Organized Car Max Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361556\n",
      "312468\n",
      "355397\n",
      "360405\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\999Na\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:31: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "C:\\Users\\999Na\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:32: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.offline as py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "data = pd.read_csv('CaseCompetitionData.csv', sep=',')\n",
    "data['actual'] = 1\n",
    "data.loc[data['subsequent_purchases'] == 0, 'actual'] = 0\n",
    "data['customer_age'] = data['customer_age'].map({'21 - 30':25, '61 - 70':65, '31 - 40':35, '41 - 50':45, '51 - 60':55, '101+':102, '71 - 80':75,\n",
    " '0 - 20':10, '81 - 90':85, '?':'?', '91 - 100':95})\n",
    "data['customer_income'] = data['customer_income'].map({'20001 - 40000':30000, '80001 - 100000':90000, '40001 - 60000':50000, '60001 - 80000':70000,\n",
    " '120001 - 140000':130000, '0 - 20000':10000, '100001 - 120000':110000, '160001 - 180000':170000,\n",
    " '200001+':220000, '140001 - 160000':150000, '180001 - 200000':190000, '?':'?'})\n",
    "data['purchase_price'] = data['purchase_price'].map({'10001 - 15000':12500, '15001 - 20000':17500, \n",
    " '20001 - 25000':22500, '25001 - 30000':27500, '30001 - 35000':32500, '0 - 5000':2500, '45001 - 50000':47500, \n",
    " '35001 - 40000':27500, '5001 - 10000':7500, '40001 - 45000':42500, '55001 - 60000':57500, '50001 - 55000':52500, \n",
    " '60001 - 65000':62500, '65001 - 70000':67500, '70001 - 75000':72500, '75001 - 80000':77500, '85001 - 90000':87500,\n",
    " '90001 - 95000':92500, '80001 - 85000':82500, '?':'?'})\n",
    "data['counter'] = 1\n",
    "price = data.loc[data['purchase_price'] != '?']\n",
    "income = data.loc[data['customer_income'] != '?']\n",
    "distance = data.loc[data['customer_distance_to_dealer'] != '?']\n",
    "age = data.loc[data['customer_age'] != '?']\n",
    "print(len(price))\n",
    "print(len(income))\n",
    "print(len(distance))\n",
    "print(len(age))\n",
    "distance['customer_distance_to_dealer'] = distance['customer_distance_to_dealer'].astype(np.int64)\n",
    "income['customer_income'] = income['customer_income'].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ee46b1aac8>"
      ]
     },
     "execution_count": 5,
     "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": [
    "subs = data.groupby('subsequent_purchases')['insert_num'].count().to_frame().reset_index()\n",
    "sns.barplot(x='subsequent_purchases', y='insert_num', data=subs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   trade_in    actual\n",
      "0         0  0.322550\n",
      "1         1  0.363858\n",
      "   customer_previous_purchase    actual\n",
      "0                           0  0.329577\n",
      "1                           1  0.387068\n",
      "   vehicle_warranty_used    actual\n",
      "0                      0  0.335684\n",
      "1                      1  0.359969\n",
      "   vehicle_financing    actual\n",
      "0                  0  0.370446\n",
      "1                  1  0.333032\n"
     ]
    }
   ],
   "source": [
    "### Quantifying subs ratio for binary features\n",
    "\n",
    "trade_in = data.groupby('trade_in')['actual'].mean().to_frame().reset_index()\n",
    "print(trade_in.head())\n",
    "customer_previous_purchase = data.groupby('customer_previous_purchase')['actual'].mean().to_frame().reset_index()\n",
    "print(customer_previous_purchase.head())\n",
    "warranty_used = data.groupby('vehicle_warranty_used')['actual'].mean().to_frame().reset_index()\n",
    "print(warranty_used.head())\n",
    "financing = data.groupby('vehicle_financing')['actual'].mean().to_frame().reset_index()\n",
    "print(financing.head())\n",
    "#trade_in.to_csv('trade_subs.csv')\n",
    "#customer_previous_purchase.to_csv('prev_subs.csv')\n",
    "#warranty_used.to_csv('war_subs.csv')\n",
    "#financing.to_csv('fin_subs.csv')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'parallel_cats.html'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Trade_in subs split distribution\n",
    "\n",
    "par_cats = data[['actual', 'trade_in']]\n",
    "par_cats.dtypes\n",
    "fig = px.parallel_categories(par_cats)\n",
    "py.offline.plot(fig, filename='parallel_cats.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "purch_subs = data.groupby()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Trade_in purchase year subs ratio\n",
    "\n",
    "purchase_year = data.loc[data['purchase_vehicle_year'] >= 2000].groupby(['trade_in', 'purchase_vehicle_year'])['actual'].mean().to_frame().reset_index()\n",
    "purchase_year\n",
    "fig = px.bar(purchase_year, x='purchase_vehicle_year', y='actual', facet_row='trade_in')\n",
    "py.offline.plot(fig, 'year_previous.html', auto_open=True)\n",
    "\n",
    "#purchase_year.to_csv('purchase_year.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Trade in purchase year subs split distribution\n",
    "\n",
    "par_cats = price.loc[price['purchase_vehicle_year'] >= 2000][['actual', 'purchase_vehicle_year', 'trade_in']]\n",
    "#par_cats.to_csv('trade_year_subs.csv')\n",
    "#print(par_cats['purchase_vehicle_year'].unique())\n",
    "#fig = px.parallel_categories(par_cats)\n",
    "#py.offline.plot(fig, filename='parallel_cats.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Trade in income subs ratio\n",
    "\n",
    "income_trade = income.groupby(['customer_income', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "#purchase_year\n",
    "#fig = px.bar(income_trade, x='customer_income', y='actual', facet_row='trade_in')\n",
