n",
              "      <td>38.8</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.8</td>\n",
              "      <td>55.4</td>\n",
              "      <td>55.8</td>\n",
              "      <td>55.7</td>\n",
              "      <td>55.7</td>\n",
              "      <td>55.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>57.3</td>\n",
              "      <td>57.5</td>\n",
              "      <td>58.9</td>\n",
              "      <td>59.1</td>\n",
              "      <td>59.2</td>\n",
              "      <td>58.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          country  2015  2016  2017  2018  2019  median\n",
              "26       Cambodia  26.3  26.2  25.9  25.5  25.1    25.9\n",
              "88        Lesotho  25.9  26.9  26.6  26.1  25.6    26.1\n",
              "165  Turkmenistan  27.2  26.8  26.7  26.4  26.2    26.7\n",
              "90          Libya  32.1  32.2  27.8  25.8  24.5    27.8\n",
              "150         Sudan  31.0  31.2  31.8  31.8  31.8    31.8\n",
              "24   Burkina Faso  33.1  32.6  32.4  32.2  31.9    32.4\n",
              "45       Djibouti  37.2  37.4  37.2  37.1  37.1    37.2\n",
              "163       Tunisia  42.1  38.8  39.0  39.0  38.7    39.0\n",
              "2         Algeria  55.8  55.4  55.8  55.7  55.7    55.7\n",
              "161         Tonga  57.3  57.5  58.9  59.1  59.2    58.9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bS-wjwhQH7Ho",
        "outputId": "1c19947e-6516-409c-c6bf-2cfd49cc38dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Using a sectioned data with years btn 2015 to 2019 and finding the median accross the years and plotting a bar plot of it\n",
        "sectioned_dataset2_desc_top10_asc.plot.barh(x='country', y='median', color='green', rot=0 )\n",
        "plt.title(\"Top countries with the highest percentage of females in industries worldwide\")\n",
        "plt.xlabel(\"Median\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d2cy6TE89YzdrF0m7SHpf7oo/GBjD8mea1glyd+lGkvpJ2pN0hn5tvctl1tf1zqfqmDVvM1I39crAY8AB+V4L63zvI3VDDiN10x4VEdPqWyQz63Vd8WZmZtY8d8WbmZmViLvi62yNNdaIkSNH1rsYZma9xtSpU1+IiOGtz9k3ObDX2ciRI2lsbKx3MczMeg1JbXlKXp/jrngzM7MScWA3MzMrEQd2MzOzEnFgNzMzKxH/jr3ONELBEfUuhZlZ94kTOhZ3JE2NiIZOKk7p+IzdzMysRPrUz93yqy3/kUffR3r1ZuVVptvnN/eYmZn1Wn0qsEfEAmAsgKQTgdci4rS6FsrMzKwT9fmueEm7S5omaaak8yWtlNPnSjpJ0v152uY5fbikv0t6UNJ5kp6QtEaedq2kqXna4fWsl5mZ9U19PbAPBC4EDoqIrUg9GEcVpr8QEdsCZwPH5bQTgH9GxGjgKmC9wvyHRcQ4oAE4Onf9L0fS4ZIaJTXyeqfWx8zM+ri+Htj7A49HxL/y+O+AnQvTr87/pwIj8/BOwGUAEXEj8GJh/qMlzQCmAOsCmzS10oiYFBENEdHA4M6ohpmZWdKnrrG3w1v5/2Ja2VaSJgB7ADtExOuSJpN6BMzMzLpNXz9jXwyMlLRxHv88cGsry9wJfApA0oeB1XL6qsCLOahvDozvgvKamZm1qK8H9jeBQ4ErJc0ElgDntLLMScCHJc0CDgSeAV4FbgRWkDQbOJXUHW9mZtat/OS5Nsp3zS+OiEWSdgDOjoix7c7PT54zsz7GT57rWr7G3nbrAVdI6ge8DXy5I5mNGzGOxhP8PnYzM+scDuxtFBGPANvUuxxmZmZN6evX2M3MzErFgd3MzKxEHNjNzMxKxIHdzMysRBzYzczMSsSB3czMrEQc2M3MzErEgd3MzKxEHNjNzMxKxM+KrzM/K97MepOOPue9M/hZ8S3zGbuZmVmJlDqwS/qEpMjvR0fSyPy61c7K/8eS9uis/MzMzDqq1IEd+AxwR/7fqST1j4gfRcTNnZ23mZlZe5U2sEsaAuwEfBH4dBPTB0u6QtJDkq6RdI+khjztw5LulnS/pCtzXkiaK+lnku4HDpR0oaQD8rQfSbpP0ixJkySp+2prZmaWlDawA/sCN0bEv4AFksZVTf8K8GJEbAH8EBgHIGkN4AfAHhGxLdAIfLOw3IKI2DYiLqvK78yI2C4itgQGAXs3VzBJh0tqlNTI6x2popmZ2bLKHNg/A1SC72Us3x2/U2V6RMwCHsjp44EtgDslTQcOBtYvLHd5M+vbNZ/1zwR2A0Y3V7CImBQRDRHRwOA21MjMzKwVK9S7AF1B0uqk4LqVpAD6AwH8ppbFgb9HRHPX5Rc2sb6BwFlAQ0TMk3QiMLA9ZTczM+uIsp6xHwD8ISLWj4iREbEu8DiwbmGeO4FPAUjaAtgqp08BdpS0cZ62sqRNW1lfJYi/kK/HH9BJ9TAzM2uTsgb2zwDXVKX9EfhuYfwsYLikh4CfAA8CL0fE88AhwKWSHgDuBjZvaWUR8RJwLjAL+BtwXyfUwczMrM367JPnJPUHBkTEm5I2Am4GNouIt7uzHA0NDdHY2NidqzQz69X85LmWlfIae40GA7dIGkC6rv6V7g7qZmZmna3PBvaIeBXwEZ+ZmZVKWa+xm5mZ9UkO7GZmZiXiwG5mZlYiDuxmZmYl4sBuZmZWIg7sZmZmJeLAbmZmViIO7GZmZiXSZx8p21NohIIj6l0Ks94vTvC+rK/wI2Vb5jN2MzOzEnFgNzMzK5HSBnZJwyRNz3/PSHqqML5iG/I5L7+vvbnpP5a0R+eU2szMrGNK+xKYiFgAjAWQdCLwWkSc1o58vtTK9B+1q4BmZmZdoLRn7E2RdKGkAwrjr+X/EyRNlnSVpDmSLpakPG2ypAZJ/fPysyTNlPSN6jwl/UjSfXmeSZU8zMzMukufCuyt2AY4BtgC2BDYsWr6WGDtiNgyIrYCLmgijzMjYruI2BIYBOzd1IokHS6pUVIjr3deBczMzBzYl7o3IuZHxBJgOjCyavpjwIaSzpC0J/BKE3nsKukeSTOB3YDRTa0oIiZFRENENDC4E2tgZmZ9Xl8L7IvIdZbUDyjeRPdWYXgxVfcfRMSLwNbAZOBI4LzidEkDgbOAA/IZ/bnAwM4tvpmZWcv6WmCfC4zLw/sAA2pdUNIaQL+I+CPwA2DbqlkqQfwFSUOAAzAzM+tmpb0rvhnnAn+SNAO4EVjYhmXXBi7IZ/oA3y1OjIiXJJ0LzAKeAe7rhPKamZm1iR8pW2cNDQ3R2NhY72KYmfUafqRsy/paV7yZmVmpObCbmZmViAO7mZlZiTiwm5mZlYgDu5mZWYk4sJuZmZWIA7uZmVmJOLCbmZmViAO7mZlZiTiwm5mZlYgfKVtnGqHgiHqXwqznihO8j7Jl+ZGyLfMZu5mZWYmUKrBLWixpuqQHJc2QdGzlbWySGiSdnof3kXR8Hr5QUqe8YlXS96rG7+qMfM3MzGpVtte2vhERYwEkvRe4BHgPcEJENAKNABFxHXBdF6z/e8BPKyMR8YEuWIeZmVmzSnXGXhQRzwGHA19TMkHS9QCSDpF0ZmH2PSQ1SvqXpL3zPAMlXSBppqRpknZtallJ1+e8TwUG5R6Di/O017qrvmZmZlC+M/ZlRMRjkvoD721l1pHA9sBGwC2SNga+mrKIrSRtDtwkadMW1nW8pK9VegxaIulw0kEHrFpTVczMzGpS2jP2NroiIpZExCPAY8DmwE7ARQARMQd4Amg2sLdFREyKiIaIaGBwZ+RoZmaWlDqwS9oQWAw818qs1b+naen3NYtYdrsNbEfRzMzMukRpA7uk4cA5wJnR+o/1D5TUT9JGwIbAw8DtwMSc16bAejl9LjA2z78uqQu/4h1JAzq3JmZmZrUr2zX2QZKmAwNIZ9Z/AH5RmN5cgH8SuJd0B/2REfGmpLOAsyXNzHkdEhFvSboTeBx4CJgN3F/IZxLwgKT7I2JiZ1bMzMysFn3myXOSPgnsExEH17ssRX7ynFnL/OQ5q+Ynz7WsbGfsTZK0D3AKcFi9y1Jt3IhxNJ7QWO9imJlZSfSJwN6FD6QxMzPrUUp785yZmVlf5MBuZmZWIg7sZmZmJeLAbmZmViIO7GZmZiXiwG5mZlYiDuxmZmYl4sBuZmZWIjUFdknDurogZmZm1nE1PSte0iPAdOAC4K81vC3NauRnxVtv5We4W734WfEtq7UrflPSm8s+Dzwi6af5VaZmZmbWg9QU2CP5e0R8BvgycDBwr6RbJe1QSx6SFkuaLmmGpPslfaCthZU0WdJyR2mSbpA0tK35NZHPIZKez+WcLun3Hc3TzMysO9X0Eph8jf1zpDP2Z4Gvk16qMha4EtighmzeiIixOb+PAP8L7FJrQSX1b25aROxVaz41uDwivtaJ+ZmZmXWbWrvi7wbeA3wiIj4WEVdHxKKIaATOacd63wO8CCBpgqTrKxMknSnpkDw8V9LPJN0PHFiYp5+kCyX9pDDfGpJGSpot6VxJD0q6SdKgPM+XJd2Xewz+KGlwLQWV9HFJ90iaJulmSWvm9F0KZ/bTJK2i5OeSZkmaKemgdmwbMzOzdms1sOcz5T9HxMkRMb96ekT8rMZ1DcpBcA5wHnByjcstiIhtI+KyPL4CcDHwSET8oIn5NwF+ExGjgZeAT+b0qyNiu4jYGpgNfLGZ9R1UCNiHAncA4yNiG+Ay4Nt5vuOAr+ZeiA8CbwD7k3oxtgb2AH4uaa3qFUg6XFKjpEZer3ErmJmZ1aDVrviIWNye6+FNKHbF7wD8XtKWNSx3edX4/wFXRMQpzcz/eERMz8NTgZF5eMt8hj8UGAL8rbn1FbviJW0FXJ4D9IrA43nSncAvJF1MOmiYL2kn4NKIWAw8K+lWYDuq3gUfEZNINyOmu+LNzMw6Sa1d8dMlXSfp85L2r/y1d6URcTewBjAcWFRVjoFVsy+sGr8L2FVS9XwVbxWGF7P04OVC4GsRsRVwUhPrac4ZwJl5uSMqy0XEqcCXgEHAnZI2rzE/MzOzLlNrYB8ILAB2Az6e//Zu70pzEOyf83wC2ELSSvnO9t1bWfy3wA3AFZJquvkvWwV4WtIAYGIbllsVeCoPH1xJlLRRRMzMlyLuAzYHbid15feXNBzYGbi3DesyMzPrkFoD43kRcWcxQdKObVzXIEmVLnIBB+cu63mSrgBmkbq5p7WWUUT8QtKqwB8k1RqkfwjcAzyf/69S43InAldKehH4J0t/AXCMpF2BJcCDwF+Bt4EdgBlAAN+OiGdqXI+ZmVmH1frkufsjYtvW0qzt/OQ566385DmrFz95rmUtnrHnm9w+AAyX9M3CpPeQutKtg8aNGEfjCY31LoaZmZVEa13xK5LuIF+BZbuuXwEO6KpCmZmZWfu0GNgj4lbgVkkXRsQT3VQmMzMza6dab55bSdIk0m/C310mInbrikKZmZlZ+9Qa2K8kPTr2PNJvw83MzKwHqjWwL4qIs7u0JGZmZtZhtT6g5s+SviJpLUmrV/66tGRmZmbWZrWesVeeuPatQloAG3ZucczMzKwjagrsEVHL+9bNzMyszmoK7JK+0FR6RPy+c4tjZmZmHVFrV/x2heGBpBe13A84sJuZmfUgNT0rfrmF0lvYLouIPTu/SH2LnxVvvYmfD289gZ8V37Ja74qvtpClbzkzMzOzHqLWa+x/Jt0FD+nlL6OAK7qqUF1F0veBz5IesrMEOCIi7qlx2blAQ0S80HUlNDMz65har7GfVhheBDwREfO7oDxdJr+pbm9g24h4S9IapJfcmJmZlUZNXfH5ZTBzSG94Ww14uysL1UXWAl6IiLcAIuKFiPiPpLk5yCOpQdLkPDxM0k2SHpR0HqBKRpKulTQ1Tzu8kP6apFMkzZA0RdKa3VpDMzPr82oK7JI+BdwLHAh8CrhHUm97betNwLqS/iXpLEm7tDL/CcAdETEauAZYrzDtsIgYBzQAR0saltNXBqZExNbAbcCXm8pY0uGSGiU18npHqmRmZrasWrvivw9sFxHPAUgaDtwMXNVVBetsEfGapHHAB4FdgcslHd/CIjsD++dl/yLpxcK0oyXtl4fXBTYBFpB6Mq7P6VOBDzVTlknAJMh3xZuZmXWSWgN7v0pQzxbQ/jvq6yYiFgOTgcmSZpIelbuIpXUZ2FoekiYAewA7RMTrueu+stw7sfT3g4upffuamZl1ilqD842S/ibpEEmHAH8Bbui6YnU+SZtJ2qSQNBZ4ApgLjMtpnyxMv410Bz2SPkq6twBgVeDFHNQ3B8Z3ZbnNzMzaosUzSkkbA2tGxLck7Q/slCfdDVzc1YXrZEOAM/LDdRYBjwKHk36691tJJ5PO5itOAi6V9CBwF/BkTr8ROFLSbOBhYEr3FN/MzKx1LT55TtL1wHcjYmZV+lbATyPi411cvtJraGiIxsbGehfDzKzX8JPnWtZaV/ya1UEdIKeN7JISmZmZWbu1FtiHtjBtUGcWxMzMzDqutcDeKGm532JL+hLp51xmZmbWg7T2c6xjgGskTWRpIG8gPYp1v2aXMjMzs7poMbBHxLPAByTtCmyZk/8SEf/s8pKZmZlZm9X0AJWIuAW4pYvLYmZmZh3U654eZ2ZmZs1zYDczMysRB3YzM7MScWA3MzMrkRYfKWtdTyMUHFHvUljZxAn+Xlt5+ZGyLfMZu5mZWYk4sJuZmZVInw7skl5rIu1ISV/Iw5MlubvHzMx6jZoeUNOXRMQ59S6DmZlZe/XpM/amSDpR0nGFpM9Lmi5plqTtJfWT9Iik4Xn+fpIelTRc0scl3SNpmqSbJa1Zp2qYmVkf5cDeusERMRb4CnB+RCwBLgIm5ul7ADMi4nngDmB8RGwDXAZ8u6kMJR0uqVFSI693fQXMzKzvcFd86y4FiIjbJL1H0lDgfOBPwK+Aw4AL8rzrAJdLWov0BrzHm8owIiYBkyD/3M3MzKyT+Iy9ddWBNyJiHvCspN2A7YG/5mlnAGdGxFbAEcDA7iummZmZA3stDgKQtBPwckS8nNPPI3XJXxkRi3PaqsBTefjgbi2lmZkZ7oofLGl+YfwXTczzpqRpwABSt3vFdaQu+AsKaScCV0p6EfgnsEHnFqNT0loAABF/SURBVNfMzKxlfqRsO+Xft/8yIj7YkXwaGhqisbGxk0plZlZ+fqRsy/r6GXu7SDoeOIqld8abmZn1CL7G3g4RcWpErB8Rd9S7LGZmZkUO7GZmZiXiwG5mZlYiDuxmZmYl4sBuZmZWIg7sZmZmJeLAbmZmViIO7GZmZiXiwG5mZlYifqRsnWmEgiPqXQrrreIEf3+t7/EjZVvmM3YzM7MS6ZLALmmYpOn57xlJTxXGV2xl2ZGSZnVFuZpZ3whJV7Uwfaikr3RXeczMzDqiS14CExELgLEAkk4EXouI01pbTlK3v5QmIv4DHNDCLEOBrwBndU+JzMzM2q/buuIlXSjpgML4a/n/BEm3S7oOeKhqmQ0lTZO0naRDJF0r6e+S5kr6mqRv5ulTJK2el9lI0o2SpuZ8Ny+s/3RJd0l6rFKWYg+BpNGS7s09Cw9I2gQ4Fdgop/1c0hBJ/5B0v6SZkvYt5DNb0rmSHpR0k6RB3bBpzczM3tVTrrFvC/xPRGxaSZC0GfBH4JCIuC8nbwnsD2wHnAK8HhHbAHcDX8jzTAK+HhHjgONY9kx7LWAnYG9SwK52JPDriBgLNADzgeOBf0fE2Ij4FvAmsF9EbAvsCvx/kpSX3wT4TUSMBl4CPtlUZSUdLqlRUiOv17aBzMzMatFT3sd+b0Q8XhgfDvwJ2D8iimfxt0TEq8Crkl4G/pzTZwJjJA0BPgBcuTTWslJh+WsjYgnwkKQ1myjH3cD3Ja0DXB0RjxTyqRDwU0k7A0uAtYFKXo9HxPQ8PBUY2VRlI2IS6QAk3RVvZmbWSbozsC8i9xBI6gcUb6JbWDXvy8CTpLPrYmB/qzC8pDC+hFSXfsBL+Yy7KcXll4vYEXGJpHuAjwE3SDoCeKxqtomkA49xEfGOpLnAwCbyXwy4K97MzLpVd3bFzwXG5eF9gAEtzPs2sB/wBUmfrXUFEfEK8LikAwGUbF3r8pI2BB6LiNNJPQZjgFeBVQqzrQo8l4P6rsD6teZvZmbW1bozsJ8L7CJpBrADy5+lLyMiFpKuhX9D0j5tWM9E4It5PQ8C+7Zh2U8BsyRNJ13P/32+w/9OSbMk/Ry4GGiQNJN0XX9OG/I3MzPrUn7yXJ35yXPWEX7ynPVFfvJcy3rKzXN91rgR42g8obHexTAzs5LoKT93MzMzs07gwG5mZlYiDuxmZmYl4sBuZmZWIg7sZmZmJeLAbmZmViIO7GZmZiXiwG5mZlYiDuxmZmYl4sBuZmZWIn5WfJ35WfHWVn4+vPV1flZ8y3zGbmZmViKlCuySXuuifMdK2qswfqKk47piXWZmZh1RqsDehcYCe7U6l5mZWZ2VPrBL2kjSjZKmSrpd0uY5/UBJsyTNkHRbThso6QJJMyVNk7SrpBWBHwMHSZou6aCc9RaSJkt6TNLRhfV9M+c7S9Ix3V5hMzPr0/rC+9gnAUdGxCOS3g+cBewG/Aj4SEQ8JWlonverQETEVvkA4CZg0zxvQ0R8DVJXPLA5sCuwCvCwpLOBMcChwPsBAfdIujUiphULJOlw4HAAVu2yepuZWR9U6sAuaQjwAeBKSZXklfL/O4ELJV0BXJ3TdgLOAIiIOZKeIAX2pvwlIt4C3pL0HLBmXv6aiFiY13818EFgmcAeEZNIBxzprngzM7NOUurATrrU8FJEjK2eEBFH5jP4jwFTJY1rY95vFYYXU/5taWZmvUCpr7FHxCvA45IOBFCydR7eKCLuiYgfAc8D6wK3AxPz9E2B9YCHgVdJXe6tuR34hKTBklYG9stpZmZm3aJsgX2wpPmFv2+SAvUXJc0AHgT2zfP+PN8kNwu4C5hBuv7eT9JM4HLgkNzdfgvpZrnizXPLiYj7gQuBe4F7gPOqr6+bmZl1JT95rs785DlrKz95zvo6P3muZb4uXGfjRoyj8YTGehfDzMxKomxd8WZmZn2aA7uZmVmJOLCbmZmViK+xm5nVyTvvvMP8+fN58803612UHmngwIGss846DBgwoN5F6VUc2M3M6mT+/PmsssoqjBw5ksLTMQ2ICBYsWMD8+fPZYIMN6l2cXsVd8WZmdfLmm28ybNgwB/UmSGLYsGHuzWgHB3YzszpyUG+et037OLCbmZmViK+xm5n1EDqpc89Qu/sphRMmTOC0006joaGBvfbai0suuYShQ4e2vqB1Kgf2Opv6n6md/mW28vLjZK23uOGGG+pdhD7LXfFmZn3Y3Llz2XzzzTnkkEPYdNNNmThxIjfffDM77rgjm2yyCffeey8LFy7ksMMOY/vtt2ebbbbhT3/6EwBvvPEGn/70pxk1ahT77bcfb7zxxrv5jhw5khdeeAGAT3ziE4wbN47Ro0czadKkd+cZMmQI3//+99l6660ZP348zz77bPdWvqQc2M3M+rhHH32UY489ljlz5jBnzhwuueQS7rjjDk477TR++tOfcsopp7Dbbrtx7733csstt/Ctb32LhQsXcvbZZzN48GBmz57NSSedxNSpU5vM//zzz2fq1Kk0NjZy+umns2DBAgAWLlzI+PHjmTFjBjvvvDPnnntud1a7tHp8YJf0PkmXSfq3pKmSbsjvSu9InidKOq4Dy18o6YA8fJ6kLTpSHjOzetpggw3Yaqut6NevH6NHj2b33XdHEltttRVz587lpptu4tRTT2Xs2LFMmDCBN998kyeffJLbbruNz33ucwCMGTOGMWPGNJn/6aef/u5Z+bx583jkkUcAWHHFFdl7770BGDduHHPnzu2W+pZdj77GrvRbh2uA30XEp3Pa1sCawL/qWbaKiPhSvctgZtYRK6200rvD/fr1e3e8X79+LFq0iP79+/PHP/6RzTbbrM15T548mZtvvpm7776bwYMHv3tgADBgwIB3f9LWv39/Fi1a1Am1sZ5+xr4r8E5EnFNJiIgZwDRJ/5B0v6SZkvYFkDRS0px8Rv0vSRdL2kPSnZIekbR9Ie+tJd2d07+cl5ekn0ualfM9qJB+pqSHJd0MvLeSiaTJkhry8NmSGiU9KOmkrt88ZmZd7yMf+QhnnHEGEenmzWnTpgGw8847c8kllwAwa9YsHnjggeWWffnll1lttdUYPHgwc+bMYcqUKd1X8D6qR5+xA1sCTV20eRPYLyJekbQGMEXSdXnaxsCBwGHAfcBngZ2AfYDvAZ/I840BxgMrkw4U/gLsAIwFtgbWAO6TdFtO3wzYgtRb8BBwfhPl+n5E/FdSf+AfksZExHItXdLhwOEArFr7xjCzcuupv3r44Q9/yDHHHMOYMWNYsmQJG2ywAddffz1HHXUUhx56KKNGjWLUqFGMGzduuWX33HNPzjnnHEaNGsVmm23G+PHj61CDvkWVI7CeSNLRwAYR8Y2q9AHAL4GdgSWkoLsBMBD4e0Rskuf7PfC3iLhY0obA1RExVtKJQL+I+FFhvquBXYCZEXF+Tv8DcCWwG/BAIf1q4JKIuErSZOC4iGiUdCQpYK8ArAV8PSIua7GOIxQc0aHNZH1IT93xW/vMnj2bUaNG1bsYPVpT20jS1IhoqFOReryefsb+IHBAE+kTgeHAuIh4R9JcUlAHeKsw35LC+BKWrW/1HrJDe0xJGwDHAdtFxIuSLiyUyczMrFv09Gvs/wRWyl3XAEgaA6wPPJeD+q55vK32lTRQ0jBgAqnb/nbgIEn9JQ0n9QjcC9xWSF+LdO2/2nuAhcDLktYEPtqOMpmZmXVIjz5jj4iQtB/wK0nfIV1bnwucCJwuaSbQCMxpR/YPALeQrqWfHBH/kXQN6Xr6DNIZ/Lcj4pmcvhvp2vqTwN1NlHWGpGm5LPOAO9tRJjPrYyLCLztpRk++VNyT9ehr7H1BQ0NDNDY21rsYZlYHjz/+OKussopf3dqEyvvYX3311eXex+5r7C3r0WfsZmZlts466zB//nyef/75ehelRxo4cCDrrLNOvYvR6ziwm5nVyYABA5Y7GzXrqJ5+85yZmZm1gQO7mZlZiTiwm5mZlYjviq8zSa8CD9e7HF1gDeCFeheiC7hevYvr1bvUWq/1I2J4Vxemt/LNc/X3cBl/tiGp0fXqPVyv3sX1spa4K97MzKxEHNjNzMxKxIG9/ibVuwBdxPXqXVyv3sX1smb55jkzM7MS8Rm7mZlZiTiwm5mZlYgDe51I2lPSw5IelXR8vcvTEZLOl/ScpFmFtNUl/V3SI/n/avUsY1tJWlfSLZIekvSgpP/J6b29XgMl3StpRq7XSTl9A0n35PZ4uaQV613W9pDUX9I0Sdfn8bLUa66kmZKmS2rMab26LQJIGirpKklzJM2WtEMZ6lVvDux1IKk/8Bvgo8AWwGckbVHfUnXIhcCeVWnHA/+IiE2Af+Tx3mQRcGxEbAGMB76aP6PeXq+3gN0iYmtgLLCnpPHAz4BfRsTGwIvAF+tYxo74H2B2Ybws9QLYNSLGFn7n3dvbIsCvgRsjYnNga9JnV4Z61ZUDe31sDzwaEY9FxNvAZcC+dS5Tu0XEbcB/q5L3BX6Xh38HfKJbC9VBEfF0RNyfh18l7XDWpvfXKyLitTw6IP8FsBtwVU7vdfUCkLQO8DHgvDwuSlCvFvTqtihpVWBn4LcAEfF2RLxEL69XT+DAXh9rA/MK4/NzWpmsGRFP5+FngDXrWZiOkDQS2Aa4hxLUK3dXTweeA/4O/Bt4KSIW5Vl6a3v8FfBtYEkeH0Y56gXp4OsmSVMlHZ7Tentb3AB4HrggXz45T9LK9P561Z0Du3W5SL+p7JW/q5Q0BPgjcExEvFKc1lvrFRGLI2IssA6p92jzOhepwyTtDTwXEVPrXZYuslNEbEu6fPdVSTsXJ/bStrgCsC1wdkRsAyykqtu9l9ar7hzY6+MpYN3C+Do5rUyelbQWQP7/XJ3L02aSBpCC+sURcXVO7vX1qsjdnrcAOwBDJVXeHdEb2+OOwD6S5pIube1Gun7b2+sFQEQ8lf8/B1xDOiDr7W1xPjA/Iu7J41eRAn1vr1fdObDXx33AJvmO3RWBTwPX1blMne064OA8fDDwpzqWpc3y9dnfArMj4heFSb29XsMlDc3Dg4APke4fuAU4IM/W6+oVEd+NiHUiYiTp+/TPiJhIL68XgKSVJa1SGQY+DMyil7fFiHgGmCdps5y0O/AQvbxePYGfPFcnkvYiXRPsD5wfEafUuUjtJulSYALplYvPAicA1wJXAOsBTwCfiojqG+x6LEk7AbcDM1l6zfZ7pOvsvbleY0g3JPUnHdhfERE/lrQh6Ux3dWAa8LmIeKt+JW0/SROA4yJi7zLUK9fhmjy6AnBJRJwiaRi9uC0CSBpLutlxReAx4FByu6QX16veHNjNzMxKxF3xZmZmJeLAbmZmViIO7GZmZiXiwG5mZlYiDuxmZmYl4sBuVkKSQtJFhfEVJD1feetZG/KZLKkhD99Q+Q28mfVcK7Q+i5n1QguBLSUNiog3SA+i6dBT1yJir04pmZl1KZ+xm5XXDaS3nQF8Bri0MiE/zez8/G72aZL2zemDJF2W3419DTCosMxcSWvk4WvzC0keLLyUBEmvSTolv+99iiS/wMOsmzmwm5XXZcCnJQ0ExpCemlfxfdJjV7cHdgV+nh9XehTwekSMIj1BcFwzeR8WEeOABuDo/BQ0gJWBKfl977cBX+7sSplZyxzYzUoqIh4ARpLO1m+omvxh4Pj8+tbJwEDSIzx3Bi4qLP9AM9kfLWkGMIX0QqNNcvrbQOU6/tS8fjPrRr7GblZu1wGnkZ7lP6yQLuCTEfFwceb07puW5Wex7wHsEBGvS5pMOjAAeCeWPqd6Md7HmHU7n7Gbldv5wEkRMbMq/W/A1/Nb7JC0TU6/DfhsTtuS1IVfbVXgxRzUNwfGd0nJzaxdHNjNSiwi5kfE6U1MOhkYADwg6cE8DnA2METSbODHpO70ajcCK+R5TiV1x5tZD+G3u5mZmZWIz9jNzMxKxIHdzMysRBzYzczMSsSB3czMrEQc2M3MzErEgd3MzKxEHNjNzMxK5P8HptFXfjAr4LMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-U1_l5tuQdwt"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "According to the graph, Tonga has the highest percentage of females in industries globally and in the Oceania region/Pacific Ocean followed by Algeria,Tunisia, Djibouti, Burkina Faso, Sudan, Libya, Turkmenistan, Lesotho and Cambodia. More so, The top 10 countries listed has majority of the countries in the African region forming 70% in total followed by 20% from the Asian region and 10% from Oceania.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2YAJ9CfCdtRx"
      },
      "source": [
        "# **Finding the top 10 countries from each continent**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVbQpBCNd-MA"
      },
      "source": [
        "**Top 10 countries with the highest percentage of females in Europe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIQ8pO2kH7IG"
      },
      "source": [
        "#getting the list of eu countries in the dataset\n",
        "eu =['Albania', 'Armenia', 'Austria', 'Azerbaijan', 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria', 'Croatia','Cyprus',\n",
        "     'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France', 'Georgia', 'Germany', 'Greece', 'Hungary', 'Iceland',\n",
        "    'Ireland','Israel', 'Italy', 'Kyrgyz Republic', 'Latvia', 'Lithuania', 'Luxembourg', 'Malta', 'Montenegro',\n",
        "     'Netherlands', \"North Macedonia\", 'Norway', 'Poland', 'Portugal', 'Moldova', 'Romania', 'Russia', 'Serbia', \"Slovak Republic\",\n",
        "     'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', \"United Kingdom\"]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdQBjJq7H7IO",
        "outputId": "73b21b93-e21a-492a-e7df-adcaeac855d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#create a new dataframe for the the eu contries with their corresponding columns\n",
        "eu_countries=sectioned_dataset2[sectioned_dataset2['country'].isin(eu)]\n",
        "\n",
        "#takiing a look at the various columns in the dataframe\n",
        "eu_countries.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.90</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>7.52</td>\n",
              "      <td>7.48</td>\n",
              "      <td>7.73</td>\n",
              "      <td>7.98</td>\n",
              "      <td>7.77</td>\n",
              "      <td>7.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Austria</td>\n",
              "      <td>12.10</td>\n",
              "      <td>12.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>11.80</td>\n",
              "      <td>11.70</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>5.87</td>\n",
              "      <td>6.02</td>\n",
              "      <td>5.99</td>\n",
              "      <td>5.86</td>\n",
              "      <td>5.82</td>\n",
              "      <td>5.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>20.70</td>\n",
              "      <td>19.70</td>\n",
              "      <td>19.40</td>\n",
              "      <td>19.20</td>\n",
              "      <td>18.90</td>\n",
              "      <td>19.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   2015   2016   2017   2018   2019  median\n",
              "1      Albania  13.90  13.70  13.80  13.80  13.70   13.80\n",
              "5      Armenia   7.52   7.48   7.73   7.98   7.77    7.73\n",
              "7      Austria  12.10  12.00  12.00  11.80  11.70   12.00\n",
              "8   Azerbaijan   5.87   6.02   5.99   5.86   5.82    5.87\n",
              "13     Belarus  20.70  19.70  19.40  19.20  18.90   19.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1ueLc59H7Ie",
        "outputId": "b5e90ba2-b9ce-4564-8b0d-d8d98f1aa169",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "eu_countries = eu_countries.sort_values(by=['median'], ascending=False) #sorting eu countries in descending order to get the top 10\n",
        "eu_countries = eu_countries.head(10) #get the top 10 of the ascending order to get the top 10 countries\n",
        "eu_countries_sorted = eu_countries.head(10) #top 10 countries placed in eu_countries_sorted\n",
        "eu_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>24.4</td>\n",
              "      <td>25.1</td>\n",
              "      <td>24.2</td>\n",
              "      <td>23.8</td>\n",
              "      <td>23.5</td>\n",
              "      <td>24.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.1</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.4</td>\n",
              "      <td>23.0</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Bulgaria</td>\n",
              "      <td>22.7</td>\n",
              "      <td>22.4</td>\n",
              "      <td>21.5</td>\n",
              "      <td>21.1</td>\n",
              "      <td>20.8</td>\n",
              "      <td>21.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Slovak Republic</td>\n",
              "      <td>20.8</td>\n",
              "      <td>21.2</td>\n",
              "      <td>21.1</td>\n",
              "      <td>20.8</td>\n",
              "      <td>20.4</td>\n",
              "      <td>20.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Romania</td>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>20.4</td>\n",
              "      <td>19.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>20.7</td>\n",
              "      <td>19.7</td>\n",
              "      <td>19.4</td>\n",
              "      <td>19.2</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>29.9</td>\n",
              "      <td>19.3</td>\n",
              "      <td>19.2</td>\n",
              "      <td>19.1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.2</td>\n",
              "      <td>18.9</td>\n",
              "      <td>18.5</td>\n",
              "      <td>18.2</td>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Slovenia</td>\n",
              "      <td>18.2</td>\n",
              "      <td>19.2</td>\n",
              "      <td>18.6</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Estonia</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.3</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.2</td>\n",
              "      <td>16.7</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    country  2015  2016  2017  2018  2019  median\n",
              "116         North Macedonia  24.4  25.1  24.2  23.8  23.5    24.2\n",
              "43           Czech Republic  23.7  24.1  23.9  23.4  23.0    23.7\n",
              "23                 Bulgaria  22.7  22.4  21.5  21.1  20.8    21.5\n",
              "139         Slovak Republic  20.8  21.2  21.1  20.8  20.4    20.8\n",
              "129                 Romania  20.0  21.0  20.4  19.7  19.3    20.0\n",
              "13                  Belarus  20.7  19.7  19.4  19.2  18.9    19.4\n",
              "19   Bosnia and Herzegovina  29.9  19.3  19.2  19.1  19.0    19.2\n",
              "69                  Hungary  18.9  19.2  18.9  18.5  18.2    18.9\n",
              "140                Slovenia  18.2  19.2  18.6  17.7  17.0    18.2\n",
              "52                  Estonia  17.5  17.3  17.5  17.2  16.7    17.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4Ng-eomH7In",
        "outputId": "5b3ce3c5-f71a-45a9-b3c2-357e1ffcb3a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "eu_countries_sorted =eu_countries_sorted.sort_values(by=['median'], ascending=True)\n",
        "eu_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Estonia</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.3</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.2</td>\n",
              "      <td>16.7</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Slovenia</td>\n",
              "      <td>18.2</td>\n",
              "      <td>19.2</td>\n",
              "      <td>18.6</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.2</td>\n",
              "      <td>18.9</td>\n",
              "      <td>18.5</td>\n",
              "      <td>18.2</td>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>29.9</td>\n",
              "      <td>19.3</td>\n",
              "      <td>19.2</td>\n",
              "      <td>19.1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>20.7</td>\n",
              "      <td>19.7</td>\n",
              "      <td>19.4</td>\n",
              "      <td>19.2</td>\n",
              "      <td>18.9</td>\n",
              "      <td>19.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Romania</td>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>20.4</td>\n",
              "      <td>19.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Slovak Republic</td>\n",
              "      <td>20.8</td>\n",
              "      <td>21.2</td>\n",
              "      <td>21.1</td>\n",
              "      <td>20.8</td>\n",
              "      <td>20.4</td>\n",
              "      <td>20.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Bulgaria</td>\n",
              "      <td>22.7</td>\n",
              "      <td>22.4</td>\n",
              "      <td>21.5</td>\n",
              "      <td>21.1</td>\n",
              "      <td>20.8</td>\n",
              "      <td>21.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>23.7</td>\n",
              "      <td>24.1</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.4</td>\n",
              "      <td>23.0</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>24.4</td>\n",
              "      <td>25.1</td>\n",
              "      <td>24.2</td>\n",
              "      <td>23.8</td>\n",
              "      <td>23.5</td>\n",
              "      <td>24.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    country  2015  2016  2017  2018  2019  median\n",
              "52                  Estonia  17.5  17.3  17.5  17.2  16.7    17.3\n",
              "140                Slovenia  18.2  19.2  18.6  17.7  17.0    18.2\n",
              "69                  Hungary  18.9  19.2  18.9  18.5  18.2    18.9\n",
              "19   Bosnia and Herzegovina  29.9  19.3  19.2  19.1  19.0    19.2\n",
              "13                  Belarus  20.7  19.7  19.4  19.2  18.9    19.4\n",
              "129                 Romania  20.0  21.0  20.4  19.7  19.3    20.0\n",
              "139         Slovak Republic  20.8  21.2  21.1  20.8  20.4    20.8\n",
              "23                 Bulgaria  22.7  22.4  21.5  21.1  20.8    21.5\n",
              "43           Czech Republic  23.7  24.1  23.9  23.4  23.0    23.7\n",
              "116         North Macedonia  24.4  25.1  24.2  23.8  23.5    24.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxaobwkxH7Iu",
        "outputId": "6bc210fc-5680-46b6-b413-abeec418cdbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries in Europe from 2015 to 2019\n",
        "eu_countries_sorted.plot.barh(x='country', y='median', color='orange', rot=0 )\n",
        "plt.title(\"Top countries with the highest percentage of females in industries in Europe\")\n",
        "plt.xlabel(\"Median\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2exHLXtC6w3-"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "\n",
        "North Macedonia has the highest percentage of females in Europe followed by Czech Republic, Bulgaria, Slovak Republic, Romania, Belarus, Bosina and Herzegovina, Hungary, Slovenia and Estonia."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_eYHBL9jvVt"
      },
      "source": [
        "**Top 10 countries with the highest percentage of females in Africa**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8f74vYeH7I7"
      },
      "source": [
        "#getting all the aftican countries in one list\n",
        "africa = [\"Algeria\", 'Angola', 'Benin', 'Botswana', 'Burkina Faso', 'Burundi', 'Cameroon', 'Cape Verde',\n",
        "'Central African Republic', 'Chad', 'Comoros', \"Congo, Dem. Rep.\", \"Congo, Rep.\", 'Djibouti', 'Egypt', 'Equatorial Guinea',\n",
        "'Eritrea', 'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', \"Guinea-Bissau\", \"Cote d'Ivoire\", \"Kenya\", \"Lesotho\", \"Liberia\",\n",
        "\"Libya\", \"Madagascar\", \"Malawi\", \"Mali\", \"Mauritania\", \"Mauritius\", \"Morocco\", \"Mozambique\", \"Namibia\", \"Niger\", \"Nigeria\",\n",
        "\"Rwanda\", \"Sao Tome and Principe\", \"Senegal\", \"Sierra Leone\", \"Somalia\", \"South Africa\", \"South Sudan\", \"Sudan\", \"Swaziland\",\n",
        "\"Tanzania\", \"Togo\", \"Tunisia\", \"Uganda\", \"Zambia\", \"Zimbabwe\"]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDuFr2HSH7JH",
        "outputId": "9c214f30-7225-447e-b7ac-6662f9e99f8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#create a new dataframe for the african contries with their corresponding columns\n",
        "african_countries=sectioned_dataset2[sectioned_dataset2['country'].isin(africa)]\n",
        "\n",
        "#takiing a look at the various columns in the dataframe\n",
        "african_countries.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.40</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Benin</td>\n",
              "      <td>16.40</td>\n",
              "      <td>16.30</td>\n",
              "      <td>17.10</td>\n",
              "      <td>17.10</td>\n",
              "      <td>17.10</td>\n",
              "      <td>17.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>4.81</td>\n",
              "      <td>4.73</td>\n",
              "      <td>4.74</td>\n",
              "      <td>4.75</td>\n",
              "      <td>4.76</td>\n",
              "      <td>4.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>33.10</td>\n",
              "      <td>32.60</td>\n",
              "      <td>32.40</td>\n",
              "      <td>32.20</td>\n",
              "      <td>31.90</td>\n",
              "      <td>32.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         country   2015   2016   2017   2018   2019  median\n",
              "2        Algeria  55.80  55.40  55.80  55.70  55.70   55.70\n",
              "3         Angola   1.25   1.27   1.27   1.29   1.30    1.27\n",
              "16         Benin  16.40  16.30  17.10  17.10  17.10   17.10\n",
              "20      Botswana   4.81   4.73   4.74   4.75   4.76    4.75\n",
              "24  Burkina Faso  33.10  32.60  32.40  32.20  31.90   32.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHu2Ssz4H7JY",
        "outputId": "228d45ec-099b-4a08-bd33-a76bff5de8a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "african_countries = african_countries.sort_values(by=['median'], ascending=False) #sorting african countries in descending order to get the top 10\n",
        "african_countries = african_countries.head(10) #get the top 10 of the ascending order to get the top 10 countries\n",
        "african_countries_sorted = african_countries.head(10) #african_countries 10 countries placed in african_countries_sorted\n",
        "african_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.8</td>\n",
              "      <td>55.4</td>\n",
              "      <td>55.8</td>\n",
              "      <td>55.7</td>\n",
              "      <td>55.7</td>\n",
              "      <td>55.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>42.1</td>\n",
              "      <td>38.8</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>37.2</td>\n",
              "      <td>37.4</td>\n",
              "      <td>37.2</td>\n",
              "      <td>37.1</td>\n",
              "      <td>37.1</td>\n",
              "      <td>37.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>33.1</td>\n",
              "      <td>32.6</td>\n",
              "      <td>32.4</td>\n",
              "      <td>32.2</td>\n",
              "      <td>31.9</td>\n",
              "      <td>32.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.2</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Libya</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.8</td>\n",
              "      <td>25.8</td>\n",
              "      <td>24.5</td>\n",
              "      <td>27.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>25.9</td>\n",
              "      <td>26.9</td>\n",
              "      <td>26.6</td>\n",
              "      <td>26.1</td>\n",
              "      <td>25.6</td>\n",
              "      <td>26.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>South Sudan</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.1</td>\n",
              "      <td>25.8</td>\n",
              "      <td>26.3</td>\n",
              "      <td>26.5</td>\n",
              "      <td>25.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Congo, Rep.</td>\n",
              "      <td>24.8</td>\n",
              "      <td>25.7</td>\n",
              "      <td>26.3</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>25.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Togo</td>\n",
              "      <td>17.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>17.6</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>17.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          country  2015  2016  2017  2018  2019  median\n",
              "2         Algeria  55.8  55.4  55.8  55.7  55.7    55.7\n",
              "163       Tunisia  42.1  38.8  39.0  39.0  38.7    39.0\n",
              "45       Djibouti  37.2  37.4  37.2  37.1  37.1    37.2\n",
              "24   Burkina Faso  33.1  32.6  32.4  32.2  31.9    32.4\n",
              "150         Sudan  31.0  31.2  31.8  31.8  31.8    31.8\n",
              "90          Libya  32.1  32.2  27.8  25.8  24.5    27.8\n",
              "88        Lesotho  25.9  26.9  26.6  26.1  25.6    26.1\n",
              "145   South Sudan  25.0  25.1  25.8  26.3  26.5    25.8\n",
              "37    Congo, Rep.  24.8  25.7  26.3  25.5  26.1    25.7\n",
              "160          Togo  17.9  17.8  17.6  17.5  17.4    17.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SSH1LGzH7Jq",
        "outputId": "99aa335a-14a9-454f-f8ef-70e98e30c5b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#sorting in ascending order again to draw the bar chart\n",
        "african_countries_sorted =african_countries_sorted.sort_values(by=['median'], ascending=True)\n",
        "african_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Togo</td>\n",
              "      <td>17.9</td>\n",
              "      <td>17.8</td>\n",
              "      <td>17.6</td>\n",
              "      <td>17.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>17.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Congo, Rep.</td>\n",
              "      <td>24.8</td>\n",
              "      <td>25.7</td>\n",
              "      <td>26.3</td>\n",
              "      <td>25.5</td>\n",
              "      <td>26.1</td>\n",
              "      <td>25.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>South Sudan</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.1</td>\n",
              "      <td>25.8</td>\n",
              "      <td>26.3</td>\n",
              "      <td>26.5</td>\n",
              "      <td>25.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>25.9</td>\n",
              "      <td>26.9</td>\n",
              "      <td>26.6</td>\n",
              "      <td>26.1</td>\n",
              "      <td>25.6</td>\n",
              "      <td>26.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Libya</td>\n",
              "      <td>32.1</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.8</td>\n",
              "      <td>25.8</td>\n",
              "      <td>24.5</td>\n",
              "      <td>27.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.2</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "      <td>31.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>33.1</td>\n",
              "      <td>32.6</td>\n",
              "      <td>32.4</td>\n",
              "      <td>32.2</td>\n",
              "      <td>31.9</td>\n",
              "      <td>32.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>37.2</td>\n",
              "      <td>37.4</td>\n",
              "      <td>37.2</td>\n",
              "      <td>37.1</td>\n",
              "      <td>37.1</td>\n",
              "      <td>37.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>42.1</td>\n",
              "      <td>38.8</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.8</td>\n",
              "      <td>55.4</td>\n",
              "      <td>55.8</td>\n",
              "      <td>55.7</td>\n",
              "      <td>55.7</td>\n",
              "      <td>55.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          country  2015  2016  2017  2018  2019  median\n",
              "160          Togo  17.9  17.8  17.6  17.5  17.4    17.6\n",
              "37    Congo, Rep.  24.8  25.7  26.3  25.5  26.1    25.7\n",
              "145   South Sudan  25.0  25.1  25.8  26.3  26.5    25.8\n",
              "88        Lesotho  25.9  26.9  26.6  26.1  25.6    26.1\n",
              "90          Libya  32.1  32.2  27.8  25.8  24.5    27.8\n",
              "150         Sudan  31.0  31.2  31.8  31.8  31.8    31.8\n",
              "24   Burkina Faso  33.1  32.6  32.4  32.2  31.9    32.4\n",
              "45       Djibouti  37.2  37.4  37.2  37.1  37.1    37.2\n",
              "163       Tunisia  42.1  38.8  39.0  39.0  38.7    39.0\n",
              "2         Algeria  55.8  55.4  55.8  55.7  55.7    55.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qWrgCA6H7J6",
        "outputId": "0a0624e9-d8fb-40b5-a4a1-16f97465c664",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries in Africa from 2015 to 2019\n",
        "african_countries_sorted.plot.barh(x='country', y='median', color='blue', rot=0 )\n",
        "plt.title(\"Top countries with the highest percentage of females in industries in Africa\")\n",
        "plt.xlabel(\"Median\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ER39H9M67wak"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "In the African region, the first 7 countries form 70% of the top 10 countries globally as seen above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_0_GD10kDUs"
      },
      "source": [
        "**Top 10 countries with the highest percentage of females in Asia**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SU2WKUs7H7KU"
      },
      "source": [
        "#getting all the asian countries in one list\n",
        "asia = [\"Afghanistan\",\"Armenia\", \"Azerbaijan\", \"Bahrain\", \"Bangladesh\", \"Bhutan\", \"Brunei\", \"Cambodia\", \"China\", \"Cyprus\",\n",
        "        \"Georgia\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Israel\", \"Japan\", \"Jordan\", \"Kazakhstan\", \"Kuwait\", 'Kyrgyz Republic',\n",
        "        \"Lao\", \"Lebanon\", \"Malaysia\", \"Maldives\", \"Mongolia\", \"Nepal\", \"North Korea\", \"Oman\", \"Pakistan\", \"Palestine\",\n",
        "        \"Philippines\", \"Qatar\", \"Saudi Arabia\", \"Singapore\", \"South Korea\", \"Sri Lanka\", \"Syria\", \"Tajikistan\", \"Thailand\",\n",
        "        \"Timor-Leste\", \"Turkey\", \"Turkmenistan\", \"United Arab Emirates\", \"Uzbekistan\", \"Vietnam\", \"Yemen\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjiraCIMH7Ki",
        "outputId": "c19e59e9-1e28-4e0f-a101-0335f25e494a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#create a new dataframe for the asian contries with their corresponding columns\n",
        "asian_countries=sectioned_dataset2[sectioned_dataset2['country'].isin(asia)]\n",
        "\n",
        "#takiing a look at the various columns in the dataframe\n",
        "asian_countries.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.56</td>\n",
              "      <td>5.52</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.46</td>\n",
              "      <td>5.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>7.52</td>\n",
              "      <td>7.48</td>\n",
              "      <td>7.73</td>\n",
              "      <td>7.98</td>\n",
              "      <td>7.77</td>\n",
              "      <td>7.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>5.87</td>\n",
              "      <td>6.02</td>\n",
              "      <td>5.99</td>\n",
              "      <td>5.86</td>\n",
              "      <td>5.82</td>\n",
              "      <td>5.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>9.21</td>\n",
              "      <td>9.39</td>\n",
              "      <td>8.90</td>\n",
              "      <td>9.22</td>\n",
              "      <td>9.48</td>\n",
              "      <td>9.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>15.90</td>\n",
              "      <td>16.20</td>\n",
              "      <td>16.30</td>\n",
              "      <td>16.60</td>\n",
