lab03.ipynb

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      "source": [
        "Lab 3: Pandas \n",
        "\n",
        "Kobie Williams\n",
        "\n",
        "2/8/2022\n",
        "\n",
        "Practice using the Pandas library to read in data, process, and visualize it. "
      ],
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        "id": "bXG1lGxRX4Zi"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f500_df = pd.read_csv('https://drive.google.com/uc?export=download&id=1qtsEdMAS2xB9L1hQBfhwqO2ydepZE2rz')"
      ],
      "metadata": {
        "id": "tm3cLdMmYrey"
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      "execution_count": 21,
      "outputs": []
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      "source": [
        "f500_df.head(6)"
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Company</th>\n",
              "      <th>Revenue (in millions)</th>\n",
              "      <th>Profit (in millions)</th>\n",
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              "      <th>0</th>\n",
              "      <td>1955</td>\n",
              "      <td>1</td>\n",
              "      <td>General Motors</td>\n",
              "      <td>9823.5</td>\n",
              "      <td>806</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1955</td>\n",
              "      <td>2</td>\n",
              "      <td>Exxon Mobil</td>\n",
              "      <td>5661.4</td>\n",
              "      <td>584.8</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1955</td>\n",
              "      <td>3</td>\n",
              "      <td>U.S. Steel</td>\n",
              "      <td>3250.4</td>\n",
              "      <td>195.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1955</td>\n",
              "      <td>4</td>\n",
              "      <td>General Electric</td>\n",
              "      <td>2959.1</td>\n",
              "      <td>212.6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1955</td>\n",
              "      <td>5</td>\n",
              "      <td>Esmark</td>\n",
              "      <td>2510.8</td>\n",
              "      <td>19.1</td>\n",
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              "      <th>5</th>\n",
              "      <td>1955</td>\n",
              "      <td>6</td>\n",
              "      <td>Chrysler</td>\n",
              "      <td>2071.6</td>\n",
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              "   Year  Rank           Company  Revenue (in millions) Profit (in millions)\n",
              "0  1955     1    General Motors                 9823.5                  806\n",
              "1  1955     2       Exxon Mobil                 5661.4                584.8\n",
              "2  1955     3        U.S. Steel                 3250.4                195.4\n",
              "3  1955     4  General Electric                 2959.1                212.6\n",
              "4  1955     5            Esmark                 2510.8                 19.1\n",
              "5  1955     6          Chrysler                 2071.6                 18.5"
            ]
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      "source": [
        "  #Description of: \n",
        "#counts \n",
        "\n",
        "#mins \n",
        "\n",
        "#maxes \n",
        "\n",
        "f500_df.describe() "
      ],
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Revenue (in millions)</th>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25500.00000</td>\n",
              "      <td>25500.000000</td>\n",
              "      <td>25500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1980.00000</td>\n",
              "      <td>250.499765</td>\n",
              "      <td>4273.329635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.71989</td>\n",
              "      <td>144.339963</td>\n",
              "      <td>11351.884979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1955.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>49.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1967.00000</td>\n",
              "      <td>125.750000</td>\n",
              "      <td>362.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1980.00000</td>\n",
              "      <td>250.500000</td>\n",
              "      <td>1019.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1993.00000</td>\n",
              "      <td>375.250000</td>\n",
              "      <td>3871.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2005.00000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>288189.000000</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "              Year          Rank  Revenue (in millions)\n",
              "count  25500.00000  25500.000000           25500.000000\n",
              "mean    1980.00000    250.499765            4273.329635\n",
              "std       14.71989    144.339963           11351.884979\n",
              "min     1955.00000      1.000000              49.700000\n",
              "25%     1967.00000    125.750000             362.300000\n",
              "50%     1980.00000    250.500000            1019.000000\n",
              "75%     1993.00000    375.250000            3871.000000\n",
              "max     2005.00000    500.000000          288189.000000"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "#Print out data types \n",
        "f500_df.dtypes"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRpGFlora6KM",
        "outputId": "02e548dd-24cb-4759-e7dc-e93759670bc9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                       int64\n",
              "Rank                       int64\n",
              "Company                   object\n",
              "Revenue (in millions)    float64\n",
              "Profit (in millions)      object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  #f: \n",
        "#i) \n",
        "profit = f500_df['Profit (in millions)'] \n",
        "bad = profit.str.contains('[^0-9.-]')\n"
      ],
      "metadata": {
        "id": "-YtENXGAbzMB"
      },