    "#py.offline.plot(fig, 'income_trade.html', auto_open=True)\n",
    "#income_trade.to_csv('income_trade_subs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Trade in purchase price subs ratio\n",
    "\n",
    "price_trade = price.groupby(['purchase_price', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "#purchase_year\n",
    "#fig = px.bar(price_trade, x='purchase_price', y='actual', facet_row='trade_in')\n",
    "#py.offline.plot(fig, 'price_trade.html', auto_open=True)\n",
    "#data['actual'].mean()\n",
    "#price_trade.to_csv('price_trade_subs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### trade in age subs ratio\n",
    "\n",
    "age_trade = age.groupby(['customer_age', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "\n",
    "fig = px.bar(age_trade, x='customer_age', y='actual', facet_row='trade_in')\n",
    "py.offline.plot(fig, 'age_trade.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### trade in distance to dealer subs ratio\n",
    "\n",
    "dist_subs = distance.loc[distance['customer_distance_to_dealer'] <= 50].groupby(['customer_distance_to_dealer', 'customer_previous_purchase'])['actual'].mean().to_frame().reset_index()\n",
    "\n",
    "fig = px.line(dist_subs, x='customer_distance_to_dealer', y='actual', facet_row='customer_previous_purchase')\n",
    "py.offline.plot(fig, 'dist_trade.html', auto_open=True)\n",
    "#dist_subs.to_csv('dist_prev_subs_50.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### trade in make subs ratio\n",
    "\n",
    "make_trade = data.groupby(['purchase_make', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "\n",
    "#fig = px.bar(make_trade, x='purchase_make', y='actual', facet_row='trade_in')\n",
    "#py.offline.plot(fig, 'make_trade.html', auto_open=True)\n",
    "\n",
    "make_subs = data.groupby('purchase_make')['actual'].mean().to_frame().reset_index()\n",
    "fig = px.bar(make_subs, x='purchase_make', y='actual')\n",
    "py.offline.plot(fig, 'make_subs.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_prev = data.groupby(['purchase_make', 'customer_previous_purchase'])['actual'].mean().to_frame().reset_index()\n",
    "fig = px.bar(make_prev, x='purchase_make', y='actual', facet_row='customer_previous_purchase')\n",
    "py.offline.plot(fig, 'make_prev.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35903806022061013\n",
      "0.5550632128474738\n"
     ]
    },
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'low_make_subs.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-94a20ba96e90>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'purchase_make'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'actual'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlow_make\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlow_make\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'actual'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\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---> 10\u001b[1;33m \u001b[0mlow_make\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'low_make_subs.csv'\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     11\u001b[0m \u001b[1;31m#makes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mlow_demand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'low_demand_dist.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal)\u001b[0m\n\u001b[0;32m   3226\u001b[0m             \u001b[0mdecimal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3227\u001b[0m         )\n\u001b[1;32m-> 3228\u001b[1;33m         \u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\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[0m\u001b[0;32m   3229\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3230\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    181\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m                 \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m                 \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompression\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    184\u001b[0m             )\n\u001b[0;32m    185\u001b[0m             \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    397\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\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    400\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m             \u001b[1;31m# No explicit encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'low_make_subs.csv'"
     ]
    },
    {
     "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": [
    "makes = data\n",
    "makes['counter'] = 1\n",
    "makes = makes.groupby('purchase_make')['counter'].sum().to_frame().reset_index()\n",
    "\n",
    "low_demand = makes.loc[makes['counter'] < 2000]\n",
    "print(data.loc[data.purchase_make.isin(low_demand['purchase_make'].unique())]['actual'].mean())\n",
    "low_make = data.loc[data.purchase_make.isin(low_demand['purchase_make'].unique())].groupby('purchase_make')['actual'].mean().to_frame().reset_index()\n",
    "sns.barplot(x='purchase_make', y='actual', data=low_make)\n",
    "print(low_make['actual'].mean())\n",
    "#low_make.to_csv('low_make_subs.csv')\n",
    "#makes\n",
    "#low_demand.to_csv('low_demand_dist.csv')\n",
    "#make_subs.groupby('purchase_make')['actual'].mean().to_frame().reset_index().to_csv('make_subs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_fin = data.groupby(['purchase_make', 'vehicle_financing'])['actual'].mean().to_frame().reset_index()\n",
    "#fig = px.bar(make_fin, x='purchase_make', y='actual', facet_row='vehicle_financing')\n",
    "#py.offline.plot(fig, 'make_fin.html', auto_open=True)\n",