              "      <td>16.70</td>\n",
              "      <td>16.30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        country   2015   2016   2017   2018   2019  median\n",
              "0   Afghanistan   5.66   5.56   5.52   5.49   5.46    5.52\n",
              "5       Armenia   7.52   7.48   7.73   7.98   7.77    7.73\n",
              "8    Azerbaijan   5.87   6.02   5.99   5.86   5.82    5.87\n",
              "10      Bahrain   9.21   9.39   8.90   9.22   9.48    9.22\n",
              "11   Bangladesh  15.90  16.20  16.30  16.60  16.70   16.30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwMs3hmbH7Kq",
        "outputId": "7d04465a-f60c-4f71-d456-719d2f739d7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "asian_countries = asian_countries.sort_values(by=['median'], ascending=False) #sorting asian countries in descending order to get the top 10\n",
        "asian_countries = asian_countries.head(10) #get the top 10 of the ascending order to get the top 10 countries\n",
        "asian_countries_sorted = asian_countries.head(10) #asian_countries 10 countries placed in asian_countries_sorted\n",
        "asian_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>27.2</td>\n",
              "      <td>26.8</td>\n",
              "      <td>26.7</td>\n",
              "      <td>26.4</td>\n",
              "      <td>26.2</td>\n",
              "      <td>26.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>26.3</td>\n",
              "      <td>26.2</td>\n",
              "      <td>25.9</td>\n",
              "      <td>25.5</td>\n",
              "      <td>25.1</td>\n",
              "      <td>25.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Iran</td>\n",
              "      <td>23.8</td>\n",
              "      <td>25.2</td>\n",
              "      <td>25.4</td>\n",
              "      <td>25.3</td>\n",
              "      <td>25.2</td>\n",
              "      <td>25.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>25.2</td>\n",
              "      <td>24.9</td>\n",
              "      <td>25.0</td>\n",
              "      <td>24.9</td>\n",
              "      <td>24.5</td>\n",
              "      <td>24.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>24.3</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.5</td>\n",
              "      <td>23.1</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Uzbekistan</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.7</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.6</td>\n",
              "      <td>23.4</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>China</td>\n",
              "      <td>22.6</td>\n",
              "      <td>22.0</td>\n",
              "      <td>21.6</td>\n",
              "      <td>21.1</td>\n",
              "      <td>20.6</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>19.3</td>\n",
              "      <td>20.8</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.2</td>\n",
              "      <td>21.1</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>20.8</td>\n",
              "      <td>19.8</td>\n",
              "      <td>19.5</td>\n",
              "      <td>19.3</td>\n",
              "      <td>19.1</td>\n",
              "      <td>19.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>19.2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.9</td>\n",
              "      <td>19.4</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          country  2015  2016  2017  2018  2019  median\n",
              "165  Turkmenistan  27.2  26.8  26.7  26.4  26.2    26.7\n",
              "26       Cambodia  26.3  26.2  25.9  25.5  25.1    25.9\n",
              "73           Iran  23.8  25.2  25.4  25.3  25.2    25.2\n",
              "147     Sri Lanka  25.2  24.9  25.0  24.9  24.5    24.9\n",
              "96       Maldives  24.3  23.9  23.9  23.5  23.1    23.9\n",
              "172    Uzbekistan  24.4  23.7  23.9  23.6  23.4    23.7\n",
              "33          China  22.6  22.0  21.6  21.1  20.6    21.6\n",
              "175       Vietnam  19.3  20.8  21.0  21.2  21.1    21.0\n",
              "158      Thailand  20.8  19.8  19.5  19.3  19.1    19.5\n",
              "95       Malaysia  19.2  20.0  19.9  19.4  19.0    19.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNwCPReZH7Ky",
        "outputId": "6cf9e2ce-eab4-4f36-be5f-3bda97cdcd54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#sorting in ascending order again to draw the bar chart\n",
        "asian_countries_sorted =asian_countries_sorted.sort_values(by=['median'], ascending=True)\n",
        "asian_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>19.2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.9</td>\n",
              "      <td>19.4</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>20.8</td>\n",
              "      <td>19.8</td>\n",
              "      <td>19.5</td>\n",
              "      <td>19.3</td>\n",
              "      <td>19.1</td>\n",
              "      <td>19.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>19.3</td>\n",
              "      <td>20.8</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.2</td>\n",
              "      <td>21.1</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>China</td>\n",
              "      <td>22.6</td>\n",
              "      <td>22.0</td>\n",
              "      <td>21.6</td>\n",
              "      <td>21.1</td>\n",
              "      <td>20.6</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Uzbekistan</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.7</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.6</td>\n",
              "      <td>23.4</td>\n",
              "      <td>23.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>24.3</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.9</td>\n",
              "      <td>23.5</td>\n",
              "      <td>23.1</td>\n",
              "      <td>23.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>25.2</td>\n",
              "      <td>24.9</td>\n",
              "      <td>25.0</td>\n",
              "      <td>24.9</td>\n",
              "      <td>24.5</td>\n",
              "      <td>24.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Iran</td>\n",
              "      <td>23.8</td>\n",
              "      <td>25.2</td>\n",
              "      <td>25.4</td>\n",
              "      <td>25.3</td>\n",
              "      <td>25.2</td>\n",
              "      <td>25.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>26.3</td>\n",
              "      <td>26.2</td>\n",
              "      <td>25.9</td>\n",
              "      <td>25.5</td>\n",
              "      <td>25.1</td>\n",
              "      <td>25.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>27.2</td>\n",
              "      <td>26.8</td>\n",
              "      <td>26.7</td>\n",
              "      <td>26.4</td>\n",
              "      <td>26.2</td>\n",
              "      <td>26.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          country  2015  2016  2017  2018  2019  median\n",
              "95       Malaysia  19.2  20.0  19.9  19.4  19.0    19.4\n",
              "158      Thailand  20.8  19.8  19.5  19.3  19.1    19.5\n",
              "175       Vietnam  19.3  20.8  21.0  21.2  21.1    21.0\n",
              "33          China  22.6  22.0  21.6  21.1  20.6    21.6\n",
              "172    Uzbekistan  24.4  23.7  23.9  23.6  23.4    23.7\n",
              "96       Maldives  24.3  23.9  23.9  23.5  23.1    23.9\n",
              "147     Sri Lanka  25.2  24.9  25.0  24.9  24.5    24.9\n",
              "73           Iran  23.8  25.2  25.4  25.3  25.2    25.2\n",
              "26       Cambodia  26.3  26.2  25.9  25.5  25.1    25.9\n",
              "165  Turkmenistan  27.2  26.8  26.7  26.4  26.2    26.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ed7--32H7LD",
        "outputId": "0dcee8d6-a649-4685-f98f-3908aec0bd2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries in Asia from 2015 to 2019\n",
        "asian_countries_sorted.plot.barh(x='country', y='median', color='purple', rot=0 )\n",
        "plt.title(\"Top countries with the highest percentage of females in industries in Asia\")\n",
        "plt.xlabel(\"Median\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yC8b659r_aaI"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "The first two countries Turkmenistan and Cambodia among the top 10 in the Asia region form part of the top 10 countries globally with the highest percentages of females in indutries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZuZ9h0vkRwK"
      },
      "source": [
        "**Top 10 countries with the highest percentage of females in North America**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "peRYCNJ5H7LO"
      },
      "source": [
        "north_america =[\n",
        "\"Bahamas\", \"Barbados\", \"Belize\", \"Canada\", \"Costa Rica\", \"Cuba\",\n",
        "\"Dominican Republic\", \"El Salvador\", \"Haiti\", \"Honduras\", \"Jamaica\", \"Mexico\",\n",
        "\"Nicaragua\", \"Panama\", \"Trinidad and Tobago\", \"United States\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-k79AYmiH7LV",
        "outputId": "2f70a4d3-f832-4e19-cdb6-cd2f1f9ac9bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#create a new dataframe for the north american contries with their corresponding columns\n",
        "north_american_countries=sectioned_dataset2[sectioned_dataset2['country'].isin(north_america)]\n",
        "\n",
        "#takiing a look at the various columns in the dataframe\n",
        "north_american_countries.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bahamas</td>\n",
              "      <td>3.86</td>\n",
              "      <td>3.70</td>\n",
              "      <td>3.77</td>\n",
              "      <td>3.75</td>\n",
              "      <td>3.75</td>\n",
              "      <td>3.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>10.00</td>\n",
              "      <td>9.39</td>\n",
              "      <td>9.25</td>\n",
              "      <td>9.22</td>\n",
              "      <td>9.16</td>\n",
              "      <td>9.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Belize</td>\n",
              "      <td>8.16</td>\n",
              "      <td>8.44</td>\n",
              "      <td>8.47</td>\n",
              "      <td>8.77</td>\n",
              "      <td>8.59</td>\n",
              "      <td>8.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Canada</td>\n",
              "      <td>8.44</td>\n",
              "      <td>8.68</td>\n",
              "      <td>8.82</td>\n",
              "      <td>8.68</td>\n",
              "      <td>8.56</td>\n",
              "      <td>8.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>9.62</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.07</td>\n",
              "      <td>8.96</td>\n",
              "      <td>8.82</td>\n",
              "      <td>9.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   2015  2016  2017  2018  2019  median\n",
              "9      Bahamas   3.86  3.70  3.77  3.75  3.75    3.75\n",
              "12    Barbados  10.00  9.39  9.25  9.22  9.16    9.25\n",
              "15      Belize   8.16  8.44  8.47  8.77  8.59    8.47\n",
              "28      Canada   8.44  8.68  8.82  8.68  8.56    8.68\n",
              "38  Costa Rica   9.62  9.09  9.07  8.96  8.82    9.07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaeFq9XpH7Lc",
        "outputId": "2794fd17-4380-4779-c6c8-ccaccf259fc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "north_american_countries = north_american_countries.sort_values(by=['median'], ascending=False) #sorting north american countries in descending order to get the top 10\n",
        "north_american_countries = north_american_countries.head(10) #get the top 10 of the descending order to get the top 10 countries\n",
        "north_american_countries_sorted = north_american_countries.head(10) #north american countries 10 countries placed in north_american_countries_sorted\n",
        "north_american_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>20.90</td>\n",
              "      <td>20.10</td>\n",
              "      <td>20.10</td>\n",
              "      <td>20.10</td>\n",
              "      <td>19.80</td>\n",
              "      <td>20.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>El Salvador</td>\n",
              "      <td>19.50</td>\n",
              "      <td>17.10</td>\n",
              "      <td>17.10</td>\n",
              "      <td>17.00</td>\n",
              "      <td>16.80</td>\n",
              "      <td>17.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>16.50</td>\n",
              "      <td>16.90</td>\n",
              "      <td>17.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>17.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Nicaragua</td>\n",
              "      <td>14.10</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.30</td>\n",
              "      <td>13.10</td>\n",
              "      <td>13.00</td>\n",
              "      <td>13.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Trinidad and Tobago</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.60</td>\n",
              "      <td>13.20</td>\n",
              "      <td>13.30</td>\n",
              "      <td>13.20</td>\n",
              "      <td>13.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Cuba</td>\n",
              "      <td>10.70</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.50</td>\n",
              "      <td>10.50</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Panama</td>\n",
              "      <td>9.83</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.74</td>\n",
              "      <td>9.33</td>\n",
              "      <td>9.04</td>\n",
              "      <td>9.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>10.00</td>\n",
              "      <td>9.39</td>\n",
              "      <td>9.25</td>\n",
              "      <td>9.22</td>\n",
              "      <td>9.16</td>\n",
              "      <td>9.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.49</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.06</td>\n",
              "      <td>8.97</td>\n",
              "      <td>9.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>9.62</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.07</td>\n",
              "      <td>8.96</td>\n",
              "      <td>8.82</td>\n",
              "      <td>9.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 country   2015   2016   2017   2018   2019  median\n",
              "68              Honduras  20.90  20.10  20.10  20.10  19.80   20.10\n",
              "49           El Salvador  19.50  17.10  17.10  17.00  16.80   17.10\n",
              "101               Mexico  16.50  16.90  17.00  17.00  17.00   17.00\n",
              "112            Nicaragua  14.10  13.40  13.30  13.10  13.00   13.30\n",
              "162  Trinidad and Tobago  13.80  13.60  13.20  13.30  13.20   13.30\n",
              "41                  Cuba  10.70  10.40  10.50  10.50  10.40   10.50\n",
              "121               Panama   9.83  10.10   9.74   9.33   9.04    9.74\n",
              "12              Barbados  10.00   9.39   9.25   9.22   9.16    9.25\n",
              "46    Dominican Republic  10.10   9.49   9.09   9.06   8.97    9.09\n",
              "38            Costa Rica   9.62   9.09   9.07   8.96   8.82    9.07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WV9giNENH7Lm",
        "outputId": "6017d990-3355-4f75-e65a-f8caef54863c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#sorting in ascending order again to draw the bar chart\n",
        "north_american_countries_sorted=north_american_countries_sorted.sort_values(by=['median'], ascending=True)\n",
        "north_american_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>9.62</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.07</td>\n",
              "      <td>8.96</td>\n",
              "      <td>8.82</td>\n",
              "      <td>9.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.49</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.06</td>\n",
              "      <td>8.97</td>\n",
              "      <td>9.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>10.00</td>\n",
              "      <td>9.39</td>\n",
              "      <td>9.25</td>\n",
              "      <td>9.22</td>\n",
              "      <td>9.16</td>\n",
              "      <td>9.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Panama</td>\n",
              "      <td>9.83</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.74</td>\n",
              "      <td>9.33</td>\n",
              "      <td>9.04</td>\n",
              "      <td>9.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Cuba</td>\n",
              "      <td>10.70</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.50</td>\n",
              "      <td>10.50</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Nicaragua</td>\n",
              "      <td>14.10</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.30</td>\n",
              "      <td>13.10</td>\n",
              "      <td>13.00</td>\n",
              "      <td>13.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Trinidad and Tobago</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.60</td>\n",
              "      <td>13.20</td>\n",
              "      <td>13.30</td>\n",
              "      <td>13.20</td>\n",
              "      <td>13.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>16.50</td>\n",
              "      <td>16.90</td>\n",
              "      <td>17.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>17.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>El Salvador</td>\n",
              "      <td>19.50</td>\n",
              "      <td>17.10</td>\n",
              "      <td>17.10</td>\n",
              "      <td>17.00</td>\n",
              "      <td>16.80</td>\n",
              "      <td>17.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>20.90</td>\n",
              "      <td>20.10</td>\n",
              "      <td>20.10</td>\n",
              "      <td>20.10</td>\n",
              "      <td>19.80</td>\n",
              "      <td>20.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 country   2015   2016   2017   2018   2019  median\n",
              "38            Costa Rica   9.62   9.09   9.07   8.96   8.82    9.07\n",
              "46    Dominican Republic  10.10   9.49   9.09   9.06   8.97    9.09\n",
              "12              Barbados  10.00   9.39   9.25   9.22   9.16    9.25\n",
              "121               Panama   9.83  10.10   9.74   9.33   9.04    9.74\n",
              "41                  Cuba  10.70  10.40  10.50  10.50  10.40   10.50\n",
              "112            Nicaragua  14.10  13.40  13.30  13.10  13.00   13.30\n",
              "162  Trinidad and Tobago  13.80  13.60  13.20  13.30  13.20   13.30\n",
              "101               Mexico  16.50  16.90  17.00  17.00  17.00   17.00\n",
              "49           El Salvador  19.50  17.10  17.10  17.00  16.80   17.10\n",
              "68              Honduras  20.90  20.10  20.10  20.10  19.80   20.10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSDRlNfoH7Lt",
        "outputId": "6306c147-c8b1-485e-c9f4-f14b903ad2c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries in north america from 2015 to 2019\n",
        "north_american_countries_sorted.plot.barh(x='country', y='median', color='black', rot=0 )\n",
        "plt.title(\"Top countries with the highest percentage of females in industries in North America\")\n",
        "plt.xlabel(\"Median\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7fCEmFF_7am"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "Interestingly, none of the countries in North America form part of the top 10 countries globally."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3TPJ74rknJI"
      },
      "source": [
        "**Top 10 countries with the highest percentage of females in South America**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNDB_cBzH7L0"
      },
      "source": [
        "south_america= [\"Argentina\", \"Bolivia\", \"Brazil\", \"Chile\", \"Colombia\", \"Ecuador\",\n",
        "\"Guyana\", \"Paraguay\", \"Peru\", \"Suriname\", \"Uruguay\", \"Venezuela\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-AfcrfnH7L_",
        "outputId": "2983927e-1d92-4256-e655-f88a52807dea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#create a new dataframe for the south american contries with their corresponding columns\n",
        "south_american_countries=sectioned_dataset2[sectioned_dataset2['country'].isin(south_america)]\n",
        "\n",
        "#takiing a look at the various columns in the dataframe\n",
        "south_american_countries.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.66</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bolivia</td>\n",
              "      <td>11.00</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.8</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>11.70</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.7</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Chile</td>\n",
              "      <td>11.20</td>\n",
              "      <td>11.10</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.7</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.20</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.10</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      country   2015   2016   2017   2018  2019  median\n",
              "4   Argentina   8.08   8.02   7.82   7.66   7.5    7.82\n",
              "18    Bolivia  11.00  11.00  10.90  11.00  10.8   11.00\n",
              "21     Brazil  11.70  10.80  10.80  10.80  10.7   10.80\n",
              "32      Chile  11.20  11.10  11.00  10.80  10.7   11.00\n",
              "34   Colombia  13.70  13.20  13.40  13.10  13.0   13.20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uK7blWzZH7MK",
        "outputId": "d1d6e052-7b8c-4311-d6d4-a6f7c748312b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "south_american_countries = south_american_countries.sort_values(by=['median'], ascending=False) #sorting south american countries in descending order to get the top 10\n",
        "south_american_countries = south_american_countries.head(10) #get the top 10 of the descending order to get the top 10 countries\n",
        "south_american_countries_sorted = south_american_countries.head(10) #south american countries 10 countries placed in south_american_countries_sorted\n",
        "south_american_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>12.60</td>\n",
              "      <td>12.60</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.20</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.10</td>\n",
              "      <td>13.00</td>\n",
              "      <td>13.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bolivia</td>\n",
              "      <td>11.00</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.80</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Chile</td>\n",
              "      <td>11.20</td>\n",
              "      <td>11.10</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.70</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>11.70</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.70</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Ecuador</td>\n",
              "      <td>11.20</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Guyana</td>\n",
              "      <td>10.20</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.97</td>\n",
              "      <td>9.83</td>\n",
              "      <td>10.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Uruguay</td>\n",
              "      <td>9.74</td>\n",
              "      <td>9.24</td>\n",
              "      <td>9.18</td>\n",
              "      <td>9.02</td>\n",
              "      <td>8.88</td>\n",
              "      <td>9.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Peru</td>\n",
              "      <td>8.99</td>\n",
              "      <td>9.06</td>\n",
              "      <td>8.97</td>\n",
              "      <td>8.92</td>\n",
              "      <td>8.83</td>\n",
              "      <td>8.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>Paraguay</td>\n",
              "      <td>9.65</td>\n",
              "      <td>8.38</td>\n",
              "      <td>8.42</td>\n",
              "      <td>8.36</td>\n",
              "      <td>8.27</td>\n",
              "      <td>8.38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   2015   2016   2017   2018   2019  median\n",
              "174  Venezuela  12.60  12.60  13.40  13.70  13.80   13.40\n",
              "34    Colombia  13.70  13.20  13.40  13.10  13.00   13.20\n",
              "18     Bolivia  11.00  11.00  10.90  11.00  10.80   11.00\n",
              "32       Chile  11.20  11.10  11.00  10.80  10.70   11.00\n",
              "21      Brazil  11.70  10.80  10.80  10.80  10.70   10.80\n",
              "47     Ecuador  11.20  10.60  10.80  10.90  10.80   10.80\n",
              "66      Guyana  10.20  10.40  10.10   9.97   9.83   10.10\n",
              "171    Uruguay   9.74   9.24   9.18   9.02   8.88    9.18\n",
              "124       Peru   8.99   9.06   8.97   8.92   8.83    8.97\n",
              "123   Paraguay   9.65   8.38   8.42   8.36   8.27    8.38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjo3O3g2H7Me",
        "outputId": "2f267a4f-622a-40c6-f809-3e3cd89ecc66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#sorting in ascending order again to draw the bar chart\n",
        "south_american_countries_sorted = south_american_countries_sorted.sort_values(by=['median'], ascending=True)\n",
        "south_american_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>Paraguay</td>\n",
              "      <td>9.65</td>\n",
              "      <td>8.38</td>\n",
              "      <td>8.42</td>\n",
              "      <td>8.36</td>\n",
              "      <td>8.27</td>\n",
              "      <td>8.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Peru</td>\n",
              "      <td>8.99</td>\n",
              "      <td>9.06</td>\n",
              "      <td>8.97</td>\n",
              "      <td>8.92</td>\n",
              "      <td>8.83</td>\n",
              "      <td>8.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Uruguay</td>\n",
              "      <td>9.74</td>\n",
              "      <td>9.24</td>\n",
              "      <td>9.18</td>\n",
              "      <td>9.02</td>\n",
              "      <td>8.88</td>\n",
              "      <td>9.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Guyana</td>\n",
              "      <td>10.20</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.97</td>\n",
              "      <td>9.83</td>\n",
              "      <td>10.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>11.70</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.70</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Ecuador</td>\n",
              "      <td>11.20</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bolivia</td>\n",
              "      <td>11.00</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.80</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Chile</td>\n",
              "      <td>11.20</td>\n",
              "      <td>11.10</td>\n",
              "      <td>11.00</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.70</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.20</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.10</td>\n",
              "      <td>13.00</td>\n",
              "      <td>13.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>12.60</td>\n",
              "      <td>12.60</td>\n",
              "      <td>13.40</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   2015   2016   2017   2018   2019  median\n",
              "123   Paraguay   9.65   8.38   8.42   8.36   8.27    8.38\n",
              "124       Peru   8.99   9.06   8.97   8.92   8.83    8.97\n",
              "171    Uruguay   9.74   9.24   9.18   9.02   8.88    9.18\n",
              "66      Guyana  10.20  10.40  10.10   9.97   9.83   10.10\n",
              "21      Brazil  11.70  10.80  10.80  10.80  10.70   10.80\n",
              "47     Ecuador  11.20  10.60  10.80  10.90  10.80   10.80\n",
              "18     Bolivia  11.00  11.00  10.90  11.00  10.80   11.00\n",
              "32       Chile  11.20  11.10  11.00  10.80  10.70   11.00\n",
              "34    Colombia  13.70  13.20  13.40  13.10  13.00   13.20\n",
              "174  Venezuela  12.60  12.60  13.40  13.70  13.80   13.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxjy_uvwH7Mv",
        "outputId": "bd423732-e26d-427d-bce9-f96e5b6f13b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries in South America from 2015 to 2019\n",
        "south_american_countries_sorted.plot.barh(x='country', y='median', color='gold', rot=0 )\n",
        "plt.title(\"Top countries with the highest percentage of females in industries in South America\")\n",
        "plt.xlabel(\"Median\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDzwdLLfk5Rz"
      },
      "source": [
        "**Top 10 countries with the highest percentage of females in Australia**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IB2_rpmH7M6"
      },
      "source": [
        "australia=[\"Australia\",\n",
        "\"Fiji\",\n",
        "\"New Zealand\",\n",
        "\"Papua New Guinea\",\n",
        "\"Samoa\",\n",
        "\"Solomon Islands\",\n",
        "\"Tonga\",\n",
        "\"Vanuatu\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UN4jJsRMH7NK",
        "outputId": "89372a70-ff65-4879-c1f2-0e746e9cda4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#create a new dataframe for the Australian contries with their corresponding columns\n",
        "australian_countries=sectioned_dataset2[sectioned_dataset2['country'].isin(australia)]\n",
        "\n",
        "#takiing a look at the various columns in the dataframe\n",
        "australian_countries.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Australia</td>\n",
              "      <td>7.67</td>\n",
              "      <td>7.92</td>\n",
              "      <td>7.72</td>\n",
              "      <td>7.59</td>\n",
              "      <td>7.49</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>8.35</td>\n",
              "      <td>8.39</td>\n",
              "      <td>8.49</td>\n",
              "      <td>8.42</td>\n",
              "      <td>8.36</td>\n",
              "      <td>8.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>10.10</td>\n",
              "      <td>8.97</td>\n",
              "      <td>9.07</td>\n",
              "      <td>8.94</td>\n",
              "      <td>8.77</td>\n",
              "      <td>8.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>2.51</td>\n",
              "      <td>2.47</td>\n",
              "      <td>2.49</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>7.31</td>\n",
              "      <td>6.97</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.04</td>\n",
              "      <td>7.31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              country   2015  2016  2017  2018  2019  median\n",
              "6           Australia   7.67  7.92  7.72  7.59  7.49    7.67\n",
              "54               Fiji   8.35  8.39  8.49  8.42  8.36    8.39\n",
              "111       New Zealand  10.10  8.97  9.07  8.94  8.77    8.97\n",
              "122  Papua New Guinea   2.51  2.47  2.49  2.48  2.46    2.48\n",
              "132             Samoa   7.31  6.97  7.64  7.85  7.04    7.31"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8TQSvHPH7Nc",
        "outputId": "6bcb5cf4-9b95-434a-ad85-891fc38c77dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "australian_countries = australian_countries.sort_values(by=['median'], ascending=False) #sorting australian countries in descending order to get the top 10\n",
        "australian_countries = australian_countries.head(10) #get the top 10 of the descending order to get the top 10 countries\n",
        "australian_countries_sorted = australian_countries.head(10) #australian countries 10 countries placed in australian_countries_sorted\n",
        "australian_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>57.30</td>\n",
              "      <td>57.50</td>\n",
              "      <td>58.90</td>\n",
              "      <td>59.10</td>\n",
              "      <td>59.20</td>\n",
              "      <td>58.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>10.10</td>\n",
              "      <td>8.97</td>\n",
              "      <td>9.07</td>\n",
              "      <td>8.94</td>\n",
              "      <td>8.77</td>\n",
              "      <td>8.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>8.35</td>\n",
              "      <td>8.39</td>\n",
              "      <td>8.49</td>\n",
              "      <td>8.42</td>\n",
              "      <td>8.36</td>\n",
              "      <td>8.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Australia</td>\n",
              "      <td>7.67</td>\n",
              "      <td>7.92</td>\n",
              "      <td>7.72</td>\n",
              "      <td>7.59</td>\n",
              "      <td>7.49</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>7.31</td>\n",
              "      <td>6.97</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.04</td>\n",
              "      <td>7.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>6.63</td>\n",
              "      <td>6.54</td>\n",
              "      <td>7.18</td>\n",
              "      <td>7.16</td>\n",
              "      <td>7.19</td>\n",
              "      <td>7.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>2.51</td>\n",
              "      <td>2.47</td>\n",
              "      <td>2.49</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>2.17</td>\n",
              "      <td>2.19</td>\n",
              "      <td>2.32</td>\n",
              "      <td>2.31</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              country   2015   2016   2017   2018   2019  median\n",
              "161             Tonga  57.30  57.50  58.90  59.10  59.20   58.90\n",
              "111       New Zealand  10.10   8.97   9.07   8.94   8.77    8.97\n",
              "54               Fiji   8.35   8.39   8.49   8.42   8.36    8.39\n",
              "6           Australia   7.67   7.92   7.72   7.59   7.49    7.67\n",
              "132             Samoa   7.31   6.97   7.64   7.85   7.04    7.31\n",
              "141   Solomon Islands   6.63   6.54   7.18   7.16   7.19    7.16\n",
              "122  Papua New Guinea   2.51   2.47   2.49   2.48   2.46    2.48\n",
              "173           Vanuatu   2.17   2.19   2.32   2.31   2.30    2.30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWL61ys7H7Nm",
        "outputId": "595da618-214e-486e-d8f5-9afa2a59115f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "#sorting in ascending order again to draw the bar chart\n",
        "australian_countries_sorted = australian_countries_sorted.sort_values(by=['median'], ascending=True)\n",
        "australian_countries_sorted"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>2.17</td>\n",
              "      <td>2.19</td>\n",
              "      <td>2.32</td>\n",
              "      <td>2.31</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>2.51</td>\n",
              "      <td>2.47</td>\n",
              "      <td>2.49</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>6.63</td>\n",
              "      <td>6.54</td>\n",
              "      <td>7.18</td>\n",
              "      <td>7.16</td>\n",
              "      <td>7.19</td>\n",
              "      <td>7.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>7.31</td>\n",
              "      <td>6.97</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.04</td>\n",
              "      <td>7.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Australia</td>\n",
              "      <td>7.67</td>\n",
              "      <td>7.92</td>\n",
              "      <td>7.72</td>\n",
              "      <td>7.59</td>\n",
              "      <td>7.49</td>\n",
              "      <td>7.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>8.35</td>\n",
              "      <td>8.39</td>\n",
              "      <td>8.49</td>\n",
              "      <td>8.42</td>\n",
              "      <td>8.36</td>\n",
              "      <td>8.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>10.10</td>\n",
              "      <td>8.97</td>\n",
              "      <td>9.07</td>\n",
              "      <td>8.94</td>\n",
              "      <td>8.77</td>\n",
              "      <td>8.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>57.30</td>\n",
              "      <td>57.50</td>\n",
              "      <td>58.90</td>\n",
              "      <td>59.10</td>\n",
              "      <td>59.20</td>\n",
              "      <td>58.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              country   2015   2016   2017   2018   2019  median\n",
              "173           Vanuatu   2.17   2.19   2.32   2.31   2.30    2.30\n",
              "122  Papua New Guinea   2.51   2.47   2.49   2.48   2.46    2.48\n",
              "141   Solomon Islands   6.63   6.54   7.18   7.16   7.19    7.16\n",
              "132             Samoa   7.31   6.97   7.64   7.85   7.04    7.31\n",
              "6           Australia   7.67   7.92   7.72   7.59   7.49    7.67\n",
              "54               Fiji   8.35   8.39   8.49   8.42   8.36    8.39\n",
              "111       New Zealand  10.10   8.97   9.07   8.94   8.77    8.97\n",
              "161             Tonga  57.30  57.50  58.90  59.10  59.20   58.90"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6bIaInNH7Nv",
        "outputId": "7db5579b-818f-48b3-d26a-56d794311db0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries in Australia from 2015 to 2019\n",
        "australian_countries_sorted.plot.barh(x='country', y='median', color='hotpink', rot=0 )\n",
        "plt.title(\"Top countries with the highest percentage of females in industries in Australia\")\n",
        "plt.xlabel(\"Median\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5Sd0eaaAsvJ"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "According to the graph above, Tonga superseeds all the countries in the Oceania region and also the country with the highest number of feamles in industries globally within the year 2015 to 2019."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEtpp2BIH7N1"
