notebooks/00-eda.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Entendiendo qué es el riesgo crediticio o ```credit risk```\n",
    "Consiste en la posibilidad de que una persona que ha prestado plata no te pague toda su deuda\n",
    "\n",
    "Un credito o prestamo está en ```default``` cuando la agencia esta razonablemente segura de que el cliente no va a pagar su cuenta\n",
    "## Perdida Esperada o ```Expected Loss``` (EL)\n",
    "La cantidad que una empresa pierde como resultado del ```loan default```\n",
    "\n",
    "La perdida esperada es un calculo realizado con los siguientes tres componentes:\n",
    "- Probabilidad de ```Default``` (```PD```)\n",
    "- Exposicion al momento del Default (```EAD```) que es la cantidad faltante al momento del ```default```\n",
    "- Loss Given Default (```LGD```) que es el ratio de la exposicion contra cualquier recuperacion de la perdida. Si un cliente debe 100.000 PYG y se logra vender el prestamo por 20.000 PYG, el LGD será 80%.\n",
    "\n",
    "La formula de la Perdida Esperada es: $$EL = PD * EAD * LGD$$\n",
    "\n",
    "## TIpos de Datos disponibles para crear un modelo de ```credit risk assesment```\n",
    "- De Aplicacion\n",
    "    - Tasa de Interes\n",
    "    - Faja\n",
    "    - Cantidad\n",
    "- De Comportamiento\n",
    "    - Hace cuantos años está empleado\n",
    "    - Defaults anteriores (historial crediticio)\n",
    "    - Ingresos\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursos:\n",
    "- [Financial Analytics: Credit Default Analysis & Predictions](http://rtuttinsights.com/portfolio/financial-analytics-credit-card-default-analysis-predictions/)\n",
    "- [Making Predictions with Data and Python : Predicting Credit Card Default | packtpub.com](https://www.youtube.com/watch?v=zUqa6KcwRhs)\n",
    "- [Credit Risk Modeling in Python\n",
    "](https://www.datacamp.com/courses/credit-risk-modeling-in-python)\n",
    "- [Home Credit Default Analysis | kaggle](https://www.kaggle.com/ashishpatel26/home-credit-default-analysis)\n",
    "- [Metodologías de cuantificación del riesgo de crédito](https://accionistaseinversores.bbva.com/microsites/bbva2012/es/Gestiondelriesgo/Metodologiasdecuantificaciondelriesgodecredito.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "cr_loan = pd.read_csv(\"data/cr_loan2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "person_age                      int64\n",
       "person_income                   int64\n",
       "person_home_ownership          object\n",
       "person_emp_length             float64\n",
       "loan_intent                    object\n",
       "loan_grade                     object\n",
       "loan_amnt                       int64\n",
       "loan_int_rate                 float64\n",
       "loan_status                     int64\n",
       "loan_percent_income           float64\n",
       "cb_person_default_on_file      object\n",
       "cb_person_cred_hist_length      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_loan.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>person_age</th>\n",
       "      <th>person_income</th>\n",
       "      <th>person_home_ownership</th>\n",
       "      <th>person_emp_length</th>\n",
       "      <th>loan_intent</th>\n",
       "      <th>loan_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_int_rate</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>loan_percent_income</th>\n",
       "      <th>cb_person_default_on_file</th>\n",
       "      <th>cb_person_cred_hist_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>59000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>123.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>D</td>\n",
       "      <td>35000</td>\n",
       "      <td>16.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.59</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>9600</td>\n",
       "      <td>OWN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>B</td>\n",
       "      <td>1000</td>\n",
       "      <td>11.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>9600</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>5500</td>\n",
       "      <td>12.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.57</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>65500</td>\n",
       "      <td>RENT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>15.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>54400</td>\n",
       "      <td>RENT</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>C</td>\n",
       "      <td>35000</td>\n",
       "      <td>14.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>9900</td>\n",
       "      <td>OWN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VENTURE</td>\n",
       "      <td>A</td>\n",
       "      <td>2500</td>\n",
       "      <td>7.14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>77100</td>\n",
       "      <td>RENT</td>\n",
       "      <td>8.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>B</td>\n",
       "      <td>35000</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0.45</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>78956</td>\n",
       "      <td>RENT</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>B</td>\n",
       "      <td>35000</td>\n",
       "      <td>11.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>83000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PERSONAL</td>\n",
       "      <td>A</td>\n",
       "      <td>35000</td>\n",
       "      <td>8.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0.42</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>10000</td>\n",
       "      <td>OWN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>VENTURE</td>\n",
       "      <td>D</td>\n",
       "      <td>1600</td>\n",
       "      <td>14.74</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>85000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>6.0</td>\n",
       "      <td>VENTURE</td>\n",
       "      <td>B</td>\n",
       "      <td>35000</td>\n",
       "      <td>10.37</td>\n",
       "      <td>1</td>\n",
       "      <td>0.41</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>10000</td>\n",
       "      <td>OWN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HOMEIMPROVEMENT</td>\n",
