Pipe.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pipe.ipynb",
      "provenance": [],
      "mount_file_id": "1di9dF18FQISi3V3BcmE0SqO2jKb073W8",
      "authorship_tag": "ABX9TyPyeLTuj0jbh0PRTQTg5A+I",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SuperDataWorld/Python/blob/main/Pipe.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihIivcD8SahC"
      },
      "source": [
        "## Imports and EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0g4dfrhFfzs"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZ_SlCD_Fr84",
        "outputId": "2e736684-78f5-4737-b6ab-58da3c31e2c8"
      },
      "source": [
        "! wget https://raw.githubusercontent.com/SuperDataWorld/Python/main/loan.csv\n",
        "df = pd.read_csv('loan.csv')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-18 14:55:18--  https://raw.githubusercontent.com/SuperDataWorld/Python/main/loan.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 38011 (37K) [text/plain]\n",
            "Saving to: ‘loan.csv.7’\n",
            "\n",
            "\rloan.csv.7            0%[                    ]       0  --.-KB/s               \rloan.csv.7          100%[===================>]  37.12K  --.-KB/s    in 0.003s  \n",
            "\n",
            "2021-09-18 14:55:18 (11.2 MB/s) - ‘loan.csv.7’ saved [38011/38011]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9abjJbXU1M0",
        "outputId": "012e1abd-2d93-4630-b9a8-6f259ed7a59f"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             601 non-null    object \n",
            " 2   Married            611 non-null    object \n",
            " 3   Dependents         599 non-null    object \n",
            " 4   Education          614 non-null    object \n",
            " 5   Self_Employed      582 non-null    object \n",
            " 6   ApplicantIncome    614 non-null    int64  \n",
            " 7   CoapplicantIncome  614 non-null    float64\n",
            " 8   LoanAmount         592 non-null    float64\n",
            " 9   Loan_Amount_Term   600 non-null    float64\n",
            " 10  Credit_History     564 non-null    float64\n",
            " 11  Property_Area      614 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 62.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "mAPPuNS-F2pE",
        "outputId": "1ec67f91-726c-4963-cbad-b23c5372af42"
      },
      "source": [
        "df.head(12)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LP001013</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2333</td>\n",
              "      <td>1516.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LP001014</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3+</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3036</td>\n",
              "      <td>2504.0</td>\n",
              "      <td>158.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LP001018</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4006</td>\n",
              "      <td>1526.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LP001020</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>12841</td>\n",
              "      <td>10968.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LP001024</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3200</td>\n",
              "      <td>700.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LP001027</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2500</td>\n",
              "      <td>1840.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Loan_ID Gender Married  ... Credit_History Property_Area Loan_Status\n",
              "0   LP001002   Male      No  ...            1.0         Urban           Y\n",
              "1   LP001003   Male     Yes  ...            1.0         Rural           N\n",
              "2   LP001005   Male     Yes  ...            1.0         Urban           Y\n",
              "3   LP001006   Male     Yes  ...            1.0         Urban           Y\n",
              "4   LP001008   Male      No  ...            1.0         Urban           Y\n",
              "5   LP001011   Male     Yes  ...            1.0         Urban           Y\n",
              "6   LP001013   Male     Yes  ...            1.0         Urban           Y\n",
              "7   LP001014   Male     Yes  ...            0.0     Semiurban           N\n",
              "8   LP001018   Male     Yes  ...            1.0         Urban           Y\n",
              "9   LP001020   Male     Yes  ...            1.0     Semiurban           N\n",
              "10  LP001024   Male     Yes  ...            1.0         Urban           Y\n",
              "11  LP001027   Male     Yes  ...            1.0         Urban           Y\n",
              "\n",
              "[12 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yoZAXuGRd9p",
        "outputId": "0c7d5ed7-25c6-4b30-fd07-c0bbb15cd7f5"
      },
      "source": [
        "df['Dependents'].value_counts()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     345\n",
              "1     102\n",
              "2     101\n",
              "3+     51\n",
              "Name: Dependents, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkA2KXyoF7D4"
      },
      "source": [
        "cols = df.columns"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuUqqDjKGFy_",
        "outputId": "a7053b79-c897-4a49-f83b-758fc9cba457"
      },
      "source": [
        "cols"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
              "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
              "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zOVJzrUSKcw"
      },
      "source": [
