Alpha Insurance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n",
    "#### This notebook was used for general data exploration and descriptive stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "dtype = {\n",
    " 'Annual_Premium': 'int64',\n",
    " 'Claim_Amount': 'float64',\n",
    " 'Claim_Cause': 'category',\n",
    " 'Claim_Date': 'category',\n",
    " 'Claim_Report_Type': 'category',\n",
    " 'Claimant__Number': 'int64',\n",
    " 'Education': 'category',\n",
    " 'Employment_Status': 'category',\n",
    " 'Fraudulent_Claim': 'category',\n",
    " 'Gender': 'category',\n",
    " 'Income': 'int64',\n",
    " 'Location': 'category',\n",
    " 'Marital_Status': 'category',\n",
    " 'Monthly_Premium': 'int64',\n",
    " 'Months_Since_Last_Claim': 'int64',\n",
    " 'Months_Since_Policy_Inception': 'int64',\n",
    " 'Outstanding_Balance': 'float64',\n",
    " 'State': 'category',\n",
    " 'State_Code': 'category',\n",
    " 'Vehicle_Class': 'category',\n",
    " 'Vehicle_Model': 'category',\n",
    " 'Vehicle_Size': 'category'}\n",
    "    \n",
    "data_train = pd.read_csv('claim_data_v2_train.csv', dtype=dtype, usecols=[*dtype])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5001\n",
       "unique       2\n",
       "top          N\n",
       "freq      4694\n",
       "Name: Fraudulent_Claim, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[\"Fraudulent_Claim\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~jeffmoise/0 or inside your plot.ly account where it is named 'data_train'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jeffmoise/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb=data_train[['Location', 'Income']].groupby('Location', as_index=False).mean()\n",
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='jeffmoise', api_key='1CzEsYh8Rr6b1VZBgi8B')\n",
    "data = [go.Bar(\n",
    "            x=gb['Location'],\n",
    "            y=gb['Income'],\n",
    "            orientation = 'v'\n",
    ")]\n",
    "\n",
    "py.iplot(data, filename='data_train')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_cat(attr, df=data_train):\n",
    "    df_i = df[['Fraudulent_Claim', attr]].groupby([attr])['Fraudulent_Claim'].agg(['count','sum', 'mean'])\n",
    "    m = 'Pct Fraud'\n",
    "    df_i = df_i.rename({'count': 'frequency', 'sum': 'Fraud', 'mean': m}, axis='columns')\n",
    "    print(df_i)\n",
    "    fig, ax1 = plt.subplots()\n",
    "    ax2 = ax1.twinx()\n",
    "    df_i['frequency'].plot.bar(ax=ax1, alpha=.8)\n",
    "    df_i[m].plot.line(ax=ax2, color='k')\n",
    "    ax1.set_ylabel('frequency')\n",
    "    ax2.set_ylabel('Pct Fraud')\n",
    "    ax2.set_ylim(bottom=0, top=1, auto=False)\n",
    "    ax2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5001\n",
       "unique       2\n",
       "top          N\n",
       "freq      4694\n",
       "Name: Fraudulent_Claim, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[\"Fraudulent_Claim\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Fraud Claim data from Y/N to 1/0 for analysis purposes\n",
    "data_train.Fraudulent_Claim.replace(('Y', 'N'), (1, 0), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Education</th>\n",
       "      <th>Claim_Date</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Since_Last_Claim</th>\n",
       "      <th>Months_Since_Policy_Inception</th>\n",
       "      <th>Claim_Cause</th>\n",
       "      <th>Claim_Report_Type</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th>Outstanding_Balance</th>\n",
       "      <th>Fraudulent_Claim</th>\n",
       "      <th>Claimant__Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>575.560000</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>56274</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>25781.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NE</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>697.953590</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>42</td>\n",
       "      <td>Scratch/Dent</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>37061.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OK</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>1288.743165</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48767</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>17236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MO</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>764.586183</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Honda</td>\n",
       "      <td>30270.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KS</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>281.369258</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>43836</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>44</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Ford</td>\n",
       "      <td>16263.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State_Code     State  Claim_Amount Education  Claim_Date Employment_Status  \\\n",
       "0         KS    Kansas    575.560000  Bachelor  12/01/2018          Employed   \n",
       "1         NE  Nebraska    697.953590  Bachelor  12/01/2018        Unemployed   \n",
       "2         OK  Oklahoma   1288.743165  Bachelor  12/01/2018          Employed   \n",
       "3         MO  Missouri    764.586183  Bachelor  12/01/2018        Unemployed   \n",
       "4         KS    Kansas    281.369258  Bachelor  12/01/2018          Employed   \n",
       "\n",
       "  Gender  Income  Location Marital_Status        ...         \\\n",
       "0      F   56274  Suburban        Married        ...          \n",
       "1      F       0       NaN         Single        ...          \n",
       "2      F   48767  Suburban        Married        ...          \n",
       "3      M       0  Suburban        Married        ...          \n",
       "4      M   43836     Rural         Single        ...          \n",
       "\n",
       "   Months_Since_Last_Claim  Months_Since_Policy_Inception   Claim_Cause  \\\n",
       "0                       32                              5     Collision   \n",
       "1                       13                             42  Scratch/Dent   \n",
       "2                       18                             38     Collision   \n",
       "3                       18                             65     Collision   \n",
       "4                       52                             44     Collision   \n",
       "\n",
       "   Claim_Report_Type  Vehicle_Class Vehicle_Size Vehicle_Model  \\\n",
       "0              Agent   Two-Door Car      Midsize        Toyota   \n",
       "1              Agent  Four-Door Car      Midsize     Chevrolet   \n",
       "2              Agent   Two-Door Car      Midsize     Chevrolet   \n",
       "3        Call Center            SUV      Midsize         Honda   \n",
       "4              Agent  Four-Door Car      Midsize          Ford   \n",
       "\n",
       "  Outstanding_Balance Fraudulent_Claim  Claimant__Number  \n",
       "0             25781.0                0              1001  \n",
       "1             37061.0                0              1002  \n",
       "2             17236.0                0              1003  \n",
       "3             30270.0                0              1004  \n",
       "4             16263.0                0              1005  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test to see if fraud claim data converted correctly \n",
    "data_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State_Code', 'State', 'Claim_Amount', 'Education', 'Claim_Date',\n",
       "       'Employment_Status', 'Gender', 'Income', 'Location', 'Marital_Status',\n",
       "       'Monthly_Premium', 'Annual_Premium', 'Months_Since_Last_Claim',\n",
       "       'Months_Since_Policy_Inception', 'Claim_Cause', 'Claim_Report_Type',\n",
       "       'Vehicle_Class', 'Vehicle_Size', 'Vehicle_Model', 'Outstanding_Balance',\n",
       "       'Fraudulent_Claim', 'Claimant__Number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Education</th>\n",