      },
      "source": [
        "#Finding the median of each row or country across the years from 1991 to 2022\n",
        "dataset['median'] = dataset.median(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxk9kmohH7N7",
        "outputId": "9ea45c33-f680-464e-d001-31af72e4964f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>6.76</td>\n",
              "      <td>4.29</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2.96</td>\n",
              "      <td>3.51</td>\n",
              "      <td>3.97</td>\n",
              "      <td>4.57</td>\n",
              "      <td>5.41</td>\n",
              "      <td>6.63</td>\n",
              "      <td>7.90</td>\n",
              "      <td>6.26</td>\n",
              "      <td>6.14</td>\n",
              "      <td>7.23</td>\n",
              "      <td>6.76</td>\n",
              "      <td>6.84</td>\n",
              "      <td>7.38</td>\n",
              "      <td>7.49</td>\n",
              "      <td>5.55</td>\n",
              "      <td>5.53</td>\n",
              "      <td>5.98</td>\n",
              "      <td>5.53</td>\n",
              "      <td>5.44</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.56</td>\n",
              "      <td>5.52</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.46</td>\n",
              "      <td>5.41</td>\n",
              "      <td>5.35</td>\n",
              "      <td>5.29</td>\n",
              "      <td>5.525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>12.10</td>\n",
              "      <td>10.30</td>\n",
              "      <td>9.70</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.60</td>\n",
              "      <td>11.90</td>\n",
              "      <td>10.40</td>\n",
              "      <td>9.93</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.70</td>\n",
              "      <td>11.50</td>\n",
              "      <td>11.80</td>\n",
              "      <td>12.20</td>\n",
              "      <td>12.50</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.30</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.40</td>\n",
              "      <td>8.74</td>\n",
              "      <td>10.20</td>\n",
              "      <td>11.70</td>\n",
              "      <td>13.90</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.60</td>\n",
              "      <td>13.50</td>\n",
              "      <td>11.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>35.10</td>\n",
              "      <td>35.50</td>\n",
              "      <td>36.30</td>\n",
              "      <td>36.90</td>\n",
              "      <td>36.10</td>\n",
              "      <td>33.00</td>\n",
              "      <td>32.30</td>\n",
              "      <td>33.40</td>\n",
              "      <td>32.50</td>\n",
              "      <td>31.00</td>\n",
              "      <td>31.30</td>\n",
              "      <td>30.10</td>\n",
              "      <td>27.20</td>\n",
              "      <td>31.00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>42.50</td>\n",
              "      <td>45.30</td>\n",
              "      <td>49.30</td>\n",
              "      <td>50.30</td>\n",
              "      <td>50.20</td>\n",
              "      <td>51.70</td>\n",
              "      <td>54.60</td>\n",
              "      <td>55.50</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.40</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.90</td>\n",
              "      <td>56.10</td>\n",
              "      <td>56.10</td>\n",
              "      <td>40.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.68</td>\n",
              "      <td>1.52</td>\n",
              "      <td>1.81</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.99</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.71</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.81</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.55</td>\n",
              "      <td>1.52</td>\n",
              "      <td>1.39</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.45</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.31</td>\n",
              "      <td>1.33</td>\n",
              "      <td>1.34</td>\n",
              "      <td>1.485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>19.80</td>\n",
              "      <td>18.80</td>\n",
              "      <td>16.10</td>\n",
              "      <td>14.40</td>\n",
              "      <td>14.90</td>\n",
              "      <td>12.30</td>\n",
              "      <td>11.50</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.12</td>\n",
              "      <td>11.10</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.20</td>\n",
              "      <td>9.62</td>\n",
              "      <td>9.31</td>\n",
              "      <td>9.95</td>\n",
              "      <td>9.43</td>\n",
              "      <td>8.88</td>\n",
              "      <td>8.32</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.66</td>\n",
              "      <td>7.50</td>\n",
              "      <td>7.35</td>\n",
              "      <td>7.23</td>\n",
              "      <td>7.13</td>\n",
              "      <td>10.100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   1991   1992   1993   1994  ...   2019   2020   2021   2022  median\n",
              "0  Afghanistan   6.76   4.29   4.25   4.00  ...   5.46   5.41   5.35   5.29   5.525\n",
              "1      Albania  12.10  10.30   9.70  10.10  ...  13.70  13.70  13.60  13.50  11.150\n",
              "2      Algeria  35.10  35.50  36.30  36.90  ...  55.70  55.90  56.10  56.10  40.750\n",
              "3       Angola   1.68   1.52   1.81   1.84  ...   1.30   1.31   1.33   1.34   1.485\n",
              "4    Argentina  19.80  18.80  16.10  14.40  ...   7.50   7.35   7.23   7.13  10.100\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yk-5m1ZZH7OB",
        "outputId": "a2868204-c254-48e9-b47e-90ffbba689c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "#Dataset after including the median column\n",
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>6.76</td>\n",
              "      <td>4.29</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2.96</td>\n",
              "      <td>3.51</td>\n",
              "      <td>3.97</td>\n",
              "      <td>4.57</td>\n",
              "      <td>5.41</td>\n",
              "      <td>6.63</td>\n",
              "      <td>7.90</td>\n",
              "      <td>6.26</td>\n",
              "      <td>6.14</td>\n",
              "      <td>7.23</td>\n",
              "      <td>6.76</td>\n",
              "      <td>6.84</td>\n",
              "      <td>7.38</td>\n",
              "      <td>7.49</td>\n",
              "      <td>5.55</td>\n",
              "      <td>5.53</td>\n",
              "      <td>5.98</td>\n",
              "      <td>5.53</td>\n",
              "      <td>5.44</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.56</td>\n",
              "      <td>5.52</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.46</td>\n",
              "      <td>5.41</td>\n",
              "      <td>5.35</td>\n",
              "      <td>5.29</td>\n",
              "      <td>5.525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>12.10</td>\n",
              "      <td>10.30</td>\n",
              "      <td>9.70</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.60</td>\n",
              "      <td>11.90</td>\n",
              "      <td>10.40</td>\n",
              "      <td>9.93</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.70</td>\n",
              "      <td>11.50</td>\n",
              "      <td>11.80</td>\n",
              "      <td>12.20</td>\n",
              "      <td>12.50</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.90</td>\n",
              "      <td>10.30</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.40</td>\n",
              "      <td>8.74</td>\n",
              "      <td>10.20</td>\n",
              "      <td>11.70</td>\n",
              "      <td>13.90</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.60</td>\n",
              "      <td>13.50</td>\n",
              "      <td>11.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>35.10</td>\n",
              "      <td>35.50</td>\n",
              "      <td>36.30</td>\n",
              "      <td>36.90</td>\n",
              "      <td>36.10</td>\n",
              "      <td>33.00</td>\n",
              "      <td>32.30</td>\n",
              "      <td>33.40</td>\n",
              "      <td>32.50</td>\n",
              "      <td>31.00</td>\n",
              "      <td>31.30</td>\n",
              "      <td>30.10</td>\n",
              "      <td>27.20</td>\n",
              "      <td>31.00</td>\n",
              "      <td>36.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>42.50</td>\n",
              "      <td>45.30</td>\n",
              "      <td>49.30</td>\n",
              "      <td>50.30</td>\n",
              "      <td>50.20</td>\n",
              "      <td>51.70</td>\n",
              "      <td>54.60</td>\n",
              "      <td>55.50</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.40</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.90</td>\n",
              "      <td>56.10</td>\n",
              "      <td>56.10</td>\n",
              "      <td>40.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.68</td>\n",
              "      <td>1.52</td>\n",
              "      <td>1.81</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.99</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.71</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.81</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.55</td>\n",
              "      <td>1.52</td>\n",
              "      <td>1.39</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.45</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.31</td>\n",
              "      <td>1.33</td>\n",
              "      <td>1.34</td>\n",
              "      <td>1.485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>19.80</td>\n",
              "      <td>18.80</td>\n",
              "      <td>16.10</td>\n",
              "      <td>14.40</td>\n",
              "      <td>14.90</td>\n",
              "      <td>12.30</td>\n",
              "      <td>11.50</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.10</td>\n",
              "      <td>10.10</td>\n",
              "      <td>9.12</td>\n",
              "      <td>11.10</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.20</td>\n",
              "      <td>9.62</td>\n",
              "      <td>9.31</td>\n",
              "      <td>9.95</td>\n",
              "      <td>9.43</td>\n",
              "      <td>8.88</td>\n",
              "      <td>8.32</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.66</td>\n",
              "      <td>7.50</td>\n",
              "      <td>7.35</td>\n",
              "      <td>7.23</td>\n",
              "      <td>7.13</td>\n",
              "      <td>10.100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   1991   1992   1993   1994  ...   2019   2020   2021   2022  median\n",
              "0  Afghanistan   6.76   4.29   4.25   4.00  ...   5.46   5.41   5.35   5.29   5.525\n",
              "1      Albania  12.10  10.30   9.70  10.10  ...  13.70  13.70  13.60  13.50  11.150\n",
              "2      Algeria  35.10  35.50  36.30  36.90  ...  55.70  55.90  56.10  56.10  40.750\n",
              "3       Angola   1.68   1.52   1.81   1.84  ...   1.30   1.31   1.33   1.34   1.485\n",
              "4    Argentina  19.80  18.80  16.10  14.40  ...   7.50   7.35   7.23   7.13  10.100\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSlvfoDXH7OQ"
      },
      "source": [
        "#picking a subset of the datasetfrom 2015 to 2020\n",
        "sectioned_dataset = dataset[[\"country\",\"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", \"median\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjZk8cS4_Qd_"
      },
      "source": [
        "# **Reading the GDP csv Dataset**\n",
        "This dataset is also obtained from gapminder at https://www.gapminder.org/data/\n",
        "The aim was to answer the question **are wealthier countries better at gender balance?**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQaHMtV_g8iU",
        "outputId": "87bce6cf-ffd2-4d39-d914-df4a00c9efc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "#Reading the gdp dataset particularly the exports perentage gdp\n",
        "gdp_dataset=pd.read_csv(r\"exports_percent_of_gdp.csv\")\n",
        "gdp_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1960</th>\n",
              "      <th>1961</th>\n",
              "      <th>1962</th>\n",
              "      <th>1963</th>\n",
              "      <th>1964</th>\n",
              "      <th>1965</th>\n",
              "      <th>1966</th>\n",
              "      <th>1967</th>\n",
              "      <th>1968</th>\n",
              "      <th>1969</th>\n",
              "      <th>1970</th>\n",
              "      <th>1971</th>\n",
              "      <th>1972</th>\n",
              "      <th>1973</th>\n",
              "      <th>1974</th>\n",
              "      <th>1975</th>\n",
              "      <th>1976</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>1987</th>\n",
              "      <th>1988</th>\n",
              "      <th>1989</th>\n",
              "      <th>1990</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0.0413</td>\n",
              "      <td>0.0445</td>\n",
              "      <td>0.0488</td>\n",
              "      <td>0.0917</td>\n",
              "      <td>0.0889</td>\n",
              "      <td>0.113</td>\n",
              "      <td>0.0857</td>\n",
              "      <td>0.0677</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.101</td>\n",
              "      <td>0.0978</td>\n",
              "      <td>0.109</td>\n",
              "      <td>0.148</td>\n",
              "      <td>0.129</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.127</td>\n",
              "      <td>0.132</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.108</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.170</td>\n",
              "      <td>0.162</td>\n",
              "      <td>0.172</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.0673</td>\n",
              "      <td>0.0969</td>\n",
              "      <td>0.0712</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.0571</td>\n",
              "      <td>0.0826</td>\n",
              "      <td>0.0775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.238</td>\n",
              "      <td>0.201</td>\n",
              "      <td>0.189</td>\n",
              "      <td>0.180</td>\n",
              "      <td>0.168</td>\n",
              "      <td>0.156</td>\n",
              "      <td>0.160</td>\n",
              "      <td>0.170</td>\n",
              "      <td>0.186</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.0748</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.160</td>\n",
              "      <td>0.120</td>\n",
              "      <td>0.126</td>\n",
              "      <td>0.116</td>\n",
              "      <td>0.102</td>\n",
              "      <td>0.115</td>\n",
              "      <td>0.168</td>\n",
              "      <td>0.201</td>\n",
              "      <td>0.213</td>\n",
              "      <td>0.209</td>\n",
              "      <td>0.208</td>\n",
              "      <td>0.222</td>\n",
              "      <td>0.230</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.282</td>\n",
              "      <td>0.253</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.289</td>\n",
              "      <td>0.2890</td>\n",
              "      <td>0.2820</td>\n",
              "      <td>0.2730</td>\n",
              "      <td>0.2900</td>\n",
              "      <td>0.3160</td>\n",
              "      <td>0.3150</td>\n",
              "      <td>0.3150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>0.3900</td>\n",
              "      <td>0.4620</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.2470</td>\n",
              "      <td>0.2510</td>\n",
              "      <td>0.226</td>\n",
              "      <td>0.2600</td>\n",
              "      <td>0.2340</td>\n",
              "      <td>0.231</td>\n",
              "      <td>0.238</td>\n",
              "      <td>0.2210</td>\n",
              "      <td>0.184</td>\n",
              "      <td>0.204</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.387</td>\n",
              "      <td>0.337</td>\n",
              "      <td>0.331</td>\n",
              "      <td>0.306</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.343</td>\n",
              "      <td>0.346</td>\n",
              "      <td>0.309</td>\n",
              "      <td>0.279</td>\n",
              "      <td>0.257</td>\n",
              "      <td>0.236</td>\n",
              "      <td>0.129</td>\n",
              "      <td>0.143</td>\n",
              "      <td>0.155</td>\n",
              "      <td>0.186</td>\n",
              "      <td>0.234</td>\n",
              "      <td>0.2910</td>\n",
              "      <td>0.253</td>\n",
              "      <td>0.218</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.262</td>\n",
              "      <td>0.298</td>\n",
              "      <td>0.309</td>\n",
              "      <td>0.226</td>\n",
              "      <td>0.281</td>\n",
              "      <td>0.421</td>\n",
              "      <td>0.367</td>\n",
              "      <td>0.355</td>\n",
              "      <td>0.382</td>\n",
              "      <td>0.401</td>\n",
              "      <td>0.472</td>\n",
              "      <td>0.488</td>\n",
              "      <td>0.471</td>\n",
              "      <td>0.480</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.384</td>\n",
              "      <td>0.388</td>\n",
              "      <td>0.369</td>\n",
              "      <td>0.3320</td>\n",
              "      <td>0.3020</td>\n",
              "      <td>0.2320</td>\n",
              "      <td>0.2090</td>\n",
              "      <td>0.2270</td>\n",
              "      <td>0.2560</td>\n",
              "      <td>0.2260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.897</td>\n",
              "      <td>0.754</td>\n",
              "      <td>0.571</td>\n",
              "      <td>0.543</td>\n",
              "      <td>0.584</td>\n",
              "      <td>0.655</td>\n",
              "      <td>0.635</td>\n",
              "      <td>0.679</td>\n",
              "      <td>0.725</td>\n",
              "      <td>0.588</td>\n",
              "      <td>0.615</td>\n",
              "      <td>0.607</td>\n",
              "      <td>0.559</td>\n",
              "      <td>0.5070</td>\n",
              "      <td>0.4470</td>\n",
              "      <td>0.2980</td>\n",
              "      <td>0.2810</td>\n",
              "      <td>0.2900</td>\n",
              "      <td>0.4080</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Antigua and Barbuda</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.407</td>\n",
              "      <td>0.525</td>\n",
              "      <td>0.692</td>\n",
              "      <td>0.564</td>\n",
              "      <td>0.616</td>\n",
              "      <td>0.569</td>\n",
              "      <td>0.516</td>\n",
              "      <td>0.622</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.709</td>\n",
              "      <td>0.673</td>\n",
              "      <td>0.666</td>\n",
              "      <td>0.663</td>\n",
              "      <td>0.752</td>\n",
              "      <td>0.7680</td>\n",
              "      <td>0.816</td>\n",
              "      <td>0.821</td>\n",
              "      <td>0.740</td>\n",
              "      <td>0.696</td>\n",
              "      <td>0.635</td>\n",
              "      <td>0.651</td>\n",
              "      <td>0.640</td>\n",
              "      <td>0.617</td>\n",
              "      <td>0.562</td>\n",
              "      <td>0.556</td>\n",
              "      <td>0.525</td>\n",
              "      <td>0.541</td>\n",
              "      <td>0.581</td>\n",
              "      <td>0.533</td>\n",
              "      <td>0.474</td>\n",
              "      <td>0.443</td>\n",
              "      <td>0.457</td>\n",
              "      <td>0.459</td>\n",
              "      <td>0.456</td>\n",
              "      <td>0.473</td>\n",
              "      <td>0.453</td>\n",
              "      <td>0.4620</td>\n",
              "      <td>0.4730</td>\n",
              "      <td>0.4510</td>\n",
              "      <td>0.4240</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               country    1960    1961    1962  ...    2016    2017    2018    2019\n",
              "0          Afghanistan  0.0413  0.0445  0.0488  ...  0.0587  0.0571  0.0826  0.0775\n",
              "1              Albania     NaN     NaN     NaN  ...  0.2900  0.3160  0.3150  0.3150\n",
              "2              Algeria  0.3900  0.4620  0.1980  ...  0.2090  0.2270  0.2560  0.2260\n",
              "3               Angola     NaN     NaN     NaN  ...  0.2810  0.2900  0.4080     NaN\n",
              "4  Antigua and Barbuda     NaN     NaN     NaN  ...  0.4240     NaN     NaN     NaN\n",
              "\n",
              "[5 rows x 61 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTII3kdoFK8_"
      },
      "source": [
        "**Descriptives on the GDP dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZbwZwhog8ib",
        "outputId": "c9defa93-cf1c-4244-f78f-abd1780cd015",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "gdp_dataset.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country     object\n",
              "1960       float64\n",
              "1961       float64\n",
              "1962       float64\n",
              "1963       float64\n",
              "            ...   \n",
              "2015       float64\n",
              "2016       float64\n",
              "2017       float64\n",
              "2018       float64\n",
              "2019       float64\n",
              "Length: 61, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oks-Vb0eg8ik",
        "outputId": "995b41d4-2f9a-4332-aed8-869d779be6f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "\n",
        "gdp_dataset.describe(include = 'all')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1960</th>\n",
              "      <th>1961</th>\n",
              "      <th>1962</th>\n",
              "      <th>1963</th>\n",
              "      <th>1964</th>\n",
              "      <th>1965</th>\n",
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              "      <th>1967</th>\n",
              "      <th>1968</th>\n",
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              "      <th>1973</th>\n",
              "      <th>1974</th>\n",
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              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>1987</th>\n",
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              "      <th>1989</th>\n",
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              "      <th>1991</th>\n",
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              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
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              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
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              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
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              "      <th>2019</th>\n",
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              "      <th>count</th>\n",
              "      <td>184</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>157.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>171.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>174.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>143.000000</td>\n",
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              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>184</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Central African Republic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.211152</td>\n",
              "      <td>0.205760</td>\n",
              "      <td>0.201029</td>\n",
              "      <td>0.210061</td>\n",
              "      <td>0.212311</td>\n",
              "      <td>0.217484</td>\n",
              "      <td>0.220762</td>\n",
              "      <td>0.217222</td>\n",
              "      <td>0.229950</td>\n",
              "      <td>0.232949</td>\n",
              "      <td>0.248049</td>\n",
              "      <td>0.247112</td>\n",
              "      <td>0.252529</td>\n",
              "      <td>0.272764</td>\n",
              "      <td>0.315879</td>\n",
              "      <td>0.295779</td>\n",
              "      <td>0.299325</td>\n",
              "      <td>0.311025</td>\n",
              "      <td>0.308192</td>\n",
              "      <td>0.326549</td>\n",
              "      <td>0.350105</td>\n",
              "      <td>0.337176</td>\n",
              "      <td>0.320284</td>\n",
              "      <td>0.312776</td>\n",
              "      <td>0.318559</td>\n",
              "      <td>0.316402</td>\n",
              "      <td>0.297153</td>\n",
              "      <td>0.308999</td>\n",
              "      <td>0.308748</td>\n",
              "      <td>0.320383</td>\n",
              "      <td>0.325367</td>\n",
              "      <td>0.315657</td>\n",
              "      <td>0.332501</td>\n",
              "      <td>0.329996</td>\n",
              "      <td>0.340368</td>\n",
              "      <td>0.344844</td>\n",
              "      <td>0.344877</td>\n",
              "      <td>0.355691</td>\n",
              "      <td>0.346131</td>\n",
              "      <td>0.353828</td>\n",
              "      <td>0.383106</td>\n",
              "      <td>0.371936</td>\n",
              "      <td>0.369573</td>\n",
              "      <td>0.375095</td>\n",
              "      <td>0.394033</td>\n",
              "      <td>0.405287</td>\n",
              "      <td>0.416442</td>\n",
              "      <td>0.420749</td>\n",
              "      <td>0.428950</td>\n",
              "      <td>0.378312</td>\n",
              "      <td>0.403678</td>\n",
              "      <td>0.428668</td>\n",
              "      <td>0.431738</td>\n",
              "      <td>0.428980</td>\n",
              "      <td>0.426382</td>\n",
              "      <td>0.411723</td>\n",
              "      <td>0.401157</td>\n",
              "      <td>0.419784</td>\n",
              "      <td>0.424689</td>\n",
              "      <td>0.442851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.214234</td>\n",
              "      <td>0.189436</td>\n",
              "      <td>0.186269</td>\n",
              "      <td>0.194117</td>\n",
              "      <td>0.172886</td>\n",
              "      <td>0.179761</td>\n",
              "      <td>0.179807</td>\n",
              "      <td>0.172459</td>\n",
              "      <td>0.192333</td>\n",
              "      <td>0.196616</td>\n",
              "      <td>0.190083</td>\n",
              "      <td>0.187447</td>\n",
              "      <td>0.178760</td>\n",
              "      <td>0.197602</td>\n",
              "      <td>0.237473</td>\n",
              "      <td>0.220563</td>\n",
              "      <td>0.218120</td>\n",
              "      <td>0.220808</td>\n",
              "      <td>0.223665</td>\n",
              "      <td>0.237124</td>\n",
              "      <td>0.281088</td>\n",
              "      <td>0.278553</td>\n",
              "      <td>0.262768</td>\n",
              "      <td>0.238124</td>\n",
              "      <td>0.217931</td>\n",
              "      <td>0.219223</td>\n",
              "      <td>0.213658</td>\n",
              "      <td>0.226406</td>\n",
              "      <td>0.231730</td>\n",
              "      <td>0.230655</td>\n",
              "      <td>0.229032</td>\n",
              "      <td>0.222818</td>\n",
              "      <td>0.235117</td>\n",
              "      <td>0.225876</td>\n",
              "      <td>0.224628</td>\n",
              "      <td>0.231445</td>\n",
              "      <td>0.230672</td>\n",
              "      <td>0.227990</td>\n",
              "      <td>0.231280</td>\n",
              "      <td>0.239924</td>\n",
              "      <td>0.261183</td>\n",
              "      <td>0.247568</td>\n",
              "      <td>0.240271</td>\n",
              "      <td>0.244427</td>\n",
              "      <td>0.253327</td>\n",
              "      <td>0.261851</td>\n",
              "      <td>0.270757</td>\n",
              "      <td>0.270069</td>\n",
              "      <td>0.284337</td>\n",
              "      <td>0.255141</td>\n",
              "      <td>0.264926</td>\n",
              "      <td>0.280029</td>\n",
              "      <td>0.284605</td>\n",
              "      <td>0.296564</td>\n",
              "      <td>0.297176</td>\n",
              "      <td>0.309357</td>\n",
              "      <td>0.297463</td>\n",
              "      <td>0.310979</td>\n",
              "      <td>0.296656</td>\n",
              "      <td>0.303579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.020600</td>\n",
              "      <td>0.038700</td>\n",
              "      <td>0.035200</td>\n",
              "      <td>0.038200</td>\n",
              "      <td>0.028000</td>\n",
              "      <td>0.033100</td>\n",
              "      <td>0.034900</td>\n",
              "      <td>0.032800</td>\n",
              "      <td>0.033000</td>\n",
              "      <td>0.030500</td>\n",
              "      <td>0.024900</td>\n",
              "      <td>0.027900</td>\n",
              "      <td>0.032500</td>\n",
              "      <td>0.042100</td>\n",
              "      <td>0.037000</td>\n",
              "      <td>0.029000</td>\n",
              "      <td>0.045100</td>\n",
              "      <td>0.038200</td>\n",
              "      <td>0.041500</td>\n",
              "      <td>0.032200</td>\n",
              "      <td>0.050600</td>\n",
              "      <td>0.047600</td>\n",
              "      <td>0.033400</td>\n",
              "      <td>0.033800</td>\n",
              "      <td>0.027600</td>\n",
              "      <td>0.040400</td>\n",
              "      <td>0.036100</td>\n",
              "      <td>0.049900</td>\n",
              "      <td>0.038000</td>\n",
              "      <td>0.053400</td>\n",
              "      <td>0.040200</td>\n",
              "      <td>0.012900</td>\n",
              "      <td>0.005820</td>\n",
              "      <td>0.000756</td>\n",
              "      <td>0.000356</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>0.000092</td>\n",
              "      <td>0.053400</td>\n",
              "      <td>0.055900</td>\n",
              "      <td>0.062200</td>\n",
              "      <td>0.005390</td>\n",
              "      <td>0.004710</td>\n",
              "      <td>0.002860</td>\n",
              "      <td>0.002080</td>\n",
              "      <td>0.001870</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>0.001570</td>\n",
              "      <td>0.001380</td>\n",
              "      <td>0.001170</td>\n",
              "      <td>0.001030</td>\n",
              "      <td>0.063800</td>\n",
              "      <td>0.000995</td>\n",
              "      <td>0.072000</td>\n",
              "      <td>0.055900</td>\n",
              "      <td>0.045700</td>\n",
              "      <td>0.030400</td>\n",
              "      <td>0.031300</td>\n",
              "      <td>0.023700</td>\n",
              "      <td>0.027400</td>\n",
              "      <td>0.077500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.113250</td>\n",
              "      <td>0.107000</td>\n",
              "      <td>0.093100</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.114000</td>\n",
              "      <td>0.109000</td>\n",
              "      <td>0.109500</td>\n",
              "      <td>0.107500</td>\n",
              "      <td>0.114000</td>\n",
              "      <td>0.125750</td>\n",
              "      <td>0.128000</td>\n",
              "      <td>0.139000</td>\n",
              "      <td>0.144250</td>\n",
              "      <td>0.144500</td>\n",
              "      <td>0.145000</td>\n",
              "      <td>0.153250</td>\n",
              "      <td>0.167750</td>\n",
              "      <td>0.165750</td>\n",
              "      <td>0.178500</td>\n",
              "      <td>0.171000</td>\n",
              "      <td>0.159500</td>\n",
              "      <td>0.150500</td>\n",
              "      <td>0.157500</td>\n",
              "      <td>0.168500</td>\n",
              "      <td>0.159500</td>\n",
              "      <td>0.154500</td>\n",
              "      <td>0.156000</td>\n",
              "      <td>0.163000</td>\n",
              "      <td>0.173000</td>\n",
              "      <td>0.171000</td>\n",
              "      <td>0.169000</td>\n",
              "      <td>0.169000</td>\n",
              "      <td>0.177000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.209000</td>\n",
              "      <td>0.202000</td>\n",
              "      <td>0.203000</td>\n",
              "      <td>0.193000</td>\n",
              "      <td>0.192000</td>\n",
              "      <td>0.207500</td>\n",
              "      <td>0.212750</td>\n",
              "      <td>0.215000</td>\n",
              "      <td>0.231000</td>\n",
              "      <td>0.237000</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.252000</td>\n",
              "      <td>0.255000</td>\n",
              "      <td>0.252000</td>\n",
              "      <td>0.217500</td>\n",
              "      <td>0.228750</td>\n",
              "      <td>0.252750</td>\n",
              "      <td>0.258000</td>\n",
              "      <td>0.248000</td>\n",
              "      <td>0.237000</td>\n",
              "      <td>0.224500</td>\n",
              "      <td>0.220250</td>\n",
              "      <td>0.226500</td>\n",
              "      <td>0.234500</td>\n",
              "      <td>0.246000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.157000</td>\n",
              "      <td>0.160500</td>\n",
              "      <td>0.151000</td>\n",
              "      <td>0.173000</td>\n",
              "      <td>0.174500</td>\n",
              "      <td>0.171000</td>\n",
              "      <td>0.176000</td>\n",
              "      <td>0.174000</td>\n",
              "      <td>0.183000</td>\n",
              "      <td>0.184000</td>\n",
              "      <td>0.214500</td>\n",
              "      <td>0.211000</td>\n",
              "      <td>0.212000</td>\n",
              "      <td>0.236000</td>\n",
              "      <td>0.256000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>0.245500</td>\n",
              "      <td>0.264500</td>\n",
              "      <td>0.252500</td>\n",
              "      <td>0.267000</td>\n",
              "      <td>0.284000</td>\n",
              "      <td>0.275000</td>\n",
              "      <td>0.263000</td>\n",
              "      <td>0.257000</td>\n",
              "      <td>0.266500</td>\n",
              "      <td>0.265000</td>\n",
              "      <td>0.256000</td>\n",
              "      <td>0.255000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.259000</td>\n",
              "      <td>0.275000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>0.272000</td>\n",
              "      <td>0.280500</td>\n",
              "      <td>0.296000</td>\n",
              "      <td>0.293500</td>\n",
              "      <td>0.284000</td>\n",
              "      <td>0.286000</td>\n",
              "      <td>0.296000</td>\n",
              "      <td>0.289000</td>\n",
              "      <td>0.329000</td>\n",
              "      <td>0.318500</td>\n",
              "      <td>0.325000</td>\n",
              "      <td>0.327000</td>\n",
              "      <td>0.351000</td>\n",
              "      <td>0.355000</td>\n",
              "      <td>0.365000</td>\n",
              "      <td>0.376000</td>\n",
              "      <td>0.379000</td>\n",
              "      <td>0.327500</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>0.366500</td>\n",
              "      <td>0.367000</td>\n",
              "      <td>0.372000</td>\n",
              "      <td>0.373000</td>\n",
              "      <td>0.339000</td>\n",
              "      <td>0.326500</td>\n",
              "      <td>0.349500</td>\n",
              "      <td>0.361000</td>\n",
              "      <td>0.372000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.263000</td>\n",
              "      <td>0.248250</td>\n",
              "      <td>0.242000</td>\n",
              "      <td>0.252000</td>\n",
              "      <td>0.281500</td>\n",
              "      <td>0.267500</td>\n",
              "      <td>0.277000</td>\n",
              "      <td>0.264000</td>\n",
              "      <td>0.267750</td>\n",
              "      <td>0.267000</td>\n",
              "      <td>0.316750</td>\n",
              "      <td>0.319000</td>\n",
              "      <td>0.322500</td>\n",
              "      <td>0.329750</td>\n",
              "      <td>0.388500</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>0.369000</td>\n",
              "      <td>0.388250</td>\n",
              "      <td>0.409250</td>\n",
              "      <td>0.412500</td>\n",
              "      <td>0.443000</td>\n",
              "      <td>0.430500</td>\n",
              "      <td>0.409000</td>\n",
              "      <td>0.387500</td>\n",
              "      <td>0.419250</td>\n",
              "      <td>0.426250</td>\n",
              "      <td>0.398500</td>\n",
              "      <td>0.408000</td>\n",
              "      <td>0.413000</td>\n",
              "      <td>0.411000</td>\n",
              "      <td>0.421000</td>\n",
              "      <td>0.406500</td>\n",
              "      <td>0.431000</td>\n",
              "      <td>0.432750</td>\n",
              "      <td>0.445000</td>\n",
              "      <td>0.421250</td>\n",
              "      <td>0.425000</td>\n",
              "      <td>0.453000</td>\n",
              "      <td>0.434000</td>\n",
