1_ML_handling_missing_data.ipynb

{
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      "id": "f9a80bb0",
      "metadata": {
        "id": "f9a80bb0"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce9c7851",
      "metadata": {
        "id": "ce9c7851"
      },
      "outputs": [],
      "source": [
        "data = {\n",
        "    'A': [1, 2, 100, 4, 5],\n",
        "    'B': [3, np.nan, 7, 8, 9],\n",
        "    'C': [np.nan, 12, 13, 14, 15]\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6497227c",
      "metadata": {
        "id": "6497227c"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "92ec06e2",
      "metadata": {
        "id": "92ec06e2",
        "outputId": "3ac2173a-02f0-4c6b-e4c7-a8e8f75f0166"
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      "outputs": [
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              "      <td>100</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>8.0</td>\n",
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              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
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            "text/plain": [
              "     A    B     C\n",
              "0    1  3.0   NaN\n",
              "1    2  NaN  12.0\n",
              "2  100  7.0  13.0\n",
              "3    4  8.0  14.0\n",
              "4    5  9.0  15.0"
            ]
          },
          "execution_count": 6,
          "metadata": {},
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        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdf5d66a",
      "metadata": {
        "id": "cdf5d66a",
        "outputId": "f3996028-2195-4c5a-ce98-cd1a9bc6ee7d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "A    0\n",
              "B    1\n",
              "C    1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e41e36ff",
      "metadata": {
        "id": "e41e36ff",
        "outputId": "4747bc26-65af-46aa-8323-7377686a6ccb"
      },
      "outputs": [
        {
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              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "     A    B     C\n",
              "2  100  7.0  13.0\n",
              "3    4  8.0  14.0\n",
              "4    5  9.0  15.0"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#drop all the rows\n",
        "df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73829d82",
      "metadata": {
        "id": "73829d82",
        "outputId": "d4956e9a-8ed2-47fc-dff5-09548c9a8e6b"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
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              "      <th>2</th>\n",
              "      <td>100</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "     A\n",
              "0    1\n",
              "1    2\n",
              "2  100\n",
              "3    4\n",
              "4    5"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#drop a column with missing value\n",
        "df.dropna(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dacb7694",
      "metadata": {
        "id": "dacb7694",
        "outputId": "7409e585-5916-4a07-ba52-8a7d848ddc89"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "6.75"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['B'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad07bad6",
      "metadata": {
        "id": "ad07bad6",
        "outputId": "2e022ae9-654f-4ee2-8ef5-5e82287659d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    3.00\n",
              "1    6.75\n",
              "2    7.00\n",
              "3    8.00\n",
              "4    9.00\n",
              "Name: B, dtype: float64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Replace/Fill/imputation of missing value with mean\n",
        "\n",
        "df['B'].fillna(df['B'].mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "24e2b086",
      "metadata": {
        "id": "24e2b086",
        "outputId": "5d118b66-57b0-4ea4-f91a-2848506f6487"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    3.0\n",
              "1    0.0\n",
              "2    7.0\n",
              "3    8.0\n",
              "4    9.0\n",
              "Name: B, dtype: float64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#imputing with constant\n",
        "df['B'].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2a02b57",
      "metadata": {
        "id": "d2a02b57",
        "outputId": "a783198f-c19e-449a-febc-7da730d63830"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    3.0\n",
              "1    7.5\n",
              "2    7.0\n",
              "3    8.0\n",
              "4    9.0\n",
              "Name: B, dtype: float64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['B'].fillna(df['B'].median())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "9469375c",
      "metadata": {
        "id": "9469375c"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "df = sns.load_dataset('titanic')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "h-n4-lqSuvMJ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-n4-lqSuvMJ",
        "outputId": "a29bec43-30ad-4a54-9406-9052ef05517d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "embarked         2\n",
              "class            0\n",
              "who              0\n",
              "adult_male       0\n",
              "deck           688\n",
              "embark_town      2\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate the missing value\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94889b6b",
      "metadata": {
        "id": "94889b6b",
        "outputId": "92ae9fda-782a-4e5a-a8c3-efe3e58e0127"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "survived        0.000000\n",
              "pclass          0.000000\n",
              "sex             0.000000\n",
              "age            19.865320\n",
              "sibsp           0.000000\n",
              "parch           0.000000\n",
              "fare            0.000000\n",
              "embarked        0.224467\n",
              "class           0.000000\n",
              "who             0.000000\n",
              "adult_male      0.000000\n",
              "deck           77.216611\n",
              "embark_town     0.224467\n",
              "alive           0.000000\n",
              "alone           0.000000\n",
              "dtype: float64"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate the missing value in percentage\n",
        "(df.isnull().sum() / len(df.index))*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc618383",
      "metadata": {
        "id": "fc618383",
        "outputId": "66310fa5-5913-477a-b1bb-bea16aca74db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(891, 15)"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check data set is huge or not?\n",
        "# data set is not huge, then don't drop the missing values.\n",
        "df.shape\n",
        "\n",
        "# 0.2% is significant on 891 rows but 0.2% is not significant on 1 lakh rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3427cb2c",
      "metadata": {
        "id": "3427cb2c",
        "outputId": "c0aedd19-3eeb-4510-8c8a-9aad56f3f243"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "embark_town\n",
              "Southampton    644\n",
              "Cherbourg      168\n",
              "Queenstown      77\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# embark_town is a categorical column\n",
        "# Count Null values in categorical column\n",
        "df['embark_town'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "Vk4wsJqkxZlb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vk4wsJqkxZlb",
        "outputId": "aedde6bf-7115-4eac-b786-10893c865de3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate the missing values\n",
        "df['embark_town'].isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "bef0c9ba",
      "metadata": {
        "id": "bef0c9ba"
      },
      "outputs": [],
      "source": [
        "# how to replace null values in categorical column?\n",
        "# Imputed missing values with mode,\n",
