Data Exploration and Quality Assurance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Catagorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"clean_data.xls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with *LIMIT_BAL* and *AGE*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002076B1AB9E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076B034898>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df['LIMIT_BAL'].hist()\n",
    "df[['LIMIT_BAL', 'AGE']].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>LIMIT_BAL</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167919.054905</td>\n",
       "      <td>35.505213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129839.453081</td>\n",
       "      <td>9.227442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140000.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240000.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>800000.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LIMIT_BAL           AGE\n",
       "count   26664.000000  26664.000000\n",
       "mean   167919.054905     35.505213\n",
       "std    129839.453081      9.227442\n",
       "min     10000.000000     21.000000\n",
       "25%     50000.000000     28.000000\n",
       "50%    140000.000000     34.000000\n",
       "75%    240000.000000     41.000000\n",
       "max    800000.000000     79.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['LIMIT_BAL', 'AGE']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDUCATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    12458\n",
       "1     9412\n",
       "3     4380\n",
       "5      245\n",
       "4      115\n",
       "6       43\n",
       "0       11\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Data Description we see that the values 0, 5 and 6 for education are undocumented. We replace these with 4 (others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EDUCATION'].replace(to_replace=[0,5,6], value=4, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    12458\n",
       "1     9412\n",
       "3     4380\n",
       "4      414\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MARRIAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14158\n",
       "1    12172\n",
       "3      286\n",
       "0       48\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Value 0 is not documented, replace with 3 (others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MARRIAGE'].replace(to_replace=0, value=3, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14158\n",
       "1    12172\n",
       "3      334\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking default rate within Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Education Level')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('EDUCATION').agg({'default payment next month':'mean'}).plot.bar()\n",
    "plt.ylabel('Default Rate')\n",
    "plt.xlabel('Education Level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing One-hot encoding (OHE) on EDUCATION for more complex models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_mapping = {1: 'graduate school', 2: 'university', 3: 'high school', 4: 'others'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EDUCATION_CAT'] = df['EDUCATION'].map(edu_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>EDUCATION</th>\n",
       "      <th>EDUCATION_CAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>high school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>high school</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EDUCATION    EDUCATION_CAT\n",
       "0          2       university\n",
       "1          2       university\n",
       "2          2       university\n",
       "3          2       university\n",
       "4          2       university\n",
       "5          1  graduate school\n",
       "6          1  graduate school\n",
       "7          2       university\n",
       "8          3      high school\n",
       "9          3      high school"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['EDUCATION', 'EDUCATION_CAT']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_ohe = pd.get_dummies(df['EDUCATION_CAT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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>graduate school</th>\n",
       "      <th>high school</th>\n",
       "      <th>others</th>\n",
       "      <th>university</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   graduate school  high school  others  university\n",
       "0                0            0       0           1\n",
       "1                0            0       0           1\n",
       "2                0            0       0           1\n",
       "3                0            0       0           1\n",
       "4                0            0       0           1\n",
       "5                1            0       0           0\n",
       "6                1            0       0           0\n",
       "7                0            0       0           1\n",
       "8                0            1       0           0\n",
       "9                0            1       0           0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_ohe.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ohe = pd.concat([df, edu_ohe], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ohe.to_excel(\"clean_data_withOHE.xls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Financial Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remaining financial features are in 3 groups: 1. Status of montly payments, 2. Billed Amound, 3. Paid Amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Status of monthly payments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pay_feat = ['PAY_1', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.017777</td>\n",
       "      <td>-0.133363</td>\n",
       "      <td>-0.167679</td>\n",
       "      <td>-0.225023</td>\n",
       "      <td>-0.269764</td>\n",
       "      <td>-0.293579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.126769</td>\n",
       "      <td>1.198640</td>\n",
       "      <td>1.199165</td>\n",
       "      <td>1.167897</td>\n",
       "      <td>1.131735</td>\n",
       "      <td>1.150229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PAY_1         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
       "count  26664.000000  26664.000000  26664.000000  26664.000000  26664.000000   \n",
       "mean      -0.017777     -0.133363     -0.167679     -0.225023     -0.269764   \n",
       "std        1.126769      1.198640      1.199165      1.167897      1.131735   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "              PAY_6  \n",
       "count  26664.000000  \n",
       "mean      -0.293579  \n",
       "std        1.150229  \n",
       "min       -2.000000  \n",
       "25%       -1.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        8.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[pay_feat].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20773888780>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PAY_1'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most people are in good standing (-2, -1, 0). Others have paid in a short time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002076ADA97B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076ABE30F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076AC0A748>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002076AC33DD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076AC644A8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076AC644E0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.dpi'] = 150\n",
    "mpl.rcParams['font.size'] = 4\n",