       "      <th>Claim_Date</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Since_Last_Claim</th>\n",
       "      <th>Months_Since_Policy_Inception</th>\n",
       "      <th>Claim_Cause</th>\n",
       "      <th>Claim_Report_Type</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th>Outstanding_Balance</th>\n",
       "      <th>Fraudulent_Claim</th>\n",
       "      <th>Claimant__Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>4990</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5.001000e+03</td>\n",
       "      <td>4998</td>\n",
       "      <td>5001</td>\n",
       "      <td>...</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>5001</td>\n",
       "      <td>4992.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "      <td>5001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>IA</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>College</td>\n",
       "      <td>12/15/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1546</td>\n",
       "      <td>1546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1492</td>\n",
       "      <td>2000</td>\n",
       "      <td>3126</td>\n",
       "      <td>2531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3156</td>\n",
       "      <td>2930</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2077</td>\n",
       "      <td>1889</td>\n",
       "      <td>2535</td>\n",
       "      <td>3523</td>\n",
       "      <td>1278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>787.763253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.131045e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>15.004199</td>\n",
       "      <td>48.234953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23728.152644</td>\n",
       "      <td>0.061388</td>\n",
       "      <td>3501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>655.963269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.276904e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>11.139649</td>\n",
       "      <td>28.096651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13827.401917</td>\n",
       "      <td>0.240064</td>\n",
       "      <td>1443.808678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.868369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>402.296353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11528.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>577.390039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.464000e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23993.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>886.114954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.300900e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35574.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4751.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7422.851604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.596780e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47996.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State_Code State  Claim_Amount Education  Claim_Date Employment_Status  \\\n",
       "count        5001  5001   5001.000000      4990        5001              5001   \n",
       "unique          5     5           NaN         5           3                 5   \n",
       "top            IA  Iowa           NaN   College  12/15/2018          Employed   \n",
       "freq         1546  1546           NaN      1492        2000              3126   \n",
       "mean          NaN   NaN    787.763253       NaN         NaN               NaN   \n",
       "std           NaN   NaN    655.963269       NaN         NaN               NaN   \n",
       "min           NaN   NaN    189.868369       NaN         NaN               NaN   \n",
       "25%           NaN   NaN    402.296353       NaN         NaN               NaN   \n",
       "50%           NaN   NaN    577.390039       NaN         NaN               NaN   \n",
       "75%           NaN   NaN    886.114954       NaN         NaN               NaN   \n",
       "max           NaN   NaN   7422.851604       NaN         NaN               NaN   \n",
       "\n",
       "       Gender        Income  Location Marital_Status        ...         \\\n",
       "count    5001  5.001000e+03      4998           5001        ...          \n",
       "unique      2           NaN         3              3        ...          \n",
       "top         M           NaN  Suburban        Married        ...          \n",
       "freq     2531           NaN      3156           2930        ...          \n",
       "mean      NaN  4.131045e+04       NaN            NaN        ...          \n",
       "std       NaN  2.276904e+05       NaN            NaN        ...          \n",
       "min       NaN  0.000000e+00       NaN            NaN        ...          \n",
       "25%       NaN  0.000000e+00       NaN            NaN        ...          \n",
       "50%       NaN  3.464000e+04       NaN            NaN        ...          \n",
       "75%       NaN  6.300900e+04       NaN            NaN        ...          \n",
       "max       NaN  1.596780e+07       NaN            NaN        ...          \n",
       "\n",
       "        Months_Since_Last_Claim  Months_Since_Policy_Inception  Claim_Cause  \\\n",
       "count               5001.000000                    5001.000000         5001   \n",
       "unique                      NaN                            NaN            5   \n",
       "top                         NaN                            NaN    Collision   \n",
       "freq                        NaN                            NaN         2077   \n",
       "mean                  15.004199                      48.234953          NaN   \n",
       "std                   11.139649                      28.096651          NaN   \n",
       "min                    0.000000                       0.000000          NaN   \n",
       "25%                    5.000000                      24.000000          NaN   \n",
       "50%                   13.000000                      48.000000          NaN   \n",
       "75%                   23.000000                      72.000000          NaN   \n",
       "max                   60.000000                      99.000000          NaN   \n",
       "\n",
       "        Claim_Report_Type  Vehicle_Class Vehicle_Size Vehicle_Model  \\\n",
       "count                5001           5001         5001          5001   \n",
       "unique                  4              6            3             4   \n",
       "top                 Agent  Four-Door Car      Midsize        Toyota   \n",
       "freq                 1889           2535         3523          1278   \n",
       "mean                  NaN            NaN          NaN           NaN   \n",
       "std                   NaN            NaN          NaN           NaN   \n",
       "min                   NaN            NaN          NaN           NaN   \n",
       "25%                   NaN            NaN          NaN           NaN   \n",
       "50%                   NaN            NaN          NaN           NaN   \n",
       "75%                   NaN            NaN          NaN           NaN   \n",
       "max                   NaN            NaN          NaN           NaN   \n",
       "\n",
       "       Outstanding_Balance Fraudulent_Claim  Claimant__Number  \n",
       "count          4992.000000      5001.000000       5001.000000  \n",
       "unique                 NaN              NaN               NaN  \n",
       "top                    NaN              NaN               NaN  \n",
       "freq                   NaN              NaN               NaN  \n",
       "mean          23728.152644         0.061388       3501.000000  \n",
       "std           13827.401917         0.240064       1443.808678  \n",
       "min               4.000000         0.000000       1001.000000  \n",
       "25%           11528.750000         0.000000       2251.000000  \n",
