Kraytonian_LS_DS_112_Make_Features_Assignment.ipynb

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        "<a href=\"https://colab.research.google.com/github/TheRealKraytonian/DS-Unit-1-Sprint-1-Data-Wrangling-and-Storytelling/blob/master/Kraytonian_LS_DS_112_Make_Features_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Assignment:\n",
        "\n",
        "- Replicate the lesson code.\n",
        "\n",
        " - This means that if you haven't followed along already, type out the things that we did in class. Forcing your fingers to hit each key will help you internalize the syntax of what we're doing. Make sure you understand each line of code that you're writing, google things that you don't fully understand.\n",
        " - [Lambda Learning Method for DS - By Ryan Herr](https://docs.google.com/document/d/1ubOw9B3Hfip27hF2ZFnW3a3z9xAgrUDRReOEo-FHCVs/edit?usp=sharing)\n",
        "- Convert the `term` column from string to integer.\n",
        "- Make a column named `loan_status_is_great`. It should contain the integer 1 if `loan_status` is \"Current\" or \"Fully Paid.\" Else it should contain the integer 0.\n",
        "- Make `last_pymnt_d_month` and `last_pymnt_d_year` columns."
      ]
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    {
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      "source": [
        "##### Begin Working Here #####\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "metadata": {
        "id": "dEiYehKsNBZs",
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      "source": [
        "source_url = 'https://raw.githubusercontent.com/ryanleeallred/datasets/master/Ames%20Housing%20Data/train.csv'"
      ],
      "execution_count": 0,
      "outputs": []
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      "metadata": {
        "id": "HppRBdB_NDXI",
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      "source": [
        "iowa = pd.read_csv(source_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "source": [
        "iowa.sample(5)"
      ],
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      "outputs": [
        {
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              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1961</td>\n",
              "      <td>1961</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>WdShing</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>BrkCmn</td>\n",
              "      <td>491.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>288</td>\n",
              "      <td>Rec</td>\n",
              "      <td>374</td>\n",
              "      <td>700</td>\n",
              "      <td>1362</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1362</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1362</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1961.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>2</td>\n",
              "      <td>504</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "      <td>COD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>163000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "764    765         120       RL  ...        WD         Normal    270000\n",
              "492    493          60       RL  ...       New        Partial    172785\n",
              "1022  1023          50       RM  ...        WD         Normal     87000\n",
              "950    951          20       RL  ...        WD         Normal    129000\n",
              "173    174          20       RL  ...       COD         Normal    163000\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AWIC_lGNDcd",
        "colab_type": "code",
        "outputId": "133b3b7d-8ed3-4289-fc39-5a76e0563742",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "iowa.shape    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 81)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zYBwjf-NDe5",
        "colab_type": "code",
        "outputId": "4ee6bdb6-ac12-4e17-f0ad-1addae904526",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "iowa[['YearBuilt', 'MSSubClass']].describe()\n"
      ],
      "execution_count": 0,
      "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>YearBuilt</th>\n",
              "      <th>MSSubClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1971.267808</td>\n",
              "      <td>56.897260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>30.202904</td>\n",
              "      <td>42.300571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1872.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1954.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1973.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2010.000000</td>\n",
              "      <td>190.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         YearBuilt   MSSubClass\n",
              "count  1460.000000  1460.000000\n",
              "mean   1971.267808    56.897260\n",
              "std      30.202904    42.300571\n",
              "min    1872.000000    20.000000\n",
              "25%    1954.000000    20.000000\n",
              "50%    1973.000000    50.000000\n",
              "75%    2000.000000    70.000000\n",
              "max    2010.000000   190.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Hyj19oKNDhc",
        "colab_type": "code",
        "outputId": "06fc4847-8864-497a-92d1-1c21bab447b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "pd.set_option('display.max_rows', 100)\n",
        "iowa.dtypes[:4]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               int64\n",
              "MSSubClass       int64\n",
              "MSZoning        object\n",
              "LotFrontage    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ib5-PCRTNDkA",
        "colab_type": "code",
        "outputId": "645472af-d862-4e23-c6de-4ca91a163da4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "iowa['BedroomAbvGr'].dtype"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wklQunLeNDmi",
        "colab_type": "code",
        "outputId": "0ef063ac-e008-43a8-f262-8fee8b9cbca0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "iowa.Fireplaces.sample(4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1133    1\n",
              "649     0\n",
              "378     1\n",
              "1026    2\n",
              "Name: Fireplaces, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AnsQIYZyNDpI",
        "colab_type": "code",
        "outputId": "bbe3ee77-c07e-4ca6-bd06-9b25d5917c2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "iowa['Fireplaces'].sample(4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "965     1\n",
              "1366    0\n",
              "404     1\n",
              "647     2\n",
              "Name: Fireplaces, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yF7-yOb3NDr2",
        "colab_type": "code",
        "outputId": "eb37fb96-cfde-45d1-edda-2dd4ce2ff86f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(iowa.shape)\n",
        "iowa['LotFrontage'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1460, 81)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "259"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRwZintBNDuq",
        "colab_type": "code",
        "outputId": "b0d5f73b-f9af-4d00-e6b7-806ac790e8ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "iowa['LotFrontage'].dropna(inplace=True)\n",
        "print(iowa.shape)\n",
        "iowa['LotFrontage'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1460, 81)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLWc5_R9NDxQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iowa.dropna(subset=['LotFrontage'], inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPmc9uP-NDz6",
        "colab_type": "code",
        "outputId": "06862c1b-0c35-4c5b-fabc-64e20a0b4548",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(iowa.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1201, 81)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GXPzSHoND2y",
        "colab_type": "code",
        "outputId": "b256e385-a36c-458f-ec4c-82520f69f42c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "iowa['LotFrontage'].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    65.0\n",
              "1    80.0\n",
              "2    68.0\n",
              "3    60.0\n",
              "4    84.0\n",
              "Name: LotFrontage, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3u_eMpBiND5j",
        "colab_type": "code",
        "outputId": "d16715b9-bc92-4624-ac13-9b79e4d9299f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "iowa['LotFrontage'].value_counts(dropna=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60.0     143\n",
              "70.0      70\n",
              "80.0      69\n",
              "50.0      57\n",
              "75.0      53\n",
              "        ... \n",
              "106.0      1\n",
              "38.0       1\n",
              "138.0      1\n",
              "140.0      1\n",
              "137.0      1\n",
              "Name: LotFrontage, Length: 110, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbLGb5JpND8Y",
        "colab_type": "code",
        "outputId": "57d0ec1a-5f96-4bbe-9fd1-5e3329d8e73e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "np.nan"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nan"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhzP4SF_ND_N",
        "colab_type": "code",
        "outputId": "d9801c39-3b31-4f12-ec85-5c9715ba2c4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(np.nan)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oo1IjaavNEB9",
        "colab_type": "code",
        "outputId": "654bd537-7cb8-4e1e-9786-71b1f2696169",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "iowa['LotFrontage'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wwR6WguNEE8",
        "colab_type": "code",
        "outputId": "62eecdb4-4860-4e10-db04-35b9d99eb985",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "iowa.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1201, 81)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhF4ipj6NEHt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iowa['LotFrontage'].dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bldzB7NsNEK3",
        "colab_type": "code",
        "outputId": "62053062-97ef-4492-a5cb-1a0fe9e4cde4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "iowa['LotFrontage'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8abkk0hNEN7",
        "colab_type": "code",
        "outputId": "5a707f1a-f695-471b-c469-1531fd1ec2d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "small=iowa[['TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'SalePrice']].copy()\n",
        "small.sample(3)"
      ],
      "execution_count": 0,
      "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>TotalBsmtSF</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>659</th>\n",
              "      <td>1486</td>\n",
              "      <td>1486</td>\n",
              "      <td>0</td>\n",
              "      <td>167000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>796</th>\n",
              "      <td>660</td>\n",
              "      <td>1285</td>\n",
              "      <td>0</td>\n",
              "      <td>143500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1040</th>\n",
              "      <td>1134</td>\n",
              "      <td>1803</td>\n",
              "      <td>0</td>\n",
              "      <td>155000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      TotalBsmtSF  1stFlrSF  2ndFlrSF  SalePrice\n",
              "659          1486      1486         0     167000\n",
              "796           660      1285         0     143500\n",
              "1040         1134      1803         0     155000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYHWfgTiNEQ_",
        "colab_type": "code",
        "outputId": "0595baed-7804-423f-ef11-81d5d8253731",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "small['TotalSF'] = small['TotalBsmtSF'] + small['1stFlrSF'] + small['2ndFlrSF']\n",
        "small.head()"
      ],
      "execution_count": 0,
      "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>TotalBsmtSF</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>SalePrice</th>\n",
              "      <th>TotalSF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>856</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>208500</td>\n",
              "      <td>2566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1262</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>181500</td>\n",
              "      <td>2524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>920</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>223500</td>\n",
              "      <td>2706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>756</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>140000</td>\n",
              "      <td>2473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1145</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>250000</td>\n",
              "      <td>3343</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   TotalBsmtSF  1stFlrSF  2ndFlrSF  SalePrice  TotalSF\n",
              "0          856       856       854     208500     2566\n",
              "1         1262      1262         0     181500     2524\n",
              "2          920       920       866     223500     2706\n",
              "3          756       961       756     140000     2473\n",
              "4         1145      1145      1053     250000     3343"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHSzlLEuRewL",
        "colab_type": "code",
        "outputId": "30835d6f-f5b0-4024-f0ea-6d581b7634de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "small['PricePerSF'] = small['SalePrice'] / small['TotalSF']\n",
        "small.sample(5)"
      ],
      "execution_count": 0,
      "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",
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              "    }\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>TotalBsmtSF</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>SalePrice</th>\n",
              "      <th>TotalSF</th>\n",
              "      <th>PricePerSF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1283</th>\n",
              "      <td>912</td>\n",
              "      <td>912</td>\n",
              "      <td>912</td>\n",
              "      <td>139000</td>\n",
              "      <td>2736</td>\n",
              "      <td>50.804094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1063</th>\n",
              "      <td>1103</td>\n",
              "      <td>1103</td>\n",
              "      <td>0</td>\n",
              "      <td>110500</td>\n",
              "      <td>2206</td>\n",
              "      <td>50.090662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841</th>\n",
              "      <td>650</td>\n",
              "      <td>958</td>\n",
              "      <td>581</td>\n",
              "      <td>157500</td>\n",
              "      <td>2189</td>\n",
              "      <td>71.950662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>960</td>\n",
              "      <td>960</td>\n",
              "      <td>1000</td>\n",
              "      <td>124500</td>\n",
              "      <td>2920</td>\n",
              "      <td>42.636986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>1215</td>\n",
              "      <td>1215</td>\n",
              "      <td>0</td>\n",
              "      <td>155000</td>\n",
              "      <td>2430</td>\n",