    "df[pay_feat].hist(layout =(2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOTE : We see 2 month payment delay in Sept (PAY_1), but no 1 month payment delay for Aug (PAY_2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "bill_feat = ['BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002076C0A8C88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076E0FF438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076E11C940>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002076DBF2F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076DC25668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076DC256A0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[bill_feat].hist(layout = (2,3), bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "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>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51405.730723</td>\n",
       "      <td>49300.001500</td>\n",
       "      <td>47026.340047</td>\n",
       "      <td>43338.894539</td>\n",
       "      <td>40338.136701</td>\n",
       "      <td>38889.872337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>73633.687106</td>\n",
       "      <td>70934.549534</td>\n",
       "      <td>68705.359524</td>\n",
       "      <td>64275.250740</td>\n",
       "      <td>60705.944083</td>\n",
       "      <td>59432.541657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-157264.000000</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3580.000000</td>\n",
       "      <td>2999.750000</td>\n",
       "      <td>2627.250000</td>\n",
       "      <td>2341.750000</td>\n",
       "      <td>1745.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22361.000000</td>\n",
       "      <td>21150.000000</td>\n",
       "      <td>20079.500000</td>\n",
       "      <td>19037.000000</td>\n",
       "      <td>18066.000000</td>\n",
       "      <td>17005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67649.750000</td>\n",
       "      <td>64395.500000</td>\n",
       "      <td>60360.000000</td>\n",
       "      <td>54727.500000</td>\n",
       "      <td>50290.500000</td>\n",
       "      <td>49253.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>746814.000000</td>\n",
       "      <td>671563.000000</td>\n",
       "      <td>855086.000000</td>\n",
       "      <td>706864.000000</td>\n",
       "      <td>823540.000000</td>\n",
       "      <td>699944.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           BILL_AMT1      BILL_AMT2      BILL_AMT3      BILL_AMT4  \\\n",
       "count   26664.000000   26664.000000   26664.000000   26664.000000   \n",
       "mean    51405.730723   49300.001500   47026.340047   43338.894539   \n",
       "std     73633.687106   70934.549534   68705.359524   64275.250740   \n",
       "min   -165580.000000  -69777.000000 -157264.000000 -170000.000000   \n",
       "25%      3580.000000    2999.750000    2627.250000    2341.750000   \n",
       "50%     22361.000000   21150.000000   20079.500000   19037.000000   \n",
       "75%     67649.750000   64395.500000   60360.000000   54727.500000   \n",
       "max    746814.000000  671563.000000  855086.000000  706864.000000   \n",
       "\n",
       "           BILL_AMT5      BILL_AMT6  \n",
       "count   26664.000000   26664.000000  \n",
       "mean    40338.136701   38889.872337  \n",
       "std     60705.944083   59432.541657  \n",
       "min    -81334.000000 -339603.000000  \n",
       "25%      1745.000000    1256.000000  \n",
       "50%     18066.000000   17005.000000  \n",
       "75%     50290.500000   49253.750000  \n",
       "max    823540.000000  699944.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[bill_feat].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No inconsistency seems to appear for the Billed Amount "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "amt_feat = ['PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>2.666400e+04</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5704.085771</td>\n",
       "      <td>5.881110e+03</td>\n",
       "      <td>5259.514964</td>\n",
       "      <td>4887.048717</td>\n",
       "      <td>4843.729973</td>\n",
       "      <td>5257.843047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16699.398632</td>\n",
       "      <td>2.121431e+04</td>\n",
       "      <td>17265.439561</td>\n",
       "      <td>15956.349371</td>\n",
       "      <td>15311.721795</td>\n",
       "      <td>17635.468185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.020000e+02</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>294.750000</td>\n",
       "      <td>242.750000</td>\n",
       "      <td>111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2114.500000</td>\n",
       "      <td>2.007000e+03</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5027.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4556.250000</td>\n",
       "      <td>4050.500000</td>\n",
       "      <td>4082.750000</td>\n",
       "      <td>4015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.227082e+06</td>\n",
       "      <td>889043.000000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PAY_AMT1      PAY_AMT2       PAY_AMT3       PAY_AMT4  \\\n",
       "count   26664.000000  2.666400e+04   26664.000000   26664.000000   \n",
       "mean     5704.085771  5.881110e+03    5259.514964    4887.048717   \n",
       "std     16699.398632  2.121431e+04   17265.439561   15956.349371   \n",
       "min         0.000000  0.000000e+00       0.000000       0.000000   \n",
       "25%      1000.000000  8.020000e+02     390.000000     294.750000   \n",
       "50%      2114.500000  2.007000e+03    1822.000000    1500.000000   \n",
       "75%      5027.000000  5.000000e+03    4556.250000    4050.500000   \n",
       "max    873552.000000  1.227082e+06  889043.000000  621000.000000   \n",
       "\n",
       "            PAY_AMT5       PAY_AMT6  \n",
       "count   26664.000000   26664.000000  \n",
       "mean     4843.729973    5257.843047  \n",
       "std     15311.721795   17635.468185  \n",
       "min         0.000000       0.000000  \n",
       "25%       242.750000     111.000000  \n",
       "50%      1500.000000    1500.000000  \n",
       "75%      4082.750000    4015.000000  \n",
       "max    426529.000000  528666.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[amt_feat].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002076E9AC898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076E9552E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076EAF45F8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002076EB1A908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076EB44C18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002076EB44C50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[amt_feat].hist(layout = (2,3), bins=25, xrot=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Not informative enough. Let's check howmany 0's are included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "zero_mask = df[amt_feat] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAY_AMT1    4656\n",
       "PAY_AMT2    4833\n",
       "PAY_AMT3    5293\n",
       "PAY_AMT4    5697\n",
       "PAY_AMT5    5981\n",
       "PAY_AMT6    6373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_mask.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We try to view the amounts as base10-log transform. This will tell us the size of the amount. It is important to not consider 0 as log(0) is undefined!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002076FD92860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000207705719E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002077059E080>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000207709D5710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000207709FDDA0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000207709FDDD8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[amt_feat][~zero_mask].apply(np.log10).hist(layout = (2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This provides more information in terms of the amounts paid. We can see amounts from 10s to Millions (six 0's)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}