datafillingtitanic.ipynb

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      "name": "Day 9",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Pranav-22-aditya/pranav/blob/main/datafillingtitanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "oO2LVpqeISdg",
        "outputId": "c99dd03d-59f4-4787-b464-58a1e2bacfda"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/drive/MyDrive/Verzeo_python/Titanic (1).csv')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe thead th {\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YjxTo0vRJygH",
        "outputId": "d19313f4-3b7e-4595-e164-02dcdd5dea82"
      },
      "source": [
        "df['Survived'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3C9cXem4KAwK",
        "outputId": "2ce0b328-2060-4ad3-c53b-8a1b87ef7955"
      },
      "source": [
        "df['Pclass'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: Pclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "nRe5fvt3KMtH",
        "outputId": "aeaf98a7-5746-4ed2-af55-89b96a8fba6c"
      },
      "source": [
        "pd.pivot_table(df, index=\"Survived\")"
      ],
      "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>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Parch</th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>SibSp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30.626179</td>\n",
              "      <td>22.117887</td>\n",
              "      <td>0.329690</td>\n",
              "      <td>447.016393</td>\n",
              "      <td>2.531876</td>\n",
              "      <td>0.553734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28.343690</td>\n",
              "      <td>48.395408</td>\n",
              "      <td>0.464912</td>\n",
              "      <td>444.368421</td>\n",
              "      <td>1.950292</td>\n",
              "      <td>0.473684</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Age       Fare     Parch  PassengerId    Pclass     SibSp\n",
              "Survived                                                                 \n",
              "0         30.626179  22.117887  0.329690   447.016393  2.531876  0.553734\n",
              "1         28.343690  48.395408  0.464912   444.368421  1.950292  0.473684"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkyjymRNMZzn"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fImxaml1MfCj"
      },
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "vm9qEBWSKt-a",
        "outputId": "223e466e-74b5-4a30-833a-125ac1df1c68"
      },
      "source": [
        "sns.catplot(x = 'Sex', hue = \"Pclass\", kind = \"count\", data=df)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "MCps4H5MMUtc",
        "outputId": "a21c10f4-464d-4cb5-a776-f962eb2c551a"
      },
      "source": [
        "sns.violinplot(x = \"Sex\", y = 'Age', hue= \"Survived\", data=df, split=True)\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": "6FmhAu8ENxpe"
      },
      "source": [
        "To find missing Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JG6iA7AVNxaq"
      },
      "source": [
        "import numpy as np\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bi_91ZVcNCpl"
      },
      "source": [
        "z1 = [1,np.nan,np.nan,4,5,np.nan,7,np.nan]\n",
        "z2 = [1,1,1,1,2,2,2,2]\n",
        "df1 = pd.DataFrame({'Cricket': z1,\n",
        "                    'Tennis': z2})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGOT4yByP6ZX"
      },
      "source": [
        "df1.to_csv(\"missing eg.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "VamAiaYZPzSo",
        "outputId": "88aeb950-089e-4f40-bdde-315d5a887127"
      },
      "source": [
        "df1"
      ],
      "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>Cricket</th>\n",
              "      <th>Tennis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cricket  Tennis\n",
              "0      1.0       1\n",
              "1      NaN       1\n",
              "2      NaN       1\n",
              "3      4.0       1\n",
              "4      5.0       2\n",
              "5      NaN       2\n",
              "6      7.0       2\n",
              "7      NaN       2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "McE5x0WsP2X8",
        "outputId": "e7fc135c-8d70-47f7-a918-87f5e82ffdea"
      },
      "source": [
        "df1.fillna(value=2)"
      ],
      "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>Cricket</th>\n",
              "      <th>Tennis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cricket  Tennis\n",
              "0      1.0       1\n",
              "1      2.0       1\n",
              "2      2.0       1\n",
              "3      4.0       1\n",
              "4      5.0       2\n",
              "5      2.0       2\n",
              "6      7.0       2\n",
              "7      2.0       2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZXEAoSbQvqY",
        "outputId": "af60b9b8-bc7b-4788-f17c-f85c5afd29c9"
      },
      "source": [
        "df1['Cricket'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "o4hxWaccRJAG",
        "outputId": "2b5e5dcd-ff33-42d3-a5e5-02e388cf6009"
      },
      "source": [
        "df1.fillna(value=4.5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>Tennis</th>\n",
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              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "   Cricket  Tennis\n",
              "0      1.0       1\n",
              "1      4.5       1\n",
              "2      4.5       1\n",
              "3      4.0       1\n",
              "4      5.0       2\n",
              "5      4.5       2\n",
              "6      7.0       2\n",
              "7      4.5       2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtGNVRNdROaA"
      },
      "source": [
        "df1['Cricket'].fillna(df1.groupby(['Tennis'])['Cricket'].transform(np.mean), inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "OtaPAJwDS3CB",