              "      <td>63.786008</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      TotalBsmtSF  1stFlrSF  2ndFlrSF  SalePrice  TotalSF  PricePerSF\n",
              "1283          912       912       912     139000     2736   50.804094\n",
              "1063         1103      1103         0     110500     2206   50.090662\n",
              "841           650       958       581     157500     2189   71.950662\n",
              "505           960       960      1000     124500     2920   42.636986\n",
              "339          1215      1215         0     155000     2430   63.786008"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fd0JYafJRezB",
        "colab_type": "code",
        "outputId": "4373fdc5-5135-4225-e474-57fc5752eb89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "iowa.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
              "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
              "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
              "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
              "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
              "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
              "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
              "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
              "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
              "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
              "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
              "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
              "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
              "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
              "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
              "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
              "       'SaleCondition', 'SalePrice'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMOXncwFRe2D",
        "colab_type": "code",
        "outputId": "0cf6ff4c-b79d-4429-cc2c-edd53d02e922",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "iowa[['LotShape', 'Alley', 'Street', 'SaleCondition']].sample(5)"
      ],
      "execution_count": 0,
      "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>LotShape</th>\n",
              "      <th>Alley</th>\n",
              "      <th>Street</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>Reg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Reg</td>\n",
              "      <td>Grvl</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>834</th>\n",
              "      <td>Reg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Reg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>Reg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    LotShape Alley Street SaleCondition\n",
              "333      Reg   NaN   Pave        Normal\n",
              "21       Reg  Grvl   Pave        Normal\n",
              "834      Reg   NaN   Pave        Normal\n",
              "25       Reg   NaN   Pave        Normal\n",
              "275      Reg   NaN   Pave        Normal"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-l1YeeK8Re4k",
        "colab_type": "code",
        "outputId": "090b234c-be1b-485f-c0a9-d183da943a31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "iowa['SaleCondition'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Normal     961\n",
              "Partial    124\n",
              "Abnorml     84\n",
              "Family      18\n",
              "Alloca      10\n",
              "AdjLand      4\n",
              "Name: SaleCondition, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5jHzMfyRe7a",
        "colab_type": "code",
        "outputId": "602baaa2-ce54-42ff-f057-5d137e5124b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "iowa['NormalCondition'] = np.where(iowa['SaleCondition']=='Normal', 1, 0)\n",
        "iowa['NormalCondition'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    961\n",
              "0    240\n",
              "Name: NormalCondition, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBh9cs2ZRe-A",
        "colab_type": "code",
        "outputId": "35d9b3bc-5034-414b-8f0f-50f72d5137ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "iowa['LotShape'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Reg    851\n",
              "IR1    317\n",
              "IR2     26\n",
              "IR3      7\n",
              "Name: LotShape, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioLjvQ8aRfBL",
        "colab_type": "code",
        "outputId": "b3be65a7-ebb3-44c8-dec4-ff3b1d1543f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "iowa['LotShape3'] = iowa['LotShape'].map({'Reg':'Regular',\n",
        "                                          'IR1':'Irregular',\n",
        "                                          'IR2':'Other',\n",
        "                                          'IR3':'Other'})\n",
        "iowa['LotShape3'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Regular      851\n",
              "Irregular    317\n",
              "Other         33\n",
              "Name: LotShape3, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMR4GzKHRfEW",
        "colab_type": "code",
        "outputId": "3c7b8301-9f70-459b-9b45-cf8d76afaafd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "iowa['Neighborhood'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NAmes      186\n",
              "CollgCr    126\n",
              "OldTown    109\n",
              "Edwards     92\n",
              "Somerst     78\n",
              "NridgHt     76\n",
              "BrkSide     51\n",
              "SawyerW     50\n",
              "Gilbert     49\n",
              "Sawyer      48\n",
              "NWAmes      45\n",
              "Crawfor     41\n",
              "Mitchel     36\n",
              "IDOTRR      34\n",
              "NoRidge     33\n",
              "Timber      30\n",
              "SWISU       23\n",
              "StoneBr     20\n",
              "BrDale      16\n",
              "MeadowV     15\n",
              "Blmngtn     14\n",
              "ClearCr     13\n",
              "Veenker      7\n",
              "NPkVill      7\n",
              "Blueste      2\n",
              "Name: Neighborhood, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPy2AkWARfGD",
        "colab_type": "code",
        "outputId": "ee202796-8ae6-48cb-d8e5-f2f8d2f08a7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "iowa['top_neighbors']='All Others'\n",
        "iowa[['top_neighbors', 'Neighborhood']].head()"
      ],
      "execution_count": 0,
      "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>top_neighbors</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>All Others</td>\n",
              "      <td>CollgCr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>All Others</td>\n",
              "      <td>Veenker</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>All Others</td>\n",
              "      <td>CollgCr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>All Others</td>\n",
              "      <td>Crawfor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>All Others</td>\n",
              "      <td>NoRidge</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  top_neighbors Neighborhood\n",
              "0    All Others      CollgCr\n",
              "1    All Others      Veenker\n",
              "2    All Others      CollgCr\n",
              "3    All Others      Crawfor\n",
              "4    All Others      NoRidge"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjT_rxeCRfIc",
        "colab_type": "code",
        "outputId": "3a13feca-ccc5-4a64-9264-68ab61c17f92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "iowa.loc[iowa['Neighborhood']=='NAmes', 'top_neighbors']='North Ames'\n",
        "iowa[['top_neighbors', 'Neighborhood']].sample(10)"
      ],
      "execution_count": 0,
      "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",
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              "    }\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>top_neighbors</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1197</th>\n",
              "      <td>All Others</td>\n",
              "      <td>OldTown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>All Others</td>\n",
              "      <td>OldTown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>All Others</td>\n",
              "      <td>ClearCr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>909</th>\n",
              "      <td>All Others</td>\n",
              "      <td>Gilbert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>552</th>\n",
              "      <td>All Others</td>\n",
              "      <td>NridgHt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>All Others</td>\n",
              "      <td>CollgCr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1076</th>\n",
              "      <td>All Others</td>\n",
              "      <td>OldTown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1209</th>\n",
              "      <td>All Others</td>\n",
              "      <td>Somerst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>992</th>\n",
              "      <td>North Ames</td>\n",
              "      <td>NAmes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>All Others</td>\n",
              "      <td>CollgCr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     top_neighbors Neighborhood\n",
              "1197    All Others      OldTown\n",
              "202     All Others      OldTown\n",
              "279     All Others      ClearCr\n",
              "909     All Others      Gilbert\n",
              "552     All Others      NridgHt\n",
              "435     All Others      CollgCr\n",
              "1076    All Others      OldTown\n",
              "1209    All Others      Somerst\n",
              "992     North Ames        NAmes\n",
              "139     All Others      CollgCr"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mxm3xbL8RfK_",
        "colab_type": "code",
        "outputId": "8d1a36c0-41e2-4aee-8c74-fb33d10a0189",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "iowa.loc[iowa['Neighborhood']=='CollgCr', 'top_neighbors']='College Circle'\n",
        "iowa.loc[iowa['Neighborhood']=='Gilbert', 'top_neighbors']='Favorite Neighborhood'\n",
        "iowa.loc[iowa['Neighborhood']=='Somerst', 'top_neighbors']='Favorite Neighborhood'\n",
        "iowa[['top_neighbors', 'Neighborhood']].sample(10)"
      ],
      "execution_count": 0,
      "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>top_neighbors</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>All Others</td>\n",
              "      <td>BrkSide</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>All Others</td>\n",
              "      <td>Crawfor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1248</th>\n",
              "      <td>All Others</td>\n",
              "      <td>OldTown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1202</th>\n",
              "      <td>All Others</td>\n",
              "      <td>BrkSide</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>North Ames</td>\n",
              "      <td>NAmes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>North Ames</td>\n",
              "      <td>NAmes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>All Others</td>\n",
              "      <td>StoneBr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>College Circle</td>\n",
              "      <td>CollgCr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449</th>\n",
              "      <td>All Others</td>\n",
              "      <td>OldTown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>431</th>\n",
              "      <td>All Others</td>\n",
              "      <td>OldTown</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       top_neighbors Neighborhood\n",
              "1255      All Others      BrkSide\n",
              "190       All Others      Crawfor\n",
              "1248      All Others      OldTown\n",
              "1202      All Others      BrkSide\n",
              "813       North Ames        NAmes\n",
              "662       North Ames        NAmes\n",
              "474       All Others      StoneBr\n",
              "1021  College Circle      CollgCr\n",
              "449       All Others      OldTown\n",
              "431       All Others      OldTown"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87a0HCBtRfN_",
        "colab_type": "code",
        "outputId": "2cd210a5-73f6-461b-9c69-83a8b7493aa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "iowa['top_neighbors'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "All Others               762\n",
              "North Ames               186\n",
              "Favorite Neighborhood    127\n",
              "College Circle           126\n",
              "Name: top_neighbors, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7M_UM3bmRfQj",
        "colab_type": "code",
        "outputId": "66716475-421f-4ba8-9cc1-71a0aac7275b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "pd.crosstab(iowa['top_neighbors'], iowa['LotShape3'])"
      ],
      "execution_count": 0,
      "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>LotShape3</th>\n",
              "      <th>Irregular</th>\n",
              "      <th>Other</th>\n",
              "      <th>Regular</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top_neighbors</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>All Others</th>\n",
              "      <td>188</td>\n",
              "      <td>14</td>\n",
              "      <td>560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>College Circle</th>\n",
              "      <td>46</td>\n",
              "      <td>10</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Favorite Neighborhood</th>\n",
              "      <td>51</td>\n",
              "      <td>8</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North Ames</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "LotShape3              Irregular  Other  Regular\n",
              "top_neighbors                                   \n",
              "All Others                   188     14      560\n",
              "College Circle                46     10       70\n",
              "Favorite Neighborhood         51      8       68\n",
              "North Ames                    32      1      153"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1GT164HRfTY",
        "colab_type": "code",
        "outputId": "02a1d73d-552f-4158-fcd7-24981ed50d10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "pd.crosstab(iowa['top_neighbors'], iowa['LotShape3'], margins=True)"
      ],
      "execution_count": 0,
      "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>LotShape3</th>\n",
              "      <th>Irregular</th>\n",
              "      <th>Other</th>\n",
              "      <th>Regular</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top_neighbors</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>All Others</th>\n",
              "      <td>188</td>\n",
              "      <td>14</td>\n",
              "      <td>560</td>\n",
              "      <td>762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>College Circle</th>\n",
              "      <td>46</td>\n",
              "      <td>10</td>\n",
              "      <td>70</td>\n",
              "      <td>126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Favorite Neighborhood</th>\n",
              "      <td>51</td>\n",
              "      <td>8</td>\n",