       "50%           23993.500000         0.000000       3501.000000  \n",
       "75%           35574.000000         0.000000       4751.000000  \n",
       "max           47996.000000         1.000000       6001.000000  \n",
       "\n",
       "[11 rows x 22 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding Missing Data Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1788a10>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data\n",
    "msno.matrix(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_Code                        0\n",
       "State                             0\n",
       "Claim_Amount                      0\n",
       "Education                        11\n",
       "Claim_Date                        0\n",
       "Employment_Status                 0\n",
       "Gender                            0\n",
       "Income                            0\n",
       "Location                          3\n",
       "Marital_Status                    0\n",
       "Monthly_Premium                   0\n",
       "Annual_Premium                    0\n",
       "Months_Since_Last_Claim           0\n",
       "Months_Since_Policy_Inception     0\n",
       "Claim_Cause                       0\n",
       "Claim_Report_Type                 0\n",
       "Vehicle_Class                     0\n",
       "Vehicle_Size                      0\n",
       "Vehicle_Model                     0\n",
       "Outstanding_Balance               9\n",
       "Fraudulent_Claim                  0\n",
       "Claimant__Number                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking for correlations with a correlation heatmap in seaborn package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "corrmat= data_train.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at individual variables with histograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce6d910>"
      ]
     },
     "execution_count": 37,
     "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": [
    "#potential log tranformation\n",
    "data_train['Claim_Amount'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cea0eb0>"
      ]
     },
     "execution_count": 38,
     "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": [
    "data_train['Income'].describe()\n",
    "data_train['Income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e1aac70>"
      ]
     },
     "execution_count": 39,
     "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": [
    "data=data_train['Income'].where(data_train['Income']<=100000,other=100001)\n",
    "data.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e1eda30>"
      ]
     },
     "execution_count": 40,
     "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": [
    "data_train['Annual_Premium'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e225ef0>"
      ]
     },
     "execution_count": 41,
     "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": [
    "data_train['Monthly_Premium'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e1e29b0>"
      ]
     },
     "execution_count": 42,
     "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": [
    "data_train['Months_Since_Last_Claim'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b997e90>"
      ]
     },
     "execution_count": 43,
     "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": [
    "data_train['Months_Since_Policy_Inception'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1325190>"
      ]
     },
     "execution_count": 44,
     "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": [
    "data_train['Outstanding_Balance'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using violin graphs in the seaborn package to further investigate fraud claim vs certain varibales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Fraudulent_Claim\", y=\"Claim_Amount\", kind=\"violin\",data=data_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Fraudulent_Claim\", y=\"Income\", kind=\"violin\",data=data_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Fraudulent_Claim\", y=\"State\", kind=\"violin\",data=data_train);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at Claim Cause within the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Collision, Scratch/Dent, Hail, Other, Fire]\n",
       "Categories (5, object): [Collision, Scratch/Dent, Hail, Other, Fire]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Claim_Cause.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAELCAYAAADKjLEqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAGolJREFUeJzt3XmcJWV97/HPlxkB2V8sSmTJYMB40SBXR9S4iwsmKC74YlwCXo1crxJz3SZorgRRo+IWFdBgEHGJkOA2xlFQUYxEcWZcgEHREREGMgKCwLDJwO/+UdXOoTk9XYfpmu6Z/rxfr3511VNP1fmd0+f079Tz1PNUqgpJkiaz2XQHIEnaOJgwJEmdmDAkSZ2YMCRJnZgwJEmdmDAkSZ2YMCRJnZgwJEmdmDAkSZ3Mne4AptLOO+9c8+bNm+4wJGmjsmzZsmurapfJ6m1SCWPevHksXbp0usOQpI1Kkl93qWeTlCSpExOGJKkTE4YkqRMThiSpExOGJKkTE4YkqRMThiSpExOGJKmTTWrgnjRTLVy4kFWrVrHrrrty/PHHT3c40r1iwpA2gFWrVnHllVdOdxjSerFJSpLUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktTJ3OkOQNLssnDhQlatWsWuu+7K8ccfP93haAQmDEkb1KpVq7jyyiunOwzdCzZJSZI6MWFIkjoxYUiSOjFhSJI6MWFIkjrpPWEkOSjJJUlWJDl6yPYtkpzRbj8/yby2/D5JTktyYZKfJnlT37FKkibW62W1SeYAJwJPA1YCS5IsqqqLB6q9HLi+qvZOsgB4N3AY8AJgi6r6syRbARcn+WxVXdZnzJpdHvvhx26Qx9n8d5uzGZtxxe+u6P0xz/ub83o9vmavvs8wDgBWVNWlVfV74HTgkHF1DgFOa5fPBA5MEqCArZPMBe4L/B64sed4JUkT6Dth7AZcMbC+si0bWqeq1gA3ADvRJI+bgf8GLgfeW1XXjX+AJEcmWZpk6TXXXDP1z0CSBPSfMDKkrDrWOQC4E3gAsBfw+iQPvEfFqpOran5Vzd9ll13WN15J0gT6ThgrgT0G1ncHrpqoTtv8tD1wHfAi4GtVdUdVXQ2cB8zvOV5J0gT6ThhLgH2S7JVkc2ABsGhcnUXAEe3yocA5VVU0zVBPSWNr4NHAz3qOd70tXLiQww8/nIULF053KJI0pXq9Sqqq1iQ5CjgLmAN8vKqWJzkOWFpVi4BTgE8lWUFzZrGg3f1E4FTgIppmq1Or6oI+450KTqwmaVPV+2y1VbUYWDyu7JiB5dtoLqEdv9/qYeWSpOnhSG9JUicmDElSJyYMSVInJgxJUicmDElSJyYMSVInJgxJUicmDElSJyYMSVInJgxJUicmDElSJyYMSVInJgxJUicmDElSJyYMSVInJgxJUicmDElSJyYMSVInJgxJUicmDElSJ3O7VkxyMPA24I/b/QJUVW3XU2xT6hFv/OQGeZxtr72JOcDl197U+2Mue8/hvR5fkgZ1ThjAPwHPAy6squopHknSDDVKk9QVwEUmC0manUY5w1gILE5yLnD7WGFVvX/Ko5IkzTijJIx3AKuBLYHN+wlH2jTVVsVd3EVt5Qm6Nl6jJIwdq+rpvUUibcLueOwd0x2CtN5G6cP4RhIThiTNUqMkjFcDX0tya5Ibk9yU5Ma+ApMkzSydm6Sqats+A5EkzWyTJowkD66qnyV5+LDtVfXDqQ9LkjTTdDnDeB1wJPC+IdsKeMqURiRpWpz7hCdukMe5de4cSLh15creH/OJ3zm31+PPNpMmjKo6sv395P7DkSTNVKNcVkuShwL70ozFAKCqNswkTZKkaTXK5IP/ADyJJmEsBp4JfBcwYUjSLDDKZbWHAgcCq6rqfwEPA7boJSpJ0owzSsK4taruAtYk2Q64GnjgZDslOSjJJUlWJDl6yPYtkpzRbj8/ybyBbfsl+V6S5UkuTLLl+P0lSRvGKAljaZIdgI8By4AfAj9Y1w5J5gAn0jRf7Qu8MMm+46q9HLi+qvYGPgC8u913LvBp4JVV9RCa5jDnV5CkaTLKwL1XtYsfTfI1YLuqumCS3Q4AVlTVpQBJTgcOAS4eqHMIcGy7fCZwQpIATwcuqKqftI//266xSpKmXpeBe0MH7I1tm2Tg3m4099EYsxJ41ER1qmpNkhuAnYAHAZXkLGAX4PSqOn5IDEfSjBNhzz33nOzpSJLupS5nGMMG7I2ZbOBeJtinS525wOOARwK3AN9Msqyqvnm3ilUnAycDzJ8/37mjJaknXQburc+AvZXAHgPruwNXTVBnZdtvsT1wXVt+blVdC5BkMfBw4JtIkja4STu9k7wkyV8NKX9FkhdNsvsSYJ8keyXZHFgALBpXZxFwRLt8KHBOexvYs4D9kmzVJpIncve+D0nSBtSlSer1wBOGlJ8BfAv414l2bPskjqL55z8H+HhVLU9yHLC0qhYBpwCfSrKC5sxiQbvv9UneT5N0ClhcVV/p/tQkSVOpS8KYU1U3jS+sqhuT3GeynatqMc3I8MGyYwaWbwNeMMG+n6a5tFaSNM26jMO4T5Ktxxcm2Rbv7S1Js0aXhHEKcOa4EdjzgNPbbZKkWaDLVVLvTbIaODfJNjT9CTcD76qqj/Qd4Mbmrs23vttvSdpUdBrpXVUfpRnhvQ2QYX0aSY6oqtOmOsCNzc37PH26Q5CkXowylxRVtXpYsmj97RTEI0maoUZKGJMYNmJbkrSJmMqE4bQckrQJ8wxDktRJ54SRZK9Jys6bkogkSTPSKGcYnxtSdubYQlUdtf7hSJJmqi73w3gw8BBg+yTPG9i0HeAtUyVplugyDuNPgYOBHYBnDZTfBLyij6AkSTNPl5HeXwK+lOQxVfW9DRCTJGkG6nxPb2BFkjcD8wb3q6qXTXVQkqSZZ5SE8SXgP4FvAHf2E44kaaYaJWFsVVV/11skkqQZbZTLav8jyV/0FokkaUYbJWH8LU3SuC3JjUluSnJjX4FJkmaWzk1SVbVtn4FIkma2UaYGSZKXJHlLu75HkgP6C02SNJOM0iR1EvAY4EXt+mrgxCmPSJI0I41yldSjqurhSX4EUFXXJ9m8p7gkSTPMKGcYdySZQ3vfiyS7AHf1EpUkacYZJWF8CPgCcL8k7wC+C/xjL1FJkmacUa6S+kySZcCBNDdLek5V/bS3yCRJM0qX6c13HFi9Gvjs4Laquq6PwCRJM0uXM4xlNP0Wg7dgHVsv4IE9xCVJmmG6TG9+j1uzSpJmn859GEmeMKy8qr4zdeFIkmaqUcZhvHFgeUvgAJrmqqdMaUSSNmk7VN3ttzYeo1wlNXh7VpLsARw/5RFJ2qS95E6Hb22sRhmHMd5K4KFTFYgkaWYbpQ/jw7SjvGkSzf7AT/oISpI084zSh7F0YHkN8NmqOm+K45EkzVCdm6Sq6rSBn890TRZJDkpySZIVSY4esn2LJGe0289PMm/c9j2TrE7yhq6xSpKmXpeR3heytinqHqpqv3XsO4dmCvSn0fR5LEmyqKouHqj2cuD6qto7yQLg3cBhA9s/AHx1sjglSf3q0iR1cPv71e3vT7W/XwzcMsm+BwArqupSgCSnA4cAgwnjEODYdvlM4IQkqapK8hzgUuDmDnFKkno0aZNUVf26qn4