Day_33_Mized_Data_Handling.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day33-handling-mixed-variables/titanic.csv')"
      ],
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      "outputs": []
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        "df.head(5)"
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      "execution_count": 3,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  Cabin            Ticket number  Survived\n",
              "0   NaN         A/5 21171      5         0\n",
              "1   C85          PC 17599      3         1\n",
              "2   NaN  STON/O2. 3101282      6         1\n",
              "3  C123            113803      3         1\n",
              "4   NaN            373450      A         0"
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              "      <th>Cabin</th>\n",
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        "df.info()"
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Cabin     204 non-null    object\n",
            " 1   Ticket    891 non-null    object\n",
            " 2   number    891 non-null    object\n",
            " 3   Survived  891 non-null    int64 \n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 28.0+ KB\n"
          ]
        }
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    {
      "cell_type": "markdown",
      "source": [
        "All the columns in the dataset except Survived can be categorized as mixed data. We have to work on all of them to seprate numerical and categorical data from them."
      ],
      "metadata": {
        "id": "zS1tSo9F9eIO"
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        "df.number.value_counts().plot(kind = 'bar')"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['number_numerical'] = pd.to_numeric(df['number'], errors = 'coerce', downcast = 'integer')"
      ],
      "metadata": {
        "id": "jpuiUAEW9xOB"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['number_categorical'] = np.where(df['number'].isnull(),df['number'],np.nan)"
      ],
      "metadata": {
        "id": "Gwtjs8kl-G2B"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "colab": {
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        "id": "4o9qIZGL-mlp",
        "outputId": "493c3593-356e-4099-986a-63bf85627aea"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cabin            Ticket number  Survived  number_numerical  \\\n",
              "0   NaN         A/5 21171      5         0               5.0   \n",
              "1   C85          PC 17599      3         1               3.0   \n",
              "2   NaN  STON/O2. 3101282      6         1               6.0   \n",
              "3  C123            113803      3         1               3.0   \n",
              "4   NaN            373450      A         0               NaN   \n",
              "\n",
              "  number_categorical  \n",
              "0                NaN  \n",
              "1                NaN  \n",
              "2                NaN  \n",
              "3                NaN  \n",
              "4                NaN  "
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.Cabin.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcHp0uTlAPdy",
        "outputId": "2a3ee33d-e491-4d78-f076-9d6135d7e3e5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
              "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
              "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
              "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
              "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
              "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
              "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
              "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
              "       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
              "       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
              "       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
              "       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
              "       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
              "       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
              "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
              "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
              "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
              "       'C148'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see there are lot of distinct values, It will be complex to apply OHE or OE in it. SO, we should use the following code to handle such data"
      ],
      "metadata": {
        "id": "_CqXe2TUAT6T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['cabin_num'] = df['Cabin'].str.extract('(\\d+)')\n",
        "df['cabin_cat'] = df['Cabin'].str[0]"
      ],
      "metadata": {
        "id": "I4lhfqwxAeO0"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "zWIrjUFxAs8N",
        "outputId": "d9bbc8c1-737c-4de0-886c-2a05b89f0beb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Cabin            Ticket number  Survived  number_numerical  \\\n",
              "0     NaN         A/5 21171      5         0               5.0   \n",
              "1     C85          PC 17599      3         1               3.0   \n",
              "2     NaN  STON/O2. 3101282      6         1               6.0   \n",
              "3    C123            113803      3         1               3.0   \n",
              "4     NaN            373450      A         0               NaN   \n",
              "..    ...               ...    ...       ...               ...   \n",
              "886   NaN            211536      3         0               3.0   \n",
              "887   B42            112053      3         1               3.0   \n",
              "888   NaN        W./C. 6607      1         0               1.0   \n",
              "889  C148            111369      2         1               2.0   \n",
              "890   NaN            370376      3         0               3.0   \n",
              "\n",
              "    number_categorical cabin_num cabin_cat  \n",
              "0                  NaN       NaN       NaN  \n",
              "1                  NaN        85         C  \n",
              "2                  NaN       NaN       NaN  \n",
              "3                  NaN       123         C  \n",
              "4                  NaN       NaN       NaN  \n",
              "..                 ...       ...       ...  \n",
              "886                NaN       NaN       NaN  \n",
              "887                NaN        42         B  \n",
              "888                NaN       NaN       NaN  \n",
              "889                NaN       148         C  \n",
              "890                NaN       NaN       NaN  \n",
              "\n",
              "[891 rows x 8 columns]"
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-653dd46c-528c-42ed-b97e-f3893cccbfe2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-653dd46c-528c-42ed-b97e-f3893cccbfe2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Ticket.unique()"
      ],
      "metadata": {
        "id": "eLMR2PAw_goY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ticket is more harder to handle due to presence o irregularity in mized data also."
      ],
      "metadata": {
        "id": "X7rVuE0OAvqJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# extract the last bit of ticket as number\n",
        "df['ticket_num'] = df['Ticket'].apply(lambda s: s.split()[-1])\n",
        "df['ticket_num'] = pd.to_numeric(df['ticket_num'],\n",
        "                                   errors='coerce',\n",
        "                                   downcast='integer')\n",
        "\n",
        "# extract the first part of ticket as category\n",
        "df['ticket_cat'] = df['Ticket'].apply(lambda s: s.split()[0])\n",
        "df['ticket_cat'] = np.where(df['ticket_cat'].str.isdigit(), np.nan,\n",
        "                              df['ticket_cat'])"
      ],
      "metadata": {
        "id": "-sJPb6yx_kVp"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3AoHHGL3B1CR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}