      "execution_count": 25,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "f500_df.head()"
      ],
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        "outputId": "50a87c96-785a-4b8d-9de4-f77238759c80"
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      "execution_count": 26,
      "outputs": [
        {
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              "      <th>Year</th>\n",
              "      <th>Rank</th>\n",
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              "      <th>0</th>\n",
              "      <td>1955</td>\n",
              "      <td>1</td>\n",
              "      <td>General Motors</td>\n",
              "      <td>9823.5</td>\n",
              "      <td>806</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1955</td>\n",
              "      <td>2</td>\n",
              "      <td>Exxon Mobil</td>\n",
              "      <td>5661.4</td>\n",
              "      <td>584.8</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1955</td>\n",
              "      <td>3</td>\n",
              "      <td>U.S. Steel</td>\n",
              "      <td>3250.4</td>\n",
              "      <td>195.4</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1955</td>\n",
              "      <td>4</td>\n",
              "      <td>General Electric</td>\n",
              "      <td>2959.1</td>\n",
              "      <td>212.6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1955</td>\n",
              "      <td>5</td>\n",
              "      <td>Esmark</td>\n",
              "      <td>2510.8</td>\n",
              "      <td>19.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4aaee48a-155a-4ba0-932f-2b1750f4f6bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4aaee48a-155a-4ba0-932f-2b1750f4f6bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4aaee48a-155a-4ba0-932f-2b1750f4f6bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Year  Rank           Company  Revenue (in millions) Profit (in millions)\n",
              "0  1955     1    General Motors                 9823.5                  806\n",
              "1  1955     2       Exxon Mobil                 5661.4                584.8\n",
              "2  1955     3        U.S. Steel                 3250.4                195.4\n",
              "3  1955     4  General Electric                 2959.1                212.6\n",
              "4  1955     5            Esmark                 2510.8                 19.1"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f500_df[bad])\n",
        "f500_df[bad].count()\n",
        "#369 bad values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmcWL3aqcejY",
        "outputId": "181c7d63-93f1-4dbd-8676-2bf8f3a4939a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Year  Rank  ... Revenue (in millions)  Profit (in millions)\n",
            "228    1955   229  ...                 135.0                  N.A.\n",
            "290    1955   291  ...                 100.0                  N.A.\n",
            "294    1955   295  ...                  97.9                  N.A.\n",
            "296    1955   297  ...                  96.0                  N.A.\n",
            "352    1955   353  ...                  77.4                  N.A.\n",
            "...     ...   ...  ...                   ...                   ...\n",
            "24242  2003   243  ...                7580.2                  N.A.\n",
            "24296  2003   297  ...                5847.0                  N.A.\n",
            "24435  2003   436  ...                3637.9                  N.A.\n",
            "24489  2003   490  ...                3029.0                  N.A.\n",
            "25485  2005   486  ...                3773.0                  N.A.\n",
            "\n",
            "[369 rows x 5 columns]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                     369\n",
              "Rank                     369\n",
              "Company                  369\n",
              "Revenue (in millions)    369\n",
              "Profit (in millions)     369\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bins = np.arange(f500_df['Year'].min(), f500_df['Year'].max())\n",
        "bins"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEZoGAMCcmB8",
        "outputId": "2145e5f8-4619-439e-e47b-dbd29da9cb1e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,\n",
              "       1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,\n",
              "       1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
              "       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
              "       1999, 2000, 2001, 2002, 2003, 2004])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#non num is where bins is equal to \n",
        "non_num = f500_df[bad]"
      ],
      "metadata": {
        "id": "HeB2lbOc3JtJ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(non_num['Year'], bins=bins)\n",
        "plt.xlabel('Year') \n",
        "plt.ylabel('Count') \n",
        "plt.title('Histogram of non-numeric profit')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "fr50-GX71VIe",
        "outputId": "e6a1a1ac-ed91-4d04-c91f-a4b0db891bdb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Histogram of non-numeric profit')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "good = ~bad "
      ],
      "metadata": {
        "id": "PlLrHOVVBuZ3"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f500_df[good].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "82cMf9zNEfjQ",
        "outputId": "265e048b-7d6c-472f-d8ff-f6f8ae38fa1e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0d65e532-b771-4ddd-bd0e-211ee65bc89d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Company</th>\n",
              "      <th>Revenue (in millions)</th>\n",
              "      <th>Profit (in millions)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1955</td>\n",
              "      <td>1</td>\n",
              "      <td>General Motors</td>\n",
              "      <td>9823.5</td>\n",
              "      <td>806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1955</td>\n",