NPLaqFlbVhe3P0cAzJtl9N+CKgfWVbdnQOlW1BrgB2CnJ1sDfAW/t9lQkSX0a5bLarZM8bmwlyZ8DW0+yT4aUjW/emqjOW4EPVNXqdT5AcmSSpUmWXnPNNZOEI0m6t0a5SurlwMeTbN+u/w542ST7rAT2GFjfHbhqgjork8wFtgeuAx4FHJrkeGAH4K4kt1XVCYM7V9XJwMkA8+fPd+ioJPVklJHey4CHJdkOSFXd0GG3JcA+SfYCrgQWsPae4GMWAUcA3wMOBc6pqgIeP1YhybHA6vHJQpK04YwycO+YcesAVNVxE+1TVWuSHAWcBcwBPl5Vy5McByytqkXAKcCnkqygObNYMPKzkCT1bpQmqcErlbakuXpq0jvuVdViYPG4smMGlm8DXjDJMY4dIU5JUg9GaZJ63+B6kvfSNCdJkmaB9Zl8cCu8254kzRqj9GEMjvieA+wCTNh/IUnatIzSh3HwwPIa4DftQDtJ0iwwSh/GrwGS3I+m0/sBSaiqy/sKTpI0c3Tuw0jy7CS/AH4FnAtchpMCStKsMUqn99uARwM/r6q9gAMB74chSbPEKAnjjqr6LbBZks2q6ls0d92TJM0Co3R6/y7JNsB3gM8kuZqm81uSNAuMcoZxCM39L14LfA34JfCsPoKSJM08nc4w2jvnfamqngrcBZzWa1SSpBmn0xlGVd0J3DIwtbkkaZYZpQ/jNuDCJF9nYCLCqnrNlEclSZpxRkkYX2l/JEmz0KQJI8meVXV5VdlvIUmzWJc+jC+OLST5XI+xSJJmsC4JIwPLTmcuSbNUl4RREyxLkmaRLp3eD0tyI82Zxn3bZdr1qqrteotOkjRjTJowqmrOhghEkjSzrc8tWiVJs4gJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktSJCUOS1IkJQ5LUiQlDktRJ7wkjyUFJLkmyIsnRQ7ZvkeSMdvv5Sea15U9LsizJhe3vp/QdqyRpYr0mjCRzgBOBZwL7Ai9Msu+4ai8Hrq+qvYEPAO9uy68FnlVVfwYcAXyqz1glSevW9xnGAcCKqrq0qn4PnA4cMq7OIcBp7fKZwIFJUlU/qqqr2vLlwJZJtug5XknSBPpOGLsBVwysr2zLhtapqjXADcBO4+o8H/hRVd3eU5ySpElMek/v9ZQhZTVKnSQPoWmmevrQB0iOBI4E2HPPPe9dlJKkSfV9hrES2GNgfXfgqonqJJkLbA9c167vDnwBOLyqfjnsAarq5KqaX1Xzd9lllykOX5I0pu+EsQTYJ8leSTYHFgCLxtVZRNOpDXAocE5VVZIdgK8Ab6qq83qOU5I0iV4TRtsncRRwFvBT4N+qanmS45I8u612CrBTkhXA64CxS2+PAvYG3pLkx+3P/fqMV5I0sb77MKiqxcDicWXHDCzfBrxgyH5vB97ed3ySpG4c6S1J6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqxIQhSerEhCFJ6sSEIUnqpPeEkeSgJJckWZHk6CHbt0hyRrv9/CTzBra9qS2/JMkz+o5VkjSxXhNGkjnAicAzgX2BFybZd1y1lwPXV9XewAeAd7f77gssAB4CHASc1B5PkjQN+j7DOABYUVWXVtXvgdOBQ8bVOQQ4rV0+EzgwSdry06vq9qr6FbCiPZ4kaRr0nTB2A64YWF/Zlg2tU1VrgBuAnTruK0naQOb2fPwMKauOdbrsS5IjgSPb1dVJLhkpwn7sDFzb94PkvUf0/RBTYYO8FhuJDfO+eM2wj86Ms2HeF/G16OiPu1TqO2GsBPYYWN8duGqCOiuTzAW2B67ruC9VdTJw8hTGvN6SLK2q+dMdx0zga7GWr8VavhZrbUyvRd9NUkuAfZLslWRzmk7sRePqLALGviofCpxTVdWWL2ivotoL2Af4Qc/xSpIm0OsZRlWtSXIUcBYwB/h4VS1PchywtKoWAacAn0qygubMYkG77/Ik/wZcDKwBXl1Vd/YZryRpYmm+zGsqJTmybSqb9Xwt1vK1WMvXYq2N6bUwYUiSOnFqEElSJyYMIMmuSU5P8sskFydZnORBE9Sdl+SidvlJSf6jXX72sKlPBvabn+RD/TyD6ZHkziQ/HviZtyk+zy6SrB63/tIkJ0yyzx/eM0mOTfKGPmPsS5Ldk3wpyS/az9AHk2yeZP8kfzFQb8Y+xyR/n2R5kgva9/Kj1vN4OyR5VYd6304y4RVSSZa1r+VlSS5sfy5O8vYkW6xHfC9N8oBR9+v7stoZrx1V/gXgtKpa0JbtD9wf+HnX47Qd+OOvABvcvhRYun7Rzji3VtX+48ouY8jzTDK3HZip1mTvmY1B+/n5PPCRqjqknb7nZOAdwHJgPrB4ih5rTh8XviR5DHAw8PCquj3JzsDmHfZb13t6B+BVwEnrEdc84Mqq+n3zMvPkqro2yTY0r/HJrL3CdFQvBS5iyFCFdfEMA54M3FFVHx0rqKofA99N8p4kF7VZ/bB1HWTwG2WSF7T7/STJd9qywbORHZN8sf028/0k+7Xlxyb5ePut49Ikr+nrSfdl3PM8NsnJSc4GPplkTvuaLmmf+/+e5nA3iCTPSjOx5o+SfCPJ/dvySc9CNgJPAW6rqlMB2n/orwX+GjgeOKz9xj72+dl32Ps7yUuS/KCt+89t4iHJ6iTHJTkfeExPz+GPgGur6vb2OVxbVVcleWSS/2o/xz9Ism37N/v3JF8Gzk6yTZJvJvlh+39ibOqjdwF/0j6f97TPZWFb5ydJ3jXw+C9oj//zJI8fKH8m8LXxwVbVauCVwHOS7Nge+40Dn6u3tmXzkvw0ycfas6ezk9w3yaE0ifwzbXz37fpCzfozDOChwLIh5c8D9gceRjMSc8nYP/8OjgGeUVVXJtlhyPa3Aj+qquckeQrwyfaxAB5Mk8S2BS5J8pGquqP709mg7pvkx+3yr6rquUPqPAJ4XFXdmmZU/g1V9cj2dPq8JGe3c4Vt7AZfC4AdWXv28F3g0VVVSf4aWAi8fkMH2JOHMO7zU1U3JrkMOBV4UFUdBc0XCIa8v4G9gcOAx1bVHUlOAl5M87nYGrioqo7p8TmcDRyT5OfAN4AzgO+1vw+rqiVJtgNubes/Btivqq5LM9j4ue1z3hn4fpJFwNHAQ8fOwJM8E3gO8KiqumXsH31rblUdkKb57h+Ap7blB9Ek33toH+9XNOPctqcZp3YAzQwZi5I8Abi8LX9hVb0izTCF51fVp9MMd3hD2/LRmQljYo8DPtt+Y/pNknOBRwIXdNj3POAT7R/o8xMc+/kAVXVOkp3aPzrAV9pvOrcnuZqmaWzlej6XvgxrkhpvUVWNfdCeDuzXfsOBZlT/PsCmkDDu9lokeSnNtzhoZik4I8kf0TR1bArPd0wYMmXPOsqHvb8PpPlisaRterkvcHVb/07gc1Md9KCqWp3kEcDjaZLZGTRNav9dVUvaOjcCtPF9vaqua3cP8I/tP+i7aOa7u/+Qh3kqcGpV3dIe77qBbWP/I5YB89rH2RzYvaouXUfoY/OePL39+VG7vg3N5+pymi9yY19k/nD8e8uE0bSzHjqk/F5PQlNVr0zTafaXwI/T9IlMduyxD9ftA2V3svH/jW4eWA7wN1V11nQFM00+DLy/qhYleRJw7PSGM6WW0375GdN+G9+D5v073rD3d2j6EN80pP5tG2LAbvsY3wa+neRC4NUMT3hw9/f0i4FdgEe0Z0eXAVsO2WeiBAprX5PBz/vjac5Mh0qyLc0//5+3x35nVf3zuDrzuOfr3bn5aRj7MOAcYIskrxgrSPJI4Hqa9tc5SXYBnkDHqUmS/ElVnd+eRl/L3efEAvgOzRuN9h/ItWPfYDZxZwH/J8l9AJI8KMnW0xzThrA9cGW7vFHMGDmCbwJbJTkc/nAPnPcBnwB+Q9P01OUYhya5X3uMHZN0mgxvKiT50yT7DBTtD/wUeED7v4C2/2LYl7ftgavbZPFk1k7idxN3f+5nAy9LslV7vB1Zt4OAr04Q7zY0nelfrKrraT5XL2vLSbLb2Gu5DuPj62Rj//a63tp25ecC/5TmEsfbaK70+b80p3Y/oflmsLCqVmXgjoDr8J72DRiaD8NPgCcObD8WODXJBcAtbHr/RCbyLzTfin6Y5tz+Gpp23U3dscC/J7kS+D6w1/SGM3UGPj8nJXkLzZfQxcCbafofjm77dt65jmNcnOT/0XQibwbcQfMN/9e9P4HGNsCH2/7GNTT33jmSpg/mw22n8K2s7VsY9Bngy0mWAj8GfgZQVb9Ncl6aS/C/WlVvbFsalib5PWtfo4k8iaYvdNC32s/NZjRXdr6tfayzk/wP4Httk9lq4CUMP8Mb8wngo0luBR4z0Gy8To70lqQZJMnuwMeq6pnTHct4JgxJUif2YUiSOjFhSJI6MWFIkjoxYUiSOjFhSJI6MWFoVskEU9m318uva78HJDlzimJ4Q5KfZe0ElYdPxXGlvs36gXuaPdpBTxNNZb9OVXUVw6eQGTWGVwJPAw5oJ5DbntkxeFGbAM8wNJtMNJX9FWPraaaE/s8001X/MMmfD5SP3TjrpWmmp/9ykl8lOSrJ69JMX/79SaZ9eDPwqrGpYKrqhqo6rT3uMWmmqL4ozbTwacv/cJOdJDu38xWR5CFZOyX4BWPTW2SCqcKl9WXC0Gwy0VT2g64GnlZVD6eZcnuiuwc+FHgRzZTS7wBuqar/STMt9tAmpnbCuG2r6pcTHPOEqnpkVT2UZpK4gyeJ9ZXAB9tZcucDK9spIsamCt+fZnqIF09yHKkTm6Sku7sPcELbVHUnMPRWvcC3quom4KYkNwBfbssvBPabYJ91zVgK8OQkC4GtaO6nsXzguMN8D/j7diqJz1fVL5Ksa6pwab2YMDSbTDSV/aDX0syy+jCaM/DbJqg3OG30XQPrdzHB56rts7g5yQPH3+cgyZY0M5DOr6or0txsaGya7DWsbQ3YcuB4/5rmTnR/CZyV5uZM65oqXFovNklpNploKvvBqbS3p7lxzl3AXwFT3f7/TuDENPeMIMl2ae5EOJYIxu7ZPJjYLqM5a2CwPMkDgUur6kM0d/fbj2meKlybNhOGZo1qZtp8LvC09rLa5TRTj181UO0k4Igk36dpjrr5HgdaPx8BvkXTZHQRcC5N/8fvgI/RNGl9EVgysM97ae4j8l80twsecxhwUTt9+IOBT1bVxcDYVOEXAF+nuWe1tN6crVaS1IlnGJKkTuz0lnqQ5ETgseOKP1hVp05HPNJUsElKktSJTVKSpE5MGJKkTkwYkqROTBiSpE5MGJKkTv4/BIy8oeE2TnIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "sns.barplot(x=\"Claim_Cause\", y=\"Fraudulent_Claim\", data=data_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Claim_Cause Fraudulent_Claim                \n",
      "                             sum      mean count\n",
      "0     Collision              142  0.068368  2077\n",
      "1          Fire                0  0.000000     1\n",
      "2          Hail              128  0.081115  1578\n",
      "3         Other               37  0.063793   580\n",
      "4  Scratch/Dent                0  0.000000   765\n"
     ]
    }
   ],
   "source": [
    "# Hail slightly higher mean than collision and other but scratch/dent is ZERO\n",
    "claim_cause1=data_train.groupby('Claim_Cause', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})\n",
    "print(claim_cause1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouping by certain variables to look at the sum and percentage of Fraudulent Claims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>102</td>\n",
       "      <td>0.065977</td>\n",
       "      <td>1546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>35</td>\n",
       "      <td>0.075269</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>75</td>\n",
       "      <td>0.051867</td>\n",
       "      <td>1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>67</td>\n",
       "      <td>0.065558</td>\n",
       "      <td>1022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>28</td>\n",
       "      <td>0.053640</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      State Fraudulent_Claim                \n",
       "                         sum      mean count\n",
       "0      Iowa              102  0.065977  1546\n",
       "1    Kansas               35  0.075269   465\n",
       "2  Missouri               75  0.051867  1446\n",
       "3  Nebraska               67  0.065558  1022\n",
       "4  Oklahoma               28  0.053640   522"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.groupby('State', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Education</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bachelor</td>\n",
       "      <td>91</td>\n",
       "      <td>0.061033</td>\n",
       "      <td>1491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>College</td>\n",
       "      <td>88</td>\n",
       "      <td>0.058981</td>\n",
       "      <td>1492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Doctor</td>\n",
       "      <td>12</td>\n",
       "      <td>0.057971</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High School or Below</td>\n",
       "      <td>87</td>\n",
       "      <td>0.061922</td>\n",