    "#make_fin.to_csv('make_fin_subs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Purchase price subs ratio\n",
    "\n",
    "purch_ratio = price.groupby('purchase_price')['actual'].mean().to_frame().reset_index()\n",
    "#purch_ratio.to_csv('purch_subs.csv')\n",
    "#fig = px.bar(purch_ratio, x='purchase_price', y='actual')\n",
    "#py.offline.plot(fig, 'price_subs.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>purchase_price</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>2944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7500</td>\n",
       "      <td>3764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12500</td>\n",
       "      <td>33753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>17500</td>\n",
       "      <td>39607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>22500</td>\n",
       "      <td>22396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>27500</td>\n",
       "      <td>13560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>32500</td>\n",
       "      <td>5199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>47500</td>\n",
       "      <td>1716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>52500</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>57500</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>62500</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>67500</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>72500</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>77500</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>82500</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>87500</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>92500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    purchase_price  actual\n",
       "0             2500    2944\n",
       "1             7500    3764\n",
       "2            12500   33753\n",
       "3            17500   39607\n",
       "4            22500   22396\n",
       "5            27500   13560\n",
       "6            32500    5199\n",
       "7            47500    1716\n",
       "8            52500     249\n",
       "9            57500     158\n",
       "10           62500      97\n",
       "11           67500      54\n",
       "12           72500      25\n",
       "13           77500      18\n",
       "14           82500       6\n",
       "15           87500       5\n",
       "16           92500       3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### purchase price subs=1 distribution \n",
    "\n",
    "purch = price.loc[data['actual'] == 1].groupby('purchase_price')['actual'].sum().to_frame().reset_index()\n",
    "fig = px.bar(purch, x='purchase_price', y='actual')\n",
    "py.offline.plot(fig, 'price_subs_1_dist.html', auto_open=True)\n",
    "purch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### purchase price distribution\n",
    "\n",
    "purch_dist = price.groupby('purchase_price')['insert_num'].count().to_frame().reset_index()\n",
    "fig = px.bar(purch_dist, x='purchase_price', y='insert_num')\n",
    "py.offline.plot(fig, 'price_dist.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### purchase price warranty subs ratio\n",
    "\n",
    "warranty_price = price.groupby(['vehicle_warranty_used', 'purchase_price'])['actual'].mean().to_frame().reset_index()\n",
    "fig = px.bar(warranty_price, x='purchase_price', y='actual', facet_row='vehicle_warranty_used')\n",
    "py.offline.plot(fig, 'warranty_price.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### purchase price year subs ratio\n",
    "\n",
    "price_year = price.loc[price['purchase_vehicle_year'] > 2000].groupby(['purchase_vehicle_year', 'purchase_price'])['actual'].mean().to_frame().reset_index()\n",
    "fig = px.bar(price_year, x='purchase_price', y='actual', facet_row='purchase_vehicle_year', width=1600, height=1600)\n",
    "py.offline.plot(fig, 'price_year.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'parallel_cats.html'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### purchase price subs split distribution\n",
    "\n",
    "par_cats = price[['actual', 'purchase_price']]\n",
    "par_cats.dtypes\n",
    "fig = px.parallel_categories(par_cats)\n",
    "py.offline.plot(fig, filename='parallel_cats.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### purchase price income subs ratio\n",
    "\n",
    "price_income = price.merge(income['insert_num'], on='insert_num')\n",
    "price_income = price_income.groupby(['purchase_price', 'customer_income'])['actual'].mean().to_frame().reset_index()\n",
    "fig = px.bar(price_income, x='purchase_price', y='actual', facet_row='customer_income', width=1600, height=1600)\n",
    "py.offline.plot(fig, 'price_income.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp-plot.html'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### purchase price income distribution\n",
    "\n",
    "price_income = price.merge(income['insert_num'], on='insert_num')\n",
    "price_income = price_income.groupby(['purchase_price', 'customer_income'])['actual'].mean().to_frame().reset_index()\n",
    "fig = px.bar(price_income, x='purchase_price', y='actual', facet_row='customer_income', width=1600, height=1600)\n",