              "      <td>68</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North Ames</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>153</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>317</td>\n",
              "      <td>33</td>\n",
              "      <td>851</td>\n",
              "      <td>1201</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "LotShape3              Irregular  Other  Regular   All\n",
              "top_neighbors                                         \n",
              "All Others                   188     14      560   762\n",
              "College Circle                46     10       70   126\n",
              "Favorite Neighborhood         51      8       68   127\n",
              "North Ames                    32      1      153   186\n",
              "All                          317     33      851  1201"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZMCbHraRfWA",
        "colab_type": "code",
        "outputId": "82ff01af-b58f-422d-bbe2-6102f36b0f6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "pd.crosstab(iowa['top_neighbors'], iowa['LotShape3'], normalize='index', margins=True)\n",
        "pd.crosstab(iowa['top_neighbors'], iowa['LotShape3'], normalize='columns', margins=True)"
      ],
      "execution_count": 0,
      "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>LotShape3</th>\n",
              "      <th>Irregular</th>\n",
              "      <th>Other</th>\n",
              "      <th>Regular</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top_neighbors</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>All Others</th>\n",
              "      <td>0.593060</td>\n",
              "      <td>0.424242</td>\n",
              "      <td>0.658049</td>\n",
              "      <td>0.634471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>College Circle</th>\n",
              "      <td>0.145110</td>\n",
              "      <td>0.303030</td>\n",
              "      <td>0.082256</td>\n",
              "      <td>0.104913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Favorite Neighborhood</th>\n",
              "      <td>0.160883</td>\n",
              "      <td>0.242424</td>\n",
              "      <td>0.079906</td>\n",
              "      <td>0.105745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North Ames</th>\n",
              "      <td>0.100946</td>\n",
              "      <td>0.030303</td>\n",
              "      <td>0.179788</td>\n",
              "      <td>0.154871</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "LotShape3              Irregular     Other   Regular       All\n",
              "top_neighbors                                                 \n",
              "All Others              0.593060  0.424242  0.658049  0.634471\n",
              "College Circle          0.145110  0.303030  0.082256  0.104913\n",
              "Favorite Neighborhood   0.160883  0.242424  0.079906  0.105745\n",
              "North Ames              0.100946  0.030303  0.179788  0.154871"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SF7YP4fS4e_",
        "colab_type": "code",
        "outputId": "01a87f7f-dc21-41fa-f963-bef0a5c58f5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "source": [
        "myresults = pd.crosstab(iowa['top_neighbors'], iowa['LotShape3'])\n",
        "myresults.plot(kind='bar');"
      ],
      "execution_count": 0,
      "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": "code",
      "metadata": {
        "id": "-7mMA5IHS4oF",
        "colab_type": "code",
        "outputId": "3bb0e207-2b70-433d-cd36-bc88f9754ba0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "pd.crosstab(iowa['OverallQual'], iowa['top_neighbors'])"
      ],
      "execution_count": 0,
      "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>top_neighbors</th>\n",
              "      <th>All Others</th>\n",
              "      <th>College Circle</th>\n",
              "      <th>Favorite Neighborhood</th>\n",
              "      <th>North Ames</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallQual</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>210</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>179</td>\n",
              "      <td>14</td>\n",
              "      <td>26</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>131</td>\n",
              "      <td>68</td>\n",
              "      <td>59</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>90</td>\n",
              "      <td>16</td>\n",
              "      <td>33</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>35</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "top_neighbors  All Others  College Circle  Favorite Neighborhood  North Ames\n",
              "OverallQual                                                                 \n",
              "1                       1               0                      0           0\n",
              "2                       3               0                      0           0\n",
              "3                      15               0                      0           2\n",
              "4                      82               1                      1          14\n",
              "5                     210              25                      2          96\n",
              "6                     179              14                     26          63\n",
              "7                     131              68                     59           8\n",
              "8                      90              16                     33           3\n",
              "9                      35               2                      5           0\n",
              "10                     16               0                      1           0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRH_1tMrS4q0",
        "colab_type": "code",
        "outputId": "1fa09f38-74e9-4c9e-d29c-838678bfafca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "myresults.plot(kind='barh');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olEd45tRS4tw",
        "colab_type": "code",
        "outputId": "1a388ba1-ecee-4f18-a73e-6b09375cd605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "! wget https://resources.lendingclub.com/LoanStats_2018Q4.csv.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-15 03:07:25--  https://resources.lendingclub.com/LoanStats_2018Q4.csv.zip\n",
            "Resolving resources.lendingclub.com (resources.lendingclub.com)... 52.38.18.179, 54.71.74.4, 54.213.209.4\n",
            "Connecting to resources.lendingclub.com (resources.lendingclub.com)|52.38.18.179|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/zip]\n",
            "Saving to: ‘LoanStats_2018Q4.csv.zip’\n",
            "\n",
            "LoanStats_2018Q4.cs     [           <=>      ]  22.05M  1.96MB/s    in 12s     \n",
            "\n",
            "2020-05-15 03:07:37 (1.90 MB/s) - ‘LoanStats_2018Q4.csv.zip’ saved [23121687]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xs1Nvw3wS4wp",
        "colab_type": "code",
        "outputId": "150e6e57-14fb-4809-b3c6-0b16c9c89f77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!unzip LoanStats_2018Q4.csv.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  LoanStats_2018Q4.csv.zip\n",
            "  inflating: LoanStats_2018Q4.csv    \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynlN7pOlS4zW",
        "colab_type": "code",
        "outputId": "cf4d6fdc-7859-4f98-a56e-3177245f9070",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "source": [
        "loans = pd.read_csv('LoanStats_2018Q4.csv')\n",
        "loans.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (0,1,2,3,4,7,13,18,19,24,25,27,28,29,30,31,32,34,36,37,38,39,40,41,42,43,44,46,49,50,51,53,54,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,113,114,115,116,117,118,119,120,121,126,127,131,132,134,135,136,141,142,143) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Notes offered by Prospectus (https://www.lendingclub.com/info/prospectus.action)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">NaN</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">NaN</th>\n",
              "      <th>5000</th>\n",
              "      <th>5000</th>\n",
              "      <th>5000</th>\n",
              "      <th>36 months</th>\n",
              "      <th>13.56%</th>\n",
              "      <th>169.83</th>\n",
              "      <th>C</th>\n",
              "      <th>C1</th>\n",
              "      <th>Payoff Clerk</th>\n",
              "      <th>10+ years</th>\n",
              "      <th>MORTGAGE</th>\n",
              "      <th>35360.0</th>\n",
              "      <th>Not Verified</th>\n",
              "      <th>Oct-2018</th>\n",
              "      <th>Current</th>\n",
              "      <th>n</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>debt_consolidation</th>\n",
              "      <th>Debt consolidation</th>\n",
              "      <th>381xx</th>\n",
              "      <th>TN</th>\n",
              "      <th>11.3</th>\n",
              "      <th>1</th>\n",
              "      <th>Jun-2006</th>\n",
              "      <th>0</th>\n",
              "      <th>21.0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>9</th>\n",
              "      <th>0</th>\n",
              "      <th>2597</th>\n",
              "      <th>27.3%</th>\n",
              "      <th>15</th>\n",
              "      <th>f</th>\n",
              "      <th>2751.79</th>\n",
              "      <th>2751.79</th>\n",
              "      <th>3053.17</th>\n",
              "      <th>3053.17</th>\n",
              "      <th>2248.21</th>\n",
              "      <th>804.96</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>Apr-2020</th>\n",
              "      <th>169.83</th>\n",
              "      <th>May-2020</th>\n",
              "      <th>Apr-2020</th>\n",
              "      <th>0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>1</th>\n",
              "      <th>Individual</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>0</th>\n",
              "      <th>1413</th>\n",
              "      <th>69785</th>\n",
              "      <th>0</th>\n",
              "      <th>2</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>16.0</th>\n",
              "      <th>2379</th>\n",
              "      <th>40.0</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>1826</th>\n",
              "      <th>32.0</th>\n",
              "      <th>9500</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>5</th>\n",
              "      <th>8723</th>\n",
              "      <th>1174.0</th>\n",
              "      <th>60.9</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>147.0</th>\n",
              "      <th>85</th>\n",
              "      <th>9</th>\n",
              "      <th>9</th>\n",
              "      <th>2</th>\n",
              "      <th>10.0</th>\n",
              "      <th>21.0</th>\n",
              "      <th>9.0</th>\n",
              "      <th>21.0</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>3</th>\n",
              "      <th>2</th>\n",
              "      <th>2</th>\n",
              "      <th>6</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>3</th>\n",
              "      <th>9</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>3</th>\n",
              "      <th>92.9</th>\n",
              "      <th>50.0</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>93908</th>\n",
              "      <th>4976</th>\n",
              "      <th>3000</th>\n",
              "      <th>6028.0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>N</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>N</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">10000</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">10000</th>\n",
              "      <th>9750</th>\n",
              "      <th>36 months</th>\n",
              "      <th>11.06%</th>\n",
              "      <th>327.68</th>\n",
              "      <th>B</th>\n",
              "      <th>B3</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>RENT</th>\n",
              "      <th>44400.0</th>\n",
              "      <th>Source Verified</th>\n",
              "      <th>Oct-2018</th>\n",
              "      <th>Current</th>\n",
              "      <th>n</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>credit_card</th>\n",
              "      <th>Credit card refinancing</th>\n",
              "      <th>980xx</th>\n",
              "      <th>WA</th>\n",
              "      <th>11.78</th>\n",
              "      <th>0</th>\n",
              "      <th>Oct-2008</th>\n",
              "      <th>2</th>\n",
              "      <th>40.0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>15</th>\n",
              "      <th>0</th>\n",
              "      <th>6269</th>\n",
              "      <th>13.1%</th>\n",
              "      <th>25</th>\n",
              "      <th>f</th>\n",
              "      <th>5411.74</th>\n",
              "      <th>5276.45</th>\n",
              "      <th>5882.88</th>\n",
              "      <th>5735.81</th>\n",
              "      <th>4588.26</th>\n",
              "      <th>1294.62</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>Apr-2020</th>\n",
              "      <th>327.68</th>\n",
              "      <th>May-2020</th>\n",
              "      <th>Apr-2020</th>\n",
              "      <th>0</th>\n",
              "      <th>53.0</th>\n",
              "      <th>1</th>\n",
              "      <th>Individual</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>0</th>\n",
              "      <th>520</th>\n",
              "      <th>16440</th>\n",
              "      <th>3</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <th>2.0</th>\n",
              "      <th>10171</th>\n",
              "      <th>100.0</th>\n",
              "      <th>2</th>\n",
              "      <th>5</th>\n",
              "      <th>404</th>\n",
              "      <th>28.0</th>\n",
              "      <th>47700</th>\n",
              "      <th>0</th>\n",
              "      <th>3</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>1265.0</th>\n",
              "      <th>20037.0</th>\n",
              "      <th>2.3</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>61.0</th>\n",
              "      <th>119</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>1.0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>1.0</th>\n",
              "      <th>40.0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>4</th>\n",
              "      <th>6</th>\n",
              "      <th>8</th>\n",
              "      <th>3</th>\n",
              "      <th>14</th>\n",
              "      <th>22</th>\n",
              "      <th>4</th>\n",
              "      <th>15</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>3</th>\n",
              "      <th>92.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>57871.0</th>\n",
              "      <th>16440</th>\n",
              "      <th>20500</th>\n",
              "      <th>10171.0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>N</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>N</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10000</th>\n",
              "      <th>36 months</th>\n",
              "      <th>16.91%</th>\n",
              "      <th>356.08</th>\n",
              "      <th>C</th>\n",
              "      <th>C5</th>\n",
              "      <th>Key Accounts Manager</th>\n",
              "      <th>2 years</th>\n",
              "      <th>RENT</th>\n",