        "outputId": "622db14f-8237-466a-909f-780ff54a7fc8"
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      "source": [
        "df1"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "   Cricket  Tennis\n",
              "0      1.0       1\n",
              "1      2.5       1\n",
              "2      2.5       1\n",
              "3      4.0       1\n",
              "4      5.0       2\n",
              "5      6.0       2\n",
              "6      7.0       2\n",
              "7      6.0       2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RzKSsWyFbsB"
      },
      "source": [
        "Missing values for 'Age' in Titanic Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
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        "id": "qR1l_IV2TCkn",
        "outputId": "ab7a2396-2933-4a63-b477-6af0fecfc71f"
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      "source": [
        "df"
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>35.0</td>\n",
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              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
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              "      <th>887</th>\n",
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              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
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              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
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              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
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              "      <td>Dooley, Mr. Patrick</td>\n",
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              "      <td>32.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCEot1r9Qyam"
      },
      "source": [
        "Adding a new column named \"Category\" based on salutation of passengers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4TW6G3cQfN7"
      },
      "source": [
        "def extract(x):\n",
        "  temp = x.split(\" \")\n",
        "  if \"Mr.\" in temp:\n",
        "    return \"Mr\"\n",
        "  elif \"Mrs.\" in temp:\n",
        "    return \"Mrs\"\n",
        "  elif \"Miss.\" in temp:\n",
        "    return \"Miss\"\n",
        "  elif \"Master.\" in temp:\n",
        "    return \"Master\"\n",
        "  elif \"Dr.\" in temp:\n",
        "    return \"Dr\"\n",
        "  else:\n",
        "    return None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhVg02UmPr4-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "8b6bfc2f-d548-4f14-e5f8-dcd6e213904c"
      },
      "source": [
        "df['Category'] = df['Name'].apply(extract)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked  Category\n",
              "0              1         0       3  ...   NaN        S        Mr\n",
              "1              2         1       1  ...   C85        C       Mrs\n",
              "2              3         1       3  ...   NaN        S      Miss\n",
              "3              4         1       1  ...  C123        S       Mrs\n",
              "4              5         0       3  ...   NaN        S        Mr\n",
              "..           ...       ...     ...  ...   ...      ...       ...\n",
              "886          887         0       2  ...   NaN        S      None\n",
              "887          888         1       1  ...   B42        S      Miss\n",
              "888          889         0       3  ...   NaN        S      Miss\n",
              "889          890         1       1  ...  C148        C        Mr\n",
              "890          891         0       3  ...   NaN        Q        Mr\n",
              "\n",
              "[891 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olfgExIePwxM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "f8eada7b-d079-494a-dabb-bbe517072190"
      },
      "source": [
        "c = df['Category'].value_counts()\n",
        "c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr        517\n",
              "Miss      182\n",
              "Mrs       125\n",
              "Master     40\n",
              "Dr          7\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S32CLg_8QrKV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a48429ef-884d-4543-dc95-1cca537e43e5"
      },
      "source": [
        "c.sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "871"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OG3QO2YRCNi"
      },
      "source": [
        "Missing Values mission"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwuVbSNOQuo7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "09d0403d-0638-4793-98f1-c53420fedb7e"
      },
      "source": [
        "df['Category'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', None, 'Dr'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRCK0a0sHLna"
      },
      "source": [
        "Calculating mean age with respect to salutations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tsZkVP9RJQ0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b2d7f70f-8119-4b21-d350-3273a308836a"
      },
      "source": [
        "mr_mean = df[df['Category'] == 'Mr']['Age'].mean()\n",
        "mr_mean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32.368090452261306"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LH5x8kPMRS0d"
      },
      "source": [
        "mrs_mean = df[df['Category'] == 'Mrs']['Age'].mean()\n",
        "miss_mean = df[df['Category'] == 'Miss']['Age'].mean()\n",
        "master_mean = df[df['Category'] == 'Master']['Age'].mean()\n",
        "dr_mean = df[df['Category'] == 'Dr']['Age'].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TtZr-geRZ8j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "e91ea62f-96c5-4f5f-9106-991d95e693f4"
      },
      "source": [
        "print(mr_mean)\n",
        "print(mrs_mean)\n",
        "print(miss_mean)\n",
        "print(master_mean)\n",