    "py.offline.plot(fig, 'price_income.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'str' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-2b966ddf6759>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mstuff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'vehicle_financing'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'customer_income'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'insert_num'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\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----> 2\u001b[1;33m \u001b[0mstuff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstuff\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'customer_income'\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[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'customer_income'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'insert_num'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstuff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36msort_values\u001b[1;34m(self, by, axis, ascending, inplace, kind, na_position)\u001b[0m\n\u001b[0;32m   4997\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4998\u001b[0m             indexer = nargsort(\n\u001b[1;32m-> 4999\u001b[1;33m                 \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mascending\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_position\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna_position\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5000\u001b[0m             )\n\u001b[0;32m   5001\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36mnargsort\u001b[1;34m(items, kind, ascending, na_position)\u001b[0m\n\u001b[0;32m    258\u001b[0m         \u001b[0mnon_nans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnon_nans\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m         \u001b[0mnon_nan_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnon_nan_idx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m     \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnon_nan_idx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnon_nans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\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[0m\u001b[0;32m    261\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mascending\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'"
     ]
    }
   ],
   "source": [
    "stuff = data.loc[data['vehicle_financing'] == 0].groupby('customer_income')['insert_num'].count().to_frame().reset_index()\n",
    "stuff = stuff.sort_values(by=['customer_income'])\n",
    "\n",
    "sns.barplot(x='customer_income', y='insert_num', data=stuff)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin_in = income.groupby('vehicle_financing')['insert_num'].count().to_frame().reset_index()\n",
    "#fin_in.to_csv('fin_in.csv')\n",
    "no_fin_in = income.loc[income['vehicle_financing'] == 0].groupby('customer_income')['insert_num'].count().to_frame().reset_index()\n",
    "#no_fin_in.to_csv('no_fin_in.csv')\n",
    "#sns.barplot(x='customer_income', y='insert_num', data=no_fin_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2691bbdcbc8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stuff = data.drop(columns=['subsequent_purchases', 'actual', 'insert_num'])\n",
    "sns.heatmap(stuff.corr(), cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fdcddc3408>"
      ]
     },
     "execution_count": 4,
     "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": [
    "income_subs = data.groupby('customer_income')['actual'].mean().to_frame().reset_index()\n",
    "sns.barplot(x='customer_income', y='actual', data=income_subs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>insert_num</th>\n",
       "      <th>purchase_make</th>\n",
       "      <th>purchase_model</th>\n",
       "      <th>purchase_vehicle_year</th>\n",
       "      <th>purchase_price</th>\n",
       "      <th>trade_in</th>\n",
       "      <th>vehicle_financing</th>\n",
       "      <th>customer_age</th>\n",
       "      <th>customer_income</th>\n",
       "      <th>customer_gender</th>\n",
       "      <th>customer_previous_purchase</th>\n",
       "      <th>customer_distance_to_dealer</th>\n",
       "      <th>post_purchase_satisfaction</th>\n",
       "      <th>vehicle_warranty_used</th>\n",
       "      <th>subsequent_purchases</th>\n",
       "      <th>actual</th>\n",
       "      <th>counter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>81690</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>CHARGER</td>\n",
       "      <td>2010</td>\n",
       "      <td>12500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>30000</td>\n",
       "      <td>U</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>109994</td>\n",
       "      <td>FORD</td>\n",
       "      <td>EXPLORER SPORT TRAC</td>\n",
       "      <td>2007</td>\n",
       "      <td>17500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>30000</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11399</td>\n",
       "      <td>BMW</td>\n",
       "      <td>328</td>\n",
       "      <td>2010</td>\n",
       "      <td>22500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>90000</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>360305</td>\n",
       "      <td>VOLVO</td>\n",
       "      <td>S40</td>\n",
       "      <td>2008</td>\n",
       "      <td>17500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>?</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>36685</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>CRUZE</td>\n",
       "      <td>2012</td>\n",
       "      <td>12500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361554</td>\n",
       "      <td>195314</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>GRAND CHEROKEE</td>\n",
       "      <td>2005</td>\n",
       "      <td>17500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>70000</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361555</td>\n",
       "      <td>58219</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>SILVERADO 1500</td>\n",
       "      <td>2013</td>\n",