              "      <th>80000.0</th>\n",
              "      <th>Not Verified</th>\n",
              "      <th>Oct-2018</th>\n",
              "      <th>Current</th>\n",
              "      <th>n</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>other</th>\n",
              "      <th>Other</th>\n",
              "      <th>021xx</th>\n",
              "      <th>MA</th>\n",
              "      <th>17.72</th>\n",
              "      <th>1</th>\n",
              "      <th>Sep-2006</th>\n",
              "      <th>0</th>\n",
              "      <th>14.0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>17</th>\n",
              "      <th>0</th>\n",
              "      <th>1942</th>\n",
              "      <th>30.8%</th>\n",
              "      <th>31</th>\n",
              "      <th>w</th>\n",
              "      <th>5626.39</th>\n",
              "      <th>5626.39</th>\n",
              "      <th>6400.05</th>\n",
              "      <th>6400.05</th>\n",
              "      <th>4373.61</th>\n",
              "      <th>2026.44</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>Apr-2020</th>\n",
              "      <th>356.08</th>\n",
              "      <th>May-2020</th>\n",
              "      <th>Apr-2020</th>\n",
              "      <th>0</th>\n",
              "      <th>25.0</th>\n",
              "      <th>1</th>\n",
              "      <th>Individual</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>59194</th>\n",
              "      <th>0</th>\n",
              "      <th>15</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <th>12.0</th>\n",
              "      <th>57252.0</th>\n",
              "      <th>85.0</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>1942</th>\n",
              "      <th>80.0</th>\n",
              "      <th>6300</th>\n",
              "      <th>0</th>\n",
              "      <th>5</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>3482</th>\n",
              "      <th>2058.0</th>\n",
              "      <th>48.5</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>144.0</th>\n",
              "      <th>142</th>\n",
              "      <th>40</th>\n",
              "      <th>12</th>\n",
              "      <th>0</th>\n",
              "      <th>131.0</th>\n",
              "      <th>30.0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>30.0</th>\n",
              "      <th>3</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <th>5</th>\n",
              "      <th>22</th>\n",
              "      <th>2</th>\n",
              "      <th>9</th>\n",
              "      <th>1</th>\n",
              "      <th>17</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>74.2</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <th>73669</th>\n",
              "      <th>59194</th>\n",
              "      <th>4000</th>\n",
              "      <th>67369</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>N</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>N</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total amount funded in policy code 1: 2050909275</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total amount funded in policy code 2: 820109297</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <th>NaN</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Notes offered by Prospectus (https://www.lendingclub.com/info/prospectus.action)\n",
              "NaN                                              NaN 5000  5000  5000   36 months  13.56% 169.83 C   C1  Payoff Clerk         10+ years MORTGAGE 35360.0 Not Verified    Oct-2018 Current n   NaN NaN debt_consolidation Debt consolidation      381xx TN  11.3  1   Jun-2006 0   21.0 NaN 9   0   2597 27.3% 15  f   2751.79 2751.79 3053.17 3053.17 2248.21 804.96  0.0 0.0 0.0 Apr-2020 169.83 May-2020 Apr-2020 0   NaN  1   Individual NaN NaN NaN 0   1413 69785 0   2   0   1   16.0 2379    40.0  3   4   1826 32.0 9500  0   0   1   5   8723   1174.0  60.9 0   0   147.0 85  9   9   2   10.0  21.0 9.0 21.0 0   1   3   2   2   6   6   7   3   9   0.0 0   0   3   92.9 50.0 0   0   93908   4976  3000  6028.0  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN                                                NaN                              \n",
              "                                                     10000 10000 9750   36 months  11.06% 327.68 B   B3  NaN                  NaN       RENT     44400.0 Source Verified Oct-2018 Current n   NaN NaN credit_card        Credit card refinancing 980xx WA  11.78 0   Oct-2008 2   40.0 NaN 15  0   6269 13.1% 25  f   5411.74 5276.45 5882.88 5735.81 4588.26 1294.62 0.0 0.0 0.0 Apr-2020 327.68 May-2020 Apr-2020 0   53.0 1   Individual NaN NaN NaN 0   520  16440 3   1   1   1   2.0  10171   100.0 2   5   404  28.0 47700 0   3   5   6   1265.0 20037.0 2.3  0   0   61.0  119 1   1   0   1.0   NaN  1.0 40.0 1   2   4   6   8   3   14  22  4   15  0.0 0   0   3   92.0 0.0  0   0   57871.0 16440 20500 10171.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN                                                NaN                              \n",
              "                                                                 10000  36 months  16.91% 356.08 C   C5  Key Accounts Manager 2 years   RENT     80000.0 Not Verified    Oct-2018 Current n   NaN NaN other              Other                   021xx MA  17.72 1   Sep-2006 0   14.0 NaN 17  0   1942 30.8% 31  w   5626.39 5626.39 6400.05 6400.05 4373.61 2026.44 0.0 0.0 0.0 Apr-2020 356.08 May-2020 Apr-2020 0   25.0 1   Individual NaN NaN NaN 0   0    59194 0   15  1   1   12.0 57252.0 85.0  0   0   1942 80.0 6300  0   5   0   1   3482   2058.0  48.5 0   0   144.0 142 40  12  0   131.0 30.0 NaN 30.0 3   1   1   1   5   22  2   9   1   17  0.0 0   0   1   74.2 0.0  0   0   73669   59194 4000  67369   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN                                                NaN                              \n",
              "Total amount funded in policy code 1: 2050909275 NaN NaN   NaN   NaN   NaN        NaN     NaN    NaN NaN NaN                  NaN       NaN      NaN     NaN             NaN      NaN     NaN NaN NaN NaN                NaN                     NaN   NaN NaN   NaN NaN      NaN NaN  NaN NaN NaN NaN  NaN   NaN NaN NaN     NaN     NaN     NaN     NaN     NaN     NaN NaN NaN NaN      NaN    NaN      NaN      NaN NaN  NaN NaN        NaN NaN NaN NaN NaN  NaN   NaN NaN NaN NaN NaN  NaN     NaN   NaN NaN NaN  NaN  NaN   NaN NaN NaN NaN NaN    NaN     NaN  NaN NaN NaN   NaN NaN NaN NaN NaN   NaN  NaN NaN  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  NaN  NaN NaN NaN     NaN   NaN   NaN     NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN                                                NaN                              \n",
              "Total amount funded in policy code 2: 820109297  NaN NaN   NaN   NaN   NaN        NaN     NaN    NaN NaN NaN                  NaN       NaN      NaN     NaN             NaN      NaN     NaN NaN NaN NaN                NaN                     NaN   NaN NaN   NaN NaN      NaN NaN  NaN NaN NaN NaN  NaN   NaN NaN NaN     NaN     NaN     NaN     NaN     NaN     NaN NaN NaN NaN      NaN    NaN      NaN      NaN NaN  NaN NaN        NaN NaN NaN NaN NaN  NaN   NaN NaN NaN NaN NaN  NaN     NaN   NaN NaN NaN  NaN  NaN   NaN NaN NaN NaN NaN    NaN     NaN  NaN NaN NaN   NaN NaN NaN NaN NaN   NaN  NaN NaN  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  NaN  NaN NaN NaN     NaN   NaN   NaN     NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN                                                NaN                              "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvaSFFjSS42h",
        "colab_type": "code",
        "outputId": "c7b39d90-aeed-4d08-9023-4d98da0e61ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "source": [
        "loans = pd.read_csv('LoanStats_2018Q4.csv', header=1, skipfooter=2)\n",
        "loans.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "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>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
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              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pymnt_plan</th>\n",
              "      <th>url</th>\n",
              "      <th>desc</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>mths_since_last_delinq</th>\n",
              "      <th>mths_since_last_record</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>...</th>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>revol_bal_joint</th>\n",
              "      <th>sec_app_earliest_cr_line</th>\n",
              "      <th>sec_app_inq_last_6mths</th>\n",
              "      <th>sec_app_mort_acc</th>\n",
              "      <th>sec_app_open_acc</th>\n",
              "      <th>sec_app_revol_util</th>\n",
              "      <th>sec_app_open_act_il</th>\n",
              "      <th>sec_app_num_rev_accts</th>\n",
              "      <th>sec_app_chargeoff_within_12_mths</th>\n",
              "      <th>sec_app_collections_12_mths_ex_med</th>\n",
              "      <th>sec_app_mths_since_last_major_derog</th>\n",
              "      <th>hardship_flag</th>\n",
              "      <th>hardship_type</th>\n",
              "      <th>hardship_reason</th>\n",
              "      <th>hardship_status</th>\n",
              "      <th>deferral_term</th>\n",
              "      <th>hardship_amount</th>\n",
              "      <th>hardship_start_date</th>\n",
              "      <th>hardship_end_date</th>\n",
              "      <th>payment_plan_start_date</th>\n",
              "      <th>hardship_length</th>\n",
              "      <th>hardship_dpd</th>\n",
              "      <th>hardship_loan_status</th>\n",
              "      <th>orig_projected_additional_accrued_interest</th>\n",
              "      <th>hardship_payoff_balance_amount</th>\n",
              "      <th>hardship_last_payment_amount</th>\n",
              "      <th>debt_settlement_flag</th>\n",
              "      <th>debt_settlement_flag_date</th>\n",
              "      <th>settlement_status</th>\n",
              "      <th>settlement_date</th>\n",
              "      <th>settlement_amount</th>\n",
              "      <th>settlement_percentage</th>\n",
              "      <th>settlement_term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>16.14%</td>\n",
              "      <td>243.93</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>Sale Consultant</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>079xx</td>\n",
              "      <td>NJ</td>\n",
              "      <td>8.80</td>\n",
              "      <td>0</td>\n",
              "      <td>Mar-1991</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>10582</td>\n",
              "      <td>58.5%</td>\n",
              "      <td>32</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10661.057727</td>\n",
              "      <td>10661.06</td>\n",
              "      <td>...</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>25600</td>\n",
              "      <td>15470</td>\n",
              "      <td>15000</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6500</td>\n",
              "      <td>6500</td>\n",
              "      <td>6500.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.80%</td>\n",
              "      <td>215.28</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Educator</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>46500.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>352xx</td>\n",
              "      <td>AL</td>\n",
              "      <td>21.01</td>\n",
              "      <td>0</td>\n",
              "      <td>Aug-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>6741</td>\n",
              "      <td>41.6%</td>\n",
              "      <td>30</td>\n",
              "      <td>w</td>\n",
              "      <td>4066.26</td>\n",
              "      <td>4066.26</td>\n",
              "      <td>3222.810000</td>\n",
              "      <td>3222.81</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50845</td>\n",
              "      <td>40223</td>\n",
              "      <td>13300</td>\n",
              "      <td>34645</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4500</td>\n",
              "      <td>4500</td>\n",
              "      <td>4500.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.31%</td>\n",
              "      <td>147.99</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Accounts Examiner III</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>38500.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>786xx</td>\n",
              "      <td>TX</td>\n",
              "      <td>4.64</td>\n",
              "      <td>0</td>\n",
              "      <td>Dec-2003</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>4472</td>\n",
              "      <td>15.3%</td>\n",
              "      <td>25</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4549.217149</td>\n",
              "      <td>4549.22</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53795</td>\n",
              "      <td>29137</td>\n",
              "      <td>15100</td>\n",
              "      <td>24595</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25000</td>\n",
              "      <td>25000</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>14.47%</td>\n",
              "      <td>587.82</td>\n",
              "      <td>C</td>\n",
              "      <td>C2</td>\n",
              "      <td>teacher</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>OWN</td>\n",
              "      <td>110000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>117xx</td>\n",
              "      <td>NY</td>\n",
              "      <td>26.43</td>\n",
              "      <td>1</td>\n",
              "      <td>Jan-1997</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>39053</td>\n",
              "      <td>45.7%</td>\n",
              "      <td>49</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5848.050000</td>\n",
              "      <td>5848.05</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>179321</td>\n",
              "      <td>95648</td>\n",
              "      <td>62800</td>\n",
              "      <td>91424</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>27.27%</td>\n",
              "      <td>920.91</td>\n",
              "      <td>E</td>\n",
              "      <td>E5</td>\n",
              "      <td>Claims Analyst</td>\n",
              "      <td>1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>857xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>23.62</td>\n",
              "      <td>0</td>\n",
              "      <td>Dec-1997</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1258</td>\n",
              "      <td>10.7%</td>\n",
              "      <td>16</td>\n",
              "      <td>w</td>\n",
              "      <td>25780.25</td>\n",
              "      <td>25780.25</td>\n",
              "      <td>13745.470000</td>\n",
              "      <td>13745.47</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46300</td>\n",
              "      <td>27364</td>\n",
              "      <td>10100</td>\n",
              "      <td>34500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 144 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  member_id  ...  settlement_percentage  settlement_term\n",
              "0 NaN        NaN  ...                    NaN              NaN\n",
              "1 NaN        NaN  ...                    NaN              NaN\n",