        "## Clean Up Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5ZpgxjxGOJi"
      },
      "source": [
        "# 1. Drop Rows with Null Columns\n",
        "def drop_nulls(df, cols):\n",
        "  df.dropna(subset = cols, inplace=True)\n",
        "  return df\n",
        "\n",
        "# 2. Fill Null Columns with Av Values \n",
        "def fill_vals(df, cols):\n",
        " for i in cols:\n",
        "   av = df[i].mean()\n",
        "   df[i].fillna(av, inplace = True)\n",
        " return df\n",
        "\n",
        "# 3. Replace Strings with numbers and convert type\n",
        "def replace_strings(df, cols):\n",
        "  for i in cols:\n",
        "    df[i].replace('3+',4, inplace = True)\n",
        "    df[i] = pd.to_numeric(df[i])\n",
        "  return df\n",
        "  "
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eG6fSoWKSPVF"
      },
      "source": [
        "# Lists and Pipe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5_cflA9R-vh"
      },
      "source": [
        "null_cols = ['Gender','Married','Dependents','Credit_History','Self_Employed','Loan_Amount_Term']\n",
        "av_cols = ['LoanAmount']\n",
        "rp_cols = ['Dependents']\n",
        "\n",
        "\n",
        "df = df.pipe(drop_nulls, null_cols).pipe(fill_vals,av_cols).pipe(replace_strings, rp_cols)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaAJMp_hSUe5"
      },
      "source": [
        "## Output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGGeFucOR-lh",
        "outputId": "0d3aa5b6-4d91-4271-ef01-11dcaeb73308"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 499 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            499 non-null    object \n",
            " 1   Gender             499 non-null    object \n",
            " 2   Married            499 non-null    object \n",
            " 3   Dependents         499 non-null    int64  \n",
            " 4   Education          499 non-null    object \n",
            " 5   Self_Employed      499 non-null    object \n",
            " 6   ApplicantIncome    499 non-null    int64  \n",
            " 7   CoapplicantIncome  499 non-null    float64\n",
            " 8   LoanAmount         499 non-null    float64\n",
            " 9   Loan_Amount_Term   499 non-null    float64\n",
            " 10  Credit_History     499 non-null    float64\n",
            " 11  Property_Area      499 non-null    object \n",
            " 12  Loan_Status        499 non-null    object \n",
            "dtypes: float64(4), int64(2), object(7)\n",
            "memory usage: 54.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "hRkJQFrNOx8d",
        "outputId": "5b1a1083-6dd5-487c-bb17-7069a8ee23b1"
      },
      "source": [
        "df.head(12)\n",
        "# Row 0 NaN in Loan Amount\n",
        "# Row 7 # 3+ in Dependents\n",
        "# Row 11 NaN in self employed"
      ],
      "execution_count": 11,
      "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>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.735417</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LP001011</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5417</td>\n",
              "      <td>4196.0</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LP001013</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2333</td>\n",
              "      <td>1516.0</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LP001014</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3036</td>\n",
              "      <td>2504.0</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LP001018</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4006</td>\n",
              "      <td>1526.0</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LP001020</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>12841</td>\n",
              "      <td>10968.0</td>\n",
              "      <td>349.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Semiurban</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LP001024</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3200</td>\n",
              "      <td>700.0</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LP001028</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>3073</td>\n",
              "      <td>8106.0</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Loan_ID Gender Married  ...  Credit_History Property_Area Loan_Status\n",
              "0   LP001002   Male      No  ...             1.0         Urban           Y\n",
              "1   LP001003   Male     Yes  ...             1.0         Rural           N\n",
              "2   LP001005   Male     Yes  ...             1.0         Urban           Y\n",
              "3   LP001006   Male     Yes  ...             1.0         Urban           Y\n",
              "4   LP001008   Male      No  ...             1.0         Urban           Y\n",
              "5   LP001011   Male     Yes  ...             1.0         Urban           Y\n",
              "6   LP001013   Male     Yes  ...             1.0         Urban           Y\n",
              "7   LP001014   Male     Yes  ...             0.0     Semiurban           N\n",
              "8   LP001018   Male     Yes  ...             1.0         Urban           Y\n",
              "9   LP001020   Male     Yes  ...             1.0     Semiurban           N\n",
              "10  LP001024   Male     Yes  ...             1.0         Urban           Y\n",
              "12  LP001028   Male     Yes  ...             1.0         Urban           Y\n",
              "\n",
              "[12 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}