       "      <td>27500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>110000</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361556</td>\n",
       "      <td>207386</td>\n",
       "      <td>KIA</td>\n",
       "      <td>OPTIMA</td>\n",
       "      <td>2012</td>\n",
       "      <td>17500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>50000</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361557</td>\n",
       "      <td>252391</td>\n",
       "      <td>MINI</td>\n",
       "      <td>COOPER</td>\n",
       "      <td>2010</td>\n",
       "      <td>17500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>50000</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361558</td>\n",
       "      <td>52652</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>MALIBU</td>\n",
       "      <td>2012</td>\n",
       "      <td>17500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>50000</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>361559 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        insert_num purchase_make       purchase_model  purchase_vehicle_year  \\\n",
       "0            81690         DODGE              CHARGER                   2010   \n",
       "1           109994          FORD  EXPLORER SPORT TRAC                   2007   \n",
       "2            11399           BMW                  328                   2010   \n",
       "3           360305         VOLVO                  S40                   2008   \n",
       "4            36685     CHEVROLET                CRUZE                   2012   \n",
       "...            ...           ...                  ...                    ...   \n",
       "361554      195314          JEEP       GRAND CHEROKEE                   2005   \n",
       "361555       58219     CHEVROLET       SILVERADO 1500                   2013   \n",
       "361556      207386           KIA               OPTIMA                   2012   \n",
       "361557      252391          MINI               COOPER                   2010   \n",
       "361558       52652     CHEVROLET               MALIBU                   2012   \n",
       "\n",
       "       purchase_price  trade_in  vehicle_financing customer_age  \\\n",
       "0               12500         0                  0           25   \n",
       "1               17500         0                  1           65   \n",
       "2               22500         1                  1           35   \n",
       "3               17500         1                  0           45   \n",
       "4               12500         1                  1           25   \n",
       "...               ...       ...                ...          ...   \n",
       "361554          17500         0                  1           35   \n",
       "361555          27500         0                  1           25   \n",
       "361556          17500         1                  1           45   \n",
       "361557          17500         1                  1           45   \n",
       "361558          17500         0                  1           35   \n",
       "\n",
       "       customer_income customer_gender  customer_previous_purchase  \\\n",
       "0                30000               U                           0   \n",
       "1                30000               M                           1   \n",
       "2                90000               F                           1   \n",
       "3                    ?               M                           0   \n",
       "4                50000               M                           0   \n",
       "...                ...             ...                         ...   \n",
       "361554           70000               M                           0   \n",
       "361555          110000               F                           0   \n",
       "361556           50000               F                           0   \n",
       "361557           50000               M                           0   \n",
       "361558           50000               F                           0   \n",
       "\n",
       "       customer_distance_to_dealer post_purchase_satisfaction  \\\n",
       "0                                9                          ?   \n",
       "1                               15                          ?   \n",
       "2                               18                          ?   \n",
       "3                                4                          ?   \n",
       "4                                2                          ?   \n",
       "...                            ...                        ...   \n",
       "361554                           5                          ?   \n",
       "361555                           8                          ?   \n",
       "361556                           4                          ?   \n",
       "361557                           5                          ?   \n",
       "361558                           8                          ?   \n",
       "\n",
       "        vehicle_warranty_used  subsequent_purchases  actual  counter  \n",
       "0                           1                     0       0        1  \n",
       "1                           0                     0       0        1  \n",
       "2                           0                     9       1        1  \n",
       "3                           0                     1       1        1  \n",
       "4                           0                     0       0        1  \n",
       "...                       ...                   ...     ...      ...  \n",
       "361554                      0                     0       0        1  \n",
       "361555                      0                     0       0        1  \n",