              "      <td>0.432000</td>\n",
              "      <td>0.485000</td>\n",
              "      <td>0.456750</td>\n",
              "      <td>0.472000</td>\n",
              "      <td>0.471000</td>\n",
              "      <td>0.512000</td>\n",
              "      <td>0.533000</td>\n",
              "      <td>0.525500</td>\n",
              "      <td>0.530000</td>\n",
              "      <td>0.540000</td>\n",
              "      <td>0.483750</td>\n",
              "      <td>0.504000</td>\n",
              "      <td>0.538750</td>\n",
              "      <td>0.541000</td>\n",
              "      <td>0.513000</td>\n",
              "      <td>0.499000</td>\n",
              "      <td>0.489000</td>\n",
              "      <td>0.476750</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.510750</td>\n",
              "      <td>0.554000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.630000</td>\n",
              "      <td>1.420000</td>\n",
              "      <td>1.380000</td>\n",
              "      <td>1.410000</td>\n",
              "      <td>1.220000</td>\n",
              "      <td>1.230000</td>\n",
              "      <td>1.230000</td>\n",
              "      <td>1.140000</td>\n",
              "      <td>1.260000</td>\n",
              "      <td>1.320000</td>\n",
              "      <td>1.260000</td>\n",
              "      <td>1.190000</td>\n",
              "      <td>1.070000</td>\n",
              "      <td>1.180000</td>\n",
              "      <td>1.490000</td>\n",
              "      <td>1.370000</td>\n",
              "      <td>1.490000</td>\n",
              "      <td>1.630000</td>\n",
              "      <td>1.650000</td>\n",
              "      <td>1.850000</td>\n",
              "      <td>2.020000</td>\n",
              "      <td>1.980000</td>\n",
              "      <td>1.850000</td>\n",
              "      <td>1.670000</td>\n",
              "      <td>1.560000</td>\n",
              "      <td>1.520000</td>\n",
              "      <td>1.490000</td>\n",
              "      <td>1.640000</td>\n",
              "      <td>1.850000</td>\n",
              "      <td>1.790000</td>\n",
              "      <td>1.770000</td>\n",
              "      <td>1.690000</td>\n",
              "      <td>1.620000</td>\n",
              "      <td>1.620000</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>1.810000</td>\n",
              "      <td>1.760000</td>\n",
              "      <td>1.690000</td>\n",
              "      <td>1.670000</td>\n",
              "      <td>1.770000</td>\n",
              "      <td>1.880000</td>\n",
              "      <td>1.830000</td>\n",
              "      <td>1.840000</td>\n",
              "      <td>2.030000</td>\n",
              "      <td>2.140000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.280000</td>\n",
              "      <td>2.130000</td>\n",
              "      <td>2.290000</td>\n",
              "      <td>1.910000</td>\n",
              "      <td>1.980000</td>\n",
              "      <td>2.030000</td>\n",
              "      <td>1.970000</td>\n",
              "      <td>1.950000</td>\n",
              "      <td>2.130000</td>\n",
              "      <td>2.210000</td>\n",
              "      <td>2.130000</td>\n",
              "      <td>2.180000</td>\n",
              "      <td>2.120000</td>\n",
              "      <td>2.090000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         country       1960  ...        2018        2019\n",
              "count                        184  65.000000  ...  168.000000  143.000000\n",
              "unique                       184        NaN  ...         NaN         NaN\n",
              "top     Central African Republic        NaN  ...         NaN         NaN\n",
              "freq                           1        NaN  ...         NaN         NaN\n",
              "mean                         NaN   0.211152  ...    0.424689    0.442851\n",
              "std                          NaN   0.214234  ...    0.296656    0.303579\n",
              "min                          NaN   0.020600  ...    0.027400    0.077500\n",
              "25%                          NaN   0.100000  ...    0.234500    0.246000\n",
              "50%                          NaN   0.157000  ...    0.361000    0.372000\n",
              "75%                          NaN   0.263000  ...    0.510750    0.554000\n",
              "max                          NaN   1.630000  ...    2.120000    2.090000\n",
              "\n",
              "[11 rows x 61 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsEs9dSNg8iv",
        "outputId": "9ceae80e-7c5f-4c09-9ec8-f7c4c4acc6fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "gdp_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(184, 61)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hycb8-ogg8i5",
        "outputId": "ffe3cd75-f451-429f-fc88-8d9e31025239",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "world_countries = gdp_dataset['country']\n",
        "world_countries"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              Afghanistan\n",
              "1                  Albania\n",
              "2                  Algeria\n",
              "3                   Angola\n",
              "4      Antigua and Barbuda\n",
              "              ...         \n",
              "179                Vanuatu\n",
              "180              Venezuela\n",
              "181                Vietnam\n",
              "182                 Zambia\n",
              "183               Zimbabwe\n",
              "Name: country, Length: 184, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9R9ZhaZ7g8jP",
        "outputId": "7b329480-c76d-4c1a-b060-aab22ce64801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "values =['Afghanistan','Albania','Algeria','Angola','Argentina','Armenia','Australia','Austria','Azerbaijan','Bahamas','Bahrain',\n",
        "    'Bangladesh','Barbados','Belarus','Belgium','Belize','Benin','Bhutan','Bolivia',\"Bosnia and Herzegovina\",'Botswana','Brazil',\n",
        "    'Brunei','Bulgaria','Burkina Faso','Burundi','Cambodia','Cameroon','Canada','Cape Verde','Central African Republic','Chad',\n",
        "    'Chile','China','Colombia','Comoros',\"Congo, Dem. Rep.\",\"Congo, Rep.\",'Costa Rica',\"Cote d'Ivoire\",'Croatia','Cuba','Cyprus',\n",
        "    'Czech Republic','Denmark','Djibouti','Dominican Republic','Ecuador','Egypt','El Salvador','Equatorial Guinea','Eritrea',\n",
        "    'Estonia','Ethiopia','Finland','France','Gabon','Gambia','Georgia','Germany','Ghana','Greece','Guatemala','Guinea',\n",
        "    \"Guinea-Bissau\",'Guyana','Haiti','Honduras','Hungary','Iceland','India','Indonesia','Iran','Iraq','Ireland','Israel','Italy',\n",
        "    'Jamaica','Japan','Jordan','Kazakhstan','Kenya','Kuwait','Kyrgyz Republic','Lao','Latvia','Lebanon','Lesotho','Liberia',\n",
        "    'Libya','Lithuania','Luxembourg','Madagascar','Malawi','Malaysia','Maldives','Mali','Malta','Mauritania','Mauritius','Mexico',\n",
        "    'Moldova','Mongolia','Montenegro','Morocco','Mozambique','Myanmar','Namibia''Nepal','Netherlands','New Zealand','Nicaragua',\n",
        "    'Niger','Nigeria','North Macedonia','Norway','Oman', 'Pakistan','Palestine','Panama','Papua New Guinea','Paraguay','Peru',\n",
        "    'Philippines','Poland','Portugal','Qatar','Romania','Russia','Rwanda','Samoa','Saudi Arabia','Senegal','Serbia',\n",
        "    'Sierra Leone','Singapore','Slovak Republic','Slovenia','Solomon Islands','Somalia','South Africa','South Korea',\n",
        "    'South Sudan','Spain','Sri Lanka',\"St. Vincent and the Grenadines\",'Sudan','Suriname','Sweden','Switzerland','Syria',\n",
        "    'Tajikistan','Tanzania','Thailand',\"Timor-Leste\",'Togo','Tonga','Tunisia','Turkey','Turkmenistan','Uganda','Ukraine',\n",
        "    \"United Arab Emirates\",'United Kingdom','United States','Uruguay','Uzbekistan','Vanuatu','Venezuela','Vietnam','Zambia',\n",
        "    'Zimbabwe']\n",
        "\n",
        "len(values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QObTUNYrqWDa"
      },
      "source": [
        "**Finding the intersection of values in both the female in industries dataset and the GDP Dataset**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oqcpl4Jg8jZ"
      },
      "source": [
        "gdp_dataset_selected2 = gdp_dataset[gdp_dataset[\"country\"].isin([\n",
        "    'Afghanistan','Albania','Algeria','Angola','Argentina','Armenia','Australia','Austria','Azerbaijan','Bahamas','Bahrain',\n",
        "    'Bangladesh','Barbados','Belarus','Belgium','Belize','Benin','Bhutan','Bolivia',\"Bosnia and Herzegovina\",'Botswana','Brazil',\n",
        "    'Brunei','Bulgaria','Burkina Faso','Burundi','Cambodia','Cameroon','Canada','Cape Verde','Central African Republic','Chad',\n",
        "    'Chile','China','Colombia','Comoros',\"Congo, Dem. Rep.\",\"Congo, Rep.\",'Costa Rica',\"Cote d'Ivoire\",'Croatia','Cuba','Cyprus',\n",
        "    'Czech Republic','Denmark','Djibouti','Dominican Republic','Ecuador','Egypt','El Salvador','Equatorial Guinea','Eritrea',\n",
        "    'Estonia','Ethiopia','Finland','France','Gabon','Gambia','Georgia','Germany','Ghana','Greece','Guatemala','Guinea',\n",
        "    \"Guinea-Bissau\",'Guyana','Haiti','Honduras','Hungary','Iceland','India','Indonesia','Iran','Iraq','Ireland','Israel','Italy',\n",
        "    'Jamaica','Japan','Jordan','Kazakhstan','Kenya','Kuwait','Kyrgyz Republic','Lao','Latvia','Lebanon','Lesotho','Liberia',\n",
        "    'Libya','Lithuania','Luxembourg','Madagascar','Malawi','Malaysia','Maldives','Mali','Malta','Mauritania','Mauritius','Mexico',\n",
        "    'Moldova','Mongolia','Montenegro','Morocco','Mozambique','Myanmar','Namibia''Nepal','Netherlands','New Zealand','Nicaragua',\n",
        "    'Niger','Nigeria','North Macedonia','Norway','Oman', 'Pakistan','Palestine','Panama','Papua New Guinea','Paraguay','Peru',\n",
        "    'Philippines','Poland','Portugal','Qatar','Romania','Russia','Rwanda','Samoa','Saudi Arabia','Senegal','Serbia',\n",
        "    'Sierra Leone','Singapore','Slovak Republic','Slovenia','Solomon Islands','Somalia','South Africa','South Korea',\n",
        "    'South Sudan','Spain','Sri Lanka',\"St. Vincent and the Grenadines\",'Sudan','Suriname','Sweden','Switzerland','Syria',\n",
        "    'Tajikistan','Tanzania','Thailand',\"Timor-Leste\",'Togo','Tonga','Tunisia','Turkey','Turkmenistan','Uganda','Ukraine',\n",
        "    \"United Arab Emirates\",'United Kingdom','United States','Uruguay','Uzbekistan','Vanuatu','Venezuela','Vietnam','Zambia',\n",
        "    'Zimbabwe'])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inDdKO_HmXwF"
      },
      "source": [
        "**Getting a section of the export GDP dataset from 2015 to 2019**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vril055Ig8jl"
      },
      "source": [
        "gdp_dataset_selected2 = gdp_dataset_selected2[[\"country\",\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPlBX5j4obtt"
      },
      "source": [
        "**Adding a median section to the gdp dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zWE6HTUg8jx"
      },
      "source": [
        "#Finding the median of each row or country across the years from 2015 to 2019\n",
        "gdp_dataset_selected2['median'] = gdp_dataset_selected2.median(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZ9xsrjyosOO"
      },
      "source": [
        "**Converting the decimal values to a percentage value to work with**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUfAoXYZzK12"
      },
      "source": [
        "for i in range (2015,2020, 1):\n",
        " gdp_dataset_selected2[str(i)] = gdp_dataset_selected2[str(i)] * 100;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjctaHUvg8ko"
      },
      "source": [
        "gdp_dataset_selected2['median'] = gdp_dataset_selected2['median'] * 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deFRHoVxzpHs",
        "outputId": "4c2dc563-6699-4056-93e0-1574861c1855",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "gdp_dataset_selected2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>7.12</td>\n",
              "      <td>5.87</td>\n",
              "      <td>5.71</td>\n",
              "      <td>8.26</td>\n",
              "      <td>7.75</td>\n",
              "      <td>7.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>27.30</td>\n",
              "      <td>29.00</td>\n",
              "      <td>31.60</td>\n",
              "      <td>31.50</td>\n",
              "      <td>31.50</td>\n",
              "      <td>31.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>23.20</td>\n",
              "      <td>20.90</td>\n",
              "      <td>22.70</td>\n",
              "      <td>25.60</td>\n",
              "      <td>22.60</td>\n",
              "      <td>22.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>29.80</td>\n",
              "      <td>28.10</td>\n",
              "      <td>29.00</td>\n",
              "      <td>40.80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>10.70</td>\n",
              "      <td>12.50</td>\n",
              "      <td>11.20</td>\n",
              "      <td>14.30</td>\n",
              "      <td>17.30</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   2015   2016   2017   2018   2019  median\n",
              "0  Afghanistan   7.12   5.87   5.71   8.26   7.75    7.12\n",
              "1      Albania  27.30  29.00  31.60  31.50  31.50   31.50\n",
              "2      Algeria  23.20  20.90  22.70  25.60  22.60   22.70\n",
              "3       Angola  29.80  28.10  29.00  40.80    NaN   29.40\n",
              "5    Argentina  10.70  12.50  11.20  14.30  17.30   12.50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pEWPTQJz5G_"
      },
      "source": [
        "merged_dataset=dataset[[\"country\",\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1tlF_aR0p16",
        "outputId": "6c98c4ce-1094-4b65-88bd-49a197c6282d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "merged_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.56</td>\n",
              "      <td>5.52</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.90</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.40</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.66</td>\n",
              "      <td>7.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   2015   2016   2017   2018   2019\n",
              "0  Afghanistan   5.66   5.56   5.52   5.49   5.46\n",
              "1      Albania  13.90  13.70  13.80  13.80  13.70\n",
              "2      Algeria  55.80  55.40  55.80  55.70  55.70\n",
              "3       Angola   1.25   1.27   1.27   1.29   1.30\n",
              "4    Argentina   8.08   8.02   7.82   7.66   7.50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENabTh-Wg8lF"
      },
      "source": [
        "merged_dataset2 = merged_dataset[merged_dataset[\"country\"].isin([\n",
        "    'Afghanistan','Albania','Algeria','Angola','Argentina','Armenia','Australia','Austria','Azerbaijan','Bahamas','Bahrain',\n",
        "    'Bangladesh','Barbados','Belarus','Belgium','Belize','Benin','Bhutan','Bolivia',\"Bosnia and Herzegovina\",'Botswana','Brazil',\n",
        "    'Brunei','Bulgaria','Burkina Faso','Burundi','Cambodia','Cameroon','Canada','Cape Verde','Central African Republic','Chad',\n",
        "    'Chile','China','Colombia','Comoros',\"Congo, Dem. Rep.\",\"Congo, Rep.\",'Costa Rica',\"Cote d'Ivoire\",'Croatia','Cuba','Cyprus',\n",
        "    'Czech Republic','Denmark','Djibouti','Dominican Republic','Ecuador','Egypt','El Salvador','Equatorial Guinea','Eritrea',\n",
        "    'Estonia','Ethiopia','Finland','France','Gabon','Gambia','Georgia','Germany','Ghana','Greece','Guatemala','Guinea',\n",
        "    \"Guinea-Bissau\",'Guyana','Haiti','Honduras','Hungary','Iceland','India','Indonesia','Iran','Iraq','Ireland','Israel','Italy',\n",
        "    'Jamaica','Japan','Jordan','Kazakhstan','Kenya','Kuwait','Kyrgyz Republic','Lao','Latvia','Lebanon','Lesotho','Liberia',\n",
        "    'Libya','Lithuania','Luxembourg','Madagascar','Malawi','Malaysia','Maldives','Mali','Malta','Mauritania','Mauritius','Mexico',\n",
        "    'Moldova','Mongolia','Montenegro','Morocco','Mozambique','Myanmar','Namibia''Nepal','Netherlands','New Zealand','Nicaragua',\n",
        "    'Niger','Nigeria','North Macedonia','Norway','Oman', 'Pakistan','Palestine','Panama','Papua New Guinea','Paraguay','Peru',\n",
        "    'Philippines','Poland','Portugal','Qatar','Romania','Russia','Rwanda','Samoa','Saudi Arabia','Senegal','Serbia',\n",
        "    'Sierra Leone','Singapore','Slovak Republic','Slovenia','Solomon Islands','Somalia','South Africa','South Korea',\n",
        "    'South Sudan','Spain','Sri Lanka',\"St. Vincent and the Grenadines\",'Sudan','Suriname','Sweden','Switzerland','Syria',\n",
        "    'Tajikistan','Tanzania','Thailand',\"Timor-Leste\",'Togo','Tonga','Tunisia','Turkey','Turkmenistan','Uganda','Ukraine',\n",
        "    \"United Arab Emirates\",'United Kingdom','United States','Uruguay','Uzbekistan','Vanuatu','Venezuela','Vietnam','Zambia',\n",
        "    'Zimbabwe'])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9j2rOu1ug8lR"
      },
      "source": [
        "#Finding the median of each row or country across the years from 2015 to 2019\n",
        "pd.set_option('mode.chained_assignment', None)   #for turning of the \"SettingWithCopyWarning\"\n",
        "merged_dataset2['median'] = merged_dataset2.median(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bxcIKHag8lY",
        "outputId": "844d19cf-4a28-4147-8b90-368585b2a6f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "merged_dataset2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.56</td>\n",
              "      <td>5.52</td>\n",
              "      <td>5.49</td>\n",
              "      <td>5.46</td>\n",
              "      <td>5.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.90</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.80</td>\n",
              "      <td>13.70</td>\n",
              "      <td>13.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.40</td>\n",
              "      <td>55.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "      <td>55.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.29</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.66</td>\n",
              "      <td>7.50</td>\n",
              "      <td>7.82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country   2015   2016   2017   2018   2019  median\n",
              "0  Afghanistan   5.66   5.56   5.52   5.49   5.46    5.52\n",
              "1      Albania  13.90  13.70  13.80  13.80  13.70   13.80\n",
              "2      Algeria  55.80  55.40  55.80  55.70  55.70   55.70\n",
              "3       Angola   1.25   1.27   1.27   1.29   1.30    1.27\n",
              "4    Argentina   8.08   8.02   7.82   7.66   7.50    7.82"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJm7T2YnwtQY"
      },
      "source": [
        "**Making sure the row count in the GDP dataset and the females in industries dataset are the same to help with merging**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UD0iLEHg8li",
        "outputId": "3d29e349-dcde-4745-f5a2-52fd58d78679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(merged_dataset2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "170"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZHH6hF5g8lr",
        "outputId": "83612f9c-8b45-4e2a-f995-52167cdb3ade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(gdp_dataset_selected2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "170"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdKw8oKeg8lz"
      },
      "source": [
        "#getting the median and country of the females in industries dataset into a new dataframe\n",
        "female_population_df = merged_dataset2[['country', 'median']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNv9kWuvg8l6",
        "outputId": "cd04bf9c-ce3f-4ce0-fee2-cb40baade475",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "#Renaming the median column\n",
        "female_population_df.rename(columns={\"median\": \"females_in_industries\"})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>females_in_industries</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>5.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>7.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>2.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>13.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>21.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>4.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>170 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         country  females_in_industries\n",
              "0    Afghanistan                   5.52\n",
              "1        Albania                  13.80\n",
              "2        Algeria                  55.70\n",
              "3         Angola                   1.27\n",
              "4      Argentina                   7.82\n",
              "..           ...                    ...\n",
              "173      Vanuatu                   2.30\n",
              "174    Venezuela                  13.40\n",
              "175      Vietnam                  21.00\n",
              "177       Zambia                   4.10\n",
              "178     Zimbabwe                   2.33\n",
              "\n",
              "[170 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tq8hr3K6g8mB"
      },
      "source": [
        "#getting the median and country of the gdp dataset into a new dataframe\n",
        "gdp_population_df = gdp_dataset_selected2[['country', 'median']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGmViO0Fg8mI",
        "outputId": "fb30c8b7-9afb-490a-a118-7e5873be9544",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "#Renaming the median section of the gdp population\n",
        "gdp_population_df.rename(columns={\"median\": \"gdp\"})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>gdp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>7.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>31.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>22.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>29.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>102.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>35.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>19.45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>170 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         country     gdp\n",
              "0    Afghanistan    7.12\n",
              "1        Albania   31.50\n",
              "2        Algeria   22.70\n",
              "3         Angola   29.40\n",
              "5      Argentina   12.50\n",
              "..           ...     ...\n",
              "179      Vanuatu     NaN\n",
              "180    Venezuela     NaN\n",
              "181      Vietnam  102.00\n",
              "182       Zambia   35.30\n",
              "183     Zimbabwe   19.45\n",
              "\n",
              "[170 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGDRI4J4g8mR",
        "outputId": "bbf6224a-0e51-4486-f00e-6dead3669baf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "final_data = pd.merge(female_population_df, gdp_population_df, on='country')\n",
        "\n",
        "final_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>median_x</th>\n",
              "      <th>median_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>5.52</td>\n",
              "      <td>7.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.80</td>\n",
              "      <td>31.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>55.70</td>\n",
              "      <td>22.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.27</td>\n",
              "      <td>29.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>7.82</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>2.30</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>13.40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>21.00</td>\n",
              "      <td>102.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>4.10</td>\n",
              "      <td>35.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2.33</td>\n",
              "      <td>19.45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>170 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         country  median_x  median_y\n",
              "0    Afghanistan      5.52      7.12\n",
              "1        Albania     13.80     31.50\n",
              "2        Algeria     55.70     22.70\n",
              "3         Angola      1.27     29.40\n",
              "4      Argentina      7.82     12.50\n",
              "..           ...       ...       ...\n",
              "165      Vanuatu      2.30       NaN\n",
              "166    Venezuela     13.40       NaN\n",
              "167      Vietnam     21.00    102.00\n",
              "168       Zambia      4.10     35.30\n",
              "169     Zimbabwe      2.33     19.45\n",
              "\n",
              "[170 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkfwqX-tg8mY",
        "outputId": "e5c50dbb-e258-4b06-8bdb-60ff59ff50f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "#Plotting GDP against the female in industries population\n",
        "plt.scatter(final_data['median_y'], final_data['median_x'])\n",
        "plt.legend(loc='best', fontsize=16)\n",
        "plt.xlabel('GDP%')\n",
        "plt.ylabel('%Female Industry workers')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYTLIJsJg8mg"
      },
      "source": [
        " #Converting final data to csv to plot in flourish\n",
        " final_data.to_csv('final_dataset1.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHqk4Rd_g8mn"
      },
      "source": [
        "gdp_dataset_selected2.to_csv('gdp_dataset.csv',encoding='utf-8', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KN2N8ufpHvE8"
      },
      "source": [
        "Graph in Flourish to answer the question this time with Labels of the countries.\n",
        "\n",
        "https://public.flourish.studio/visualisation/3875268/\n",
        "\n",
        "Looking at the graph, it can be seen that there wasnt much of a correlation between wealtheir contries and the number of females in industries.\n",
        "\n",
        "A greater portion of the datapoints were concerntrated around the center of the graph ranging from around 7dollars to 200 dollars in GDP. With Tonga and Algeria recording the highest percentage of female in industries although recording values at the lowest part of th gdp range thats approximately above $20. Also as of 2015 the United Nations classified Algeria and Tonga as part of the developing countries\n",
        "\n",
        "source: https://www.un.org/en/development/desa/policy/wesp/wesp_current/2014wesp_country_classification.pdf\n",
        "\n",
        "\n",
        "**A snapshot of the graph**\n",
        "![Are Wealthier countries Better at Gender Balance_.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clAYCm_G5m5N"
      },
      "source": [
        "# **How did Ireland fare over the years from 1991 to 2019 in terms of the percentage of women in industries?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPcKl8qRus9Q",
        "outputId": "26425c19-e03e-4d89-b2a4-77ae8348d8c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "\n",
        "ireland = dataset[dataset.country == 'Ireland']\n",
        "ireland.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>18.4</td>\n",
              "      <td>18.5</td>\n",
              "      <td>16.7</td>\n",
              "      <td>17.3</td>\n",
              "      <td>17.3</td>\n",
              "      <td>16.2</td>\n",
              "      <td>17.2</td>\n",
              "      <td>16.5</td>\n",
              "      <td>15.6</td>\n",
              "      <td>15.3</td>\n",
              "      <td>14.8</td>\n",
              "      <td>13.4</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>11.9</td>\n",
              "      <td>11.4</td>\n",
              "      <td>10.7</td>\n",
              "      <td>9.92</td>\n",
              "      <td>9.38</td>\n",
              "      <td>8.77</td>\n",
              "      <td>8.73</td>\n",
              "      <td>8.74</td>\n",
              "      <td>8.74</td>\n",
              "      <td>8.45</td>\n",
              "      <td>8.51</td>\n",
              "      <td>8.97</td>\n",
              "      <td>8.47</td>\n",
              "      <td>8.26</td>\n",
              "      <td>8.12</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.93</td>\n",
              "      <td>7.86</td>\n",
              "      <td>11.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    country  1991  1992  1993  1994  1995  ...  2018  2019  2020  2021  2022  median\n",
              "75  Ireland  18.4  18.5  16.7  17.3  17.3  ...  8.26  8.12  8.02  7.93  7.86   11.05\n",
              "\n",
              "[1 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CddDH7Z6gOh",
        "outputId": "9f45d0c5-0ea3-4305-8181-7393070cb1ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ireland.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbuRfBKR384a",
        "outputId": "d8b2766c-0981-480a-d2e6-de122024eff9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "#ireland.plot(kind='line')\n",
        "ireland = ireland[ireland.columns[0:30]]\n",
        "ireland"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>18.4</td>\n",
              "      <td>18.5</td>\n",
              "      <td>16.7</td>\n",
              "      <td>17.3</td>\n",
              "      <td>17.3</td>\n",
              "      <td>16.2</td>\n",
              "      <td>17.2</td>\n",
              "      <td>16.5</td>\n",
              "      <td>15.6</td>\n",
              "      <td>15.3</td>\n",
              "      <td>14.8</td>\n",
              "      <td>13.4</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>11.9</td>\n",
              "      <td>11.4</td>\n",
              "      <td>10.7</td>\n",
              "      <td>9.92</td>\n",
              "      <td>9.38</td>\n",
              "      <td>8.77</td>\n",
              "      <td>8.73</td>\n",
              "      <td>8.74</td>\n",
              "      <td>8.74</td>\n",
              "      <td>8.45</td>\n",
              "      <td>8.51</td>\n",
              "      <td>8.97</td>\n",
              "      <td>8.47</td>\n",
              "      <td>8.26</td>\n",
              "      <td>8.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    country  1991  1992  1993  1994  1995  ...  2014  2015  2016  2017  2018  2019\n",
              "75  Ireland  18.4  18.5  16.7  17.3  17.3  ...  8.45  8.51  8.97  8.47  8.26  8.12\n",
              "\n",
              "[1 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUxusQF3TeS8",
        "outputId": "a2bf8656-f7b8-4edb-c872-ce6e46d7757c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "Year = [1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019]\n",
        "Pecentage_Female_in_Industries  = [18.4,18.5,16.7,17.3,17.3,16.2,17.2,16.5,15.6,15.3,14.8,13.4,13.0,13.2,11.9,11.4,10.7,9.92,9.38,8.77,8.73,8.74,8.74,8.45,8.51,8.97,8.47,8.26,8.12]\n",
        "#plt.figure(figsize=(14, 4))\n",
        "plt.plot(Year, Pecentage_Female_in_Industries, color='saddlebrown')\n",
        "plt.title('Percentage of Female Employment in industries for Ireland')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Percentage of Females in Industries')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3UTfGmAIvlEcQo4Hz0/V7BnhEVVsBD7mf85WGnXoDsMqqmYwxRVzIEoSqTgV2p+8NxLrdZYEtoVp+sMpVrUNcvWasnGnVTMaYoi3c5yDuAJ4VkY3Ac8B9mY0oIte71VBzduzYEbYAwTmK2Lz4dw7t2Z79yMYYU0iFO0HcBNypqjWBO4F3MhtRVd9S1XhVjY+LC/qZ20Fp2Kk3qLJ61g9hXa4xxuQn2SYIEbldRGLF8Y6IzBWR7kEubwgw3u3+HMh3J6kBKtVtStmqdVhprZmMMUWYlyOIq1V1P9AdKA9cCTwd5PK2AGe73ecAK4OcT0iJCA079WLDvKkcO3TA73CMMcYXXhJE2uXIvYAPVXVxQL/MJxIZA8wCGovIJhG5BrgOeF5E5gNPAtcHF3boNezUm5Sk46z9Y7LfoRhjjC+83KwvQUQmAXWB+0QkBkjNbiJVHZzJoLY5iM831ZqcTunylVkx41tO7TrA73CMMSbsvCSIa4BWwBpVPSwiFYFhoQ3LfxIRQcOOPVnyyziSjx+lWFRJv0Myxpiw8lLFpEBT4Db3czRQJH4tG3TqTdKRQ6yf+5vfoRhjTNh5SRCvAR2AtCqjA8CrIYsoH6nVsjMlomOtNZMxpkjyUsXUTlXbiMhfAKq6R0SiQhxXvhBZPIp6Z3Rj9ewfSE1JJiKyUD9fyRhjTuLlCCJJRCJxqpoQkTg8nKQuLBp06s2RfbvYvPh3v0Mxxpiw8pIg/gt8CVQWkSeA6ThNVIuEuqefQ7GokqycbjfvM8YULdnWmajqxyKSAJyLc/1Df1UtMo9ciypVhtptu7By5nd0vemJHD2lLjUlmZkfPcvezWs8jR8RWYz2l99NhRoNgg3XGGPyTKYJQkRiVXW/iFQAtgNjAoZVUNX0d2ottBp26s3qWT+wbeV8TmnUytM0mprK98/+i6W/jKNctbpIRPYHa/u3beLowb0MeGxMtuMaY0yoZXUE8QnQB0jAPf/gEvdzvRDGla/Ub98DiYhk1YzvPCUIVeWnl//D0l/G0XnoCNpfdpen5fw+5kWmvfc4W5b8SbWmp+c2bAB2bVjBn5+/wlnXPkzpshXzZJ7GmKIh07+1qtpHnPqUs1W1XsCrrqoWmeQAUCq2AjWad2DlzOzPQ6gqU956iPnfvs8Zl95Ou8F3el5O6/7XUbpcHNNH580pHlVl0ot3sejHT/jm8atJSU7Kk/kaY4qGLOs9VFUBOzuLU820a/1ydm/M+v6CMz98hoQvXqd1v2s58+oHcnTOIqpUNO0G3c6GedPY8NfU3IbMimkT2LxoNg069mLj/Bn8+sYDuZ6nMabo8NKKaa6I5E19RwHWoGMvgCwvmvvz81eY9dGzNOs+mHNuejJHySFNyz5DialUjWmjn8TJz8FJPn6U3/43krh6zbjgwfeIv+gW5k14hwXffRD0PI0xRYuXBNEOmCUiq0VkgYgsFJEFoQ4sv4mtXJ1TGrdmZSbPqp438T1++99IGp/djx53vujppHRGikWVpP3ld5O4dA5rfp8UdLwJX7zB/m0b6Xrj40RERnLWNQ9RJ/5cfnrlHjYtmh30fI0xRYeXX7EeQH2c5zf0xTlx3TeUQeVXDTv2ZuvyuRzYmXhS/8U/fcZPLw+nXrtu9Br+OhGRkblazmk9LqNctbpMH/0UmprzaxIP7trK7LEv0KBjL2q1OhOAiMhI+ox4i7JVajLh0WHs374pVzEaYwo/LwnicVVdH/gCHg91YPlRg069AVg18+9qphXTvuGH5/5FrZad6fvAu0QWz/1dSCKLFafjFf9hx5pFLJ82IcfTTx/9FClJxzn7upEn9S9Zpiz9H/mY5ONH+WrklSQdPZzrWI0xhZeXBNEs8IN7240C8UyHvFaxVkMq1Gx4opppzR8/MfGp66l6alv6P/IhxUuUyrNlndp1IBVrN2bm+0+TmpLsebptK+ezaNIntOl/PeWr/7OxWcVaDekz4i22r17ED8/flqvzHMaYwi3TBCEi94nIAaCFiOx3XwdwLpr7OmwR5jMNO/Vm4/wZrJz+LRMeHUql2qcy4PGxRJUqk6fLiYiMpNNV97J70yqW/DzO0zSqyq+v30+p2Ip0uPzuTMerd0Y3zrz6QZb/9hV/jH0pr0I2xhQyWV0H8ZSqxgDPqmqs+4pR1Yqqel8YY8xXGnbujaam8PWjQ4g9pRYXPfU5JcuUDdGy+lClQQtmfvgMKUnHsx1/xbRv2LRoNp2H3keJ6Ngsxz3jkls5tesApo1+gtWzf8yrkI0xhYiXKqaJIhINICJXiMgoEakd4rjyrSoNW1GuWl3KVq3DxU9/QelylUK2LBGh09AR7N+2gYU/fJTluIHNWpuff4Wnefe480Wq1G/Ot0/fwK4NK/IqbGNMIeElQbwOHBaRlsDdwGqgyDamFxEGj5rIkNd/JaZS1ZAvr+7p51K9WTtmfzKKpGNHMh0vYfyb7N+2ga43POa5FVXxkqXpN/JDipUozZcPXc7RA3vzKmxjTCHgJUEku1dU9wNeUdVXgZjQhpW/RVeoQlTp8KwCEaHzsBEc3LWVed+8m+E4h3ZvY/aYUTTo0JNarc/K0fxjK1en30PvsX/7JiY+eV2OTogbYwo3LwnigIjcB1wBfCsiEUDx0IZlAtVs0Ynabbrwx6f/5fjhA/8YPn30k06z1utHBjX/6s3acd6tz7Au4Vd+e/uRXEZrjCksvCSIS4FjwDWquhWoATwb0qjMP3QeOoIj+3aRMP7Nk/pvW7WAhT9+Qpv+11G+ev2g59+i55W0uuAaEr54PU/uA2WMKfiyTRCqulVVR6nqNPfzBlUtsucg/FL11DbU73A+f457lSP79wCBzVor0P6yzJu1enX2dSMpV60uk166O8vzHcaYoiHbBCEiBwKugzgqIikisi8cwZmTdR5yH8ePHGTOuFcBWDl9IpsWzqLTkHvzpKlt8RKl6Hb78+zdspbZn4zK9fyMMQWblyOImLTrIIBSwECclk0mzOLqNePUs/uT8OVb7N++id/+N5JKdZvSoueVebaM2q3Poln3wfz52cvsWLM4z+ZrjCl4cnTLUXV8hXMDP+ODjlfdQ0rSMcbc1Yd9W9e7d2vN9tHiOdLl+kcoUaYsk168i9SUlDydtzGm4PBSxTQg4HWRiDwNHA1DbCYDFWo0oFm3QRzYvon6Hc6ndg6btXpRKrYCXW98nMRlCcyb+F6ez98YUzB4+esZeGvvZGAdzjURxiedhtxLyvGjdBo6ImTLaHLORSz5+XOmvfsYDTr0JLZy9ZAtyxiTP0mo7uYpIu/iPDtiu6qeFtD/VuAWIAX4VlWHZzev+Ph4nTNnTkjiNJnbm7ie0defSe3WZ9L/kY+CekKeMcY/IpKgqvHBTp/pEYSIvAxkmj1U9bZs5j0aeIWA23KISFeco4+WqnpMRCrnKFoTVuWq1qbTkHv47a2HWTHtGxqfdYHfIRljwiircxBzgASgJNAGWOm+WgHZPhVHVacCu9P1vgl4WlWPueNsDyJmE0ZtL7yByg2a88tr93L0oLVuNqYoyep23++r6vtAC6CLqr6sqi8D5+IkiWA0As4Ukd9F5DcROT2zEUXkehGZIyJzduzYEeTiTG5FRBaj+x0vcHjvTqa986jf4RhjwshLM9fyQODDBcq4/YJRDKgAtAf+A3wmmVRsq+pbqhqvqvFxcXFBLs7khVMataLtgBuZ/+37bFo4y+9wjDFh4iVBPA38JSKjReR9YC7wZJDL2wSMd6+n+ANIBUL3QAWTZzpeeQ+xVWox6cW7SD5+zO9wjDFh4OVK6veAdsCXwHigg1v1FIyvgK4AItII51zGziDnZcIoqlQ03W57lt0bV/L72Bf9DscYEwZer6SOBHYAe4BGIpLt1VkiMgaYBTQWkU0icg3wLlBPRBYBY4EhGqp2tibP1T39XJp0HcjvY1+0J9AZUwRkex2EiPwfzi2/F+NUCYFz142wtXm06yDyj0N7dvDetR2pWKsRg57/BonI0d1ajDFhlNvrILx8u/sDjVW1t6r2dV/WIL6Iii4fR5cbHmXz4t+Z/+1ov8MxxoSQlwSxBnuCnAnQrNsgarU+i59eHs43T1zD7k2r/A7JGBMCXu7FdBiYJyI/4zxZDvB0JbUppESEfg+9z5+fv0LC+DdYMW0ip3UfRIcrhts9m4wpRLycgxiSUf9ctGTKMTsHkX8d2rOD38e+yHz3rq+t+gyj3eA7KV3OWi8b47fcnoMI2c368pIliPxv//ZNzPzwWRZPHkOxEqVoO+BGTr/oFkpEx2Y/sTEmJEKWIERkIVnfrK9FsAvNKUsQBceuDSuZ8cHTrJj6NSVjytNu0O20uuAaipco5XdoxhQ5oUwQtbOaUFXXB7vQnLIEUfBsXTGP6aOfYt2cnylT8RTOvv4RmnQd6HdYxhQpVsVk8rWNC2Yy9e1HSFyWQIcr/kPHK4fbcyWMCZNwXAdhTNBqtujIoFETOa3HZcz66Fl+eO5WUpKO+x2WMcaDvH3avTEZiCxWnB53vURs5RrM/PAZDu5M5IKHRlMiOsbv0IwxWbAjCBMWIkLHK4dz/r9fZuOCGYy9uw8HdmzxOyxjTBayTRAi0klEJovIChFZIyJrRWRNOIIzhc9p3Qcz4PGx7Nu6no9v78GONYv9DskYkwkvRxDvAKOAzsDpQLz7bkxQ6rTtwuBR3wIw5q7erEuY4m9AxpgMeUkQ+1T1e1Xdrqq70l4hj8wUanH1mnH5Sz8SW6UW4x8YxKJJY/wOyRiTjpcE8auIPCsiHUSkTdor5JGZQi8mrhqDR02kRouO/PDcrcz86FkKQrNrY4oKL62Y2rnvgW1pFTgn78MxRU2J6FgGPj6WSS/excwP/o/92zbR7fbniCxmNxA2xm/ZJghV7RqOQEzRFVk8ivP//TKxVWow66PniCoVzTk3B/vYc2NMXsk0QYjIFar6kYjcldFwVR0VurBMUSMidLrqXvZt3cjCHz+m89D7iCpt10kY46eszkFEu+8xmbyMyXOtL7iGpCOHWPLz536HYkyRl+kRhKq+6b4/Er5wTFF3SuPWVG7QnHkTR9OyzzC7b5MxPrIrqU2+IiK06jOMnWuXkLjUbtBojJ8sQZh859SuA4gqXYZ57lPqjDH+sARh8p2oUmVoet6lLP/ta47s3+13OMYUWV7uxXS7iMSK4x0RmSsi3cMRnCm6WvYeQkrSMbvC2hgfeTmCuFpV9wPdgfLAlcDTIY3KFHlxdZtSvVk7Fnz7Ppqa6nc4xhRJXhJEWjOSXsCHqro4oJ8xIdOyz1D2bF7DhvnT/Q7FmCLJS4JIEJFJOAniRxGJAewvnQm5Rmf2pVRsBeZ9YyerjfGDlwRxDXAvcLqqHgaigGEhjcoYoFhUSU7rcRmrZn7HwV2JfodjTJHjJUEo0BS4zf0cDZQMWUTGBGjR6yo0NYWF33/sdyjGFDleEsRrQAdgsPv5APBqdhOJyLsisl1EFmUw7G4RURGplKNoTZFTvno9arfpwoLvPiA1JdnvcIwpUrwkiHaqegtwFEBV9+BUM2VnNHB++p4iUhOnRdQG72GaoqxVn2Ec2LmFtX/85HcoxhQpXhJEkohE4lQ1ISJxeDhJrapTgYyucnoBGJ42P2OyU699d8pUPIV5E0f7HYoxRYqXBPFf4Eugsog8AUwHgrpZv4j0Azar6nwP414vInNEZM6OHTuCWZwpJCKLFad5zytYO+dn9iau9zscY4qMbBOEqn6M84//KSAR6K+qOb4Xs4iUBkYAD3kZX1XfUtV4VY2Pi4vL6eJMIdOi51WICAu++8DvUIwpMjJNECJSIe0FbAfGAJ8A29x+OVUfqAvMF5F1QA1groicEsS8TBETE1eN+u16sPCHj0lJOu53OMYUCVk9cjQB5zxB4FXTaZ8VqJeTBanqQqBy2mc3ScSr6s6czMcUXS37DmPVrO9ZOeNbTu1yod/hGFPoZXoEoap1VbWe+1433edsk4OIjAFmAY1FZJOIXJOXgZuip06bLpQ9pbbdBtyYMMnqCOIEESkPNCTgAjm3lVKmVHVwNsPreFm2MWkkIoIWvYcw7Z1H2bVhBRVrNfI7JGMKNS+3+74WmAr8CDzivo8MbVjGZKx5j8uIKFac+USOflAAAB0FSURBVNbk1ZiQ89LM9XbgdGC9qnYFWgN7QxqVMZkoXa4Sjc7sy+LJY0k6etjvcIwp1LwkiKOqehRAREqo6jKgcWjDMiZzrfoM49ih/Sz77Su/QzGmUPOSIDaJSDngK2CyiHwN2NVKxjfVT2tPxdqNmW8nq40JKS8Xyl2oqntVdSTwIPAO0D/UgRmTGRGhZe+hbF3+F4nL5/odjjGFlpcjCESkvIi0wLmT6ybgtJBGZUw2mnW7lBJlyjL+/kGsmD7R73CMKZS8tGJ6DFgAvAw8776eC3FcxmSpRHQsl734A7FVajLh0aH88PxtHD98wO+wjClURDXrm6qKyHKguar6dn+D+Ph4nTNnjl+LN/lYStJxZn30HL9/+iKxlWvS657XqN6snd9hGZMviEiCqsYHO72XKqZFQLlgF2BMKEUWj6LzsBEMev4bAMbe3Zfp7z1JSnJSjueVmpLM2jm/MOuj56wJrTF4u5L6KeAv98lwx9J6quoFIYvKmByq3qwdV70+hV/fuJ/ZY0axds7P9LrnDSrWapjldKrKliV/svTXL1j+29cc2efcGqx4qWjiB94UjtCNybe8VDEtBt4EFhLwoCBV/S20of3NqphMTqyYPpFJL9xJ8vGjnH3dI7TqOwwROWmcHWuXsPSXL1g25Uv2b9tAsaiS1GvfgyZdBzJn3Ksc2JnItaP/JCIy0qdSGJN7ua1i8nIEcVhV/xvsAowJt0ad+1CtSVt+eP52fn5lOGt+n8T5d79E0rGjLJsynmW/jmfnuqVIRCS123Sh05B7adixJ1GlYwBQTWXCo0NZNet7GnXu43NpjPGPlyOIUThVSxM4uYopbA3Q7QjCBENV+WvCO0z930iQCJKPOecVqjdrx6ldB9L4rAsoXa7SP6ZLTUnh7aGnExtXjUGjrAmtKbjCcQTR2n1vH9BPgXOCXagx4SAitOl3LbVbn8XvY1+iYu3GnNrlQspWqZnldBGRkbTpfx1T3nyQrSvmcUqjVmGK2Jj8JdsjiPzAjiBMuB07tJ83LmtOw4696HXP636HY0xQQt7MVUSqiMg7IvK9+7mpPfzHFHYlomNpfv4VLPvtKw7uSvQ7HGN84eU6iNE4z4Co5n5eAdwRqoCMyS/a9LuW1JRk5k2wmwKaoslLgqikqp/hNnFV1WQgJaRRGZMPlKtWlwYdejL/29EkHTvidzjGhJ2XBHFIRCrinJhGRNoD+0IalTH5RNsBN3Bk/26W/jLO71CMCTsvCeIunCau9UVkBvABcGtIozImn6jRvCOV6zcnYfybFIQGHcbkpUwThIjUghPXO5wNdARuAJqp6oLwhGeMv0SEtgNuZNf6ZayfG7abBxiTL2R1BBH4PMdPVXWxqi5S1ZzfBc2YAqzx2f0pXb4yCePf8DsUY8IqqwQRePOaeqEOxJj8qlhUCVr1HcbaP39i14aVfodjTNhklSA0k25jipyWvYcSWbwEc796y+9QjAmbrBJESxHZLyIHgBZu934ROSAi+8MVoDH5QXT5OJqccxGLJ3/Kkf17/A7HmLDINEGoaqSqxqpqjKoWc7vTPseGM0hj8oO2A24g+dhhFnz/od+hGBMWXpq5GmOAuLpNqdX6LP76+n9BPbHOmILGEoQxOdD2whs4uDORldO+8TsUY0Iuq+sgSoQzEGMKgnpndKN89XokfPmm36EYE3JZHUHMAhCRoCpcReRdEdnuPss6rd+zIrJMRBaIyJciUi6YeRvjF4mIoM2FN5C4LIEtS/70OxxjQiqrBBElIpcBHUVkQPqXh3mPBs5P128ycJqqtsC5K+x9QUVtjI+adbuUEmXKkvClXThnCresEsSNwJlAOaBvule2D+pV1anA7nT9Jrl3gwWYDdQIImZjfBVVqgwtel7JimkT2b99k9/hGBMyWTVzna6qNwHDVXVYutfVebDsq4HvMxsoIteLyBwRmbNjx448WJwxead1v2sB+Ovrt32OxJjQ8dKK6UMRuU1ExrmvW0WkeG4WKiL3A8nAx5mNo6pvqWq8qsbHxcXlZnHG5LnYyjVodGYfFnz3AXs2r/E7HGNCwkuCeA1o676/BrQBgn5Ir4gMxamiulzt/smmAOtwxXAiIovxyR097YS1KZS8JIjTVXWIqv7ivoYBpwezMBE5HxgOXKCqh4OZhzH5RaXajbnspe8pER3LZ8MvZOX0b/0OyZg85SVBpIhI/bQPIlIPD48cFZExOE1lG4vIJhG5BngFiAEmi8g8EbFmIKZAK1+9Ppe9+D1x9Zrx9WNDmful3czPFB7FPIzzH+BXEVmDcwvw2sCw7CZS1cEZ9H4nZ+EZk/+VLleJS575ku/+7yZ+eX0E+7ZtoMv1jyIRdqMCU7BlmyBU9WcRaQg0dnstV9VjoQ3LmIKleMnS9H3gXaa8+SAJ499g//bN9LrnNYqXKOV3aMYEzdNfHFU9pqoL3JclB2MyEBEZyTk3P0mXGx5j5YyJfH7PQA7v2+V3WMYEzY6Bjclj8QNvou/977Bt5XzG3NGTvVvW+h2SMUGxBGFMCDQ+6wIueWY8Rw/s5ePbe5K4NMHvkIzJsWwThDiuEJGH3M+1ROSM0IdmTMFWvVk7Br/4HVGly/Dp8P6smvmd3yEZkyNeL5TrAKS1SjoAvBqyiIwpRCrUaOA0g63blAmPXc3GBTP8DskYz7wkiHaqegtwFEBV9wBRIY3KmEIkunwcFz31OeWq1WXCY1ezb+sGv0MyxhMvCSJJRCIBBRCROCA1pFEZU8iUiI6l/yMfkpqcxFcjr+T4kUN+h2RMtrwkiP8CXwKVReQJYDrwZEijMqYQqlCjAX3uf5ud65byw3O3YrciM/ldtglCVT/GuX/SU0Ai0F9VPw91YMYURnXjz+Gsax9mxbQJzP74eb/DMSZL2V5JLSIVgO3AmIB+xVU1KZSBGVNYxQ+8mR2rFzHjg6eJq9eUBh17+R2SMRnyUsU0F9iB84jQlW73OhGZKyJtQxmcMYWRiNDtjlGc0rg13/7fTexYu9TvkIzJkJcEMRnopaqVVLUi0BOYCNyM0wTWGJNDxUuUot/DHxBVqgxfjbySI/t3Zz+RMWHmJUG0V9Uf0z6o6iSgg6rOBkqELDJjCrmYSlXp9/D7HNy5hW+euJbUlOTsJzImjLwkiEQRuUdEaruv4cA2t+mrNXc1JheqNYmn2+3Ps+GvqUx562G/wzHmJF4SxGVADeAr91XL7RcJXBK60IwpGk7rPpi2F97A3C/fZOGPn/gdjjEneHkexE7g1kwGr8rbcIwpms6+/hF2rlvGT//9NxVrNqRa06Ce6mtMnvJys744EXlWRL4TkV/SXuEIzpiiIiKyGH3uf5uYuOp89cgQDuzY4ndIxniqYvoYWAbUBR4B1gF/hjAmY4qkUrHl6T/yQ5KOHuLrR4eQknTc75BMEeclQVRU1XeAJFX9TVWvBs4JcVzGFEmV6pxKz3+/wtblfzHtvSf8DscUcZ5u1ue+J4pIbxFpDVQIYUzGFGmNzuxLyz7DmDPuVdb++bPf4ZgizEuCeFxEygJ3A/8G3gbuCGlUxhRxXW54lEp1m/LdM7dwcNdWv8MxRZSXBLFHVfep6iJV7aqqbQG77NOYECpeohR9RvyPpKOH+P6ZW9BUu+TIhJ+XBPGyx37GmDxUqXZjzrn5Sdb/9Rt/fGZfORN+mV4HISIdgI5AnIjcFTAoFuciOWNMiDU//wrWz53C9NFPUrNFR7s+woRVVkcQUUAZnCQSE/DaD1wU+tCMMSJC9zteICauOhOfup6jB/f5HZIpQiS7p1qJSG1VXR+meDIUHx+vc+bM8TMEY3y1Zekcxt7VhwadetP3/rcREb9DMgWAiCSoanyw03s5B1FCRN4SkUl2JbUx/qjWJJ5OQ0ewYurXLPz+Q7/DMUVEtvdiAj4H3sBp3poS2nCMMZk54+J/seGvqfzy2giqNT2DSnVO9TskU8h5OYJIVtXXVfUPVU1Ie2U3kYi8KyLbRWRRQL8KIjJZRFa67+VzFb0xRYhERNBr+KtElY7hmyeuJenYEb9DMoWclwTxjYjcLCJV3R/4Cu5zqrMzGjg/Xb97gZ9VtSHws/vZGONRdIUq9Br+GrvWL2PKmw/6HY4p5LwkiCHAf4CZQIL7yvaMsapO5Z8X1PUD3ne73wf6e47UGANAnfiunH7xv5g/cTQrpn3jdzimEPPyPIi6ebi8Kqqa6HZvBapkNqKIXA9cD1CrVq08DMGYgq/z0BFsXDCTH1+4gyqNWlG2Sk2/QzKFkJfnQZQWkQdE5C33c0MR6ZPbBavTvjbTNraq+paqxqtqfFxcXG4XZ0yhElk8ij4j3kJTU/nyocs5tGe73yGZQshLFdN7wHGcq6oBNgOPB7m8bSJSFcB9t73amCCVq1qHfg+/z94t6xh7d1/2b9/kd0imkPGSIOqr6jO4t/1W1cNAsFfpTMA5p4H7/nWQ8zHGALVbn8XFT3/Ood3bGXNXH/ZsXu13SKYQ8ZIgjotIKdzqIBGpDxzLbiIRGQPMAhqLyCYRuQZ4GugmIiuB89zPxphcqN6sHZc+9zXJx44w5q6+7Fi7xO+QTCHh5VYb3YAHgKbAJKATMFRVp4Q8OpfdasOY7O3asILP7xlA8vFjDHzyU6o2buN3SMZnIb/VhqpOBgYAQ4ExQHw4k4MxxpuKtRoxaNS3lIiO5bPhF7JxwQy/QzIFnJdWTBfiXE39rapOBJJFxK5fMCYfKle1NoNGTSQmrjpfjLiUNX9M9jskU4B5OQfxsKqeuMewqu4FHg5dSMaY3IipVJVBz02gQq2GfDXyKpZPtbYgJjheEkRG43i5yZ8xxiely1Xi0me/pmrjNkx88joW/viJ3yGZAshLgpgjIqNEpL77GoVzuw1jTD5WIjqWgU9+Rq1WZ/Hj87cx98u3/A7JFDBeEsStOBfKfQqMBY4Ct4QyKGNM3ogqFc2Fj35Mg469+OX1EfZsa5MjWVYViUgkMFFVu4YpHmNMHisWVYILHnyXb5++kalvP0KJ0jG07DPU77BMAZBlglDVFBFJFZGygSeqjTEFS0RkMXrd8zpJRw8x+eX/EBUdQ5OuA/0Oy+RzXk42HwQWishk4FBaT1W9LWRRGWPyXGSx4vR94F2+GHEJ3z9zC1GlY6jfrrvfYZl8zMs5iPHAg8BU/n4ehJ2kNqYAKl6iFBc++glx9ZrxzWNX28V0JkvZ3moDwL0XUy1VXR76kP7JbrVhTN46vG8XY+/uy8GdW7jkmS85pVFrv0MyIRDyW22ISF9gHvCD+7mViEwIdoHGGP+VLluRi58aR8mY8nwx4lJ2rvflv5/J57xUMY0EzgD2AqjqPKBeCGMyxoRBTFw1Ln76CySyGOPuu4h9Wzf4HVK+4qV2pbDzkiCSMmjBlBqKYIwx4VW+ej0ufupzko4e5vN7B3Jw11a/Q8oXdm9cyfs3nMW4+y7m6MGi24DTS4JYLCKXAZHu40ZfBmaGOC5jTJjE1WvGwCc+5dDu7YwbcQlH9u/xOyRfrZ79Ix/d2p1Du7exYf50xtzZm/3bN/sdli+8XkndDOchQZ8A+4A7QhmUMSa8qjWJp//ID9izaRXjHxzM8SMH/Q4p7FSVWR8/z5cPX0H5anW58rVfuOiJTzmwYzOf3N6D7asX+h1i2GWaIESkpIjcATwDbAA6qOrpqvqAqh4NW4TGmLCo3eZs+tz3P7Yun8tXI69i5/rlHNqznZSk436HFnLHjxxkwmNXM+P9p2jSdSCDRk0ktnINarU+i8EvfIdIBGPu6sPaOb/4HWpYZdrMVUQ+xXkO9TSgJ7BOVX05crBmrsaEz6JJY/nhuX+d1K94qWhKxpSnVEx5SsaUo2TAe4kyZUGV1JRkUlOSSE1JITU5yf2cfKI7JTkZTU3xHEfJmPK06X8t5avXz+sinmTvlrV8NfIqdm1YzlnXPkz8wJsRkZPGObAzkfEPDGbnuqV0v/15mve8IqQx5ZXcNnPNKkEsVNXmbncx4A9V9eUZhpYgjAmvbasWsGfjKo4c2MvRA3vc194T70cCPqcmJ500bURkMSKKFSciMtLpjnS7ixVHIiL/8eObmYM7E0lJTqLpeZfQ4fK7KVe1Tp6Xc13CFCY+eS0AfUa8TZ22XTId99ihA3zzxDWsm/ML7S+/m05X3eu5LH4JZYKYG5gQ0n8OJ0sQxuRPqkrysSNIRAQRkcVylACyc2jPdv749L/Mnzia1JRkmnUfRPvL7qZslZq5nreqMmfcq0x951Eq1mpM/0c+9JSAUpKTmPzSv1n048c0Pe9Setz5ApHFo3IdT6iEMkGk8Pe9lwQoBRx2u1VVY4NdaE5ZgjCm6Dq4K5Hfx/6XBd+9j6rS/PzLaTfoTmIrVw9qfklHDzPphTtZ+usXNOzcl57/eZmoUmU8T6+qzP74eWZ88DS1Wp9Fv4dGUyI6bD+HORKyBJGfWIIwxhzYsYXZY15g4Q8fISK06HUV7QbdTpmKVbOdNjUlmUN7drB/2wZ+fvVetq9eROch99Fu8J1BH/EsmjSWSS/cQYWaDRnw+NigE1YoWYIwxhQp+7Zt5PdPRrFo0hgiIovRsvcQmna7lKP793BgZyIHdyVycGciB3dtPdF9aM92NNW5vjeqdAy9732D+u175DqW9XN/4+tHhxBVqgzdbnuOumecR0Rk/nkisyUIY0yRtDdxHbM+fp4lP3164sc/TYkyZSlTsSoxlapSplJVylSsSplKpxBTsSpVGrWiTMVT8iyOHWsWM/6hyzmwfROly1emSdcBNOt2KZXrN8+zZQTLEoQxpkjbs3kN21bMI7piFcpUPIUyFatSvGTpsMaQknSctX/+xOKfPmP17B9JTU4irl4zmp53CU26DszThJQTliCMMSYfObJ/N8umfMWSnz4lcVkCEhFB7TZdadbtEhp06BnW5GUJwhhj8qndG1ey5KfPWfzzZxzYvomo0mVodGY/6rXrRo3mHShdtmJIl28Jwhhj8jlNTWXjghks/ukzVkybQNIR5wqCirVPpWaLjtRo3oGaLToSXaFKni7XEoQxxhQgKUnH2briLzYumMmmhbPYvPj3EwmjfI361GzekRotOlKjecdcN50tkAlCRO4ErgUUWAgMy+oGgJYgjDGFVWpKMttWLmDjghlOwlg0m2OH9gNQ9pTa9LjrRWq1OjOoeec2QYS9wa6IVAduA5qq6hER+QwYBIwOdyzGGOO3iMhiVD21DVVPbcMZl9xKakoKO9YuZpN7hOFXCyjwIUEELLeUiCQBpYEtPsVhjDH5SkRkJFUatKBKgxa0HXCjv7GEe4Gquhl4DucZE4nAPlWdlH48EbleROaIyJwdO3aEO0xjjCnywp4gRKQ80A+oC1QDokXkHzdXV9W3VDVeVePj4uLCHaYxxhR5YU8QwHnAWlXdoapJwHigow9xGGOMyYIfCWID0F5ESotzG8VzgaU+xGGMMSYLfpyD+B0YB8zFaeIaAbwV7jiMMcZkzZdWTKr6MPCwH8s2xhjjjR9VTMYYYwoASxDGGGMyVCDuxSQiO4D1Ab0qATt9CifUCmvZrFwFT2EtW1EqV21VDfo6gQKRINITkTm5ub9IflZYy2blKngKa9msXN5ZFZMxxpgMWYIwxhiToYKaIArzdROFtWxWroKnsJbNyuVRgTwHYYwxJvQK6hGEMcaYELMEYYwxJkP5JkGIyLsisl1EFgX0aykis0RkoYh8IyKxbv8oEXnP7T9fRLoETNPW7b9KRP7r3hDQN3lYrikislxE5rmvyj4U5wQRqSkiv4rIEhFZLCK3u/0riMhkEVnpvpd3+4u7PVaJyAIRaRMwryHu+CtFZIhfZXJjyctypQRsrwl+lSkgnpyW7VR3Pz0mIv9ON6/z3f1xlYjc60d5AmLJy3Ktc79/80TE9+ccB1G2y939cKGIzBSRlgHzyvk2U9V88QLOAtoAiwL6/Qmc7XZfDTzmdt8CvOd2VwYSgAj38x9Ae0CA74GehaRcU4B4v7dTQBmqAm3c7hhgBdAUeAa41+1/L/B/bncvd3uIu31+d/tXANa47+Xd7vIFvVzusIN+b6dclq0ycDrwBPDvgPlEAquBekAUMB/nEcIFulzusHVAJb+3VS7K1jHt+wP0DPieBbXN8s0RhKpOBXan690ImOp2TwYGut1NgV/c6bYDe4F4EakKxKrqbHXWygdA/1DHnpW8KFcYwswxVU1U1blu9wGcW7ZXx3kY1PvuaO/z9/rvB3ygjtlAOXd79QAmq+puVd2Dsz7OD2NRTpKH5cp3clo2Vd2uqn8CSelmdQawSlXXqOpxYKw7D1/kYbnynSDKNtP9HgHMBmq43UFts3yTIDKxmL8LcTFQ0+2eD1wgIsVEpC7Q1h1WHdgUMP0mt19+k9NypXnPPfR90O+qs0AiUgdoDfwOVFHVRHfQVqCK210d2BgwWdq2yay/73JZLoCS4jw2d7aI+PpHJT2PZctMQd9mWVFgkogkiMj1IQkySEGU7Rqco1sIcpvl9wRxNXCziCTgHF4dd/u/i1PAOcCLwEwgxZcIgxNMuS5X1ebAme7ryrBGnAkRKQN8AdyhqvsDh7lHcQWyHXUelau2Orc+uAx4UUTq532kOWfbLEudVbUNTvXMLSJyVt5HmnM5LZuIdMVJEPfkZrn5OkGo6jJV7a6qbYExOHVoqGqyqt6pqq1UtR9QDqdubjN/H1Lhdm8Od9zZCaJcqOpm9/0A8AnOIaOvRKQ4zk77saqOd3tvS6ticd+3u/03c/LRUNq2yay/b/KoXIHbbA3OOaTWIQ8+GzksW2YK+jbLVMA22w58ScH7niEiLYC3gX6qusvtHdQ2y9cJQtyWOiISATwAvOF+Li0i0W53NyBZVZe4h1z7RaS9WwVzFfC1P9FnLqflcqucKrn9iwN9gEUZzjxM3PX7DrBUVUcFDJoApLVEGsLf638CcJU42gP73O31I9BdRMq7LTG6u/18kVflcstTwp1nJaATsCQshchEEGXLzJ9AQxGpKyJRwCB3Hr7Iq3KJSLSIxKR14+yLBep7JiK1gPHAlaq6ImD84LaZ17PpoX7h/JNOxDlxtAnn8Oh2nH/QK4Cn+fvK7zrAcpwTNj/hHMqnzSceZ6OuBl5Jm6YglwuIxmnRtADn/MVLQKTP5eqMc1i7AJjnvnoBFYGfgZVuGSq44wvwqrtdFhLQIgunym2V+xpWGMqF05pkIc55pYXANX6WK8iyneLus/txGkxswmkEgjvdCrfc9xeGcuG08Jnvvhb7Xa4gy/Y2sCdg3DkB88rxNrNbbRhjjMlQvq5iMsYY4x9LEMYYYzJkCcIYY0yGLEEYY4zJkCUIY4wxGbIEYYzLvZZhuoj0DOh3sYj84GdcxvjFmrkaE0BETgM+x7nquRjwF3C+qq4OYl7FVDU5j0M0JmwsQRiTjog8AxzCuUDxEFAbOA0oDoxU1a/dG6d96I4D8C9VnSnOMzwew7lY6VScRPMZzq0NInFu7f5p2ApjTC5YgjAmHfc2C3NxbqI4EVisqh+JSDmc5420xrm6NVVVj4pIQ2CMqsa7CeJb4DRVXSsiA3GOQK5z511WVff5UCxjcswShDEZEJFHgYPAJUBJIK2qqALOMyy24NzKpRXOHXcbqWppN0E8rKpd3fk0AiYBnwITVXVaOMthTG4U8zsAY/KpVPclwEBVXR44UERGAtuAljiNPY4GDD6U1qGqK8R5DGkv4HER+VlVHw1x7MbkCWvFZEzWfgRuTXtAk4ik3bK7LJCoqqk4z+aIzGhiEakGHFbVj4BncR4/a0yBYEcQxmTtMZyHNy1wb8++Fud2668BX4jIVcAPBBw1pNMceFZEUnHu6HtT6EM2Jm/YOQhjjDEZsiomY4wxGbIEYYwxJkOWIIwxxmTIEoQxxpgMWYIwxhiTIUsQxhhjMmQJwhhjTIb+H10lLFUojN+1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iKYmfcU6Fp3"
      },
      "source": [
        "# **Analysis**\n",
        "\n",
        "From the graph it can be seen that Ireland has seen an overall drop in percentage of females in industries over the years. Even though there were a few peaks around 1997 and 2004, compared to the top 10 countries there hasn't been much improvement over the years unfortunately."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YKycwysp0Vj"
      },
      "source": [
        "# **Economic Crisis in 2008**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQuVQbSM3zRc"
      },
      "source": [
        " **Finding the difference in years between 2007-2008**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUXT4ZxHre4h",
        "outputId": "2d5ba1a7-eb71-4e17-9c78-595de850afe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Extracting 2007 and 2008 column\n",
        "crisis_2008= dataset[[\"country\",\"2007\", \"2008\"]]\n",
        "crisis_2008"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>7.380</td>\n",
              "      <td>7.490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>10.900</td>\n",
              "      <td>10.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>42.500</td>\n",
              "      <td>45.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.390</td>\n",
              "      <td>1.290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>10.400</td>\n",
              "      <td>10.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>7.800</td>\n",
              "      <td>6.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Australia</td>\n",
              "      <td>9.320</td>\n",
              "      <td>9.310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Austria</td>\n",
              "      <td>13.000</td>\n",
              "      <td>12.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>3.890</td>\n",
              "      <td>6.160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bahamas</td>\n",
              "      <td>5.380</td>\n",
              "      <td>4.180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>9.870</td>\n",
              "      <td>9.830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>12.400</td>\n",
              "      <td>12.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>7.910</td>\n",
              "      <td>8.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>25.900</td>\n",
              "      <td>27.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>11.000</td>\n",
              "      <td>10.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Belize</td>\n",
              "      <td>11.100</td>\n",
              "      <td>12.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Benin</td>\n",
              "      <td>7.960</td>\n",
              "      <td>7.190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Bhutan</td>\n",
              "      <td>6.420</td>\n",
              "      <td>8.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bolivia</td>\n",
              "      <td>9.330</td>\n",
              "      <td>10.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>15.600</td>\n",
              "      <td>15.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>10.600</td>\n",
              "      <td>10.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>13.100</td>\n",
              "      <td>13.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Brunei</td>\n",
              "      <td>8.440</td>\n",
              "      <td>9.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Bulgaria</td>\n",
              "      <td>28.500</td>\n",
              "      <td>28.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>5.530</td>\n",
              "      <td>6.380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>0.850</td>\n",
              "      <td>0.778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>10.400</td>\n",
              "      <td>9.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Cameroon</td>\n",
              "      <td>6.540</td>\n",
              "      <td>6.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Canada</td>\n",
              "      <td>10.000</td>\n",
              "      <td>9.530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Cape Verde</td>\n",
              "      <td>3.590</td>\n",
              "      <td>3.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Central African Republic</td>\n",
              "      <td>5.010</td>\n",
              "      <td>5.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Chad</td>\n",
              "      <td>3.630</td>\n",
              "      <td>3.570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Chile</td>\n",
              "      <td>10.900</td>\n",
              "      <td>11.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>China</td>\n",
              "      <td>29.200</td>\n",
              "      <td>28.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>15.800</td>\n",
              "      <td>15.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Comoros</td>\n",
              "      <td>12.200</td>\n",
              "      <td>13.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Congo, Dem. Rep.</td>\n",
              "      <td>11.800</td>\n",
              "      <td>12.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Congo, Rep.</td>\n",
              "      <td>22.700</td>\n",
              "      <td>22.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>12.900</td>\n",
              "      <td>13.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Cote d'Ivoire</td>\n",
              "      <td>2.830</td>\n",
              "      <td>2.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Croatia</td>\n",
              "      <td>18.100</td>\n",
              "      <td>18.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Cuba</td>\n",
              "      <td>12.300</td>\n",
              "      <td>11.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Cyprus</td>\n",
              "      <td>9.490</td>\n",
              "      <td>9.620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>26.800</td>\n",
              "      <td>26.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>12.600</td>\n",
              "      <td>12.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>36.700</td>\n",
              "      <td>37.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>14.100</td>\n",
              "      <td>14.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Ecuador</td>\n",
              "      <td>11.200</td>\n",
              "      <td>11.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>6.140</td>\n",
              "      <td>5.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>El Salvador</td>\n",
              "      <td>20.300</td>\n",
              "      <td>20.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Equatorial Guinea</td>\n",
              "      <td>2.100</td>\n",
              "      <td>2.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Eritrea</td>\n",
              "      <td>5.030</td>\n",
              "      <td>5.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Estonia</td>\n",
              "      <td>21.800</td>\n",
              "      <td>22.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>7.840</td>\n",
              "      <td>7.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>9.130</td>\n",
              "      <td>9.440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Finland</td>\n",
              "      <td>11.500</td>\n",
              "      <td>10.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>France</td>\n",
              "      <td>11.400</td>\n",
              "      <td>10.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Gabon</td>\n",
              "      <td>3.040</td>\n",
              "      <td>3.050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Gambia</td>\n",
              "      <td>4.610</td>\n",
              "      <td>4.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>3.690</td>\n",
              "      <td>3.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Germany</td>\n",
              "      <td>16.000</td>\n",
              "      <td>15.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>14.500</td>\n",
              "      <td>13.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Greece</td>\n",
              "      <td>9.840</td>\n",
              "      <td>9.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Guatemala</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Guinea</td>\n",
              "      <td>1.860</td>\n",
              "      <td>1.880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Guinea-Bissau</td>\n",
              "      <td>4.510</td>\n",
              "      <td>4.440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Guyana</td>\n",
              "      <td>11.800</td>\n",
              "      <td>11.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Haiti</td>\n",
              "      <td>6.680</td>\n",
              "      <td>6.550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>21.900</td>\n",
              "      <td>21.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>21.100</td>\n",
              "      <td>20.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>8.620</td>\n",
              "      <td>8.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>India</td>\n",
              "      <td>15.100</td>\n",
              "      <td>15.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>15.000</td>\n",
              "      <td>14.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Iran</td>\n",
              "      <td>29.100</td>\n",
              "      <td>27.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Iraq</td>\n",
              "      <td>7.200</td>\n",
              "      <td>3.560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>10.700</td>\n",
              "      <td>9.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Israel</td>\n",
              "      <td>10.000</td>\n",
              "      <td>10.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Italy</td>\n",
              "      <td>16.500</td>\n",
              "      <td>15.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Jamaica</td>\n",
              "      <td>5.930</td>\n",
              "      <td>5.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Japan</td>\n",
              "      <td>17.600</td>\n",
              "      <td>16.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>18.500</td>\n",
              "      <td>18.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Kazakhstan</td>\n",
              "      <td>12.000</td>\n",
              "      <td>11.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>9.820</td>\n",
              "      <td>9.430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Kuwait</td>\n",
              "      <td>2.920</td>\n",
              "      <td>3.730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Kyrgyz Republic</td>\n",
              "      <td>11.000</td>\n",
              "      <td>11.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Lao</td>\n",
              "      <td>5.140</td>\n",
              "      <td>5.640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Latvia</td>\n",
              "      <td>15.500</td>\n",
              "      <td>17.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Lebanon</td>\n",
              "      <td>7.050</td>\n",
              "      <td>6.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>31.200</td>\n",
              "      <td>34.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Liberia</td>\n",
              "      <td>4.440</td>\n",
              "      <td>4.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Libya</td>\n",
              "      <td>21.100</td>\n",
              "      <td>22.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Lithuania</td>\n",
              "      <td>19.700</td>\n",
              "      <td>19.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>5.460</td>\n",
              "      <td>6.140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Madagascar</td>\n",
              "      <td>2.740</td>\n",
              "      <td>3.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Malawi</td>\n",
              "      <td>9.090</td>\n",
              "      <td>8.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>22.700</td>\n",
              "      <td>22.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>26.000</td>\n",
              "      <td>23.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Mali</td>\n",
              "      <td>12.100</td>\n",
              "      <td>10.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Malta</td>\n",
              "      <td>13.300</td>\n",
              "      <td>11.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Mauritania</td>\n",
              "      <td>3.380</td>\n",
              "      <td>3.530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Mauritius</td>\n",
              "      <td>25.900</td>\n",
              "      <td>25.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>17.800</td>\n",
              "      <td>17.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Moldova</td>\n",
              "      <td>12.400</td>\n",
              "      <td>13.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>10.600</td>\n",
              "      <td>11.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Montenegro</td>\n",
              "      <td>10.100</td>\n",
              "      <td>12.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>14.600</td>\n",
              "      <td>14.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Mozambique</td>\n",
              "      <td>0.315</td>\n",
              "      <td>0.309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Myanmar</td>\n",
              "      <td>11.600</td>\n",
              "      <td>12.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>9.710</td>\n",
              "      <td>9.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Nepal</td>\n",
              "      <td>5.250</td>\n",
              "      <td>5.990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.800</td>\n",
              "      <td>7.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>10.100</td>\n",
              "      <td>10.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Nicaragua</td>\n",
              "      <td>19.400</td>\n",
              "      <td>18.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Niger</td>\n",
              "      <td>10.300</td>\n",
              "      <td>10.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>7.720</td>\n",
              "      <td>8.930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>North Korea</td>\n",
              "      <td>17.000</td>\n",
              "      <td>18.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>28.700</td>\n",
              "      <td>27.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Norway</td>\n",
              "      <td>8.050</td>\n",
              "      <td>7.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Oman</td>\n",
              "      <td>8.360</td>\n",
              "      <td>7.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>Palestine</td>\n",
              "      <td>9.630</td>\n",
              "      <td>9.860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Panama</td>\n",
              "      <td>9.680</td>\n",
              "      <td>9.320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>2.240</td>\n",
              "      <td>2.410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>Paraguay</td>\n",
              "      <td>8.790</td>\n",
              "      <td>9.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Peru</td>\n",
              "      <td>10.400</td>\n",
              "      <td>10.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>11.100</td>\n",
              "      <td>10.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>Poland</td>\n",
              "      <td>17.800</td>\n",
              "      <td>17.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Portugal</td>\n",
              "      <td>18.100</td>\n",
              "      <td>16.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Qatar</td>\n",
              "      <td>5.150</td>\n",
              "      <td>4.350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Romania</td>\n",
              "      <td>24.700</td>\n",
              "      <td>23.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Russia</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Rwanda</td>\n",
              "      <td>1.130</td>\n",
              "      <td>1.310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>8.300</td>\n",
              "      <td>8.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Sao Tome and Principe</td>\n",
              "      <td>6.190</td>\n",
              "      <td>6.680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Senegal</td>\n",
              "      <td>7.990</td>\n",
              "      <td>9.290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Serbia</td>\n",
              "      <td>19.600</td>\n",
              "      <td>16.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>0.918</td>\n",
              "      <td>0.894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>17.500</td>\n",
              "      <td>16.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Slovak Republic</td>\n",
              "      <td>24.300</td>\n",
              "      <td>24.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Slovenia</td>\n",
              "      <td>23.000</td>\n",
              "      <td>22.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>6.370</td>\n",
              "      <td>6.380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Somalia</td>\n",
              "      <td>5.070</td>\n",
              "      <td>5.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>13.400</td>\n",
              "      <td>13.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>15.600</td>\n",
              "      <td>15.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>South Sudan</td>\n",
              "      <td>22.000</td>\n",
              "      <td>22.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Spain</td>\n",
              "      <td>11.600</td>\n",
              "      <td>11.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>27.500</td>\n",
              "      <td>25.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>St. Lucia</td>\n",
              "      <td>9.170</td>\n",
              "      <td>9.120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>St. Vincent and the Grenadines</td>\n",
              "      <td>6.740</td>\n",
              "      <td>6.610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>35.300</td>\n",
              "      <td>34.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>Suriname</td>\n",
              "      <td>9.880</td>\n",
              "      <td>9.530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Swaziland</td>\n",
              "      <td>8.640</td>\n",
              "      <td>8.630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>9.170</td>\n",
              "      <td>8.230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>10.700</td>\n",
              "      <td>11.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Syria</td>\n",
              "      <td>7.780</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Tajikistan</td>\n",
              "      <td>4.750</td>\n",
              "      <td>4.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Tanzania</td>\n",
              "      <td>2.940</td>\n",
              "      <td>2.960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>19.300</td>\n",
              "      <td>18.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>Timor-Leste</td>\n",
              "      <td>7.320</td>\n",
              "      <td>7.740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Togo</td>\n",
              "      <td>20.300</td>\n",
              "      <td>19.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>57.300</td>\n",
              "      <td>56.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Trinidad and Tobago</td>\n",
              "      <td>15.100</td>\n",
              "      <td>16.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>39.300</td>\n",
              "      <td>41.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>16.200</td>\n",
              "      <td>15.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>21.600</td>\n",
              "      <td>29.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>3.490</td>\n",
              "      <td>4.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Ukraine</td>\n",
              "      <td>18.000</td>\n",
              "      <td>16.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>5.750</td>\n",
              "      <td>6.050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>9.430</td>\n",
              "      <td>9.410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>United States</td>\n",
              "      <td>9.510</td>\n",
              "      <td>9.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Uruguay</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Uzbekistan</td>\n",
              "      <td>22.500</td>\n",
              "      <td>24.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>2.280</td>\n",
              "      <td>2.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>12.100</td>\n",
              "      <td>11.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>16.700</td>\n",
              "      <td>16.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Yemen</td>\n",
              "      <td>11.000</td>\n",
              "      <td>12.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>2.580</td>\n",
              "      <td>2.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>4.280</td>\n",
              "      <td>4.220</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            country    2007    2008\n",
              "0                       Afghanistan   7.380   7.490\n",
              "1                           Albania  10.900  10.900\n",
              "2                           Algeria  42.500  45.300\n",
              "3                            Angola   1.390   1.290\n",
              "4                         Argentina  10.400  10.200\n",
              "5                           Armenia   7.800   6.500\n",
              "6                         Australia   9.320   9.310\n",
              "7                           Austria  13.000  12.300\n",
              "8                        Azerbaijan   3.890   6.160\n",
              "9                           Bahamas   5.380   4.180\n",
              "10                          Bahrain   9.870   9.830\n",
              "11                       Bangladesh  12.400  12.500\n",
              "12                         Barbados   7.910   8.400\n",
              "13                          Belarus  25.900  27.100\n",
              "14                          Belgium  11.000  10.300\n",
              "15                           Belize  11.100  12.200\n",
              "16                            Benin   7.960   7.190\n",
              "17                           Bhutan   6.420   8.060\n",
              "18                          Bolivia   9.330  10.900\n",
              "19           Bosnia and Herzegovina  15.600  15.800\n",
              "20                         Botswana  10.600  10.200\n",
              "21                           Brazil  13.100  13.400\n",
              "22                           Brunei   8.440   9.250\n",
              "23                         Bulgaria  28.500  28.600\n",
              "24                     Burkina Faso   5.530   6.380\n",
              "25                          Burundi   0.850   0.778\n",
              "26                         Cambodia  10.400   9.070\n",
              "27                         Cameroon   6.540   6.580\n",
              "28                           Canada  10.000   9.530\n",
              "29                       Cape Verde   3.590   3.660\n",
              "30         Central African Republic   5.010   5.080\n",
              "31                             Chad   3.630   3.570\n",
              "32                            Chile  10.900  11.400\n",
              "33                            China  29.200  28.800\n",
              "34                         Colombia  15.800  15.500\n",
              "35                          Comoros  12.200  13.000\n",
              "36                 Congo, Dem. Rep.  11.800  12.400\n",
              "37                      Congo, Rep.  22.700  22.600\n",
              "38                       Costa Rica  12.900  13.000\n",
              "39                    Cote d'Ivoire   2.830   2.750\n",
              "40                          Croatia  18.100  18.000\n",
              "41                             Cuba  12.300  11.900\n",
              "42                           Cyprus   9.490   9.620\n",
              "43                   Czech Republic  26.800  26.700\n",
              "44                          Denmark  12.600  12.200\n",
              "45                         Djibouti  36.700  37.600\n",
              "46               Dominican Republic  14.100  14.600\n",
              "47                          Ecuador  11.200  11.300\n",
              "48                            Egypt   6.140   5.600\n",
              "49                      El Salvador  20.300  20.600\n",
              "50                Equatorial Guinea   2.100   2.100\n",
              "51                          Eritrea   5.030   5.790\n",
              "52                          Estonia  21.800  22.100\n",
              "53                         Ethiopia   7.840   7.390\n",
              "54                             Fiji   9.130   9.440\n",
              "55                          Finland  11.500  10.400\n",
              "56                           France  11.400  10.800\n",
              "57                            Gabon   3.040   3.050\n",
              "58                           Gambia   4.610   4.220\n",
              "59                          Georgia   3.690   3.700\n",
              "60                          Germany  16.000  15.000\n",
              "61                            Ghana  14.500  13.800\n",
              "62                           Greece   9.840   9.360\n",
              "63                        Guatemala  20.200  19.800\n",
              "64                           Guinea   1.860   1.880\n",
              "65                    Guinea-Bissau   4.510   4.440\n",
              "66                           Guyana  11.800  11.500\n",
              "67                            Haiti   6.680   6.550\n",
              "68                         Honduras  21.900  21.300\n",
              "69                          Hungary  21.100  20.900\n",
              "70                          Iceland   8.620   8.580\n",
              "71                            India  15.100  15.100\n",
              "72                        Indonesia  15.000  14.600\n",
              "73                             Iran  29.100  27.300\n",
              "74                             Iraq   7.200   3.560\n",
              "75                          Ireland  10.700   9.920\n",
              "76                           Israel  10.000  10.600\n",
              "77                            Italy  16.500  15.900\n",
              "78                          Jamaica   5.930   5.770\n",
              "79                            Japan  17.600  16.500\n",
              "80                           Jordan  18.500  18.100\n",
              "81                       Kazakhstan  12.000  11.500\n",
              "82                            Kenya   9.820   9.430\n",
              "83                           Kuwait   2.920   3.730\n",
              "84                  Kyrgyz Republic  11.000  11.000\n",
              "85                              Lao   5.140   5.640\n",
              "86                           Latvia  15.500  17.400\n",
              "87                          Lebanon   7.050   6.710\n",
              "88                          Lesotho  31.200  34.200\n",
              "89                          Liberia   4.440   4.130\n",
              "90                            Libya  21.100  22.000\n",
              "91                        Lithuania  19.700  19.000\n",
              "92                       Luxembourg   5.460   6.140\n",
              "93                       Madagascar   2.740   3.660\n",
              "94                           Malawi   9.090   8.080\n",
              "95                         Malaysia  22.700  22.700\n",
              "96                         Maldives  26.000  23.200\n",
              "97                             Mali  12.100  10.800\n",
              "98                            Malta  13.300  11.000\n",
              "99                       Mauritania   3.380   3.530\n",
              "100                       Mauritius  25.900  25.300\n",
              "101                          Mexico  17.800  17.700\n",
              "102                         Moldova  12.400  13.500\n",