       "      <td>1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Master</td>\n",
       "      <td>29</td>\n",
       "      <td>0.073418</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Education Fraudulent_Claim                \n",
       "                                     sum      mean count\n",
       "0              Bachelor               91  0.061033  1491\n",
       "1               College               88  0.058981  1492\n",
       "2                Doctor               12  0.057971   207\n",
       "3  High School or Below               87  0.061922  1405\n",
       "4                Master               29  0.073418   395"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.groupby('Education', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>0.025101</td>\n",
       "      <td>2470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>245</td>\n",
       "      <td>0.096800</td>\n",
       "      <td>2531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Fraudulent_Claim                \n",
       "                      sum      mean count\n",
       "0      F               62  0.025101  2470\n",
       "1      M              245  0.096800  2531"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#males have much higher fraud claim mean\n",
    "data_train.groupby('Gender', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning:\n",
      "\n",
      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "\n"
     ]
    },
    {
     "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": [
    "%matplotlib inline\n",
    "\n",
    "sns.barplot(x=\"Gender\", y=\"Fraudulent_Claim\", data=data_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>212</td>\n",
       "      <td>0.083629</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Luxury Car</td>\n",
       "      <td>2</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Luxury SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>0.026087</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUV</td>\n",
       "      <td>3</td>\n",
       "      <td>0.003036</td>\n",
       "      <td>988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sports Car</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>87</td>\n",
       "      <td>0.084548</td>\n",
       "      <td>1029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Vehicle_Class Fraudulent_Claim                \n",
       "                              sum      mean count\n",
       "0  Four-Door Car              212  0.083629  2535\n",
       "1     Luxury Car                2  0.025641    78\n",
       "2     Luxury SUV                3  0.026087   115\n",
       "3            SUV                3  0.003036   988\n",
       "4     Sports Car                0  0.000000   256\n",
       "5   Two-Door Car               87  0.084548  1029"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Four-Door and Two-Door Cars much higher means but also much higher counts\n",
    "data_train.groupby('Vehicle_Class', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Compact</td>\n",
       "      <td>50</td>\n",
       "      <td>0.051813</td>\n",
       "      <td>965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Luxury</td>\n",
       "      <td>32</td>\n",
       "      <td>0.062378</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Midsize</td>\n",
       "      <td>225</td>\n",
       "      <td>0.063866</td>\n",
       "      <td>3523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Vehicle_Size Fraudulent_Claim                \n",
       "                            sum      mean count\n",
       "0      Compact               50  0.051813   965\n",
       "1       Luxury               32  0.062378   513\n",
       "2      Midsize              225  0.063866  3523"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.groupby('Vehicle_Size', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>77</td>\n",
       "      <td>0.060773</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ford</td>\n",
       "      <td>77</td>\n",
       "      <td>0.060870</td>\n",
       "      <td>1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>78</td>\n",
       "      <td>0.065491</td>\n",
       "      <td>1191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>75</td>\n",
       "      <td>0.058685</td>\n",
       "      <td>1278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Vehicle_Model Fraudulent_Claim                \n",
       "                             sum      mean count\n",
       "0     Chevrolet               77  0.060773  1267\n",
       "1          Ford               77  0.060870  1265\n",
       "2         Honda               78  0.065491  1191\n",
       "3        Toyota               75  0.058685  1278"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.groupby('Vehicle_Model', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disabled</td>\n",
       "      <td>12</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Employed</td>\n",
       "      <td>174</td>\n",
       "      <td>0.055662</td>\n",
       "      <td>3126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medical Leave</td>\n",
       "      <td>15</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Retired</td>\n",
       "      <td>7</td>\n",
       "      <td>0.044304</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Unemployed</td>\n",
       "      <td>99</td>\n",
       "      <td>0.077953</td>\n",
       "      <td>1270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employment_Status Fraudulent_Claim                \n",
       "                                 sum      mean count\n",
       "0          Disabled               12  0.056338   213\n",
       "1          Employed              174  0.055662  3126\n",
       "2     Medical Leave               15  0.064103   234\n",
       "3           Retired                7  0.044304   158\n",
       "4        Unemployed               99  0.077953  1270"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unemployed showing highest fraud claim\n",
    "data_train.groupby('Employment_Status', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>37</td>\n",
       "      <td>0.049333</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Married</td>\n",
       "      <td>156</td>\n",
       "      <td>0.053242</td>\n",
       "      <td>2930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Single</td>\n",
       "      <td>114</td>\n",
       "      <td>0.086298</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Marital_Status Fraudulent_Claim                \n",
       "                              sum      mean count\n",
       "0       Divorced               37  0.049333   750\n",
       "1        Married              156  0.053242  2930\n",
       "2         Single              114  0.086298  1321"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Single tends to show a higher mean of fraud claims\n",
    "data_train.groupby('Marital_Status', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Fraudulent_Claim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rural</td>\n",
       "      <td>47</td>\n",
       "      <td>0.047910</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Suburban</td>\n",
       "      <td>201</td>\n",
       "      <td>0.063688</td>\n",
       "      <td>3156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Urban</td>\n",
       "      <td>59</td>\n",
       "      <td>0.068525</td>\n",
       "      <td>861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location Fraudulent_Claim                \n",
       "                         sum      mean count\n",
       "0     Rural               47  0.047910   981\n",
       "1  Suburban              201  0.063688  3156\n",
       "2     Urban               59  0.068525   861"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.groupby('Location', as_index=False).agg({\"Fraudulent_Claim\":[\"sum\",\"mean\",\"count\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heatmap using Cufflinks library\n",