       "361556                      0                     0       0        1  \n",
       "361557                      1                     0       0        1  \n",
       "361558                      0                     0       0        1  \n",
       "\n",
       "[361559 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   vehicle_financing  trade_in    actual\n",
      "0                  0         0  0.356170\n",
      "1                  0         1  0.389012\n",
      "2                  1         0  0.311624\n",
      "3                  1         1  0.356862\n",
      "   vehicle_warranty_used  trade_in    actual\n",
      "0                      0         0  0.322565\n",
      "1                      0         1  0.351245\n",
      "2                      1         0  0.322503\n",
      "3                      1         1  0.399762\n",
      "Empty DataFrame\n",
      "Columns: [customer_gender, trade_in, actual]\n",
      "Index: []\n",
      "   customer_previous_purchase  trade_in    actual\n",
      "0                           0         0  0.316459\n",
      "1                           0         1  0.347226\n",
      "2                           1         0  0.355612\n",
      "3                           1         1  0.407549\n",
      "   customer_previous_purchase  vehicle_warranty_used    actual\n",
      "0                           0                      0  0.323996\n",
      "1                           0                      1  0.346280\n",
      "2                           1                      0  0.378868\n",
      "3                           1                      1  0.412610\n",
      "   survey    actual\n",
      "0     0.0  0.341702\n",
      "1     1.0  0.349467\n"
     ]
    }
   ],
   "source": [
    "fin_trade = data.groupby(['vehicle_financing', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "print(fin_trade)\n",
    "war_trade = data.groupby(['vehicle_warranty_used', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "print(war_trade)\n",
    "data['customer_gender'] = data['customer_gender'].map({'M':0, 'F':1, 'U':2})\n",
    "gen_trade = data.groupby(['customer_gender', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "print(gen_trade)\n",
    "data.loc[data['post_purchase_satisfaction'] != '?', 'survey'] = 1\n",
    "data['survey'].fillna(value=0, inplace=True)\n",
    "#surv_trade = data.groupby(['survey', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "#print(surv_trade)\n",
    "trade_prev = data.groupby(['customer_previous_purchase', 'trade_in'])['actual'].mean().to_frame().reset_index()\n",
    "print(trade_prev)\n",
    "prev_war = data.groupby(['customer_previous_purchase', 'vehicle_warranty_used'])['actual'].mean().to_frame().reset_index()\n",
    "print(prev_war)\n",
    "#war_fin = data.groupby(['vehicle_warranty_used', 'vehicle_financing'])['actual'].mean().to_frame().reset_index()\n",
    "#print(war_fin)\n",
    "#surv = data.groupby('survey')['actual'].mean().to_frame().reset_index()\n",
    "#print(surv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dist_miss</th>\n",
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       "      <td>0.212574</td>\n",
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       "      <td>0.003368</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>174934.584550</td>\n",
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       "      <td>0.394190</td>\n",
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       "      <td>0.224765</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              insert_num  purchase_vehicle_year  trade_in  vehicle_financing  \\\n",
       "dist_miss                                                                      \n",
       "0.0        180881.349899            2009.392356  0.465488           0.769621   \n",
       "1.0        174934.584550            2009.332360  0.394190           0.648491   \n",
       "\n",
       "           customer_gender  customer_previous_purchase  vehicle_warranty_used  \\\n",
       "dist_miss                                                                       \n",
       "0.0                    NaN                    0.212574               0.249316   \n",
       "1.0                    NaN                    0.141188               0.224765   \n",
       "\n",
       "           subsequent_purchases    actual  counter    survey  \n",
       "dist_miss                                                     \n",
       "0.0                    0.534113  0.330315      1.0  0.003368  \n",
       "1.0                    1.000000  1.000000      1.0  0.003570  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['customer_distance_to_dealer'] == '?']['actual'].mean()\n",
    "len(data.loc[data['customer_distance_to_dealer'] == '?'])\n",
    "# duely noted\n",
    "data.loc[data['customer_distance_to_dealer'] == '?', 'dist_miss'] = 1\n",
    "data['dist_miss'].fillna(value=0, inplace=True)\n",
    "data.groupby('dist_miss').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\999Na\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
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       "      <th>vehicle_financing</th>\n",
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       "      <td>2</td>\n",
       "      <td>177322.590273</td>\n",
       "      <td>2008.517128</td>\n",
       "      <td>17803.483547</td>\n",
       "      <td>0.516356</td>\n",
       "      <td>0.668725</td>\n",
       "      <td>0.328766</td>\n",
       "      <td>0.265174</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>179982.065906</td>\n",
       "      <td>2009.146341</td>\n",