              "103                        Mongolia  10.600  11.500\n",
              "104                      Montenegro  10.100  12.000\n",
              "105                         Morocco  14.600  14.800\n",
              "106                      Mozambique   0.315   0.309\n",
              "107                         Myanmar  11.600  12.600\n",
              "108                         Namibia   9.710   9.330\n",
              "109                           Nepal   5.250   5.990\n",
              "110                     Netherlands   7.800   7.360\n",
              "111                     New Zealand  10.100  10.300\n",
              "112                       Nicaragua  19.400  18.800\n",
              "113                           Niger  10.300  10.400\n",
              "114                         Nigeria   7.720   8.930\n",
              "115                     North Korea  17.000  18.100\n",
              "116                 North Macedonia  28.700  27.100\n",
              "117                          Norway   8.050   7.920\n",
              "118                            Oman   8.360   7.650\n",
              "119                        Pakistan  12.500  12.000\n",
              "120                       Palestine   9.630   9.860\n",
              "121                          Panama   9.680   9.320\n",
              "122                Papua New Guinea   2.240   2.410\n",
              "123                        Paraguay   8.790   9.600\n",
              "124                            Peru  10.400  10.600\n",
              "125                     Philippines  11.100  10.400\n",
              "126                          Poland  17.800  17.800\n",
              "127                        Portugal  18.100  16.600\n",
              "128                           Qatar   5.150   4.350\n",
              "129                         Romania  24.700  23.800\n",
              "130                          Russia  20.200  19.100\n",
              "131                          Rwanda   1.130   1.310\n",
              "132                           Samoa   8.300   8.080\n",
              "133           Sao Tome and Principe   6.190   6.680\n",
              "134                    Saudi Arabia   1.380   1.270\n",
              "135                         Senegal   7.990   9.290\n",
              "136                          Serbia  19.600  16.000\n",
              "137                    Sierra Leone   0.918   0.894\n",
              "138                       Singapore  17.500  16.700\n",
              "139                 Slovak Republic  24.300  24.400\n",
              "140                        Slovenia  23.000  22.700\n",
              "141                 Solomon Islands   6.370   6.380\n",
              "142                         Somalia   5.070   5.150\n",
              "143                    South Africa  13.400  13.100\n",
              "144                     South Korea  15.600  15.200\n",
              "145                     South Sudan  22.000  22.000\n",
              "146                           Spain  11.600  11.200\n",
              "147                       Sri Lanka  27.500  25.900\n",
              "148                       St. Lucia   9.170   9.120\n",
              "149  St. Vincent and the Grenadines   6.740   6.610\n",
              "150                           Sudan  35.300  34.100\n",
              "151                        Suriname   9.880   9.530\n",
              "152                       Swaziland   8.640   8.630\n",
              "153                          Sweden   9.170   8.230\n",
              "154                     Switzerland  10.700  11.400\n",
              "155                           Syria   7.780  10.000\n",
              "156                      Tajikistan   4.750   4.500\n",
              "157                        Tanzania   2.940   2.960\n",
              "158                        Thailand  19.300  18.200\n",
              "159                     Timor-Leste   7.320   7.740\n",
              "160                            Togo  20.300  19.900\n",
              "161                           Tonga  57.300  56.200\n",
              "162             Trinidad and Tobago  15.100  16.000\n",
              "163                         Tunisia  39.300  41.600\n",
              "164                          Turkey  16.200  15.800\n",
              "165                    Turkmenistan  21.600  29.100\n",
              "166                          Uganda   3.490   4.000\n",
              "167                         Ukraine  18.000  16.600\n",
              "168            United Arab Emirates   5.750   6.050\n",
              "169                  United Kingdom   9.430   9.410\n",
              "170                   United States   9.510   9.060\n",
              "171                         Uruguay  12.500  12.100\n",
              "172                      Uzbekistan  22.500  24.900\n",
              "173                         Vanuatu   2.280   2.400\n",
              "174                       Venezuela  12.100  11.200\n",
              "175                         Vietnam  16.700  16.300\n",
              "176                           Yemen  11.000  12.000\n",
              "177                          Zambia   2.580   2.660\n",
              "178                        Zimbabwe   4.280   4.220"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDM8DcvxsTpd",
        "outputId": "44d27dd8-4f51-490f-bc2d-1340df13fc06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Finding the difference between 2008 to 2007\n",
        "crisis_2008['difference'] = crisis_2008['2008'].sub(crisis_2008['2007'], axis=0)\n",
        "#df1['Score_diff'] = df1['score1'].sub(df1['score2'], axis = 0)\n",
        "crisis_2008"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>7.380</td>\n",
              "      <td>7.490</td>\n",
              "      <td>0.110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>10.900</td>\n",
              "      <td>10.900</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>42.500</td>\n",
              "      <td>45.300</td>\n",
              "      <td>2.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.390</td>\n",
              "      <td>1.290</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>10.400</td>\n",
              "      <td>10.200</td>\n",
              "      <td>-0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>7.800</td>\n",
              "      <td>6.500</td>\n",
              "      <td>-1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Australia</td>\n",
              "      <td>9.320</td>\n",
              "      <td>9.310</td>\n",
              "      <td>-0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Austria</td>\n",
              "      <td>13.000</td>\n",
              "      <td>12.300</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>3.890</td>\n",
              "      <td>6.160</td>\n",
              "      <td>2.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bahamas</td>\n",
              "      <td>5.380</td>\n",
              "      <td>4.180</td>\n",
              "      <td>-1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>9.870</td>\n",
              "      <td>9.830</td>\n",
              "      <td>-0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>12.400</td>\n",
              "      <td>12.500</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>7.910</td>\n",
              "      <td>8.400</td>\n",
              "      <td>0.490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>25.900</td>\n",
              "      <td>27.100</td>\n",
              "      <td>1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>11.000</td>\n",
              "      <td>10.300</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Belize</td>\n",
              "      <td>11.100</td>\n",
              "      <td>12.200</td>\n",
              "      <td>1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Benin</td>\n",
              "      <td>7.960</td>\n",
              "      <td>7.190</td>\n",
              "      <td>-0.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Bhutan</td>\n",
              "      <td>6.420</td>\n",
              "      <td>8.060</td>\n",
              "      <td>1.640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bolivia</td>\n",
              "      <td>9.330</td>\n",
              "      <td>10.900</td>\n",
              "      <td>1.570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>15.600</td>\n",
              "      <td>15.800</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>10.600</td>\n",
              "      <td>10.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>13.100</td>\n",
              "      <td>13.400</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Brunei</td>\n",
              "      <td>8.440</td>\n",
              "      <td>9.250</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Bulgaria</td>\n",
              "      <td>28.500</td>\n",
              "      <td>28.600</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>5.530</td>\n",
              "      <td>6.380</td>\n",
              "      <td>0.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>0.850</td>\n",
              "      <td>0.778</td>\n",
              "      <td>-0.072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>10.400</td>\n",
              "      <td>9.070</td>\n",
              "      <td>-1.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Cameroon</td>\n",
              "      <td>6.540</td>\n",
              "      <td>6.580</td>\n",
              "      <td>0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Canada</td>\n",
              "      <td>10.000</td>\n",
              "      <td>9.530</td>\n",
              "      <td>-0.470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Cape Verde</td>\n",
              "      <td>3.590</td>\n",
              "      <td>3.660</td>\n",
              "      <td>0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Central African Republic</td>\n",
              "      <td>5.010</td>\n",
              "      <td>5.080</td>\n",
              "      <td>0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Chad</td>\n",
              "      <td>3.630</td>\n",
              "      <td>3.570</td>\n",
              "      <td>-0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Chile</td>\n",
              "      <td>10.900</td>\n",
              "      <td>11.400</td>\n",
              "      <td>0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>China</td>\n",
              "      <td>29.200</td>\n",
              "      <td>28.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>15.800</td>\n",
              "      <td>15.500</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Comoros</td>\n",
              "      <td>12.200</td>\n",
              "      <td>13.000</td>\n",
              "      <td>0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Congo, Dem. Rep.</td>\n",
              "      <td>11.800</td>\n",
              "      <td>12.400</td>\n",
              "      <td>0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Congo, Rep.</td>\n",
              "      <td>22.700</td>\n",
              "      <td>22.600</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>12.900</td>\n",
              "      <td>13.000</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Cote d'Ivoire</td>\n",
              "      <td>2.830</td>\n",
              "      <td>2.750</td>\n",
              "      <td>-0.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Croatia</td>\n",
              "      <td>18.100</td>\n",
              "      <td>18.000</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Cuba</td>\n",
              "      <td>12.300</td>\n",
              "      <td>11.900</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Cyprus</td>\n",
              "      <td>9.490</td>\n",
              "      <td>9.620</td>\n",
              "      <td>0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>26.800</td>\n",
              "      <td>26.700</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>12.600</td>\n",
              "      <td>12.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>36.700</td>\n",
              "      <td>37.600</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>14.100</td>\n",
              "      <td>14.600</td>\n",
              "      <td>0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Ecuador</td>\n",
              "      <td>11.200</td>\n",
              "      <td>11.300</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>6.140</td>\n",
              "      <td>5.600</td>\n",
              "      <td>-0.540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>El Salvador</td>\n",
              "      <td>20.300</td>\n",
              "      <td>20.600</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Equatorial Guinea</td>\n",
              "      <td>2.100</td>\n",
              "      <td>2.100</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Eritrea</td>\n",
              "      <td>5.030</td>\n",
              "      <td>5.790</td>\n",
              "      <td>0.760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Estonia</td>\n",
              "      <td>21.800</td>\n",
              "      <td>22.100</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>7.840</td>\n",
              "      <td>7.390</td>\n",
              "      <td>-0.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>9.130</td>\n",
              "      <td>9.440</td>\n",
              "      <td>0.310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Finland</td>\n",
              "      <td>11.500</td>\n",
              "      <td>10.400</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>France</td>\n",
              "      <td>11.400</td>\n",
              "      <td>10.800</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Gabon</td>\n",
              "      <td>3.040</td>\n",
              "      <td>3.050</td>\n",
              "      <td>0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Gambia</td>\n",
              "      <td>4.610</td>\n",
              "      <td>4.220</td>\n",
              "      <td>-0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>3.690</td>\n",
              "      <td>3.700</td>\n",
              "      <td>0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Germany</td>\n",
              "      <td>16.000</td>\n",
              "      <td>15.000</td>\n",
              "      <td>-1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>14.500</td>\n",
              "      <td>13.800</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Greece</td>\n",
              "      <td>9.840</td>\n",
              "      <td>9.360</td>\n",
              "      <td>-0.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Guatemala</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Guinea</td>\n",
              "      <td>1.860</td>\n",
              "      <td>1.880</td>\n",
              "      <td>0.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Guinea-Bissau</td>\n",
              "      <td>4.510</td>\n",
              "      <td>4.440</td>\n",
              "      <td>-0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Guyana</td>\n",
              "      <td>11.800</td>\n",
              "      <td>11.500</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Haiti</td>\n",
              "      <td>6.680</td>\n",
              "      <td>6.550</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>21.900</td>\n",
              "      <td>21.300</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>21.100</td>\n",
              "      <td>20.900</td>\n",
              "      <td>-0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>8.620</td>\n",
              "      <td>8.580</td>\n",
              "      <td>-0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>India</td>\n",
              "      <td>15.100</td>\n",
              "      <td>15.100</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>15.000</td>\n",
              "      <td>14.600</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Iran</td>\n",
              "      <td>29.100</td>\n",
              "      <td>27.300</td>\n",
              "      <td>-1.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Iraq</td>\n",
              "      <td>7.200</td>\n",
              "      <td>3.560</td>\n",
              "      <td>-3.640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>10.700</td>\n",
              "      <td>9.920</td>\n",
              "      <td>-0.780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Israel</td>\n",
              "      <td>10.000</td>\n",
              "      <td>10.600</td>\n",
              "      <td>0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Italy</td>\n",
              "      <td>16.500</td>\n",
              "      <td>15.900</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Jamaica</td>\n",
              "      <td>5.930</td>\n",
              "      <td>5.770</td>\n",
              "      <td>-0.160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Japan</td>\n",
              "      <td>17.600</td>\n",
              "      <td>16.500</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>18.500</td>\n",
              "      <td>18.100</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Kazakhstan</td>\n",
              "      <td>12.000</td>\n",
              "      <td>11.500</td>\n",
              "      <td>-0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>9.820</td>\n",
              "      <td>9.430</td>\n",
              "      <td>-0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Kuwait</td>\n",
              "      <td>2.920</td>\n",
              "      <td>3.730</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Kyrgyz Republic</td>\n",
              "      <td>11.000</td>\n",
              "      <td>11.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Lao</td>\n",
              "      <td>5.140</td>\n",
              "      <td>5.640</td>\n",
              "      <td>0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Latvia</td>\n",
              "      <td>15.500</td>\n",
              "      <td>17.400</td>\n",
              "      <td>1.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Lebanon</td>\n",
              "      <td>7.050</td>\n",
              "      <td>6.710</td>\n",
              "      <td>-0.340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>31.200</td>\n",
              "      <td>34.200</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Liberia</td>\n",
              "      <td>4.440</td>\n",
              "      <td>4.130</td>\n",
              "      <td>-0.310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Libya</td>\n",
              "      <td>21.100</td>\n",
              "      <td>22.000</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Lithuania</td>\n",
              "      <td>19.700</td>\n",
              "      <td>19.000</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>5.460</td>\n",
              "      <td>6.140</td>\n",
              "      <td>0.680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Madagascar</td>\n",
              "      <td>2.740</td>\n",
              "      <td>3.660</td>\n",
              "      <td>0.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Malawi</td>\n",
              "      <td>9.090</td>\n",
              "      <td>8.080</td>\n",
              "      <td>-1.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>22.700</td>\n",
              "      <td>22.700</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>26.000</td>\n",
              "      <td>23.200</td>\n",
              "      <td>-2.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Mali</td>\n",
              "      <td>12.100</td>\n",
              "      <td>10.800</td>\n",
              "      <td>-1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Malta</td>\n",
              "      <td>13.300</td>\n",
              "      <td>11.000</td>\n",
              "      <td>-2.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Mauritania</td>\n",
              "      <td>3.380</td>\n",
              "      <td>3.530</td>\n",
              "      <td>0.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Mauritius</td>\n",
              "      <td>25.900</td>\n",
              "      <td>25.300</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>17.800</td>\n",
              "      <td>17.700</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Moldova</td>\n",
              "      <td>12.400</td>\n",
              "      <td>13.500</td>\n",
              "      <td>1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>10.600</td>\n",
              "      <td>11.500</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Montenegro</td>\n",
              "      <td>10.100</td>\n",
              "      <td>12.000</td>\n",
              "      <td>1.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>14.600</td>\n",
              "      <td>14.800</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Mozambique</td>\n",
              "      <td>0.315</td>\n",
              "      <td>0.309</td>\n",
              "      <td>-0.006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Myanmar</td>\n",
              "      <td>11.600</td>\n",
              "      <td>12.600</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>9.710</td>\n",
              "      <td>9.330</td>\n",
              "      <td>-0.380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Nepal</td>\n",
              "      <td>5.250</td>\n",
              "      <td>5.990</td>\n",
              "      <td>0.740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.800</td>\n",
              "      <td>7.360</td>\n",
              "      <td>-0.440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>10.100</td>\n",
              "      <td>10.300</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Nicaragua</td>\n",
              "      <td>19.400</td>\n",
              "      <td>18.800</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Niger</td>\n",
              "      <td>10.300</td>\n",
              "      <td>10.400</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>7.720</td>\n",
              "      <td>8.930</td>\n",
              "      <td>1.210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>North Korea</td>\n",
              "      <td>17.000</td>\n",
              "      <td>18.100</td>\n",
              "      <td>1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>28.700</td>\n",
              "      <td>27.100</td>\n",
              "      <td>-1.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Norway</td>\n",
              "      <td>8.050</td>\n",
              "      <td>7.920</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Oman</td>\n",
              "      <td>8.360</td>\n",
              "      <td>7.650</td>\n",
              "      <td>-0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.000</td>\n",
              "      <td>-0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>Palestine</td>\n",
              "      <td>9.630</td>\n",
              "      <td>9.860</td>\n",
              "      <td>0.230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Panama</td>\n",
              "      <td>9.680</td>\n",
              "      <td>9.320</td>\n",
              "      <td>-0.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>2.240</td>\n",
              "      <td>2.410</td>\n",
              "      <td>0.170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>Paraguay</td>\n",
              "      <td>8.790</td>\n",
              "      <td>9.600</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Peru</td>\n",
              "      <td>10.400</td>\n",
              "      <td>10.600</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>11.100</td>\n",
              "      <td>10.400</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>Poland</td>\n",
              "      <td>17.800</td>\n",
              "      <td>17.800</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Portugal</td>\n",
              "      <td>18.100</td>\n",
              "      <td>16.600</td>\n",
              "      <td>-1.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Qatar</td>\n",
              "      <td>5.150</td>\n",
              "      <td>4.350</td>\n",
              "      <td>-0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Romania</td>\n",
              "      <td>24.700</td>\n",
              "      <td>23.800</td>\n",
              "      <td>-0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Russia</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.100</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Rwanda</td>\n",
              "      <td>1.130</td>\n",
              "      <td>1.310</td>\n",
              "      <td>0.180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>8.300</td>\n",
              "      <td>8.080</td>\n",
              "      <td>-0.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Sao Tome and Principe</td>\n",
              "      <td>6.190</td>\n",
              "      <td>6.680</td>\n",
              "      <td>0.490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.270</td>\n",
              "      <td>-0.110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Senegal</td>\n",
              "      <td>7.990</td>\n",
              "      <td>9.290</td>\n",
              "      <td>1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Serbia</td>\n",
              "      <td>19.600</td>\n",
              "      <td>16.000</td>\n",
              "      <td>-3.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>0.918</td>\n",
              "      <td>0.894</td>\n",
              "      <td>-0.024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>17.500</td>\n",
              "      <td>16.700</td>\n",
              "      <td>-0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Slovak Republic</td>\n",
              "      <td>24.300</td>\n",
              "      <td>24.400</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Slovenia</td>\n",
              "      <td>23.000</td>\n",
              "      <td>22.700</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>6.370</td>\n",
              "      <td>6.380</td>\n",
              "      <td>0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Somalia</td>\n",
              "      <td>5.070</td>\n",
              "      <td>5.150</td>\n",
              "      <td>0.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>13.400</td>\n",
              "      <td>13.100</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>15.600</td>\n",
              "      <td>15.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>South Sudan</td>\n",
              "      <td>22.000</td>\n",
              "      <td>22.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Spain</td>\n",
              "      <td>11.600</td>\n",
              "      <td>11.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>27.500</td>\n",
              "      <td>25.900</td>\n",
              "      <td>-1.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>St. Lucia</td>\n",
              "      <td>9.170</td>\n",
              "      <td>9.120</td>\n",
              "      <td>-0.050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>St. Vincent and the Grenadines</td>\n",
              "      <td>6.740</td>\n",
              "      <td>6.610</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>35.300</td>\n",
              "      <td>34.100</td>\n",
              "      <td>-1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>Suriname</td>\n",
              "      <td>9.880</td>\n",
              "      <td>9.530</td>\n",
              "      <td>-0.350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Swaziland</td>\n",
              "      <td>8.640</td>\n",
              "      <td>8.630</td>\n",
              "      <td>-0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>9.170</td>\n",
              "      <td>8.230</td>\n",
              "      <td>-0.940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>10.700</td>\n",
              "      <td>11.400</td>\n",
              "      <td>0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Syria</td>\n",
              "      <td>7.780</td>\n",
              "      <td>10.000</td>\n",
              "      <td>2.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Tajikistan</td>\n",
              "      <td>4.750</td>\n",
              "      <td>4.500</td>\n",
              "      <td>-0.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Tanzania</td>\n",
              "      <td>2.940</td>\n",
              "      <td>2.960</td>\n",
              "      <td>0.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>19.300</td>\n",
              "      <td>18.200</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>Timor-Leste</td>\n",
              "      <td>7.320</td>\n",
              "      <td>7.740</td>\n",
              "      <td>0.420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Togo</td>\n",
              "      <td>20.300</td>\n",
              "      <td>19.900</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>57.300</td>\n",
              "      <td>56.200</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Trinidad and Tobago</td>\n",
              "      <td>15.100</td>\n",
              "      <td>16.000</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>39.300</td>\n",
              "      <td>41.600</td>\n",
              "      <td>2.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>16.200</td>\n",
              "      <td>15.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>21.600</td>\n",
              "      <td>29.100</td>\n",
              "      <td>7.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>3.490</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Ukraine</td>\n",
              "      <td>18.000</td>\n",
              "      <td>16.600</td>\n",
              "      <td>-1.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>5.750</td>\n",
              "      <td>6.050</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>9.430</td>\n",
              "      <td>9.410</td>\n",
              "      <td>-0.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>United States</td>\n",
              "      <td>9.510</td>\n",
              "      <td>9.060</td>\n",
              "      <td>-0.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Uruguay</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.100</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Uzbekistan</td>\n",
              "      <td>22.500</td>\n",
              "      <td>24.900</td>\n",
              "      <td>2.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>2.280</td>\n",
              "      <td>2.400</td>\n",
              "      <td>0.120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>12.100</td>\n",
              "      <td>11.200</td>\n",
              "      <td>-0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>16.700</td>\n",
              "      <td>16.300</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Yemen</td>\n",
              "      <td>11.000</td>\n",
              "      <td>12.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>2.580</td>\n",
              "      <td>2.660</td>\n",
              "      <td>0.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>4.280</td>\n",
              "      <td>4.220</td>\n",
              "      <td>-0.060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            country    2007    2008  difference\n",
              "0                       Afghanistan   7.380   7.490       0.110\n",
              "1                           Albania  10.900  10.900       0.000\n",
              "2                           Algeria  42.500  45.300       2.800\n",
              "3                            Angola   1.390   1.290      -0.100\n",
              "4                         Argentina  10.400  10.200      -0.200\n",
              "5                           Armenia   7.800   6.500      -1.300\n",
              "6                         Australia   9.320   9.310      -0.010\n",
              "7                           Austria  13.000  12.300      -0.700\n",
              "8                        Azerbaijan   3.890   6.160       2.270\n",
              "9                           Bahamas   5.380   4.180      -1.200\n",
              "10                          Bahrain   9.870   9.830      -0.040\n",
              "11                       Bangladesh  12.400  12.500       0.100\n",
              "12                         Barbados   7.910   8.400       0.490\n",
              "13                          Belarus  25.900  27.100       1.200\n",
              "14                          Belgium  11.000  10.300      -0.700\n",
              "15                           Belize  11.100  12.200       1.100\n",
              "16                            Benin   7.960   7.190      -0.770\n",
              "17                           Bhutan   6.420   8.060       1.640\n",
              "18                          Bolivia   9.330  10.900       1.570\n",
              "19           Bosnia and Herzegovina  15.600  15.800       0.200\n",
              "20                         Botswana  10.600  10.200      -0.400\n",
              "21                           Brazil  13.100  13.400       0.300\n",
              "22                           Brunei   8.440   9.250       0.810\n",
              "23                         Bulgaria  28.500  28.600       0.100\n",
              "24                     Burkina Faso   5.530   6.380       0.850\n",
              "25                          Burundi   0.850   0.778      -0.072\n",
              "26                         Cambodia  10.400   9.070      -1.330\n",
              "27                         Cameroon   6.540   6.580       0.040\n",
              "28                           Canada  10.000   9.530      -0.470\n",
              "29                       Cape Verde   3.590   3.660       0.070\n",
              "30         Central African Republic   5.010   5.080       0.070\n",
              "31                             Chad   3.630   3.570      -0.060\n",
              "32                            Chile  10.900  11.400       0.500\n",
              "33                            China  29.200  28.800      -0.400\n",
              "34                         Colombia  15.800  15.500      -0.300\n",
              "35                          Comoros  12.200  13.000       0.800\n",
              "36                 Congo, Dem. Rep.  11.800  12.400       0.600\n",
              "37                      Congo, Rep.  22.700  22.600      -0.100\n",
              "38                       Costa Rica  12.900  13.000       0.100\n",
              "39                    Cote d'Ivoire   2.830   2.750      -0.080\n",
              "40                          Croatia  18.100  18.000      -0.100\n",
              "41                             Cuba  12.300  11.900      -0.400\n",
              "42                           Cyprus   9.490   9.620       0.130\n",
              "43                   Czech Republic  26.800  26.700      -0.100\n",
              "44                          Denmark  12.600  12.200      -0.400\n",
              "45                         Djibouti  36.700  37.600       0.900\n",
              "46               Dominican Republic  14.100  14.600       0.500\n",
              "47                          Ecuador  11.200  11.300       0.100\n",
              "48                            Egypt   6.140   5.600      -0.540\n",
              "49                      El Salvador  20.300  20.600       0.300\n",
              "50                Equatorial Guinea   2.100   2.100       0.000\n",
              "51                          Eritrea   5.030   5.790       0.760\n",
              "52                          Estonia  21.800  22.100       0.300\n",
              "53                         Ethiopia   7.840   7.390      -0.450\n",
              "54                             Fiji   9.130   9.440       0.310\n",
              "55                          Finland  11.500  10.400      -1.100\n",
              "56                           France  11.400  10.800      -0.600\n",
              "57                            Gabon   3.040   3.050       0.010\n",
              "58                           Gambia   4.610   4.220      -0.390\n",
              "59                          Georgia   3.690   3.700       0.010\n",
              "60                          Germany  16.000  15.000      -1.000\n",
              "61                            Ghana  14.500  13.800      -0.700\n",
              "62                           Greece   9.840   9.360      -0.480\n",
              "63                        Guatemala  20.200  19.800      -0.400\n",
              "64                           Guinea   1.860   1.880       0.020\n",
              "65                    Guinea-Bissau   4.510   4.440      -0.070\n",
              "66                           Guyana  11.800  11.500      -0.300\n",
              "67                            Haiti   6.680   6.550      -0.130\n",
              "68                         Honduras  21.900  21.300      -0.600\n",
              "69                          Hungary  21.100  20.900      -0.200\n",
              "70                          Iceland   8.620   8.580      -0.040\n",
              "71                            India  15.100  15.100       0.000\n",
              "72                        Indonesia  15.000  14.600      -0.400\n",
              "73                             Iran  29.100  27.300      -1.800\n",
              "74                             Iraq   7.200   3.560      -3.640\n",
              "75                          Ireland  10.700   9.920      -0.780\n",
              "76                           Israel  10.000  10.600       0.600\n",
              "77                            Italy  16.500  15.900      -0.600\n",
              "78                          Jamaica   5.930   5.770      -0.160\n",
              "79                            Japan  17.600  16.500      -1.100\n",
              "80                           Jordan  18.500  18.100      -0.400\n",
              "81                       Kazakhstan  12.000  11.500      -0.500\n",
              "82                            Kenya   9.820   9.430      -0.390\n",