    "Cufflinks is an extension of plotly that makes it more compatable with pandas dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>State_Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Claim_Amount</th>\n",
       "      <th>Education</th>\n",
       "      <th>Claim_Date</th>\n",
       "      <th>Employment_Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Since_Last_Claim</th>\n",
       "      <th>Months_Since_Policy_Inception</th>\n",
       "      <th>Claim_Cause</th>\n",
       "      <th>Claim_Report_Type</th>\n",
       "      <th>Vehicle_Class</th>\n",
       "      <th>Vehicle_Size</th>\n",
       "      <th>Vehicle_Model</th>\n",
       "      <th>Outstanding_Balance</th>\n",
       "      <th>Fraudulent_Claim</th>\n",
       "      <th>Claimant__Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>IA</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>265.228965</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>12/15/2018</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>41587.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>MO</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>360.093433</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Retired</td>\n",
       "      <td>F</td>\n",
       "      <td>25147</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>54</td>\n",
       "      <td>Collision</td>\n",
       "      <td>Agent</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Honda</td>\n",
       "      <td>31067.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>MO</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>292.497672</td>\n",
       "      <td>College</td>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>64459</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "      <td>Hail</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Ford</td>\n",
       "      <td>42427.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State_Code     State  Claim_Amount             Education  Claim_Date  \\\n",
       "3315         IA      Iowa    265.228965                Doctor  12/15/2018   \n",
       "1151         MO  Missouri    360.093433  High School or Below  12/01/2018   \n",
       "206          MO  Missouri    292.497672               College  12/01/2018   \n",
       "\n",
       "     Employment_Status Gender  Income  Location Marital_Status  \\\n",
       "3315        Unemployed      F       0  Suburban         Single   \n",
       "1151           Retired      F   25147  Suburban         Single   \n",
       "206           Employed      F   64459     Urban        Married   \n",
       "\n",
       "            ...         Months_Since_Last_Claim  \\\n",
       "3315        ...                               2   \n",
       "1151        ...                               7   \n",
       "206         ...                              16   \n",
       "\n",
       "      Months_Since_Policy_Inception  Claim_Cause  Claim_Report_Type  \\\n",
       "3315                             35    Collision             Branch   \n",
       "1151                             54    Collision              Agent   \n",
       "206                              64         Hail             Branch   \n",
       "\n",
       "      Vehicle_Class Vehicle_Size Vehicle_Model Outstanding_Balance  \\\n",
       "3315  Four-Door Car      Midsize        Toyota             41587.0   \n",
       "1151            SUV      Midsize         Honda             31067.0   \n",
       "206   Four-Door Car      Midsize          Ford             42427.0   \n",
       "\n",
       "     Fraudulent_Claim  Claimant__Number  \n",
       "3315                N              4316  \n",
       "1151                N              2152  \n",
       "206                 N              1207  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_csv('claim_data_v2_train.csv')\n",
    "data_train.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf\n",
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='cd412', api_key='kOyvGEmesjT6yen20LcW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_heatmap(a, b, agg='count', colorscale='Greys'):\n",
    "    \"\"\"Make heatmap from two categorical variables\"\"\"\n",
    "    df = data_train[[a, b, 'Fraudulent_Claim']].copy(deep=True)\n",
    "    df['Fraudulent_Claim'] = df['Fraudulent_Claim'].replace(('Y', 'N'), (1, 0))\n",
    "    ct = pd.crosstab(df[a], df[b], values=df['Fraudulent_Claim'], aggfunc=agg)\n",
    "    df_hm = pd.DataFrame(data=ct.values, index=ct.index.astype('str'), columns=ct.columns)\n",
    "    print(df_hm)\n",
    "    return df_hm.iplot(kind='heatmap', colorscale=colorscale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Education  Bachelor   College    Doctor  High School or Below    Master\n",
      "State                                                                  \n",
      "Iowa       0.069620  0.058947  0.069444              0.073892  0.051282\n",
      "Kansas     0.070866  0.076336  0.000000              0.078014  0.111111\n",
      "Missouri   0.056306  0.049550  0.078431              0.047382  0.049505\n",
      "Nebraska   0.056291  0.074830  0.022222              0.060811  0.108434\n",
      "Oklahoma   0.048611  0.040541  0.105263              0.055901  0.081633\n"
     ]
    },
    {
     "ename": "PlotlyRequestError",
     "evalue": "Account limit reached: Your account is limited to creating 25 charts. To continue, you can override or delete existing charts or you can upgrade your account at: https://plot.ly/products/cloud",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPlotlyRequestError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-69-a98dd4c6633c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmake_heatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'State'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Education'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mean'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Reds'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-68-d4d34856e379>\u001b[0m in \u001b[0;36mmake_heatmap\u001b[1;34m(a, b, agg, colorscale)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mdf_hm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'str'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_hm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mdf_hm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'heatmap'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolorscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolorscale\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\cufflinks\\plotlytools.py\u001b[0m in \u001b[0;36m_iplot\u001b[1;34m(self, kind, data, layout, filename, sharing, title, xTitle, yTitle, zTitle, theme, colors, colorscale, fill, width, dash, mode, interpolation, symbol, size, barmode, sortbars, bargap, bargroupgap, bins, histnorm, histfunc, orientation, boxpoints, annotations, keys, bestfit, bestfit_colors, mean, mean_colors, categories, x, y, z, text, gridcolor, zerolinecolor, margin, labels, values, secondary_y, secondary_y_title, subplots, shape, error_x, error_y, error_type, locations, lon, lat, asFrame, asDates, asFigure, asImage, dimensions, asPlot, asUrl, online, **kwargs)\u001b[0m\n\u001b[0;32m   1198\u001b[0m \t\treturn iplot(figure,validate=validate,sharing=sharing,filename=filename,\n\u001b[0;32m   1199\u001b[0m                          \u001b[0monline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0monline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masImage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masImage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masUrl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masUrl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masPlot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masPlot\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1200\u001b[1;33m \t\t\t dimensions=dimensions,display_image=kwargs.get('display_image',True))\n\u001b[0m\u001b[0;32m   