       "      <td>18877.789310</td>\n",
       "      <td>0.490400</td>\n",
       "      <td>0.723923</td>\n",
       "      <td>0.266995</td>\n",
       "      <td>0.270109</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>180955.217775</td>\n",
       "      <td>2009.427671</td>\n",
       "      <td>18536.873621</td>\n",
       "      <td>0.453199</td>\n",
       "      <td>0.752915</td>\n",
       "      <td>0.207060</td>\n",
       "      <td>0.233218</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>180618.648503</td>\n",
       "      <td>2009.305351</td>\n",
       "      <td>18719.931271</td>\n",
       "      <td>0.439863</td>\n",
       "      <td>0.732941</td>\n",
       "      <td>0.217477</td>\n",
       "      <td>0.228768</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>180278.304104</td>\n",
       "      <td>2009.235075</td>\n",
       "      <td>18894.589552</td>\n",
       "      <td>0.483209</td>\n",
       "      <td>0.759328</td>\n",
       "      <td>0.240672</td>\n",
       "      <td>0.267724</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>177596.619485</td>\n",
       "      <td>2009.397059</td>\n",
       "      <td>18474.264706</td>\n",
       "      <td>0.463235</td>\n",
       "      <td>0.737132</td>\n",
       "      <td>0.222426</td>\n",
       "      <td>0.262868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>182326.128155</td>\n",
       "      <td>2009.395146</td>\n",
       "      <td>18407.766990</td>\n",
       "      <td>0.428155</td>\n",
       "      <td>0.751456</td>\n",
       "      <td>0.217476</td>\n",
       "      <td>0.238835</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>176858.056725</td>\n",
       "      <td>2009.354986</td>\n",
       "      <td>18469.807868</td>\n",
       "      <td>0.445563</td>\n",
       "      <td>0.763038</td>\n",
       "      <td>0.207685</td>\n",
       "      <td>0.251601</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>182880.320448</td>\n",
       "      <td>2009.483215</td>\n",
       "      <td>19066.632757</td>\n",
       "      <td>0.466938</td>\n",
       "      <td>0.763988</td>\n",
       "      <td>0.236012</td>\n",
       "      <td>0.264496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         insert_num  purchase_vehicle_year  purchase_price  \\\n",
       "subsequent_purchases                                                         \n",
       "2                     177322.590273            2008.517128    17803.483547   \n",
       "3                     179982.065906            2009.146341    18877.789310   \n",
       "4                     180955.217775            2009.427671    18536.873621   \n",
       "5                     180618.648503            2009.305351    18719.931271   \n",
       "6                     180278.304104            2009.235075    18894.589552   \n",
       "7                     177596.619485            2009.397059    18474.264706   \n",
       "8                     182326.128155            2009.395146    18407.766990   \n",
       "9                     176858.056725            2009.354986    18469.807868   \n",
       "10                    182880.320448            2009.483215    19066.632757   \n",
       "\n",
       "                      trade_in  vehicle_financing  customer_previous_purchase  \\\n",
       "subsequent_purchases                                                            \n",
       "2                     0.516356           0.668725                    0.328766   \n",
       "3                     0.490400           0.723923                    0.266995   \n",
       "4                     0.453199           0.752915                    0.207060   \n",
       "5                     0.439863           0.732941                    0.217477   \n",
       "6                     0.483209           0.759328                    0.240672   \n",
       "7                     0.463235           0.737132                    0.222426   \n",
       "8                     0.428155           0.751456                    0.217476   \n",
       "9                     0.445563           0.763038                    0.207685   \n",
       "10                    0.466938           0.763988                    0.236012   \n",
       "\n",
       "                      vehicle_warranty_used  actual  counter  \n",
       "subsequent_purchases                                          \n",
       "2                                  0.265174     1.0      1.0  \n",
       "3                                  0.270109     1.0      1.0  \n",
       "4                                  0.233218     1.0      1.0  \n",
       "5                                  0.228768     1.0      1.0  \n",
       "6                                  0.267724     1.0      1.0  \n",
       "7                                  0.262868     1.0      1.0  \n",
       "8                                  0.238835     1.0      1.0  \n",
       "9                                  0.251601     1.0      1.0  \n",
       "10                                 0.264496     1.0      1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price['purchase_price'] = price['purchase_price'].astype(np.int64)\n",
    "\n",
    "price.loc[(data['subsequent_purchases'] > 1)].groupby('subsequent_purchases').mean()\n",
    "#data.loc[data['subsequent_purchases'] > 1]\n",
    "#data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\999Na\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning:\n",
      "\n",
      "Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x3600 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,4,figsize=(100,50),sharex=True,sharey=False)\n",
    "axs = axs.flatten()\n",
    "num=0\n",