              "83                           Kuwait   2.920   3.730       0.810\n",
              "84                  Kyrgyz Republic  11.000  11.000       0.000\n",
              "85                              Lao   5.140   5.640       0.500\n",
              "86                           Latvia  15.500  17.400       1.900\n",
              "87                          Lebanon   7.050   6.710      -0.340\n",
              "88                          Lesotho  31.200  34.200       3.000\n",
              "89                          Liberia   4.440   4.130      -0.310\n",
              "90                            Libya  21.100  22.000       0.900\n",
              "91                        Lithuania  19.700  19.000      -0.700\n",
              "92                       Luxembourg   5.460   6.140       0.680\n",
              "93                       Madagascar   2.740   3.660       0.920\n",
              "94                           Malawi   9.090   8.080      -1.010\n",
              "95                         Malaysia  22.700  22.700       0.000\n",
              "96                         Maldives  26.000  23.200      -2.800\n",
              "97                             Mali  12.100  10.800      -1.300\n",
              "98                            Malta  13.300  11.000      -2.300\n",
              "99                       Mauritania   3.380   3.530       0.150\n",
              "100                       Mauritius  25.900  25.300      -0.600\n",
              "101                          Mexico  17.800  17.700      -0.100\n",
              "102                         Moldova  12.400  13.500       1.100\n",
              "103                        Mongolia  10.600  11.500       0.900\n",
              "104                      Montenegro  10.100  12.000       1.900\n",
              "105                         Morocco  14.600  14.800       0.200\n",
              "106                      Mozambique   0.315   0.309      -0.006\n",
              "107                         Myanmar  11.600  12.600       1.000\n",
              "108                         Namibia   9.710   9.330      -0.380\n",
              "109                           Nepal   5.250   5.990       0.740\n",
              "110                     Netherlands   7.800   7.360      -0.440\n",
              "111                     New Zealand  10.100  10.300       0.200\n",
              "112                       Nicaragua  19.400  18.800      -0.600\n",
              "113                           Niger  10.300  10.400       0.100\n",
              "114                         Nigeria   7.720   8.930       1.210\n",
              "115                     North Korea  17.000  18.100       1.100\n",
              "116                 North Macedonia  28.700  27.100      -1.600\n",
              "117                          Norway   8.050   7.920      -0.130\n",
              "118                            Oman   8.360   7.650      -0.710\n",
              "119                        Pakistan  12.500  12.000      -0.500\n",
              "120                       Palestine   9.630   9.860       0.230\n",
              "121                          Panama   9.680   9.320      -0.360\n",
              "122                Papua New Guinea   2.240   2.410       0.170\n",
              "123                        Paraguay   8.790   9.600       0.810\n",
              "124                            Peru  10.400  10.600       0.200\n",
              "125                     Philippines  11.100  10.400      -0.700\n",
              "126                          Poland  17.800  17.800       0.000\n",
              "127                        Portugal  18.100  16.600      -1.500\n",
              "128                           Qatar   5.150   4.350      -0.800\n",
              "129                         Romania  24.700  23.800      -0.900\n",
              "130                          Russia  20.200  19.100      -1.100\n",
              "131                          Rwanda   1.130   1.310       0.180\n",
              "132                           Samoa   8.300   8.080      -0.220\n",
              "133           Sao Tome and Principe   6.190   6.680       0.490\n",
              "134                    Saudi Arabia   1.380   1.270      -0.110\n",
              "135                         Senegal   7.990   9.290       1.300\n",
              "136                          Serbia  19.600  16.000      -3.600\n",
              "137                    Sierra Leone   0.918   0.894      -0.024\n",
              "138                       Singapore  17.500  16.700      -0.800\n",
              "139                 Slovak Republic  24.300  24.400       0.100\n",
              "140                        Slovenia  23.000  22.700      -0.300\n",
              "141                 Solomon Islands   6.370   6.380       0.010\n",
              "142                         Somalia   5.070   5.150       0.080\n",
              "143                    South Africa  13.400  13.100      -0.300\n",
              "144                     South Korea  15.600  15.200      -0.400\n",
              "145                     South Sudan  22.000  22.000       0.000\n",
              "146                           Spain  11.600  11.200      -0.400\n",
              "147                       Sri Lanka  27.500  25.900      -1.600\n",
              "148                       St. Lucia   9.170   9.120      -0.050\n",
              "149  St. Vincent and the Grenadines   6.740   6.610      -0.130\n",
              "150                           Sudan  35.300  34.100      -1.200\n",
              "151                        Suriname   9.880   9.530      -0.350\n",
              "152                       Swaziland   8.640   8.630      -0.010\n",
              "153                          Sweden   9.170   8.230      -0.940\n",
              "154                     Switzerland  10.700  11.400       0.700\n",
              "155                           Syria   7.780  10.000       2.220\n",
              "156                      Tajikistan   4.750   4.500      -0.250\n",
              "157                        Tanzania   2.940   2.960       0.020\n",
              "158                        Thailand  19.300  18.200      -1.100\n",
              "159                     Timor-Leste   7.320   7.740       0.420\n",
              "160                            Togo  20.300  19.900      -0.400\n",
              "161                           Tonga  57.300  56.200      -1.100\n",
              "162             Trinidad and Tobago  15.100  16.000       0.900\n",
              "163                         Tunisia  39.300  41.600       2.300\n",
              "164                          Turkey  16.200  15.800      -0.400\n",
              "165                    Turkmenistan  21.600  29.100       7.500\n",
              "166                          Uganda   3.490   4.000       0.510\n",
              "167                         Ukraine  18.000  16.600      -1.400\n",
              "168            United Arab Emirates   5.750   6.050       0.300\n",
              "169                  United Kingdom   9.430   9.410      -0.020\n",
              "170                   United States   9.510   9.060      -0.450\n",
              "171                         Uruguay  12.500  12.100      -0.400\n",
              "172                      Uzbekistan  22.500  24.900       2.400\n",
              "173                         Vanuatu   2.280   2.400       0.120\n",
              "174                       Venezuela  12.100  11.200      -0.900\n",
              "175                         Vietnam  16.700  16.300      -0.400\n",
              "176                           Yemen  11.000  12.000       1.000\n",
              "177                          Zambia   2.580   2.660       0.080\n",
              "178                        Zimbabwe   4.280   4.220      -0.060"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1rrWVf3l4Tsq"
      },
      "source": [
        "**Finding the percentage of all Countries who were significantly affected by the 2008 crisis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eT7n4gh3KsrV",
        "outputId": "9e8cb398-a48e-4237-c633-34d30fb67a2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Sorting the crisis_2008 dataframe in ascending order to find those with the largest differences\n",
        "crisis_2008= crisis_2008.sort_values(by=['difference'], ascending=True)\n",
        "crisis_2008"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Iraq</td>\n",
              "      <td>7.200</td>\n",
              "      <td>3.560</td>\n",
              "      <td>-3.640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Serbia</td>\n",
              "      <td>19.600</td>\n",
              "      <td>16.000</td>\n",
              "      <td>-3.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>26.000</td>\n",
              "      <td>23.200</td>\n",
              "      <td>-2.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Malta</td>\n",
              "      <td>13.300</td>\n",
              "      <td>11.000</td>\n",
              "      <td>-2.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Iran</td>\n",
              "      <td>29.100</td>\n",
              "      <td>27.300</td>\n",
              "      <td>-1.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>27.500</td>\n",
              "      <td>25.900</td>\n",
              "      <td>-1.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>28.700</td>\n",
              "      <td>27.100</td>\n",
              "      <td>-1.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Portugal</td>\n",
              "      <td>18.100</td>\n",
              "      <td>16.600</td>\n",
              "      <td>-1.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Ukraine</td>\n",
              "      <td>18.000</td>\n",
              "      <td>16.600</td>\n",
              "      <td>-1.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>10.400</td>\n",
              "      <td>9.070</td>\n",
              "      <td>-1.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>7.800</td>\n",
              "      <td>6.500</td>\n",
              "      <td>-1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Mali</td>\n",
              "      <td>12.100</td>\n",
              "      <td>10.800</td>\n",
              "      <td>-1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bahamas</td>\n",
              "      <td>5.380</td>\n",
              "      <td>4.180</td>\n",
              "      <td>-1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>35.300</td>\n",
              "      <td>34.100</td>\n",
              "      <td>-1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Japan</td>\n",
              "      <td>17.600</td>\n",
              "      <td>16.500</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>19.300</td>\n",
              "      <td>18.200</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Finland</td>\n",
              "      <td>11.500</td>\n",
              "      <td>10.400</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Russia</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.100</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>57.300</td>\n",
              "      <td>56.200</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Malawi</td>\n",
              "      <td>9.090</td>\n",
              "      <td>8.080</td>\n",
              "      <td>-1.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Germany</td>\n",
              "      <td>16.000</td>\n",
              "      <td>15.000</td>\n",
              "      <td>-1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>9.170</td>\n",
              "      <td>8.230</td>\n",
              "      <td>-0.940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>12.100</td>\n",
              "      <td>11.200</td>\n",
              "      <td>-0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Romania</td>\n",
              "      <td>24.700</td>\n",
              "      <td>23.800</td>\n",
              "      <td>-0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>17.500</td>\n",
              "      <td>16.700</td>\n",
              "      <td>-0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Qatar</td>\n",
              "      <td>5.150</td>\n",
              "      <td>4.350</td>\n",
              "      <td>-0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>10.700</td>\n",
              "      <td>9.920</td>\n",
              "      <td>-0.780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Benin</td>\n",
              "      <td>7.960</td>\n",
              "      <td>7.190</td>\n",
              "      <td>-0.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Oman</td>\n",
              "      <td>8.360</td>\n",
              "      <td>7.650</td>\n",
              "      <td>-0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>11.000</td>\n",
              "      <td>10.300</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Austria</td>\n",
              "      <td>13.000</td>\n",
              "      <td>12.300</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>11.100</td>\n",
              "      <td>10.400</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Lithuania</td>\n",
              "      <td>19.700</td>\n",
              "      <td>19.000</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>14.500</td>\n",
              "      <td>13.800</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Mauritius</td>\n",
              "      <td>25.900</td>\n",
              "      <td>25.300</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>France</td>\n",
              "      <td>11.400</td>\n",
              "      <td>10.800</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Italy</td>\n",
              "      <td>16.500</td>\n",
              "      <td>15.900</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Nicaragua</td>\n",
              "      <td>19.400</td>\n",
              "      <td>18.800</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>21.900</td>\n",
              "      <td>21.300</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>6.140</td>\n",
              "      <td>5.600</td>\n",
              "      <td>-0.540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Kazakhstan</td>\n",
              "      <td>12.000</td>\n",
              "      <td>11.500</td>\n",
              "      <td>-0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.000</td>\n",
              "      <td>-0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Greece</td>\n",
              "      <td>9.840</td>\n",
              "      <td>9.360</td>\n",
              "      <td>-0.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Canada</td>\n",
              "      <td>10.000</td>\n",
              "      <td>9.530</td>\n",
              "      <td>-0.470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>7.840</td>\n",
              "      <td>7.390</td>\n",
              "      <td>-0.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>United States</td>\n",
              "      <td>9.510</td>\n",
              "      <td>9.060</td>\n",
              "      <td>-0.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.800</td>\n",
              "      <td>7.360</td>\n",
              "      <td>-0.440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Togo</td>\n",
              "      <td>20.300</td>\n",
              "      <td>19.900</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Cuba</td>\n",
              "      <td>12.300</td>\n",
              "      <td>11.900</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>12.600</td>\n",
              "      <td>12.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>15.600</td>\n",
              "      <td>15.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>15.000</td>\n",
              "      <td>14.600</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>10.600</td>\n",
              "      <td>10.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Uruguay</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.100</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Spain</td>\n",
              "      <td>11.600</td>\n",
              "      <td>11.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>18.500</td>\n",
              "      <td>18.100</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>16.200</td>\n",
              "      <td>15.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Guatemala</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>16.700</td>\n",
              "      <td>16.300</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>China</td>\n",
              "      <td>29.200</td>\n",
              "      <td>28.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>9.820</td>\n",
              "      <td>9.430</td>\n",
              "      <td>-0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Gambia</td>\n",
              "      <td>4.610</td>\n",
              "      <td>4.220</td>\n",
              "      <td>-0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>9.710</td>\n",
              "      <td>9.330</td>\n",
              "      <td>-0.380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Panama</td>\n",
              "      <td>9.680</td>\n",
              "      <td>9.320</td>\n",
              "      <td>-0.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>Suriname</td>\n",
              "      <td>9.880</td>\n",
              "      <td>9.530</td>\n",
              "      <td>-0.350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Lebanon</td>\n",
              "      <td>7.050</td>\n",
              "      <td>6.710</td>\n",
              "      <td>-0.340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Liberia</td>\n",
              "      <td>4.440</td>\n",
              "      <td>4.130</td>\n",
              "      <td>-0.310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>15.800</td>\n",
              "      <td>15.500</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Slovenia</td>\n",
              "      <td>23.000</td>\n",
              "      <td>22.700</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Guyana</td>\n",
              "      <td>11.800</td>\n",
              "      <td>11.500</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>13.400</td>\n",
              "      <td>13.100</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Tajikistan</td>\n",
              "      <td>4.750</td>\n",
              "      <td>4.500</td>\n",
              "      <td>-0.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>8.300</td>\n",
              "      <td>8.080</td>\n",
              "      <td>-0.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>21.100</td>\n",
              "      <td>20.900</td>\n",
              "      <td>-0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>10.400</td>\n",
              "      <td>10.200</td>\n",
              "      <td>-0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Jamaica</td>\n",
              "      <td>5.930</td>\n",
              "      <td>5.770</td>\n",
              "      <td>-0.160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>St. Vincent and the Grenadines</td>\n",
              "      <td>6.740</td>\n",
              "      <td>6.610</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Norway</td>\n",
              "      <td>8.050</td>\n",
              "      <td>7.920</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Haiti</td>\n",
              "      <td>6.680</td>\n",
              "      <td>6.550</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.270</td>\n",
              "      <td>-0.110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Croatia</td>\n",
              "      <td>18.100</td>\n",
              "      <td>18.000</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>17.800</td>\n",
              "      <td>17.700</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.390</td>\n",
              "      <td>1.290</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Congo, Rep.</td>\n",
              "      <td>22.700</td>\n",
              "      <td>22.600</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>26.800</td>\n",
              "      <td>26.700</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Cote d'Ivoire</td>\n",
              "      <td>2.830</td>\n",
              "      <td>2.750</td>\n",
              "      <td>-0.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>0.850</td>\n",
              "      <td>0.778</td>\n",
              "      <td>-0.072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Guinea-Bissau</td>\n",
              "      <td>4.510</td>\n",
              "      <td>4.440</td>\n",
              "      <td>-0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Chad</td>\n",
              "      <td>3.630</td>\n",
              "      <td>3.570</td>\n",
              "      <td>-0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>4.280</td>\n",
              "      <td>4.220</td>\n",
              "      <td>-0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>St. Lucia</td>\n",
              "      <td>9.170</td>\n",
              "      <td>9.120</td>\n",
              "      <td>-0.050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>9.870</td>\n",
              "      <td>9.830</td>\n",
              "      <td>-0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>8.620</td>\n",
              "      <td>8.580</td>\n",
              "      <td>-0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>0.918</td>\n",
              "      <td>0.894</td>\n",
              "      <td>-0.024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>9.430</td>\n",
              "      <td>9.410</td>\n",
              "      <td>-0.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Australia</td>\n",
              "      <td>9.320</td>\n",
              "      <td>9.310</td>\n",
              "      <td>-0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Swaziland</td>\n",
              "      <td>8.640</td>\n",
              "      <td>8.630</td>\n",
              "      <td>-0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Mozambique</td>\n",
              "      <td>0.315</td>\n",
              "      <td>0.309</td>\n",
              "      <td>-0.006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>10.900</td>\n",
              "      <td>10.900</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>Poland</td>\n",
              "      <td>17.800</td>\n",
              "      <td>17.800</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>22.700</td>\n",
              "      <td>22.700</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>India</td>\n",
              "      <td>15.100</td>\n",
              "      <td>15.100</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>South Sudan</td>\n",
              "      <td>22.000</td>\n",
              "      <td>22.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Equatorial Guinea</td>\n",
              "      <td>2.100</td>\n",
              "      <td>2.100</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Kyrgyz Republic</td>\n",
              "      <td>11.000</td>\n",
              "      <td>11.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>6.370</td>\n",
              "      <td>6.380</td>\n",
              "      <td>0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Gabon</td>\n",
              "      <td>3.040</td>\n",
              "      <td>3.050</td>\n",
              "      <td>0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>3.690</td>\n",
              "      <td>3.700</td>\n",
              "      <td>0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Tanzania</td>\n",
              "      <td>2.940</td>\n",
              "      <td>2.960</td>\n",
              "      <td>0.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Guinea</td>\n",
              "      <td>1.860</td>\n",
              "      <td>1.880</td>\n",
              "      <td>0.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Cameroon</td>\n",
              "      <td>6.540</td>\n",
              "      <td>6.580</td>\n",
              "      <td>0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Cape Verde</td>\n",
              "      <td>3.590</td>\n",
              "      <td>3.660</td>\n",
              "      <td>0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Central African Republic</td>\n",
              "      <td>5.010</td>\n",
              "      <td>5.080</td>\n",
              "      <td>0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Somalia</td>\n",
              "      <td>5.070</td>\n",
              "      <td>5.150</td>\n",
              "      <td>0.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>2.580</td>\n",
              "      <td>2.660</td>\n",
              "      <td>0.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Slovak Republic</td>\n",
              "      <td>24.300</td>\n",
              "      <td>24.400</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Niger</td>\n",
              "      <td>10.300</td>\n",
              "      <td>10.400</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>12.900</td>\n",
              "      <td>13.000</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>12.400</td>\n",
              "      <td>12.500</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Ecuador</td>\n",
              "      <td>11.200</td>\n",
              "      <td>11.300</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Bulgaria</td>\n",
              "      <td>28.500</td>\n",
              "      <td>28.600</td>\n",
              "      <td>0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>7.380</td>\n",
              "      <td>7.490</td>\n",
              "      <td>0.110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>2.280</td>\n",
              "      <td>2.400</td>\n",
              "      <td>0.120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Cyprus</td>\n",
              "      <td>9.490</td>\n",
              "      <td>9.620</td>\n",
              "      <td>0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Mauritania</td>\n",
              "      <td>3.380</td>\n",
              "      <td>3.530</td>\n",
              "      <td>0.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>2.240</td>\n",
              "      <td>2.410</td>\n",
              "      <td>0.170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Rwanda</td>\n",
              "      <td>1.130</td>\n",
              "      <td>1.310</td>\n",
              "      <td>0.180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Peru</td>\n",
              "      <td>10.400</td>\n",
              "      <td>10.600</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>15.600</td>\n",
              "      <td>15.800</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>10.100</td>\n",
              "      <td>10.300</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>14.600</td>\n",
              "      <td>14.800</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>Palestine</td>\n",
              "      <td>9.630</td>\n",
              "      <td>9.860</td>\n",
              "      <td>0.230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>13.100</td>\n",
              "      <td>13.400</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>5.750</td>\n",
              "      <td>6.050</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Estonia</td>\n",
              "      <td>21.800</td>\n",
              "      <td>22.100</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>El Salvador</td>\n",
              "      <td>20.300</td>\n",
              "      <td>20.600</td>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>9.130</td>\n",
              "      <td>9.440</td>\n",
              "      <td>0.310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>Timor-Leste</td>\n",
              "      <td>7.320</td>\n",
              "      <td>7.740</td>\n",
              "      <td>0.420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Sao Tome and Principe</td>\n",
              "      <td>6.190</td>\n",
              "      <td>6.680</td>\n",
              "      <td>0.490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>7.910</td>\n",
              "      <td>8.400</td>\n",
              "      <td>0.490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Lao</td>\n",
              "      <td>5.140</td>\n",
              "      <td>5.640</td>\n",
              "      <td>0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Chile</td>\n",
              "      <td>10.900</td>\n",
              "      <td>11.400</td>\n",
              "      <td>0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>14.100</td>\n",
              "      <td>14.600</td>\n",
              "      <td>0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>3.490</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Israel</td>\n",
              "      <td>10.000</td>\n",
              "      <td>10.600</td>\n",
              "      <td>0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Congo, Dem. Rep.</td>\n",
              "      <td>11.800</td>\n",
              "      <td>12.400</td>\n",
              "      <td>0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>5.460</td>\n",
              "      <td>6.140</td>\n",
              "      <td>0.680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>10.700</td>\n",
              "      <td>11.400</td>\n",
              "      <td>0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Nepal</td>\n",
              "      <td>5.250</td>\n",
              "      <td>5.990</td>\n",
              "      <td>0.740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Eritrea</td>\n",
              "      <td>5.030</td>\n",
              "      <td>5.790</td>\n",
              "      <td>0.760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Comoros</td>\n",
              "      <td>12.200</td>\n",
              "      <td>13.000</td>\n",
              "      <td>0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>Paraguay</td>\n",
              "      <td>8.790</td>\n",
              "      <td>9.600</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Kuwait</td>\n",
              "      <td>2.920</td>\n",
              "      <td>3.730</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Brunei</td>\n",
              "      <td>8.440</td>\n",
              "      <td>9.250</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>5.530</td>\n",
              "      <td>6.380</td>\n",
              "      <td>0.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Libya</td>\n",
              "      <td>21.100</td>\n",
              "      <td>22.000</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>36.700</td>\n",
              "      <td>37.600</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>10.600</td>\n",
              "      <td>11.500</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Trinidad and Tobago</td>\n",
              "      <td>15.100</td>\n",
              "      <td>16.000</td>\n",
              "      <td>0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Madagascar</td>\n",
              "      <td>2.740</td>\n",
              "      <td>3.660</td>\n",
              "      <td>0.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Myanmar</td>\n",
              "      <td>11.600</td>\n",
              "      <td>12.600</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Yemen</td>\n",
              "      <td>11.000</td>\n",
              "      <td>12.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Belize</td>\n",
              "      <td>11.100</td>\n",
              "      <td>12.200</td>\n",
              "      <td>1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Moldova</td>\n",
              "      <td>12.400</td>\n",
              "      <td>13.500</td>\n",
              "      <td>1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>North Korea</td>\n",
              "      <td>17.000</td>\n",
              "      <td>18.100</td>\n",
              "      <td>1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>25.900</td>\n",
              "      <td>27.100</td>\n",
              "      <td>1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>7.720</td>\n",
              "      <td>8.930</td>\n",
              "      <td>1.210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Senegal</td>\n",
              "      <td>7.990</td>\n",
              "      <td>9.290</td>\n",
              "      <td>1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Bolivia</td>\n",
              "      <td>9.330</td>\n",
              "      <td>10.900</td>\n",
              "      <td>1.570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Bhutan</td>\n",
              "      <td>6.420</td>\n",
              "      <td>8.060</td>\n",
              "      <td>1.640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Latvia</td>\n",
              "      <td>15.500</td>\n",
              "      <td>17.400</td>\n",
              "      <td>1.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Montenegro</td>\n",
              "      <td>10.100</td>\n",
              "      <td>12.000</td>\n",
              "      <td>1.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Syria</td>\n",
              "      <td>7.780</td>\n",
              "      <td>10.000</td>\n",
              "      <td>2.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>3.890</td>\n",
              "      <td>6.160</td>\n",
              "      <td>2.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>39.300</td>\n",
              "      <td>41.600</td>\n",
              "      <td>2.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Uzbekistan</td>\n",
              "      <td>22.500</td>\n",
              "      <td>24.900</td>\n",
              "      <td>2.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>42.500</td>\n",
              "      <td>45.300</td>\n",
              "      <td>2.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>31.200</td>\n",
              "      <td>34.200</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>21.600</td>\n",
              "      <td>29.100</td>\n",
              "      <td>7.500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            country    2007    2008  difference\n",
              "74                             Iraq   7.200   3.560      -3.640\n",
              "136                          Serbia  19.600  16.000      -3.600\n",
              "96                         Maldives  26.000  23.200      -2.800\n",
              "98                            Malta  13.300  11.000      -2.300\n",
              "73                             Iran  29.100  27.300      -1.800\n",
              "147                       Sri Lanka  27.500  25.900      -1.600\n",
              "116                 North Macedonia  28.700  27.100      -1.600\n",
              "127                        Portugal  18.100  16.600      -1.500\n",
              "167                         Ukraine  18.000  16.600      -1.400\n",
              "26                         Cambodia  10.400   9.070      -1.330\n",
              "5                           Armenia   7.800   6.500      -1.300\n",
              "97                             Mali  12.100  10.800      -1.300\n",
              "9                           Bahamas   5.380   4.180      -1.200\n",
              "150                           Sudan  35.300  34.100      -1.200\n",
              "79                            Japan  17.600  16.500      -1.100\n",
              "158                        Thailand  19.300  18.200      -1.100\n",
              "55                          Finland  11.500  10.400      -1.100\n",
              "130                          Russia  20.200  19.100      -1.100\n",
              "161                           Tonga  57.300  56.200      -1.100\n",
              "94                           Malawi   9.090   8.080      -1.010\n",
              "60                          Germany  16.000  15.000      -1.000\n",
              "153                          Sweden   9.170   8.230      -0.940\n",
              "174                       Venezuela  12.100  11.200      -0.900\n",
              "129                         Romania  24.700  23.800      -0.900\n",
              "138                       Singapore  17.500  16.700      -0.800\n",
              "128                           Qatar   5.150   4.350      -0.800\n",
              "75                          Ireland  10.700   9.920      -0.780\n",
              "16                            Benin   7.960   7.190      -0.770\n",
              "118                            Oman   8.360   7.650      -0.710\n",
              "14                          Belgium  11.000  10.300      -0.700\n",
              "7                           Austria  13.000  12.300      -0.700\n",
              "125                     Philippines  11.100  10.400      -0.700\n",
              "91                        Lithuania  19.700  19.000      -0.700\n",
              "61                            Ghana  14.500  13.800      -0.700\n",
              "100                       Mauritius  25.900  25.300      -0.600\n",
              "56                           France  11.400  10.800      -0.600\n",
              "77                            Italy  16.500  15.900      -0.600\n",
              "112                       Nicaragua  19.400  18.800      -0.600\n",
              "68                         Honduras  21.900  21.300      -0.600\n",
              "48                            Egypt   6.140   5.600      -0.540\n",
              "81                       Kazakhstan  12.000  11.500      -0.500\n",
              "119                        Pakistan  12.500  12.000      -0.500\n",
              "62                           Greece   9.840   9.360      -0.480\n",
              "28                           Canada  10.000   9.530      -0.470\n",
              "53                         Ethiopia   7.840   7.390      -0.450\n",
              "170                   United States   9.510   9.060      -0.450\n",
              "110                     Netherlands   7.800   7.360      -0.440\n",
              "160                            Togo  20.300  19.900      -0.400\n",