       "      <td>A</td>\n",
       "      <td>4500</td>\n",
       "      <td>8.63</td>\n",
       "      <td>1</td>\n",
       "      <td>0.45</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>95000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VENTURE</td>\n",
       "      <td>A</td>\n",
       "      <td>35000</td>\n",
       "      <td>7.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0.37</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>108160</td>\n",
       "      <td>RENT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>E</td>\n",
       "      <td>35000</td>\n",
       "      <td>18.39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>115000</td>\n",
       "      <td>RENT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>A</td>\n",
       "      <td>35000</td>\n",
       "      <td>7.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    person_age  person_income person_home_ownership  person_emp_length  \\\n",
       "0           22          59000                  RENT              123.0   \n",
       "1           21           9600                   OWN                5.0   \n",
       "2           25           9600              MORTGAGE                1.0   \n",
       "3           23          65500                  RENT                4.0   \n",
       "4           24          54400                  RENT                8.0   \n",
       "5           21           9900                   OWN                2.0   \n",
       "6           26          77100                  RENT                8.0   \n",
       "7           24          78956                  RENT                5.0   \n",
       "8           24          83000                  RENT                8.0   \n",
       "9           21          10000                   OWN                6.0   \n",
       "10          22          85000                  RENT                6.0   \n",
       "11          21          10000                   OWN                2.0   \n",
       "12          23          95000                  RENT                2.0   \n",
       "13          26         108160                  RENT                4.0   \n",
       "14          23         115000                  RENT                2.0   \n",
       "\n",
       "        loan_intent loan_grade  loan_amnt  loan_int_rate  loan_status  \\\n",
       "0          PERSONAL          D      35000          16.02            1   \n",
       "1         EDUCATION          B       1000          11.14            0   \n",
       "2           MEDICAL          C       5500          12.87            1   \n",
       "3           MEDICAL          C      35000          15.23            1   \n",
       "4           MEDICAL          C      35000          14.27            1   \n",
       "5           VENTURE          A       2500           7.14            1   \n",
       "6         EDUCATION          B      35000          12.42            1   \n",
       "7           MEDICAL          B      35000          11.11            1   \n",
       "8          PERSONAL          A      35000           8.90            1   \n",
       "9           VENTURE          D       1600          14.74            1   \n",
       "10          VENTURE          B      35000          10.37            1   \n",
       "11  HOMEIMPROVEMENT          A       4500           8.63            1   \n",
       "12          VENTURE          A      35000           7.90            1   \n",
       "13        EDUCATION          E      35000          18.39            1   \n",
       "14        EDUCATION          A      35000           7.90            0   \n",
       "\n",
       "    loan_percent_income cb_person_default_on_file  cb_person_cred_hist_length  \n",
       "0                  0.59                         Y                           3  \n",
       "1                  0.10                         N                           2  \n",
       "2                  0.57                         N                           3  \n",
       "3                  0.53                         N                           2  \n",
       "4                  0.55                         Y                           4  \n",
       "5                  0.25                         N                           2  \n",
       "6                  0.45                         N                           3  \n",
       "7                  0.44                         N                           4  \n",
       "8                  0.42                         N                           2  \n",
       "9                  0.16                         N                           3  \n",
       "10                 0.41                         N                           4  \n",
       "11                 0.45                         N                           2  \n",
       "12                 0.37                         N                           2  \n",
       "13                 0.32                         N                           4  \n",
       "14                 0.30                         N                           4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_loan.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "n, bins, patches = plt.hist(x=cr_loan['loan_status'], bins='auto', color='blue',alpha=0.7, rwidth=0.85)\n",
    "plt.xlabel(\"Loan Defaults\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "n, bins, patches = plt.hist(x=cr_loan['loan_amnt'], bins='auto', color='blue',alpha=0.7, rwidth=0.85)\n",
    "plt.xlabel(\"Loan Amount\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(x=cr_loan['person_income'], y=cr_loan['person_age'], c=cr_loan['loan_status'])\n",
    "plt.xlabel(\"Persons Income\")\n",
    "plt.ylabel(\"Persons Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(cr_loan['person_income'], cr_loan['person_age'],c='blue', alpha=0.5)\n",
    "plt.xlabel('Personal Income')\n",
    "plt.ylabel('Persone Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crosstab entre el ```tiempo de empleo de una persona``` y si la persona ```tiene una casa```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person_home_ownership  MORTGAGE  OTHER   OWN   RENT    All\n",
      "person_emp_length                                         \n",