        "print(dr_mean)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32.368090452261306\n",
            "35.898148148148145\n",
            "21.773972602739725\n",
            "4.574166666666667\n",
            "42.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KiO_KHsyHVYg"
      },
      "source": [
        "Groupby Function application"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKUOV0iTSBTv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "b7a039bd-d54a-4bc0-b079-1777c17a1c6d"
      },
      "source": [
        "df['Age'].fillna(df.groupby(['Category'])['Age'].transform(np.mean),inplace=True)\n",
        "df"
      ],
      "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "  </thead>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.000000</td>\n",
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              "      <td>female</td>\n",
              "      <td>38.000000</td>\n",
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              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
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              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000000</td>\n",
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              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>35.000000</td>\n",
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              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>886</th>\n",
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              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
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              "      <td>211536</td>\n",
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              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.000000</td>\n",
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              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
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              "      <td>2</td>\n",
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              "      <td>30.0000</td>\n",
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              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
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              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
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              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked  Category\n",
              "0              1         0       3  ...   NaN        S        Mr\n",
              "1              2         1       1  ...   C85        C       Mrs\n",
              "2              3         1       3  ...   NaN        S      Miss\n",
              "3              4         1       1  ...  C123        S       Mrs\n",
              "4              5         0       3  ...   NaN        S        Mr\n",
              "..           ...       ...     ...  ...   ...      ...       ...\n",
              "886          887         0       2  ...   NaN        S      None\n",
              "887          888         1       1  ...   B42        S      Miss\n",
              "888          889         0       3  ...   NaN        S      Miss\n",
              "889          890         1       1  ...  C148        C        Mr\n",
              "890          891         0       3  ...   NaN        Q        Mr\n",
              "\n",
              "[891 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qH2p2zXSF_4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "e55cec47-12f9-4859-e299-839ac3f88b08"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.754659</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>13.277179</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>21.773973</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.898148</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ph3j6cOISJlA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f228ef9f-8a9e-4a06-fc8f-1ec3951769d0"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "Category        20\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBy8SrPfHdba"
      },
      "source": [
        "Comparing begore and after values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_kevmw4SMRQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "26d01b54-482f-4a15-b7a9-70ad3fa59edb"
      },
      "source": [
        "a_young = np.sum((df['Age']>=0) & (df['Age']<20))\n",
        "a_adult = np.sum((df['Age']>=20) & (df['Age']<40))\n",
        "a_midage = np.sum((df['Age']>=40) & (df['Age']<60))\n",
        "a_old = np.sum((df['Age']>=60))\n",
        "print('After Missing Values')\n",
        "print(f'Age 0 to 20 years : {a_young}')\n",
        "print(f'Age 20 to 40 years : {a_adult}')\n",
        "print(f'Age 40 to 60 years : {a_midage}')\n",
        "print(f'Age 60 to 80 years : {a_old}')\n",
        "print(a_young+a_adult+a_midage+a_old)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "After Missing Values\n",
            "Age 0 to 20 years : 168\n",
            "Age 20 to 40 years : 559\n",
            "Age 40 to 60 years : 138\n",
            "Age 60 to 80 years : 26\n",
            "891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEAUrlzeSPBs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "c08dbdab-8ab0-4381-a857-ac37e65839b6"
      },
      "source": [
        "print('Before Missing Values')\n",
        "print(f'Age 0 to 20 years : {b_young}')\n",
        "print(f'Age 20 to 40 years : {b_adult}')\n",
        "print(f'Age 40 to 60 years : {b_midage}')\n",
        "print(f'Age 60 to 80 years : {b_old}')\n",
        "print(b_young+b_adult+b_midage+b_old)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before Missing Values\n",
            "Age 0 to 20 years : 164\n",
            "Age 20 to 40 years : 387\n",
            "Age 40 to 60 years : 137\n",
            "Age 60 to 80 years : 26\n",
            "714\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}