              "41                             Cuba  12.300  11.900      -0.400\n",
              "44                          Denmark  12.600  12.200      -0.400\n",
              "144                     South Korea  15.600  15.200      -0.400\n",
              "72                        Indonesia  15.000  14.600      -0.400\n",
              "20                         Botswana  10.600  10.200      -0.400\n",
              "171                         Uruguay  12.500  12.100      -0.400\n",
              "146                           Spain  11.600  11.200      -0.400\n",
              "80                           Jordan  18.500  18.100      -0.400\n",
              "164                          Turkey  16.200  15.800      -0.400\n",
              "63                        Guatemala  20.200  19.800      -0.400\n",
              "175                         Vietnam  16.700  16.300      -0.400\n",
              "33                            China  29.200  28.800      -0.400\n",
              "82                            Kenya   9.820   9.430      -0.390\n",
              "58                           Gambia   4.610   4.220      -0.390\n",
              "108                         Namibia   9.710   9.330      -0.380\n",
              "121                          Panama   9.680   9.320      -0.360\n",
              "151                        Suriname   9.880   9.530      -0.350\n",
              "87                          Lebanon   7.050   6.710      -0.340\n",
              "89                          Liberia   4.440   4.130      -0.310\n",
              "34                         Colombia  15.800  15.500      -0.300\n",
              "140                        Slovenia  23.000  22.700      -0.300\n",
              "66                           Guyana  11.800  11.500      -0.300\n",
              "143                    South Africa  13.400  13.100      -0.300\n",
              "156                      Tajikistan   4.750   4.500      -0.250\n",
              "132                           Samoa   8.300   8.080      -0.220\n",
              "69                          Hungary  21.100  20.900      -0.200\n",
              "4                         Argentina  10.400  10.200      -0.200\n",
              "78                          Jamaica   5.930   5.770      -0.160\n",
              "149  St. Vincent and the Grenadines   6.740   6.610      -0.130\n",
              "117                          Norway   8.050   7.920      -0.130\n",
              "67                            Haiti   6.680   6.550      -0.130\n",
              "134                    Saudi Arabia   1.380   1.270      -0.110\n",
              "40                          Croatia  18.100  18.000      -0.100\n",
              "101                          Mexico  17.800  17.700      -0.100\n",
              "3                            Angola   1.390   1.290      -0.100\n",
              "37                      Congo, Rep.  22.700  22.600      -0.100\n",
              "43                   Czech Republic  26.800  26.700      -0.100\n",
              "39                    Cote d'Ivoire   2.830   2.750      -0.080\n",
              "25                          Burundi   0.850   0.778      -0.072\n",
              "65                    Guinea-Bissau   4.510   4.440      -0.070\n",
              "31                             Chad   3.630   3.570      -0.060\n",
              "178                        Zimbabwe   4.280   4.220      -0.060\n",
              "148                       St. Lucia   9.170   9.120      -0.050\n",
              "10                          Bahrain   9.870   9.830      -0.040\n",
              "70                          Iceland   8.620   8.580      -0.040\n",
              "137                    Sierra Leone   0.918   0.894      -0.024\n",
              "169                  United Kingdom   9.430   9.410      -0.020\n",
              "6                         Australia   9.320   9.310      -0.010\n",
              "152                       Swaziland   8.640   8.630      -0.010\n",
              "106                      Mozambique   0.315   0.309      -0.006\n",
              "1                           Albania  10.900  10.900       0.000\n",
              "126                          Poland  17.800  17.800       0.000\n",
              "95                         Malaysia  22.700  22.700       0.000\n",
              "71                            India  15.100  15.100       0.000\n",
              "145                     South Sudan  22.000  22.000       0.000\n",
              "50                Equatorial Guinea   2.100   2.100       0.000\n",
              "84                  Kyrgyz Republic  11.000  11.000       0.000\n",
              "141                 Solomon Islands   6.370   6.380       0.010\n",
              "57                            Gabon   3.040   3.050       0.010\n",
              "59                          Georgia   3.690   3.700       0.010\n",
              "157                        Tanzania   2.940   2.960       0.020\n",
              "64                           Guinea   1.860   1.880       0.020\n",
              "27                         Cameroon   6.540   6.580       0.040\n",
              "29                       Cape Verde   3.590   3.660       0.070\n",
              "30         Central African Republic   5.010   5.080       0.070\n",
              "142                         Somalia   5.070   5.150       0.080\n",
              "177                          Zambia   2.580   2.660       0.080\n",
              "139                 Slovak Republic  24.300  24.400       0.100\n",
              "113                           Niger  10.300  10.400       0.100\n",
              "38                       Costa Rica  12.900  13.000       0.100\n",
              "11                       Bangladesh  12.400  12.500       0.100\n",
              "47                          Ecuador  11.200  11.300       0.100\n",
              "23                         Bulgaria  28.500  28.600       0.100\n",
              "0                       Afghanistan   7.380   7.490       0.110\n",
              "173                         Vanuatu   2.280   2.400       0.120\n",
              "42                           Cyprus   9.490   9.620       0.130\n",
              "99                       Mauritania   3.380   3.530       0.150\n",
              "122                Papua New Guinea   2.240   2.410       0.170\n",
              "131                          Rwanda   1.130   1.310       0.180\n",
              "124                            Peru  10.400  10.600       0.200\n",
              "19           Bosnia and Herzegovina  15.600  15.800       0.200\n",
              "111                     New Zealand  10.100  10.300       0.200\n",
              "105                         Morocco  14.600  14.800       0.200\n",
              "120                       Palestine   9.630   9.860       0.230\n",
              "21                           Brazil  13.100  13.400       0.300\n",
              "168            United Arab Emirates   5.750   6.050       0.300\n",
              "52                          Estonia  21.800  22.100       0.300\n",
              "49                      El Salvador  20.300  20.600       0.300\n",
              "54                             Fiji   9.130   9.440       0.310\n",
              "159                     Timor-Leste   7.320   7.740       0.420\n",
              "133           Sao Tome and Principe   6.190   6.680       0.490\n",
              "12                         Barbados   7.910   8.400       0.490\n",
              "85                              Lao   5.140   5.640       0.500\n",
              "32                            Chile  10.900  11.400       0.500\n",
              "46               Dominican Republic  14.100  14.600       0.500\n",
              "166                          Uganda   3.490   4.000       0.510\n",
              "76                           Israel  10.000  10.600       0.600\n",
              "36                 Congo, Dem. Rep.  11.800  12.400       0.600\n",
              "92                       Luxembourg   5.460   6.140       0.680\n",
              "154                     Switzerland  10.700  11.400       0.700\n",
              "109                           Nepal   5.250   5.990       0.740\n",
              "51                          Eritrea   5.030   5.790       0.760\n",
              "35                          Comoros  12.200  13.000       0.800\n",
              "123                        Paraguay   8.790   9.600       0.810\n",
              "83                           Kuwait   2.920   3.730       0.810\n",
              "22                           Brunei   8.440   9.250       0.810\n",
              "24                     Burkina Faso   5.530   6.380       0.850\n",
              "90                            Libya  21.100  22.000       0.900\n",
              "45                         Djibouti  36.700  37.600       0.900\n",
              "103                        Mongolia  10.600  11.500       0.900\n",
              "162             Trinidad and Tobago  15.100  16.000       0.900\n",
              "93                       Madagascar   2.740   3.660       0.920\n",
              "107                         Myanmar  11.600  12.600       1.000\n",
              "176                           Yemen  11.000  12.000       1.000\n",
              "15                           Belize  11.100  12.200       1.100\n",
              "102                         Moldova  12.400  13.500       1.100\n",
              "115                     North Korea  17.000  18.100       1.100\n",
              "13                          Belarus  25.900  27.100       1.200\n",
              "114                         Nigeria   7.720   8.930       1.210\n",
              "135                         Senegal   7.990   9.290       1.300\n",
              "18                          Bolivia   9.330  10.900       1.570\n",
              "17                           Bhutan   6.420   8.060       1.640\n",
              "86                           Latvia  15.500  17.400       1.900\n",
              "104                      Montenegro  10.100  12.000       1.900\n",
              "155                           Syria   7.780  10.000       2.220\n",
              "8                        Azerbaijan   3.890   6.160       2.270\n",
              "163                         Tunisia  39.300  41.600       2.300\n",
              "172                      Uzbekistan  22.500  24.900       2.400\n",
              "2                           Algeria  42.500  45.300       2.800\n",
              "88                          Lesotho  31.200  34.200       3.000\n",
              "165                    Turkmenistan  21.600  29.100       7.500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eq0_med2pU3M",
        "outputId": "eb25d744-5c42-4fca-a3e8-05432f1f2f1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "crisis_2008.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(179, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5e2LiIV90R5s",
        "outputId": "bb868059-a379-4831-f788-d051c0bd808e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Selecting all the rows that have their differences in the negatives\n",
        "all_least_crisis_2008 = crisis_2008[crisis_2008.difference < 0.000]\n",
        "all_least_crisis_2008"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Iraq</td>\n",
              "      <td>7.200</td>\n",
              "      <td>3.560</td>\n",
              "      <td>-3.640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Serbia</td>\n",
              "      <td>19.600</td>\n",
              "      <td>16.000</td>\n",
              "      <td>-3.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>26.000</td>\n",
              "      <td>23.200</td>\n",
              "      <td>-2.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Malta</td>\n",
              "      <td>13.300</td>\n",
              "      <td>11.000</td>\n",
              "      <td>-2.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Iran</td>\n",
              "      <td>29.100</td>\n",
              "      <td>27.300</td>\n",
              "      <td>-1.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>27.500</td>\n",
              "      <td>25.900</td>\n",
              "      <td>-1.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>28.700</td>\n",
              "      <td>27.100</td>\n",
              "      <td>-1.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Portugal</td>\n",
              "      <td>18.100</td>\n",
              "      <td>16.600</td>\n",
              "      <td>-1.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Ukraine</td>\n",
              "      <td>18.000</td>\n",
              "      <td>16.600</td>\n",
              "      <td>-1.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>10.400</td>\n",
              "      <td>9.070</td>\n",
              "      <td>-1.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>7.800</td>\n",
              "      <td>6.500</td>\n",
              "      <td>-1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Mali</td>\n",
              "      <td>12.100</td>\n",
              "      <td>10.800</td>\n",
              "      <td>-1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bahamas</td>\n",
              "      <td>5.380</td>\n",
              "      <td>4.180</td>\n",
              "      <td>-1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>35.300</td>\n",
              "      <td>34.100</td>\n",
              "      <td>-1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Japan</td>\n",
              "      <td>17.600</td>\n",
              "      <td>16.500</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>19.300</td>\n",
              "      <td>18.200</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Finland</td>\n",
              "      <td>11.500</td>\n",
              "      <td>10.400</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Russia</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.100</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>57.300</td>\n",
              "      <td>56.200</td>\n",
              "      <td>-1.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Malawi</td>\n",
              "      <td>9.090</td>\n",
              "      <td>8.080</td>\n",
              "      <td>-1.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Germany</td>\n",
              "      <td>16.000</td>\n",
              "      <td>15.000</td>\n",
              "      <td>-1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>9.170</td>\n",
              "      <td>8.230</td>\n",
              "      <td>-0.940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>12.100</td>\n",
              "      <td>11.200</td>\n",
              "      <td>-0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Romania</td>\n",
              "      <td>24.700</td>\n",
              "      <td>23.800</td>\n",
              "      <td>-0.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>17.500</td>\n",
              "      <td>16.700</td>\n",
              "      <td>-0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Qatar</td>\n",
              "      <td>5.150</td>\n",
              "      <td>4.350</td>\n",
              "      <td>-0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>10.700</td>\n",
              "      <td>9.920</td>\n",
              "      <td>-0.780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Benin</td>\n",
              "      <td>7.960</td>\n",
              "      <td>7.190</td>\n",
              "      <td>-0.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Oman</td>\n",
              "      <td>8.360</td>\n",
              "      <td>7.650</td>\n",
              "      <td>-0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>11.000</td>\n",
              "      <td>10.300</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Austria</td>\n",
              "      <td>13.000</td>\n",
              "      <td>12.300</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>11.100</td>\n",
              "      <td>10.400</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Lithuania</td>\n",
              "      <td>19.700</td>\n",
              "      <td>19.000</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>14.500</td>\n",
              "      <td>13.800</td>\n",
              "      <td>-0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Mauritius</td>\n",
              "      <td>25.900</td>\n",
              "      <td>25.300</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>France</td>\n",
              "      <td>11.400</td>\n",
              "      <td>10.800</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Italy</td>\n",
              "      <td>16.500</td>\n",
              "      <td>15.900</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Nicaragua</td>\n",
              "      <td>19.400</td>\n",
              "      <td>18.800</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>21.900</td>\n",
              "      <td>21.300</td>\n",
              "      <td>-0.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>6.140</td>\n",
              "      <td>5.600</td>\n",
              "      <td>-0.540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Kazakhstan</td>\n",
              "      <td>12.000</td>\n",
              "      <td>11.500</td>\n",
              "      <td>-0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.000</td>\n",
              "      <td>-0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Greece</td>\n",
              "      <td>9.840</td>\n",
              "      <td>9.360</td>\n",
              "      <td>-0.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Canada</td>\n",
              "      <td>10.000</td>\n",
              "      <td>9.530</td>\n",
              "      <td>-0.470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>7.840</td>\n",
              "      <td>7.390</td>\n",
              "      <td>-0.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>United States</td>\n",
              "      <td>9.510</td>\n",
              "      <td>9.060</td>\n",
              "      <td>-0.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.800</td>\n",
              "      <td>7.360</td>\n",
              "      <td>-0.440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Togo</td>\n",
              "      <td>20.300</td>\n",
              "      <td>19.900</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Cuba</td>\n",
              "      <td>12.300</td>\n",
              "      <td>11.900</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>12.600</td>\n",
              "      <td>12.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>15.600</td>\n",
              "      <td>15.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>15.000</td>\n",
              "      <td>14.600</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>10.600</td>\n",
              "      <td>10.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Uruguay</td>\n",
              "      <td>12.500</td>\n",
              "      <td>12.100</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Spain</td>\n",
              "      <td>11.600</td>\n",
              "      <td>11.200</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>18.500</td>\n",
              "      <td>18.100</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>16.200</td>\n",
              "      <td>15.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Guatemala</td>\n",
              "      <td>20.200</td>\n",
              "      <td>19.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>16.700</td>\n",
              "      <td>16.300</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>China</td>\n",
              "      <td>29.200</td>\n",
              "      <td>28.800</td>\n",
              "      <td>-0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>9.820</td>\n",
              "      <td>9.430</td>\n",
              "      <td>-0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Gambia</td>\n",
              "      <td>4.610</td>\n",
              "      <td>4.220</td>\n",
              "      <td>-0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>9.710</td>\n",
              "      <td>9.330</td>\n",
              "      <td>-0.380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Panama</td>\n",
              "      <td>9.680</td>\n",
              "      <td>9.320</td>\n",
              "      <td>-0.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>Suriname</td>\n",
              "      <td>9.880</td>\n",
              "      <td>9.530</td>\n",
              "      <td>-0.350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Lebanon</td>\n",
              "      <td>7.050</td>\n",
              "      <td>6.710</td>\n",
              "      <td>-0.340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Liberia</td>\n",
              "      <td>4.440</td>\n",
              "      <td>4.130</td>\n",
              "      <td>-0.310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Colombia</td>\n",
              "      <td>15.800</td>\n",
              "      <td>15.500</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Slovenia</td>\n",
              "      <td>23.000</td>\n",
              "      <td>22.700</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Guyana</td>\n",
              "      <td>11.800</td>\n",
              "      <td>11.500</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>13.400</td>\n",
              "      <td>13.100</td>\n",
              "      <td>-0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Tajikistan</td>\n",
              "      <td>4.750</td>\n",
              "      <td>4.500</td>\n",
              "      <td>-0.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>8.300</td>\n",
              "      <td>8.080</td>\n",
              "      <td>-0.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>21.100</td>\n",
              "      <td>20.900</td>\n",
              "      <td>-0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>10.400</td>\n",
              "      <td>10.200</td>\n",
              "      <td>-0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Jamaica</td>\n",
              "      <td>5.930</td>\n",
              "      <td>5.770</td>\n",
              "      <td>-0.160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>St. Vincent and the Grenadines</td>\n",
              "      <td>6.740</td>\n",
              "      <td>6.610</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Norway</td>\n",
              "      <td>8.050</td>\n",
              "      <td>7.920</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Haiti</td>\n",
              "      <td>6.680</td>\n",
              "      <td>6.550</td>\n",
              "      <td>-0.130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.270</td>\n",
              "      <td>-0.110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Croatia</td>\n",
              "      <td>18.100</td>\n",
              "      <td>18.000</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>17.800</td>\n",
              "      <td>17.700</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Angola</td>\n",
              "      <td>1.390</td>\n",
              "      <td>1.290</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Congo, Rep.</td>\n",
              "      <td>22.700</td>\n",
              "      <td>22.600</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>26.800</td>\n",
              "      <td>26.700</td>\n",
              "      <td>-0.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Cote d'Ivoire</td>\n",
              "      <td>2.830</td>\n",
              "      <td>2.750</td>\n",
              "      <td>-0.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>0.850</td>\n",
              "      <td>0.778</td>\n",
              "      <td>-0.072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Guinea-Bissau</td>\n",
              "      <td>4.510</td>\n",
              "      <td>4.440</td>\n",
              "      <td>-0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Chad</td>\n",
              "      <td>3.630</td>\n",
              "      <td>3.570</td>\n",
              "      <td>-0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>4.280</td>\n",
              "      <td>4.220</td>\n",
              "      <td>-0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>St. Lucia</td>\n",
              "      <td>9.170</td>\n",
              "      <td>9.120</td>\n",
              "      <td>-0.050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>9.870</td>\n",
              "      <td>9.830</td>\n",
              "      <td>-0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>8.620</td>\n",
              "      <td>8.580</td>\n",
              "      <td>-0.040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>0.918</td>\n",
              "      <td>0.894</td>\n",
              "      <td>-0.024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>9.430</td>\n",
              "      <td>9.410</td>\n",
              "      <td>-0.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Australia</td>\n",
              "      <td>9.320</td>\n",
              "      <td>9.310</td>\n",
              "      <td>-0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Swaziland</td>\n",
              "      <td>8.640</td>\n",
              "      <td>8.630</td>\n",
              "      <td>-0.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Mozambique</td>\n",
              "      <td>0.315</td>\n",
              "      <td>0.309</td>\n",
              "      <td>-0.006</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            country    2007    2008  difference\n",
              "74                             Iraq   7.200   3.560      -3.640\n",
              "136                          Serbia  19.600  16.000      -3.600\n",
              "96                         Maldives  26.000  23.200      -2.800\n",
              "98                            Malta  13.300  11.000      -2.300\n",
              "73                             Iran  29.100  27.300      -1.800\n",
              "147                       Sri Lanka  27.500  25.900      -1.600\n",
              "116                 North Macedonia  28.700  27.100      -1.600\n",
              "127                        Portugal  18.100  16.600      -1.500\n",
              "167                         Ukraine  18.000  16.600      -1.400\n",
              "26                         Cambodia  10.400   9.070      -1.330\n",
              "5                           Armenia   7.800   6.500      -1.300\n",
              "97                             Mali  12.100  10.800      -1.300\n",
              "9                           Bahamas   5.380   4.180      -1.200\n",
              "150                           Sudan  35.300  34.100      -1.200\n",
              "79                            Japan  17.600  16.500      -1.100\n",
              "158                        Thailand  19.300  18.200      -1.100\n",
              "55                          Finland  11.500  10.400      -1.100\n",
              "130                          Russia  20.200  19.100      -1.100\n",
              "161                           Tonga  57.300  56.200      -1.100\n",
              "94                           Malawi   9.090   8.080      -1.010\n",
              "60                          Germany  16.000  15.000      -1.000\n",
              "153                          Sweden   9.170   8.230      -0.940\n",
              "174                       Venezuela  12.100  11.200      -0.900\n",
              "129                         Romania  24.700  23.800      -0.900\n",
              "138                       Singapore  17.500  16.700      -0.800\n",
              "128                           Qatar   5.150   4.350      -0.800\n",
              "75                          Ireland  10.700   9.920      -0.780\n",
              "16                            Benin   7.960   7.190      -0.770\n",
              "118                            Oman   8.360   7.650      -0.710\n",
              "14                          Belgium  11.000  10.300      -0.700\n",
              "7                           Austria  13.000  12.300      -0.700\n",
              "125                     Philippines  11.100  10.400      -0.700\n",
              "91                        Lithuania  19.700  19.000      -0.700\n",
              "61                            Ghana  14.500  13.800      -0.700\n",
              "100                       Mauritius  25.900  25.300      -0.600\n",
              "56                           France  11.400  10.800      -0.600\n",
              "77                            Italy  16.500  15.900      -0.600\n",
              "112                       Nicaragua  19.400  18.800      -0.600\n",
              "68                         Honduras  21.900  21.300      -0.600\n",
              "48                            Egypt   6.140   5.600      -0.540\n",
              "81                       Kazakhstan  12.000  11.500      -0.500\n",
              "119                        Pakistan  12.500  12.000      -0.500\n",
              "62                           Greece   9.840   9.360      -0.480\n",
              "28                           Canada  10.000   9.530      -0.470\n",
              "53                         Ethiopia   7.840   7.390      -0.450\n",
              "170                   United States   9.510   9.060      -0.450\n",
              "110                     Netherlands   7.800   7.360      -0.440\n",
              "160                            Togo  20.300  19.900      -0.400\n",
              "41                             Cuba  12.300  11.900      -0.400\n",
              "44                          Denmark  12.600  12.200      -0.400\n",
              "144                     South Korea  15.600  15.200      -0.400\n",
              "72                        Indonesia  15.000  14.600      -0.400\n",
              "20                         Botswana  10.600  10.200      -0.400\n",
              "171                         Uruguay  12.500  12.100      -0.400\n",
              "146                           Spain  11.600  11.200      -0.400\n",
              "80                           Jordan  18.500  18.100      -0.400\n",
              "164                          Turkey  16.200  15.800      -0.400\n",
              "63                        Guatemala  20.200  19.800      -0.400\n",
              "175                         Vietnam  16.700  16.300      -0.400\n",
              "33                            China  29.200  28.800      -0.400\n",
              "82                            Kenya   9.820   9.430      -0.390\n",
              "58                           Gambia   4.610   4.220      -0.390\n",
              "108                         Namibia   9.710   9.330      -0.380\n",
              "121                          Panama   9.680   9.320      -0.360\n",
              "151                        Suriname   9.880   9.530      -0.350\n",
              "87                          Lebanon   7.050   6.710      -0.340\n",
              "89                          Liberia   4.440   4.130      -0.310\n",
              "34                         Colombia  15.800  15.500      -0.300\n",
              "140                        Slovenia  23.000  22.700      -0.300\n",
              "66                           Guyana  11.800  11.500      -0.300\n",
              "143                    South Africa  13.400  13.100      -0.300\n",
              "156                      Tajikistan   4.750   4.500      -0.250\n",
              "132                           Samoa   8.300   8.080      -0.220\n",
              "69                          Hungary  21.100  20.900      -0.200\n",
              "4                         Argentina  10.400  10.200      -0.200\n",
              "78                          Jamaica   5.930   5.770      -0.160\n",
              "149  St. Vincent and the Grenadines   6.740   6.610      -0.130\n",
              "117                          Norway   8.050   7.920      -0.130\n",
              "67                            Haiti   6.680   6.550      -0.130\n",
              "134                    Saudi Arabia   1.380   1.270      -0.110\n",
              "40                          Croatia  18.100  18.000      -0.100\n",
              "101                          Mexico  17.800  17.700      -0.100\n",
              "3                            Angola   1.390   1.290      -0.100\n",
              "37                      Congo, Rep.  22.700  22.600      -0.100\n",
              "43                   Czech Republic  26.800  26.700      -0.100\n",
              "39                    Cote d'Ivoire   2.830   2.750      -0.080\n",
              "25                          Burundi   0.850   0.778      -0.072\n",
              "65                    Guinea-Bissau   4.510   4.440      -0.070\n",
              "31                             Chad   3.630   3.570      -0.060\n",
              "178                        Zimbabwe   4.280   4.220      -0.060\n",
              "148                       St. Lucia   9.170   9.120      -0.050\n",
              "10                          Bahrain   9.870   9.830      -0.040\n",
              "70                          Iceland   8.620   8.580      -0.040\n",
              "137                    Sierra Leone   0.918   0.894      -0.024\n",
              "169                  United Kingdom   9.430   9.410      -0.020\n",
              "6                         Australia   9.320   9.310      -0.010\n",
              "152                       Swaziland   8.640   8.630      -0.010\n",
              "106                      Mozambique   0.315   0.309      -0.006"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eh0qs3f-1Lw9",
        "outputId": "49f64c99-58ed-4c70-d8e4-c11141a096b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "all_least_crisis_2008.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2n6DuT41sMQ",
        "outputId": "918dd804-940f-4ab3-cb39-cc127c8c6852",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Percentage of countries worldwide by the 2008 crisis interms of percentage of females in industries.\n",
        "(98/179)*100\n",
        "#where the 98 signifies the number of countries who had negative differences when 2007 column was subtracted from 2008. Whiles the 179 represent all the countries worldwide"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54.7486033519553"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASwb-eEM2pb8"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "The proportion of women in industries were affected during the 2008 crisis with Approximately 54.7% of countries affected worldwide"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooNnA6WVVLqk",
        "outputId": "0082c200-bc0f-468b-d609-86f36aa7ef9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries who were largely affected by the 2008 crisis\n",
        "crisis_2008_sort=crisis_2008.head(10)\n",
        "crisis_2008_sort"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Iraq</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.56</td>\n",
              "      <td>-3.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Serbia</td>\n",
              "      <td>19.6</td>\n",
              "      <td>16.00</td>\n",
              "      <td>-3.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>26.0</td>\n",
              "      <td>23.20</td>\n",
              "      <td>-2.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Malta</td>\n",
              "      <td>13.3</td>\n",
              "      <td>11.00</td>\n",
              "      <td>-2.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Iran</td>\n",
              "      <td>29.1</td>\n",
              "      <td>27.30</td>\n",
              "      <td>-1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>27.5</td>\n",
              "      <td>25.90</td>\n",
              "      <td>-1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>28.7</td>\n",
              "      <td>27.10</td>\n",
              "      <td>-1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Portugal</td>\n",
              "      <td>18.1</td>\n",
              "      <td>16.60</td>\n",
              "      <td>-1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Ukraine</td>\n",
              "      <td>18.0</td>\n",
              "      <td>16.60</td>\n",
              "      <td>-1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>10.4</td>\n",
              "      <td>9.07</td>\n",
              "      <td>-1.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             country  2007   2008  difference\n",
              "74              Iraq   7.2   3.56       -3.64\n",
              "136           Serbia  19.6  16.00       -3.60\n",
              "96          Maldives  26.0  23.20       -2.80\n",
              "98             Malta  13.3  11.00       -2.30\n",
              "73              Iran  29.1  27.30       -1.80\n",
              "147        Sri Lanka  27.5  25.90       -1.60\n",
              "116  North Macedonia  28.7  27.10       -1.60\n",
              "127         Portugal  18.1  16.60       -1.50\n",
              "167          Ukraine  18.0  16.60       -1.40\n",
              "26          Cambodia  10.4   9.07       -1.33"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHjo3M_p45O_"
      },
      "source": [
        "**Finding the Top 10 countries severely affected by the 2008 crisis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKZ39QJKhxxF",
        "outputId": "de0271d7-9f4f-4020-e9d9-8a10b9f2ca38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "#Plotting a bar chart of the top 10 countries significantly impacted by the 2008 crisis\n",
        "crisis_2008_sort.plot.barh(x='country', y='difference', color='red', rot=0 )\n",
        "plt.title(\"Top 10 countries significantly impacted during the 2008 crisis\")\n",
        "plt.xlabel(\"Difference in value between 2007 and 2008\")\n",
        "plt.ylabel(\"Country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSbMZ4n0jOM0"
      },
      "source": [
        "**Analysis**\n",
        "\n",
        "According to the graph Iraq, Serbia, Maldives and Malta recorded the largest difference indicating the negative impact of the crisis on the percentage of females in the industries."
      ]
    }
  ]
}