              "2 NaN        NaN  ...                    NaN              NaN\n",
              "3 NaN        NaN  ...                    NaN              NaN\n",
              "4 NaN        NaN  ...                    NaN              NaN\n",
              "\n",
              "[5 rows x 144 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgAmaqkPTbGm",
        "colab_type": "code",
        "outputId": "e68a3907-bb74-4e4c-a3a6-59bd65f17ed3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "loans.tail()"
      ],
      "execution_count": 0,
      "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",
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              "    }\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>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pymnt_plan</th>\n",
              "      <th>url</th>\n",
              "      <th>desc</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>mths_since_last_delinq</th>\n",
              "      <th>mths_since_last_record</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>...</th>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>revol_bal_joint</th>\n",
              "      <th>sec_app_earliest_cr_line</th>\n",
              "      <th>sec_app_inq_last_6mths</th>\n",
              "      <th>sec_app_mort_acc</th>\n",
              "      <th>sec_app_open_acc</th>\n",
              "      <th>sec_app_revol_util</th>\n",
              "      <th>sec_app_open_act_il</th>\n",
              "      <th>sec_app_num_rev_accts</th>\n",
              "      <th>sec_app_chargeoff_within_12_mths</th>\n",
              "      <th>sec_app_collections_12_mths_ex_med</th>\n",
              "      <th>sec_app_mths_since_last_major_derog</th>\n",
              "      <th>hardship_flag</th>\n",
              "      <th>hardship_type</th>\n",
              "      <th>hardship_reason</th>\n",
              "      <th>hardship_status</th>\n",
              "      <th>deferral_term</th>\n",
              "      <th>hardship_amount</th>\n",
              "      <th>hardship_start_date</th>\n",
              "      <th>hardship_end_date</th>\n",
              "      <th>payment_plan_start_date</th>\n",
              "      <th>hardship_length</th>\n",
              "      <th>hardship_dpd</th>\n",
              "      <th>hardship_loan_status</th>\n",
              "      <th>orig_projected_additional_accrued_interest</th>\n",
              "      <th>hardship_payoff_balance_amount</th>\n",
              "      <th>hardship_last_payment_amount</th>\n",
              "      <th>debt_settlement_flag</th>\n",
              "      <th>debt_settlement_flag_date</th>\n",
              "      <th>settlement_status</th>\n",
              "      <th>settlement_date</th>\n",
              "      <th>settlement_amount</th>\n",
              "      <th>settlement_percentage</th>\n",
              "      <th>settlement_term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>128407</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23000</td>\n",
              "      <td>23000</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.02%</td>\n",
              "      <td>797.53</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>Tax Consultant</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>75000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>352xx</td>\n",
              "      <td>AL</td>\n",
              "      <td>20.95</td>\n",
              "      <td>1</td>\n",
              "      <td>Aug-1985</td>\n",
              "      <td>2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>22465</td>\n",
              "      <td>43.6%</td>\n",
              "      <td>28</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1547.08</td>\n",
              "      <td>1547.08</td>\n",
              "      <td>...</td>\n",
              "      <td>14.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>296500</td>\n",
              "      <td>40614</td>\n",
              "      <td>47100</td>\n",
              "      <td>21000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128408</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.02%</td>\n",
              "      <td>346.76</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>security guard</td>\n",
              "      <td>5 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>38000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>443xx</td>\n",
              "      <td>OH</td>\n",
              "      <td>13.16</td>\n",
              "      <td>3</td>\n",
              "      <td>Jul-1982</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>5634</td>\n",
              "      <td>37.1%</td>\n",
              "      <td>16</td>\n",
              "      <td>w</td>\n",
              "      <td>5557.25</td>\n",
              "      <td>5557.25</td>\n",
              "      <td>6233.34</td>\n",
              "      <td>6233.34</td>\n",
              "      <td>...</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>91403</td>\n",
              "      <td>9323</td>\n",
              "      <td>9100</td>\n",
              "      <td>2000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128409</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.56%</td>\n",
              "      <td>169.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>Payoff Clerk</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>35360.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>381xx</td>\n",
              "      <td>TN</td>\n",
              "      <td>11.30</td>\n",
              "      <td>1</td>\n",
              "      <td>Jun-2006</td>\n",
              "      <td>0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2597</td>\n",
              "      <td>27.3%</td>\n",
              "      <td>15</td>\n",
              "      <td>f</td>\n",
              "      <td>2751.79</td>\n",
              "      <td>2751.79</td>\n",
              "      <td>3053.17</td>\n",
              "      <td>3053.17</td>\n",
              "      <td>...</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93908</td>\n",
              "      <td>4976</td>\n",
              "      <td>3000</td>\n",
              "      <td>6028</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128410</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>9750.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.06%</td>\n",
              "      <td>327.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RENT</td>\n",
              "      <td>44400.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>980xx</td>\n",
              "      <td>WA</td>\n",
              "      <td>11.78</td>\n",
              "      <td>0</td>\n",
              "      <td>Oct-2008</td>\n",
              "      <td>2</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>6269</td>\n",
              "      <td>13.1%</td>\n",
              "      <td>25</td>\n",
              "      <td>f</td>\n",
              "      <td>5411.74</td>\n",
              "      <td>5276.45</td>\n",
              "      <td>5882.88</td>\n",
              "      <td>5735.81</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>57871</td>\n",
              "      <td>16440</td>\n",
              "      <td>20500</td>\n",
              "      <td>10171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128411</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>16.91%</td>\n",
              "      <td>356.08</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Key Accounts Manager</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>other</td>\n",
              "      <td>Other</td>\n",
              "      <td>021xx</td>\n",
              "      <td>MA</td>\n",
              "      <td>17.72</td>\n",
              "      <td>1</td>\n",
              "      <td>Sep-2006</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>1942</td>\n",
              "      <td>30.8%</td>\n",
              "      <td>31</td>\n",
              "      <td>w</td>\n",
              "      <td>5626.39</td>\n",
              "      <td>5626.39</td>\n",
              "      <td>6400.05</td>\n",
              "      <td>6400.05</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>73669</td>\n",
              "      <td>59194</td>\n",
              "      <td>4000</td>\n",
              "      <td>67369</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 144 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  member_id  ...  settlement_percentage  settlement_term\n",
              "128407 NaN        NaN  ...                    NaN              NaN\n",
              "128408 NaN        NaN  ...                    NaN              NaN\n",
              "128409 NaN        NaN  ...                    NaN              NaN\n",
              "128410 NaN        NaN  ...                    NaN              NaN\n",
              "128411 NaN        NaN  ...                    NaN              NaN\n",
              "\n",
              "[5 rows x 144 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7E-xznbTbJR",
        "colab_type": "code",
        "outputId": "0b1448d9-26b3-4c6d-ffa8-8aa40b407d87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loans.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128412, 144)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ly42YgtvTbMA",
        "colab_type": "code",
        "outputId": "4c729341-84ff-4f6f-dd7f-da1d89ba15a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "pd.set_option('display.max_rows', 143)\n",
        "loans.isnull().sum().sort_values(ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                          128412\n",
              "member_id                   128412\n",
              "url                         128412\n",
              "desc                        128412\n",
              "settlement_percentage       127661\n",
              "                             ...  \n",
              "acc_open_past_24mths             0\n",
              "chargeoff_within_12_mths         0\n",
              "delinq_amnt                      0\n",
              "mo_sin_old_rev_tl_op             0\n",
              "inq_fi                           0\n",
              "Length: 144, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9yaeH6bTbOn",
        "colab_type": "code",
        "outputId": "0b6b3e92-a219-4898-87bd-5cb02168c9e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "loans.tail()"
      ],
      "execution_count": 0,
      "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",
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              "        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>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pymnt_plan</th>\n",
              "      <th>url</th>\n",
              "      <th>desc</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>mths_since_last_delinq</th>\n",
              "      <th>mths_since_last_record</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>...</th>\n",
              "      <th>percent_bc_gt_75</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>revol_bal_joint</th>\n",
              "      <th>sec_app_earliest_cr_line</th>\n",
              "      <th>sec_app_inq_last_6mths</th>\n",
              "      <th>sec_app_mort_acc</th>\n",
              "      <th>sec_app_open_acc</th>\n",
              "      <th>sec_app_revol_util</th>\n",
              "      <th>sec_app_open_act_il</th>\n",
              "      <th>sec_app_num_rev_accts</th>\n",
              "      <th>sec_app_chargeoff_within_12_mths</th>\n",
              "      <th>sec_app_collections_12_mths_ex_med</th>\n",
              "      <th>sec_app_mths_since_last_major_derog</th>\n",
              "      <th>hardship_flag</th>\n",
              "      <th>hardship_type</th>\n",
              "      <th>hardship_reason</th>\n",
              "      <th>hardship_status</th>\n",
              "      <th>deferral_term</th>\n",
              "      <th>hardship_amount</th>\n",
              "      <th>hardship_start_date</th>\n",
              "      <th>hardship_end_date</th>\n",
              "      <th>payment_plan_start_date</th>\n",
              "      <th>hardship_length</th>\n",
              "      <th>hardship_dpd</th>\n",
              "      <th>hardship_loan_status</th>\n",
              "      <th>orig_projected_additional_accrued_interest</th>\n",
              "      <th>hardship_payoff_balance_amount</th>\n",
              "      <th>hardship_last_payment_amount</th>\n",
              "      <th>debt_settlement_flag</th>\n",
              "      <th>debt_settlement_flag_date</th>\n",
              "      <th>settlement_status</th>\n",
              "      <th>settlement_date</th>\n",
              "      <th>settlement_amount</th>\n",
              "      <th>settlement_percentage</th>\n",
              "      <th>settlement_term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>128407</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23000</td>\n",
              "      <td>23000</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.02%</td>\n",
              "      <td>797.53</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>Tax Consultant</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>75000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>352xx</td>\n",
              "      <td>AL</td>\n",
              "      <td>20.95</td>\n",
              "      <td>1</td>\n",
              "      <td>Aug-1985</td>\n",
              "      <td>2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>22465</td>\n",
              "      <td>43.6%</td>\n",
              "      <td>28</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1547.08</td>\n",
              "      <td>1547.08</td>\n",
              "      <td>...</td>\n",
              "      <td>14.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>296500</td>\n",
              "      <td>40614</td>\n",
              "      <td>47100</td>\n",
              "      <td>21000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128408</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.02%</td>\n",
              "      <td>346.76</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>security guard</td>\n",
              "      <td>5 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>38000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>443xx</td>\n",
              "      <td>OH</td>\n",
              "      <td>13.16</td>\n",
              "      <td>3</td>\n",
              "      <td>Jul-1982</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>5634</td>\n",
              "      <td>37.1%</td>\n",
              "      <td>16</td>\n",
              "      <td>w</td>\n",
              "      <td>5557.25</td>\n",
              "      <td>5557.25</td>\n",
              "      <td>6233.34</td>\n",
              "      <td>6233.34</td>\n",
              "      <td>...</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>91403</td>\n",
              "      <td>9323</td>\n",
              "      <td>9100</td>\n",
              "      <td>2000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128409</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.56%</td>\n",
              "      <td>169.83</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>Payoff Clerk</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>35360.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>381xx</td>\n",
              "      <td>TN</td>\n",
              "      <td>11.30</td>\n",
              "      <td>1</td>\n",
              "      <td>Jun-2006</td>\n",
              "      <td>0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2597</td>\n",