1201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\cufflinks\\plotlytools.py\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure, validate, sharing, filename, online, asImage, asUrl, asPlot, dimensions, display_image, **kwargs)\u001b[0m\n\u001b[0;32m   1443\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1444\u001b[0m \t\treturn py.iplot(figure,validate=validate,sharing=sharing,\n\u001b[1;32m-> 1445\u001b[1;33m \t\t\t\t\t\tfilename=filename)\n\u001b[0m\u001b[0;32m   1446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m    140\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m     \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'layout'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'layout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 239\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclientresp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m     \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\clientresp.py\u001b[0m in \u001b[0;36mclientresp\u001b[1;34m(data, **kwargs)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'{plotly_domain}/clientresp'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpayload\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;31m# Old functionality, just keeping it around.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     47\u001b[0m             \u001b[1;33m@\u001b[0m\u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mRetrying\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_reject\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattempt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mattempt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wrap_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m             \u001b[0mdelay_since_first_attempt_ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, wrap_exception)\u001b[0m\n\u001b[0;32m    245\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m                 \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\six.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    691\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 693\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    694\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    198\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m                 \u001b[0mattempt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAttempt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattempt_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    201\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m                 \u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[0mcontent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m     \u001b[0mvalidate_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mvalidate_response\u001b[1;34m(response)\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No Content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPlotlyRequestError\u001b[0m: Account limit reached: Your account is limited to creating 25 charts. To continue, you can override or delete existing charts or you can upgrade your account at: https://plot.ly/products/cloud"
     ]
    }
   ],
   "source": [
    "make_heatmap('State', 'Education', 'mean', 'Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Education  Bachelor  College  Doctor  High School or Below  Master\n",
      "State                                                             \n",
      "Iowa            474      475      72                   406     117\n",
      "Kansas          127      131      20                   141      45\n",
      "Missouri        444      444      51                   401     101\n",
      "Nebraska        302      294      45                   296      83\n",
      "Oklahoma        144      148      19                   161      49\n"
     ]
    },
    {
     "ename": "PlotlyRequestError",
     "evalue": "Account limit reached: Your account is limited to creating 25 charts. To continue, you can override or delete existing charts or you can upgrade your account at: https://plot.ly/products/cloud",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPlotlyRequestError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-70-41c03cc9a02c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmake_heatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'State'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Education'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-68-d4d34856e379>\u001b[0m in \u001b[0;36mmake_heatmap\u001b[1;34m(a, b, agg, colorscale)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mdf_hm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'str'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_hm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mdf_hm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'heatmap'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolorscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolorscale\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\cufflinks\\plotlytools.py\u001b[0m in \u001b[0;36m_iplot\u001b[1;34m(self, kind, data, layout, filename, sharing, title, xTitle, yTitle, zTitle, theme, colors, colorscale, fill, width, dash, mode, interpolation, symbol, size, barmode, sortbars, bargap, bargroupgap, bins, histnorm, histfunc, orientation, boxpoints, annotations, keys, bestfit, bestfit_colors, mean, mean_colors, categories, x, y, z, text, gridcolor, zerolinecolor, margin, labels, values, secondary_y, secondary_y_title, subplots, shape, error_x, error_y, error_type, locations, lon, lat, asFrame, asDates, asFigure, asImage, dimensions, asPlot, asUrl, online, **kwargs)\u001b[0m\n\u001b[0;32m   1198\u001b[0m \t\treturn iplot(figure,validate=validate,sharing=sharing,filename=filename,\n\u001b[0;32m   1199\u001b[0m                          \u001b[0monline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0monline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masImage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masImage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masUrl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masUrl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masPlot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masPlot\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1200\u001b[1;33m \t\t\t dimensions=dimensions,display_image=kwargs.get('display_image',True))\n\u001b[0m\u001b[0;32m   1201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\cufflinks\\plotlytools.py\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure, validate, sharing, filename, online, asImage, asUrl, asPlot, dimensions, display_image, **kwargs)\u001b[0m\n\u001b[0;32m   1443\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1444\u001b[0m \t\treturn py.iplot(figure,validate=validate,sharing=sharing,\n\u001b[1;32m-> 1445\u001b[1;33m \t\t\t\t\t\tfilename=filename)\n\u001b[0m\u001b[0;32m   1446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m    140\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m     \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'layout'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'layout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 239\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclientresp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m     \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\clientresp.py\u001b[0m in \u001b[0;36mclientresp\u001b[1;34m(data, **kwargs)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'{plotly_domain}/clientresp'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpayload\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;31m# Old functionality, just keeping it around.