        "# Mode is having highest frequency(Southampton    644), then this is the true replacement of missing value\n",
        "# so Southampton will be replaced with missing values\n",
        "df['embark_town'] = df['embark_town'].fillna(\"Southampton\")\n",
        "\n",
        "# if embark town is having same frequency of mode,\n",
        "# then replace with any one of missing value,\n",
        "# before doing that consult business team\n",
        "# Example:\n",
        "# Southampton    777\n",
        "# Cherbourg      777\n",
        "# Queenstown     777"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "5c4a167a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5c4a167a",
        "outputId": "6b4673cd-3266-4888-dca2-9a0d91f03beb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate the missing values\n",
        "df['embark_town'].isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99a799df",
      "metadata": {
        "id": "99a799df",
        "outputId": "5d0a1d45-c460-4ff3-a84c-8e252a8f29c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "embarked         2\n",
              "class            0\n",
              "who              0\n",
              "adult_male       0\n",
              "deck           688\n",
              "embark_town      0\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e213ef4",
      "metadata": {
        "id": "2e213ef4"
      },
      "outputs": [],
      "source": [
        "# dropping missing values in deck column\n",
        "df.drop('deck', axis = 1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb20ace7",
      "metadata": {
        "id": "eb20ace7",
        "outputId": "2c368a12-7ace-40e2-ccec-9a99c8c953dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "embarked         2\n",
              "class            0\n",
              "who              0\n",
              "adult_male       0\n",
              "embark_town      0\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6d93191d",
      "metadata": {
        "id": "6d93191d",
        "outputId": "4c76372f-a4af-4c42-a9fd-5794379fca1b"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
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              "      <th>embark_town</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>Second</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
              "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
              "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
              "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
              "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
              "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
              "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
              "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
              "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
              "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
              "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
              "\n",
              "       who  adult_male  embark_town alive  alone  \n",
              "0      man        True  Southampton    no  False  \n",
              "1    woman       False    Cherbourg   yes  False  \n",
              "2    woman       False  Southampton   yes   True  \n",
              "3    woman       False  Southampton   yes  False  \n",
              "4      man        True  Southampton    no   True  \n",
              "..     ...         ...          ...   ...    ...  \n",
              "886    man        True  Southampton    no   True  \n",
              "887  woman       False  Southampton   yes   True  \n",
              "888  woman       False  Southampton    no  False  \n",
              "889    man        True    Cherbourg   yes   True  \n",
              "890    man        True   Queenstown    no   True  \n",
              "\n",
              "[891 rows x 14 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "60df3be4",
      "metadata": {
        "id": "60df3be4",
        "outputId": "fa100e01-f238-426f-cab9-a33f416c5951"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\ajayk\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
            "  with pd.option_context('mode.use_inf_as_na', True):\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='Count'>"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.histplot(df.age, kde = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d4cd8b53",
      "metadata": {
        "id": "d4cd8b53",
        "outputId": "339eb3b7-e9e2-48dd-a8ea-e8e029ece457"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# normal distribution data, no outlier\n",
        "# replace missing values(NaN) with mean value\n",
        "\n",
        "df['age'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "31cfe8a2",
      "metadata": {
        "id": "31cfe8a2"
      },
      "outputs": [],
      "source": [
        "# replaced missing values with mean value\n",
        "df['age'] = df['age'].fillna(df['age'].mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a861d3a4",
      "metadata": {
        "id": "a861d3a4",
        "outputId": "7b8014d8-d84a-4d8f-9cbd-a82d80aafb89"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "survived       0\n",
              "pclass         0\n",
              "sex            0\n",
              "age            0\n",
              "sibsp          0\n",
              "parch          0\n",
              "fare           0\n",
              "embarked       2\n",
              "class          0\n",
              "who            0\n",
              "adult_male     0\n",
              "embark_town    0\n",
              "alive          0\n",
              "alone          0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48bc09ae",
      "metadata": {
        "id": "48bc09ae",
        "outputId": "418e4c8f-5f08-4b4f-e015-1e1d95cbd48c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "embarked\n",
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['embarked'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4a1a8a2b",
      "metadata": {
        "id": "4a1a8a2b"
      },
      "outputs": [],
      "source": [
        "df['embarked'] = df['embarked'].fillna(\"S\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "786f095b",
      "metadata": {
        "id": "786f095b",
        "outputId": "dedae55d-2918-4f63-f2a9-cea03d251615"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "survived       0\n",
              "pclass         0\n",
              "sex            0\n",
              "age            0\n",
              "sibsp          0\n",
              "parch          0\n",
              "fare           0\n",
              "embarked       0\n",
              "class          0\n",
              "who            0\n",
              "adult_male     0\n",
              "embark_town    0\n",
              "alive          0\n",
              "alone          0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0a7c3455",
      "metadata": {
        "id": "0a7c3455"
      },
      "outputs": [],
      "source": [
        "# homework>> create a separate column with flag 1 and 0\n",
        "# Where age column is having null values,\n",
        "# then create flag column, null value should represent in flag column as 1 other wise 0\n",
        "df[\"flag\"]=df.age.apply(lambda x: 1 if pd.isna(x) else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "30c13558",
      "metadata": {
        "id": "30c13558",
        "outputId": "bd53fba5-c5c9-461b-e32c-f02d90226dda"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "886    0\n",
              "887    0\n",
              "888    0\n",
              "889    0\n",
              "890    0\n",
              "Name: flag, Length: 891, dtype: int64"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['flag']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "83fda83a",
      "metadata": {
        "id": "83fda83a"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
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    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
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