              "      <td>27.3%</td>\n",
              "      <td>15</td>\n",
              "      <td>f</td>\n",
              "      <td>2751.79</td>\n",
              "      <td>2751.79</td>\n",
              "      <td>3053.17</td>\n",
              "      <td>3053.17</td>\n",
              "      <td>...</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93908</td>\n",
              "      <td>4976</td>\n",
              "      <td>3000</td>\n",
              "      <td>6028</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128410</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>9750.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.06%</td>\n",
              "      <td>327.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RENT</td>\n",
              "      <td>44400.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>980xx</td>\n",
              "      <td>WA</td>\n",
              "      <td>11.78</td>\n",
              "      <td>0</td>\n",
              "      <td>Oct-2008</td>\n",
              "      <td>2</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>6269</td>\n",
              "      <td>13.1%</td>\n",
              "      <td>25</td>\n",
              "      <td>f</td>\n",
              "      <td>5411.74</td>\n",
              "      <td>5276.45</td>\n",
              "      <td>5882.88</td>\n",
              "      <td>5735.81</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>57871</td>\n",
              "      <td>16440</td>\n",
              "      <td>20500</td>\n",
              "      <td>10171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128411</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>16.91%</td>\n",
              "      <td>356.08</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Key Accounts Manager</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>other</td>\n",
              "      <td>Other</td>\n",
              "      <td>021xx</td>\n",
              "      <td>MA</td>\n",
              "      <td>17.72</td>\n",
              "      <td>1</td>\n",
              "      <td>Sep-2006</td>\n",
              "      <td>0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>1942</td>\n",
              "      <td>30.8%</td>\n",
              "      <td>31</td>\n",
              "      <td>w</td>\n",
              "      <td>5626.39</td>\n",
              "      <td>5626.39</td>\n",
              "      <td>6400.05</td>\n",
              "      <td>6400.05</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>73669</td>\n",
              "      <td>59194</td>\n",
              "      <td>4000</td>\n",
              "      <td>67369</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 144 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  member_id  ...  settlement_percentage  settlement_term\n",
              "128407 NaN        NaN  ...                    NaN              NaN\n",
              "128408 NaN        NaN  ...                    NaN              NaN\n",
              "128409 NaN        NaN  ...                    NaN              NaN\n",
              "128410 NaN        NaN  ...                    NaN              NaN\n",
              "128411 NaN        NaN  ...                    NaN              NaN\n",
              "\n",
              "[5 rows x 144 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4L1p3UbfTbRK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loans.drop('url',axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCEAKOhnTbUJ",
        "colab_type": "code",
        "outputId": "40923fd0-ab8a-46a5-a78f-563504163651",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "loans['int_rate'].head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     16.14%\n",
              "1     11.80%\n",
              "2     11.31%\n",
              "3     14.47%\n",
              "4     27.27%\n",
              "5     16.14%\n",
              "6     12.98%\n",
              "7     12.98%\n",
              "8     10.33%\n",
              "9     23.40%\n",
              "Name: int_rate, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHsJpMNZTbXE",
        "colab_type": "code",
        "outputId": "aaab2f8b-f397-4d2b-e9f9-7a7c3f540dbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loans['int_rate'][3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' 14.47%'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSV3qECBS449",
        "colab_type": "code",
        "outputId": "f75243b1-64b6-4c35-fd1a-79ae7faceb92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loans['int_rate'].dtype"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQqzyC0-S475",
        "colab_type": "code",
        "outputId": "d14f6775-a74b-4c14-e53b-40b4cc4ec38f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(loans['int_rate'][3])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYfY112xS4_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "int_rate = ' 14.47%'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlYrwOWRS5Ck",
        "colab_type": "code",
        "outputId": "b55b2994-22d1-4a33-b126-165167141502",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "int_rate.strip()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'14.47%'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ub7sN_eDNETo",
        "colab_type": "code",
        "outputId": "fa7730cd-d544-407b-a26a-48f960c90964",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "int_rate.strip('%')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' 14.47'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQZsXyz-NEWG",
        "colab_type": "code",
        "outputId": "dc27b748-cbd1-441d-d361-f7473c035a47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "int_rate.strip().strip('%')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'14.47'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGWWTQwWUZvW",
        "colab_type": "code",
        "outputId": "fb0bf923-7da1-4e62-d624-411158066397",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "float('14.47')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sS90KMD3UbBT",
        "colab_type": "code",
        "outputId": "8bc55ae1-d1e6-484f-e055-391ba4be23e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "float(int_rate.strip().strip('%'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFBjNnPuUbhZ",
        "colab_type": "code",
        "outputId": "f690b9a3-e9cd-4afb-9490-970a747a5b3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(float(int_rate.strip().strip('%')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElpF6omjUbuq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def myfunc(my_input_value):\n",
        "    return float(my_input_value.strip().strip('%'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4HmQO06Ub9r",
        "colab_type": "code",
        "outputId": "ce1f6376-d751-4931-9a80-1511cce39f15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "myfunc(int_rate)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrLvf6pRUcBc",
        "colab_type": "code",
        "outputId": "526304cf-b441-4900-f10e-3053751145ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(myfunc(int_rate))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wQ0OIexUcHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_list=[]\n",
        "for cell in loans['int_rate']:\n",
        "  new_list.append(myfunc(cell))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgu2qMPJUcYu",
        "colab_type": "code",
        "outputId": "bcda8b65-6023-4731-da2c-3eda4915180b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "new_list[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[16.14, 11.8, 11.31, 14.47, 27.27, 16.14, 12.98, 12.98, 10.33, 23.4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1FHBrBcU64n",
        "colab_type": "code",
        "outputId": "c365f6d4-a872-4184-c72f-908f85673242",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "loans['int_rate'].head()\n",
        "list(loans['int_rate'][:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[' 16.14%',\n",
              " ' 11.80%',\n",
              " ' 11.31%',\n",
              " ' 14.47%',\n",
              " ' 27.27%',\n",
              " ' 16.14%',\n",
              " ' 12.98%',\n",
              " ' 12.98%',\n",
              " ' 10.33%',\n",
              " ' 23.40%']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLxa0FuFU7LE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loans['int_rate_clean']=pd.Series(new_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VdXWUzOeU7Nw",
        "colab_type": "code",
        "outputId": "4c842b9b-ade7-4d4e-be48-390fb38eed14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "loans[['int_rate', 'int_rate_clean']].sample(7)"
      ],
      "execution_count": 0,
      "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>int_rate</th>\n",
              "      <th>int_rate_clean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15268</th>\n",
              "      <td>16.14%</td>\n",
              "      <td>16.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74492</th>\n",
              "      <td>8.46%</td>\n",
              "      <td>8.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96503</th>\n",
              "      <td>8.46%</td>\n",
              "      <td>8.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5232</th>\n",
              "      <td>7.02%</td>\n",
              "      <td>7.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115338</th>\n",
              "      <td>28.72%</td>\n",
              "      <td>28.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82669</th>\n",
              "      <td>13.56%</td>\n",
              "      <td>13.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64231</th>\n",
              "      <td>8.19%</td>\n",
              "      <td>8.19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       int_rate  int_rate_clean\n",
              "15268    16.14%           16.14\n",
              "74492     8.46%            8.46\n",
              "96503     8.46%            8.46\n",
              "5232      7.02%            7.02\n",
              "115338   28.72%           28.72\n",
              "82669    13.56%           13.56\n",
              "64231     8.19%            8.19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m31VZwsoU7Qa",
        "colab_type": "code",
        "outputId": "cb5322c7-aee8-4071-fd09-454162476834",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loans['int_rate_clean'].dtype"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kV57AHCU7St",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loans['int_rate_clean2'] = loans['int_rate'].apply(myfunc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lu2B8xrgU7VE",
        "colab_type": "code",
        "outputId": "004f2ee8-8805-429d-bffc-97157aab9a56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "loans[['int_rate', 'int_rate_clean', 'int_rate_clean2']].sample(7)"
      ],
      "execution_count": 0,
      "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>int_rate</th>\n",
              "      <th>int_rate_clean</th>\n",
              "      <th>int_rate_clean2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95972</th>\n",
              "      <td>20.89%</td>\n",
              "      <td>20.89</td>\n",
              "      <td>20.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87199</th>\n",
              "      <td>11.55%</td>\n",
              "      <td>11.55</td>\n",
              "      <td>11.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20349</th>\n",
              "      <td>10.72%</td>\n",
              "      <td>10.72</td>\n",
              "      <td>10.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17876</th>\n",
              "      <td>13.56%</td>\n",
              "      <td>13.56</td>\n",
              "      <td>13.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123524</th>\n",
              "      <td>11.55%</td>\n",
              "      <td>11.55</td>\n",
              "      <td>11.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3746</th>\n",
              "      <td>7.02%</td>\n",
              "      <td>7.02</td>\n",
              "      <td>7.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4952</th>\n",
              "      <td>8.81%</td>\n",
              "      <td>8.81</td>\n",
              "      <td>8.81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       int_rate  int_rate_clean  int_rate_clean2\n",
              "95972    20.89%           20.89            20.89\n",
              "87199    11.55%           11.55            11.55\n",
              "20349    10.72%           10.72            10.72\n",
              "17876    13.56%           13.56            13.56\n",
              "123524   11.55%           11.55            11.55\n",
              "3746      7.02%            7.02             7.02\n",
              "4952      8.81%            8.81             8.81"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QajVoWvHU7Xd",
        "colab_type": "code",
        "outputId": "61ee4644-11ab-4021-9d62-9923d55ffd4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "loans['emp_title'].head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                          Sale Consultant\n",
              "1                                Educator \n",
              "2                    Accounts Examiner III\n",
              "3                                  teacher\n",
              "4                           Claims Analyst\n",
              "5                         Training Manager\n",
              "6                             Tire builder\n",
              "7               Program Support Assistant \n",
              "8                                   Driver\n",
              "9                                   BANKER\n",
              "10                        Business Analyst\n",
              "11                                OPERATOR\n",
              "12                        Shift Supervisor\n",
              "13                                 Driver \n",
              "14    Assistant to the Treasurer (Payroll)\n",
              "15               Reimbursement specialist \n",
              "16                       Financial Advisor\n",
              "17                                Engineer\n",
              "18                   Sales Account Manager\n",
              "19                                     Cna\n",
              "Name: emp_title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KkTk0ujU7cJ",
        "colab_type": "code",
        "outputId": "c2011bfc-447c-4ac9-9534-ba618448d3c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "loans['emp_title'].value_counts(dropna=False).head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NaN                   20947\n",
              "Teacher                2090\n",
              "Manager                1773\n",
              "Registered Nurse        952\n",
              "Driver                  924\n",
              "RN                      726\n",
              "Supervisor              697\n",
              "Sales                   580\n",
              "Project Manager         526\n",
              "General Manager         523\n",
              "Office Manager          521\n",
              "Owner                   420\n",
              "Director                402\n",
              "Operations Manager      387\n",
              "Truck Driver            387\n",
              "Nurse                   326\n",
              "Engineer                325\n",
              "Sales Manager           304\n",