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     47\u001b[0m             \u001b[1;33m@\u001b[0m\u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mRetrying\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_reject\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattempt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mattempt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wrap_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m             \u001b[0mdelay_since_first_attempt_ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, wrap_exception)\u001b[0m\n\u001b[0;32m    245\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m                 \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\six.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    691\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 693\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    694\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    198\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m                 \u001b[0mattempt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAttempt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattempt_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    201\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m                 \u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[0mcontent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m     \u001b[0mvalidate_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mvalidate_response\u001b[1;34m(response)\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No Content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPlotlyRequestError\u001b[0m: Account limit reached: Your account is limited to creating 25 charts. To continue, you can override or delete existing charts or you can upgrade your account at: https://plot.ly/products/cloud"
     ]
    }
   ],
   "source": [
    "make_heatmap('State', 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vehicle_Model  Chevrolet  Ford  Honda  Toyota\n",
      "Vehicle_Size                                 \n",
      "Compact              225   259    244     237\n",
      "Luxury               148   121    116     128\n",
      "Midsize              894   885    831     913\n"
     ]
    },
    {
     "ename": "PlotlyRequestError",
     "evalue": "Account limit reached: Your account is limited to creating 25 charts. To continue, you can override or delete existing charts or you can upgrade your account at: https://plot.ly/products/cloud",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPlotlyRequestError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-2fdf6c0114c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmake_heatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Vehicle_Size'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Vehicle_Model'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-68-d4d34856e379>\u001b[0m in \u001b[0;36mmake_heatmap\u001b[1;34m(a, b, agg, colorscale)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mdf_hm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'str'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mct\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_hm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mdf_hm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'heatmap'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolorscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolorscale\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\cufflinks\\plotlytools.py\u001b[0m in \u001b[0;36m_iplot\u001b[1;34m(self, kind, data, layout, filename, sharing, title, xTitle, yTitle, zTitle, theme, colors, colorscale, fill, width, dash, mode, interpolation, symbol, size, barmode, sortbars, bargap, bargroupgap, bins, histnorm, histfunc, orientation, boxpoints, annotations, keys, bestfit, bestfit_colors, mean, mean_colors, categories, x, y, z, text, gridcolor, zerolinecolor, margin, labels, values, secondary_y, secondary_y_title, subplots, shape, error_x, error_y, error_type, locations, lon, lat, asFrame, asDates, asFigure, asImage, dimensions, asPlot, asUrl, online, **kwargs)\u001b[0m\n\u001b[0;32m   1198\u001b[0m \t\treturn iplot(figure,validate=validate,sharing=sharing,filename=filename,\n\u001b[0;32m   1199\u001b[0m                          \u001b[0monline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0monline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masImage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masImage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masUrl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masUrl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0masPlot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0masPlot\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1200\u001b[1;33m \t\t\t dimensions=dimensions,display_image=kwargs.get('display_image',True))\n\u001b[0m\u001b[0;32m   1201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\cufflinks\\plotlytools.py\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure, validate, sharing, filename, online, asImage, asUrl, asPlot, dimensions, display_image, **kwargs)\u001b[0m\n\u001b[0;32m   1443\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1444\u001b[0m \t\treturn py.iplot(figure,validate=validate,sharing=sharing,\n\u001b[1;32m-> 1445\u001b[1;33m \t\t\t\t\t\tfilename=filename)\n\u001b[0m\u001b[0;32m   1446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m    140\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m     \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'layout'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'layout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 239\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclientresp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m     \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\clientresp.py\u001b[0m in \u001b[0;36mclientresp\u001b[1;34m(data, **kwargs)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'{plotly_domain}/clientresp'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpayload\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;31m# Old functionality, just keeping it around.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     47\u001b[0m             \u001b[1;33m@\u001b[0m\u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mRetrying\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_reject\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattempt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mattempt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wrap_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m             \u001b[0mdelay_since_first_attempt_ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, wrap_exception)\u001b[0m\n\u001b[0;32m    245\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m                 \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\six.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    691\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 693\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    694\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    198\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m                 \u001b[0mattempt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAttempt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattempt_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    201\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m                 \u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[0mcontent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m     \u001b[0mvalidate_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\student\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mvalidate_response\u001b[1;34m(response)\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No Content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPlotlyRequestError\u001b[0m: Account limit reached: Your account is limited to creating 25 charts. To continue, you can override or delete existing charts or you can upgrade your account at: https://plot.ly/products/cloud"
     ]
    }
   ],
   "source": [
    "make_heatmap('Vehicle_Size', 'Vehicle_Model')\n"
   ]
  },
  {
   "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
}