      "0.0                        1092     23   324   2666   4105\n",
      "1.0                         806     16   218   1875   2915\n",
      "2.0                        1189     11   255   2394   3849\n",
      "3.0                        1244     12   212   1988   3456\n",
      "4.0                        1083      9   205   1577   2874\n",
      "5.0                        1298      9   219   1420   2946\n",
      "6.0                        1322      9   191   1144   2666\n",
      "7.0                        1151      3   181    861   2196\n",
      "8.0                         902      5   161    619   1687\n",
      "9.0                         791      1    92    483   1367\n",
      "10.0                        409      2    76    209    696\n",
      "11.0                        439      4    66    231    740\n",
      "12.0                        369      1    47    158    575\n",
      "13.0                        269      0    46    111    426\n",
      "14.0                        212      0    31     92    335\n",
      "15.0                        143      1    22     72    238\n",
      "16.0                        100      0    12     53    165\n",
      "17.0                         78      0    10     41    129\n",
      "18.0                         67      0    11     26    104\n",
      "19.0                         40      0     6     18     64\n",
      "20.0                         27      0     7      8     42\n",
      "21.0                         22      0     4     12     38\n",
      "22.0                         11      0     4      4     19\n",
      "23.0                          4      0     3      3     10\n",
      "24.0                          3      1     3      3     10\n",
      "25.0                          5      0     2      1      8\n",
      "26.0                          2      0     1      3      6\n",
      "27.0                          3      0     0      2      5\n",
      "28.0                          3      0     0      0      3\n",
      "29.0                          1      0     0      0      1\n",
      "30.0                          2      0     0      0      2\n",
      "31.0                          3      0     1      0      4\n",
      "34.0                          1      0     0      0      1\n",
      "38.0                          1      0     0      0      1\n",
      "41.0                          0      0     0      1      1\n",
      "123.0                         1      0     0      1      2\n",
      "All                       13093    107  2410  16076  31686\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(cr_loan['person_emp_length'], cr_loan['person_home_ownership'], margins = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crosstab entre ```Intencion del Prestamo``` y si ```pagó o no el prestamo```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_status            0     1    All\n",
      "loan_intent                          \n",
      "DEBTCONSOLIDATION   3722  1490   5212\n",
      "EDUCATION           5342  1111   6453\n",
      "HOMEIMPROVEMENT     2664   941   3605\n",
      "MEDICAL             4450  1621   6071\n",
      "PERSONAL            4423  1098   5521\n",
      "VENTURE             4872   847   5719\n",
      "All                25473  7108  32581\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(cr_loan['loan_intent'], cr_loan['loan_status'], margins = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crosstab entre si ```fue o no default``` el presamo y el ```grado``` que se le dio al prestamo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_grade       A      B     C     D    E    F   G    All\n",
      "loan_status                                               \n",
      "0             9704   8750  5119  1485  343   71   1  25473\n",
      "1             1073   1701  1339  2141  621  170  63   7108\n",
      "All          10777  10451  6458  3626  964  241  64  32581\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(cr_loan['loan_status'], cr_loan['loan_grade'], margins = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_status               0                                   1              \\\n",
      "loan_grade                A     B     C     D    E   F  G     A     B     C   \n",
      "person_home_ownership                                                         \n",
      "MORTGAGE               5219  3729  1934   658  178  36  0   239   324   321   \n",
      "OTHER                    23    29    11     9    2   0  0     3     5     6   \n",
      "OWN                     860   770   464   264   26   7  0    66    34    31   \n",
      "RENT                   3602  4222  2710   554  137  28  1   765  1338   981   \n",
      "All                    9704  8750  5119  1485  343  71  1  1073  1701  1339   \n",
      "\n",
      "loan_status                                  All  \n",
      "loan_grade                D    E    F   G         \n",
      "person_home_ownership                             \n",
      "MORTGAGE                553  161   61  31  13444  \n",
      "OTHER                    11    6    2   0    107  \n",
      "OWN                      18   31    8   5   2584  \n",
      "RENT                   1559  423   99  27  16446  \n",
      "All                    2141  621  170  63  32581  \n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(cr_loan['person_home_ownership'], [cr_loan['loan_status'], cr_loan['loan_grade']], margins = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_status                   0         1\n",
      "person_home_ownership                    \n",
      "MORTGAGE               0.146504  0.184882\n",
      "OTHER                  0.143784  0.300000\n",
      "OWN                    0.180013  0.297358\n",
      "RENT                   0.144611  0.264859\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(cr_loan['person_home_ownership'], cr_loan['loan_status'],\n",
    "              values=cr_loan['loan_percent_income'], aggfunc='mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "cr_loan.boxplot(column = ['loan_percent_income'], by = 'loan_status')\n",
    "plt.title('Average Percent Income by Loan Status')\n",
    "plt.suptitle('')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}