              "manager                 301\n",
              "Supervisor              270\n",
              "Name: emp_title, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLSBV9c_U7fP",
        "colab_type": "code",
        "outputId": "dcdd3292-911f-4725-d53a-27c1be3d61c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(loans.shape)\n",
        "len(loans['emp_title'].unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(128412, 145)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43893"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cOm6XpOU7kc",
        "colab_type": "code",
        "outputId": "36959672-7a84-4ab0-8755-62790c15b191",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loans['emp_title'].nunique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43892"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otO0U6iwU_WH",
        "colab_type": "code",
        "outputId": "fc64dc35-be06-442c-b544-a6cbe10cc04f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loans['emp_title'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20947"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ly08noEgU_eA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "examples = ['owner', 'Supervisor', ' Project Manager', np.nan]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnZNzwl-U_ho",
        "colab_type": "code",
        "outputId": "a9c148bb-9ac9-4176-d5d4-c6ab27f521c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type('Supervisor')\n",
        "isinstance('Supervisor', str)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDlpDM_cU_l8",
        "colab_type": "code",
        "outputId": "593ba4d8-0a60-4acf-c481-e66cbe4382f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "' supervisor'.title().strip()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Supervisor'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzkoTCXFU7aM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clean_title(jobtitle):\n",
        "  if isinstance(jobtitle, str):\n",
        "    return (jobtitle.title().strip())\n",
        "  else:\n",
        "    return 'Unknown'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVSM4EQXVmng",
        "colab_type": "code",
        "outputId": "b2419365-75e9-4245-8843-fcc00a83d071",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "clean_title(' superviSOR')\n",
        "clean_title(np.nan)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Unknown'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klot3G78Vmwa",
        "colab_type": "code",
        "outputId": "2697a8d9-1e24-4a31-d7e7-bba00d60dc22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mylist = []\n",
        "for jobtitle in examples:\n",
        "  mylist.append(clean_title(jobtitle))\n",
        "mylist"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Owner', 'Supervisor', 'Project Manager', 'Unknown']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzxSFlD_VmzU",
        "colab_type": "code",
        "outputId": "77283488-56b7-403c-857e-83bfff306fd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "[clean_title(jobtitle) for jobtitle in examples]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Owner', 'Supervisor', 'Project Manager', 'Unknown']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOzj7hiQVm2I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loans['clean_title'] = loans['emp_title'].apply(clean_title)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3wmR54qVm8l",
        "colab_type": "code",
        "outputId": "e5e9fb71-21a9-4829-af68-91078641756e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "loans[['emp_title','clean_title']].sample(20)"
      ],
      "execution_count": 0,
      "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>emp_title</th>\n",
              "      <th>clean_title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60392</th>\n",
              "      <td>Owner</td>\n",
              "      <td>Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25738</th>\n",
              "      <td>Teacher Assistant</td>\n",
              "      <td>Teacher Assistant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70982</th>\n",
              "      <td>Outside Sales</td>\n",
              "      <td>Outside Sales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8101</th>\n",
              "      <td>Sr Business Banking Coordinator</td>\n",
              "      <td>Sr Business Banking Coordinator</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85446</th>\n",
              "      <td>Shoreside tankerman</td>\n",
              "      <td>Shoreside Tankerman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115751</th>\n",
              "      <td>RN</td>\n",
              "      <td>Rn</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40165</th>\n",
              "      <td>Office Administrator</td>\n",
              "      <td>Office Administrator</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49338</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46503</th>\n",
              "      <td>Inside sales</td>\n",
              "      <td>Inside Sales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81082</th>\n",
              "      <td>Store Manager</td>\n",
              "      <td>Store Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64374</th>\n",
              "      <td>Executive Secretary</td>\n",
              "      <td>Executive Secretary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9021</th>\n",
              "      <td>Grocery Manager</td>\n",
              "      <td>Grocery Manager</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83438</th>\n",
              "      <td>Account Executive</td>\n",
              "      <td>Account Executive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51006</th>\n",
              "      <td>Truck driver</td>\n",
              "      <td>Truck Driver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19938</th>\n",
              "      <td>Teacher</td>\n",
              "      <td>Teacher</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54630</th>\n",
              "      <td>Human Resources Coordinator</td>\n",
              "      <td>Human Resources Coordinator</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119688</th>\n",
              "      <td>Claims Administrator</td>\n",
              "      <td>Claims Administrator</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32461</th>\n",
              "      <td>Plans Examiner</td>\n",
              "      <td>Plans Examiner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56222</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92321</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              emp_title                      clean_title\n",
              "60392                             Owner                            Owner\n",
              "25738                 Teacher Assistant                Teacher Assistant\n",
              "70982                     Outside Sales                    Outside Sales\n",
              "8101    Sr Business Banking Coordinator  Sr Business Banking Coordinator\n",
              "85446               Shoreside tankerman              Shoreside Tankerman\n",
              "115751                               RN                               Rn\n",
              "40165              Office Administrator             Office Administrator\n",
              "49338                               NaN                          Unknown\n",
              "46503                      Inside sales                     Inside Sales\n",
              "81082                     Store Manager                    Store Manager\n",
              "64374               Executive Secretary              Executive Secretary\n",
              "9021                    Grocery Manager                  Grocery Manager\n",
              "83438                 Account Executive                Account Executive\n",
              "51006                      Truck driver                     Truck Driver\n",
              "19938                          Teacher                           Teacher\n",
              "54630       Human Resources Coordinator      Human Resources Coordinator\n",
              "119688             Claims Administrator             Claims Administrator\n",
              "32461                    Plans Examiner                   Plans Examiner\n",
              "56222                               NaN                          Unknown\n",
              "92321                               NaN                          Unknown"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjV80TrJV_86",
        "colab_type": "code",
        "outputId": "b3634136-4fd1-4700-cdb7-045a517233dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "loans.head()"
      ],
      "execution_count": 0,
      "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>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>pymnt_plan</th>\n",
              "      <th>desc</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>addr_state</th>\n",
              "      <th>dti</th>\n",
              "      <th>delinq_2yrs</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>mths_since_last_delinq</th>\n",
              "      <th>mths_since_last_record</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>out_prncp</th>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <th>...</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>total_bal_ex_mort</th>\n",
              "      <th>total_bc_limit</th>\n",
              "      <th>total_il_high_credit_limit</th>\n",
              "      <th>revol_bal_joint</th>\n",
              "      <th>sec_app_earliest_cr_line</th>\n",
              "      <th>sec_app_inq_last_6mths</th>\n",
              "      <th>sec_app_mort_acc</th>\n",
              "      <th>sec_app_open_acc</th>\n",
              "      <th>sec_app_revol_util</th>\n",
              "      <th>sec_app_open_act_il</th>\n",
              "      <th>sec_app_num_rev_accts</th>\n",
              "      <th>sec_app_chargeoff_within_12_mths</th>\n",
              "      <th>sec_app_collections_12_mths_ex_med</th>\n",
              "      <th>sec_app_mths_since_last_major_derog</th>\n",
              "      <th>hardship_flag</th>\n",
              "      <th>hardship_type</th>\n",
              "      <th>hardship_reason</th>\n",
              "      <th>hardship_status</th>\n",
              "      <th>deferral_term</th>\n",
              "      <th>hardship_amount</th>\n",
              "      <th>hardship_start_date</th>\n",
              "      <th>hardship_end_date</th>\n",
              "      <th>payment_plan_start_date</th>\n",
              "      <th>hardship_length</th>\n",
              "      <th>hardship_dpd</th>\n",
              "      <th>hardship_loan_status</th>\n",
              "      <th>orig_projected_additional_accrued_interest</th>\n",
              "      <th>hardship_payoff_balance_amount</th>\n",
              "      <th>hardship_last_payment_amount</th>\n",
              "      <th>debt_settlement_flag</th>\n",
              "      <th>debt_settlement_flag_date</th>\n",
              "      <th>settlement_status</th>\n",
              "      <th>settlement_date</th>\n",
              "      <th>settlement_amount</th>\n",
              "      <th>settlement_percentage</th>\n",
              "      <th>settlement_term</th>\n",
              "      <th>int_rate_clean</th>\n",
              "      <th>int_rate_clean2</th>\n",
              "      <th>clean_title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>16.14%</td>\n",
              "      <td>243.93</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>Sale Consultant</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>079xx</td>\n",
              "      <td>NJ</td>\n",
              "      <td>8.80</td>\n",
              "      <td>0</td>\n",
              "      <td>Mar-1991</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>10582</td>\n",
              "      <td>58.5%</td>\n",
              "      <td>32</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10661.057727</td>\n",
              "      <td>10661.06</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>...</td>\n",
              "      <td>25600</td>\n",
              "      <td>15470</td>\n",
              "      <td>15000</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.14</td>\n",
              "      <td>16.14</td>\n",
              "      <td>Sale Consultant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6500</td>\n",
              "      <td>6500</td>\n",
              "      <td>6500.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.80%</td>\n",
              "      <td>215.28</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Educator</td>\n",
              "      <td>2 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>46500.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>352xx</td>\n",
              "      <td>AL</td>\n",
              "      <td>21.01</td>\n",
              "      <td>0</td>\n",
              "      <td>Aug-2011</td>\n",
              "      <td>1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>6741</td>\n",
              "      <td>41.6%</td>\n",
              "      <td>30</td>\n",
              "      <td>w</td>\n",
              "      <td>4066.26</td>\n",
              "      <td>4066.26</td>\n",
              "      <td>3222.810000</td>\n",
              "      <td>3222.81</td>\n",
              "      <td>2433.74</td>\n",
              "      <td>...</td>\n",
              "      <td>50845</td>\n",
              "      <td>40223</td>\n",
              "      <td>13300</td>\n",
              "      <td>34645</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.80</td>\n",
              "      <td>11.80</td>\n",
              "      <td>Educator</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4500</td>\n",
              "      <td>4500</td>\n",
              "      <td>4500.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.31%</td>\n",
              "      <td>147.99</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Accounts Examiner III</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>38500.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>786xx</td>\n",
              "      <td>TX</td>\n",
              "      <td>4.64</td>\n",
              "      <td>0</td>\n",
              "      <td>Dec-2003</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>4472</td>\n",
              "      <td>15.3%</td>\n",
              "      <td>25</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4549.217149</td>\n",
              "      <td>4549.22</td>\n",
              "      <td>4500.00</td>\n",
              "      <td>...</td>\n",
              "      <td>53795</td>\n",
              "      <td>29137</td>\n",
              "      <td>15100</td>\n",
              "      <td>24595</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.31</td>\n",
              "      <td>11.31</td>\n",
              "      <td>Accounts Examiner Iii</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25000</td>\n",
              "      <td>25000</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>14.47%</td>\n",
              "      <td>587.82</td>\n",
              "      <td>C</td>\n",
              "      <td>C2</td>\n",
              "      <td>teacher</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>OWN</td>\n",
              "      <td>110000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>117xx</td>\n",
              "      <td>NY</td>\n",