              "      <td>2</td>\n",
              "      <td>Exxon Mobil</td>\n",
              "      <td>5661.4</td>\n",
              "      <td>584.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1955</td>\n",
              "      <td>3</td>\n",
              "      <td>U.S. Steel</td>\n",
              "      <td>3250.4</td>\n",
              "      <td>195.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1955</td>\n",
              "      <td>4</td>\n",
              "      <td>General Electric</td>\n",
              "      <td>2959.1</td>\n",
              "      <td>212.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1955</td>\n",
              "      <td>5</td>\n",
              "      <td>Esmark</td>\n",
              "      <td>2510.8</td>\n",
              "      <td>19.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d65e532-b771-4ddd-bd0e-211ee65bc89d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d65e532-b771-4ddd-bd0e-211ee65bc89d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Year  Rank           Company  Revenue (in millions) Profit (in millions)\n",
              "0  1955     1    General Motors                 9823.5                  806\n",
              "1  1955     2       Exxon Mobil                 5661.4                584.8\n",
              "2  1955     3        U.S. Steel                 3250.4                195.4\n",
              "3  1955     4  General Electric                 2959.1                212.6\n",
              "4  1955     5            Esmark                 2510.8                 19.1"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we changed the variable  to where the good data is only in the dataframe\n",
        "f500_df = f500_df[good]"
      ],
      "metadata": {
        "id": "vRLG9PwrE3YX"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f500_df['Profit (in millions)'] = f500_df['Profit (in millions)'].astype(float)"
      ],
      "metadata": {
        "id": "HZJ62yf9FL4r"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f500_df.describe() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "4b-kHmXZ6JKY",
        "outputId": "fc1befa2-f117-4437-fb05-9dc1f9c80e2e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <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>Year</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Revenue (in millions)</th>\n",
              "      <th>Profit (in millions)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25131.000000</td>\n",
              "      <td>25131.000000</td>\n",
              "      <td>25131.000000</td>\n",
              "      <td>25131.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1979.926784</td>\n",
              "      <td>249.744777</td>\n",
              "      <td>4304.961780</td>\n",
              "      <td>207.903677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.764827</td>\n",
              "      <td>144.443000</td>\n",
              "      <td>11396.723687</td>\n",
              "      <td>1173.695947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1955.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>49.700000</td>\n",
              "      <td>-98696.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1967.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>357.900000</td>\n",
              "      <td>8.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1980.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>1017.600000</td>\n",
              "      <td>35.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1993.000000</td>\n",
              "      <td>375.000000</td>\n",
              "      <td>3916.100000</td>\n",
              "      <td>150.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2005.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>288189.000000</td>\n",
              "      <td>25330.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          const element = document.querySelector('#df-62ec27ae-03b9-4311-8777-5b9530c0f96f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "               Year          Rank  Revenue (in millions)  Profit (in millions)\n",
              "count  25131.000000  25131.000000           25131.000000          25131.000000\n",
              "mean    1979.926784    249.744777            4304.961780            207.903677\n",
              "std       14.764827    144.443000           11396.723687           1173.695947\n",
              "min     1955.000000      1.000000              49.700000         -98696.000000\n",
              "25%     1967.000000    124.000000             357.900000              8.900000\n",
              "50%     1980.000000    250.000000            1017.600000             35.500000\n",
              "75%     1993.000000    375.000000            3916.100000            150.500000\n",
              "max     2005.000000    500.000000          288189.000000          25330.000000"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f500_df.plot( x = 'Year', y = 'Profit (in millions)', kind= 'scatter', c = 'red')\n",
        "ax = plt.gca() #need access to the axis. gca = get current axis \n",
        "f500_df.plot( x = 'Year', y = 'Revenue (in millions)', kind= 'scatter', c = 'black', alpha=0.05, ax = ax)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "xSyCFmDW6Ql0",
        "outputId": "e087aaad-0ab8-45c5-f184-1ccc0a6d2b99"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78e7994d50>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "lDJ6jzLD9qzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "I notice that the revenue for the fortune 500 grows considerably over time but the profit doesn't grow that much. \n",
        "I make another observation that this might be because of costs and inflation and you wouldn't expect the profit margins for a business to grow 2X because you still have to provide a cost-efficient product to consumers. "
      ],
      "metadata": {
        "id": "lJbr9_SBCW65"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ANXzWx6dDD4n"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}