              "      <td>26.43</td>\n",
              "      <td>1</td>\n",
              "      <td>Jan-1997</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>39053</td>\n",
              "      <td>45.7%</td>\n",
              "      <td>49</td>\n",
              "      <td>w</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5848.050000</td>\n",
              "      <td>5848.05</td>\n",
              "      <td>3024.11</td>\n",
              "      <td>...</td>\n",
              "      <td>179321</td>\n",
              "      <td>95648</td>\n",
              "      <td>62800</td>\n",
              "      <td>91424</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.47</td>\n",
              "      <td>14.47</td>\n",
              "      <td>Teacher</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>27.27%</td>\n",
              "      <td>920.91</td>\n",
              "      <td>E</td>\n",
              "      <td>E5</td>\n",
              "      <td>Claims Analyst</td>\n",
              "      <td>1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>857xx</td>\n",
              "      <td>AZ</td>\n",
              "      <td>23.62</td>\n",
              "      <td>0</td>\n",
              "      <td>Dec-1997</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1258</td>\n",
              "      <td>10.7%</td>\n",
              "      <td>16</td>\n",
              "      <td>w</td>\n",
              "      <td>25780.25</td>\n",
              "      <td>25780.25</td>\n",
              "      <td>13745.470000</td>\n",
              "      <td>13745.47</td>\n",
              "      <td>4219.75</td>\n",
              "      <td>...</td>\n",
              "      <td>46300</td>\n",
              "      <td>27364</td>\n",
              "      <td>10100</td>\n",
              "      <td>34500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.27</td>\n",
              "      <td>27.27</td>\n",
              "      <td>Claims Analyst</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 146 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  member_id  ...  int_rate_clean2            clean_title\n",
              "0 NaN        NaN  ...            16.14        Sale Consultant\n",
              "1 NaN        NaN  ...            11.80               Educator\n",
              "2 NaN        NaN  ...            11.31  Accounts Examiner Iii\n",
              "3 NaN        NaN  ...            14.47                Teacher\n",
              "4 NaN        NaN  ...            27.27         Claims Analyst\n",
              "\n",
              "[5 rows x 146 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwuegUmiV__1",
        "colab_type": "code",
        "outputId": "53cfaa3a-41a4-4ff5-9bfc-97f68e24e28f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "loans.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
              "       'term', 'int_rate', 'installment', 'grade', 'sub_grade',\n",
              "       ...\n",
              "       'debt_settlement_flag', 'debt_settlement_flag_date',\n",
              "       'settlement_status', 'settlement_date', 'settlement_amount',\n",
              "       'settlement_percentage', 'settlement_term', 'int_rate_clean',\n",
              "       'int_rate_clean2', 'clean_title'],\n",
              "      dtype='object', length=146)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8G1WtrOWACc",
        "colab_type": "code",
        "outputId": "89e38cbe-5c83-48ef-c5f9-7dd9f67e658a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "loans['issue_d'].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Dec-2018\n",
              "1    Dec-2018\n",
              "2    Dec-2018\n",
              "3    Dec-2018\n",
              "4    Dec-2018\n",
              "Name: issue_d, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkJZMANaWAFP",
        "colab_type": "code",
        "outputId": "183009cd-0013-4b76-f275-9e2d78299935",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mylst = []\n",
        "for col in loans.columns:\n",
        "  if col.endswith('_d'):\n",
        "    mylst.append(col)\n",
        "mylst"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['issue_d', 'last_pymnt_d', 'next_pymnt_d', 'last_credit_pull_d']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2qqb63ZWAH7",
        "colab_type": "code",
        "outputId": "3e7c3458-ebc2-4611-b9be-fc0a84cb6112",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "my_date_cols = [col for col in loans.columns if col.endswith('_d')]\n",
        "my_date_cols"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['issue_d', 'last_pymnt_d', 'next_pymnt_d', 'last_credit_pull_d']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzlgIecSWAPO",
        "colab_type": "code",
        "outputId": "e9127faa-3cf6-46d5-bb29-83bf20c7a4e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(loans['issue_d'][0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iq1w85kVm_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loans['new_issue_d'] = pd.to_datetime(loans['issue_d'], infer_datetime_format=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKLyETsvVnB1",
        "colab_type": "code",
        "outputId": "f6a2878e-ea42-43f2-cdfe-6241758968f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(loans['new_issue_d'][3])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas._libs.tslibs.timestamps.Timestamp"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1htlAtrVnE0",
        "colab_type": "code",
        "outputId": "86829ad7-6268-460a-aafc-42108f424f50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(loans['new_issue_d'].dtype)\n",
        "loans['new_issue_d'].dtype"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datetime64[ns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('<M8[ns]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7k_zH-66VriJ",
        "colab_type": "code",
        "outputId": "0d8cb2cb-adc7-4334-d1a2-969bf7c21711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loans['new_issue_d'].head(1).values"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2018-12-01T00:00:00.000000000'], dtype='datetime64[ns]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1Sf6G1XVrrQ",
        "colab_type": "code",
        "outputId": "447e0c08-2f1e-49b0-f118-57d33e65e61f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "loans['year'] = loans['new_issue_d'].dt.year\n",
        "loans[['issue_d', 'new_issue_d', 'year']].head()"
      ],
      "execution_count": 0,
      "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>issue_d</th>\n",
              "      <th>new_issue_d</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    issue_d new_issue_d  year\n",
              "0  Dec-2018  2018-12-01  2018\n",
              "1  Dec-2018  2018-12-01  2018\n",
              "2  Dec-2018  2018-12-01  2018\n",
              "3  Dec-2018  2018-12-01  2018\n",
              "4  Dec-2018  2018-12-01  2018"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qd1N6sLRVm6q",
        "colab_type": "code",
        "outputId": "87ce3556-044b-445f-9e46-cb9e0d317a4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "loans['month'] = loans['new_issue_d'].dt.month\n",
        "loans[['issue_d', 'new_issue_d', 'year', 'month']].head()"
      ],
      "execution_count": 0,
      "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>issue_d</th>\n",
              "      <th>new_issue_d</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    issue_d new_issue_d  year  month\n",
              "0  Dec-2018  2018-12-01  2018     12\n",
              "1  Dec-2018  2018-12-01  2018     12\n",
              "2  Dec-2018  2018-12-01  2018     12\n",
              "3  Dec-2018  2018-12-01  2018     12\n",
              "4  Dec-2018  2018-12-01  2018     12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYN_wYvmVm4m",
        "colab_type": "code",
        "outputId": "58fb63be-ab11-46f6-9dac-36c34515acfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "loans['weekday'] = loans['new_issue_d'].dt.weekday\n",
        "loans[['issue_d', 'new_issue_d', 'year', 'month', 'weekday']].sample(10)"
      ],
      "execution_count": 0,
      "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>issue_d</th>\n",
              "      <th>new_issue_d</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12793</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48250</th>\n",
              "      <td>Nov-2018</td>\n",
              "      <td>2018-11-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29440</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53871</th>\n",
              "      <td>Nov-2018</td>\n",
              "      <td>2018-11-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118387</th>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117570</th>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111738</th>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89153</th>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92490</th>\n",
              "      <td>Oct-2018</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37308</th>\n",
              "      <td>Dec-2018</td>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         issue_d new_issue_d  year  month  weekday\n",
              "12793   Dec-2018  2018-12-01  2018     12        5\n",
              "48250   Nov-2018  2018-11-01  2018     11        3\n",
              "29440   Dec-2018  2018-12-01  2018     12        5\n",
              "53871   Nov-2018  2018-11-01  2018     11        3\n",
              "118387  Oct-2018  2018-10-01  2018     10        0\n",
              "117570  Oct-2018  2018-10-01  2018     10        0\n",
              "111738  Oct-2018  2018-10-01  2018     10        0\n",
              "89153   Oct-2018  2018-10-01  2018     10        0\n",
              "92490   Oct-2018  2018-10-01  2018     10        0\n",
              "37308   Dec-2018  2018-12-01  2018     12        5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO2q3rvDW4xL",
        "colab_type": "code",
        "outputId": "8e8ab6fd-bf6c-4644-fc09-7178ff9e0e75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "loans['new_pull'] = pd.to_datetime(loans['last_credit_pull_d'])\n",
        "loans['month_pull'] = loans['new_pull'].dt.month\n",
        "loans[['last_credit_pull_d', 'new_pull', 'month_pull']].head()"
      ],
      "execution_count": 0,
      "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>last_credit_pull_d</th>\n",
              "      <th>new_pull</th>\n",
              "      <th>month_pull</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dec-2019</td>\n",
              "      <td>2019-12-01</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apr-2020</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Feb-2020</td>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apr-2020</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apr-2020</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  last_credit_pull_d   new_pull  month_pull\n",
              "0           Dec-2019 2019-12-01        12.0\n",
              "1           Apr-2020 2020-04-01         4.0\n",
              "2           Feb-2020 2020-02-01         2.0\n",
              "3           Apr-2020 2020-04-01         4.0\n",
              "4           Apr-2020 2020-04-01         4.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gkl2MxnW42q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loans['day_diff'] = loans['new_issue_d'] - loans['new_pull']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YILpTAbTW45g",
        "colab_type": "code",
        "outputId": "109dfdfd-42b1-4c40-a249-d76657e94ebf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "loans[['last_credit_pull_d', 'new_pull', 'day_diff']].head()"
      ],
      "execution_count": 0,
      "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>last_credit_pull_d</th>\n",
              "      <th>new_pull</th>\n",
              "      <th>day_diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dec-2019</td>\n",
              "      <td>2019-12-01</td>\n",
              "      <td>-365 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apr-2020</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>-487 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Feb-2020</td>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>-427 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apr-2020</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>-487 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apr-2020</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>-487 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  last_credit_pull_d   new_pull  day_diff\n",
              "0           Dec-2019 2019-12-01 -365 days\n",
              "1           Apr-2020 2020-04-01 -487 days\n",
              "2           Feb-2020 2020-02-01 -427 days\n",
              "3           Apr-2020 2020-04-01 -487 days\n",
              "4           Apr-2020 2020-04-01 -487 days"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98s1b3UjW48l",
        "colab_type": "code",
        "outputId": "089f22df-043d-4873-a113-7568324ea99c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "loans['day_diff'].value_counts().sort_index().tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-30 days     87\n",
              "0 days      475\n",
              "30 days      73\n",
              "31 days      33\n",
              "61 days       1\n",
              "Name: day_diff, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKv-3jDNXFM9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "L8k0LiHmo5EU"
      },
      "source": [
        "# Stretch Goals\n",
        "\n",
        "You can do more with the LendingClub or Instacart datasets.\n",
        "\n",
        "LendingClub options:\n",
        "- There's one other column in the dataframe with percent signs. Remove them and convert to floats. You'll need to handle missing values.\n",
        "- Modify the `emp_title` column to replace titles with 'Other' if the title is not in the top 20. \n",
        "- Take initiatve and work on your own ideas!\n",
        "\n",
        "Instacart options:\n",
        "- Read [Instacart Market Basket Analysis, Winner's Interview: 2nd place, Kazuki Onodera](http://blog.kaggle.com/2017/09/21/instacart-market-basket-analysis-winners-interview-2nd-place-kazuki-onodera/), especially the **Feature Engineering** section. (Can you choose one feature from his bulleted lists, and try to engineer it with pandas code?)\n",
        "- Read and replicate parts of [Simple Exploration Notebook - Instacart](https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-instacart). (It's the Python Notebook with the most upvotes for this Kaggle competition.)\n",
        "- Take initiative and work on your own ideas!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0_7PXF7lpEXg"
      },
      "source": [
        "You can uncomment and run the cells below to re-download and extract the Instacart data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urIePNa0yNG6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !wget https://s3.amazonaws.com/instacart-datasets/instacart_online_grocery_shopping_2017_05_01.tar.gz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9zEyu-uyNG8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !tar --gunzip --extract --verbose --file=instacart_online_grocery_shopping_2017_05_01.tar.gz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3IqrhlpyNG-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# %cd instacart_2017_05_01"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}