    "\n",
    "for subs in sorted(distance.subsequent_purchases.unique()):\n",
    "    ax = sns.lineplot(x='customer_distance_to_dealer',y='counter',data=distance.loc[(distance.subsequent_purchases == subs) & (distance['customer_distance_to_dealer'] <= 150)].groupby('customer_distance_to_dealer')['counter'].sum().to_frame().reset_index(), ax=axs[num])\n",
    "    ax.set_title('# Subsequent Purchases - {}'.format(subs))\n",
    "    ax.set_xlabel('Distance')\n",
    "    ax.set_ylabel('Loyal Customer Rate')\n",
    "    ax.set_xticks([12,20,30,40,50,60,70,80,90,100,110,120,130,140,150])\n",
    "    num+=1\n",
    "fig.show()\n",
    "plt.savefig('distance_sub_purchases_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3840"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(distance.loc[distance['customer_distance_to_dealer'] >= 100])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\999Na\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: UserWarning:\n",
      "\n",
      "Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x3600 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = 10\n",
    "y = 0\n",
    "z = 1\n",
    "norm_dist = distance.loc[distance['customer_distance_to_dealer'] <= 150]\n",
    "while x <= 150:\n",
    "    \n",
    "    norm_dist.loc[(norm_dist['customer_distance_to_dealer'] >= y) & (norm_dist['customer_distance_to_dealer'] < x), 'dist_by_tens' ] = z\n",
    "    \n",
    "    x+=10\n",
    "    y+=10\n",
    "    z+=1\n",
    "norm_dist    \n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(3,4,figsize=(100,50),sharex=True,sharey=False)\n",
    "axs = axs.flatten()\n",
    "num=0\n",
    "\n",
    "for subs in sorted(norm_dist.subsequent_purchases.unique()):\n",
    "    ax = sns.barplot(x='dist_by_tens',y='counter',data=norm_dist.loc[(norm_dist.subsequent_purchases == subs)].groupby('dist_by_tens')['counter'].sum().to_frame().reset_index(), ax=axs[num])\n",
    "    ax.set_title('# Subsequent Purchases - {}'.format(subs))\n",
    "    ax.set_xlabel('Distance')\n",
    "    ax.set_ylabel('Loyal Customer Rate')\n",
    "    ax.set_xticks([12,20,30,40,50,60,70,80,90,100,110,120,130,140,150])\n",
    "    num+=1\n",
    "fig.show()\n",
    "#plt.savefig('distance_sub_purchases_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6162\n",
      "2166\n"
     ]
    }
   ],
   "source": [
    "print(len(data.loc[data['customer_distance_to_dealer'] == '?']))\n",
    "print(len(data.loc[(data['customer_distance_to_dealer'] == '?') &(data['vehicle_financing'] == 0)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subs_price = sns.heatmap(data=pd.pivot_table(data=data.loc[data['purchase_price'] != '?'], index='subsequent_purchases', columns=['purchase_price'], values='trade_in', aggfunc='mean'),cmap='Blues',square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\999Na\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_pop = data.purchase_model.value_counts().to_frame().reset_index()\n",
    "model_pop.rename(columns={'purchase_model':'model_popularity', 'index':'purchase_model'},inplace=True)\n",
    "model_pop\n",
    "subs_model_pop = data.merge(model_pop, on='purchase_model', how='left')\n",
    "subs_model_pop.head()\n",
    "#subs_model_pop = data.drop(columns=['model_popularity_x', 'model_popularity_y'])\n",
    "#subs_model_pop\n",
    "subs_model_pop = subs_model_pop.groupby(['purchase_make','purchase_model', 'model_popularity'])['actual'].mean().to_frame().reset_index()\n",
    "#subs_model_pop.sort_values(by=['model_popularity'], inplace=True, ascending=True)\n",
    "len(subs_model_pop['purchase_make'].unique())\n",
    "#fig, axs = plt.subplots(nrows=6,ncols=8,figsize=(500,250))\n",
    "#axs = axs.flatten()\n",
    "#num=0\n",
    "for make in subs_model_pop.purchase_make.unique():\n",
    "    sub_make = subs_model_pop.loc[subs_model_pop['purchase_make'] == make]\n",
    "    sub_make.sort_values(by=['model_popularity'], inplace=True, ascending=True)\n",
    "    sub_make.to_csv('make_graphs/'+make+'.csv')\n",
    "    #ax = sns.lineplot(x='purchase_model',y='actual',data=sub_make, ax=axs[num])\n",
    "    #ax.set_title('Make - {}'.format(make))\n",
    "    #ax.set_xlabel('Model')\n",
    "    #ax.set_ylabel('Loyalty Rate')\n",
    "    #num+=1\n",
    "\n",
    "#fig.show()\n",
    "#plt.savefig('model_subs.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_pop = data.purchase_model.value_counts().to_frame().reset_index()\n",
    "model_pop.rename(columns={'purchase_model':'model_popularity', 'index':'purchase_model'},inplace=True)\n",
    "model_pop\n",
    "subs_model_pop = data.merge(model_pop, on='purchase_model', how='left')\n",
    "subs_model_pop = subs_model_pop.groupby(['model_popularity', 'purchase_model'])['actual'].mean().to_frame().reset_index()\n",
    "subs_model_pop.sort_values(by=['model_popularity'], ascending=True, inplace=True)\n",
    "\n",
    "sns.lineplot(x='purchase_model', y='actual', data=subs_model_pop)\n",
    "subs_model_pop.to_csv('model_pop_loyalty.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pop = data.purchase_model.value_counts().to_frame().reset_index()\n",
    "model_pop.rename(columns={'purchase_model':'model_popularity', 'index':'purchase_model'},inplace=True)\n",
    "#model_pop.model_popularity = model_pop.model_popularity/model_pop.model_popularity.sum()\n",
    "model_pop\n",
    "subs_model_pop = data.merge(model_pop, on='purchase_model', how='left')\n",
    "subs_model_pop = subs_model_pop.groupby(['model_popularity', 'purchase_model'])['actual'].mean().to_frame().reset_index()\n",
    "subs_model_pop.sort_values(by=['model_popularity'], ascending=True, inplace=True)\n",
    "\n",
    "subs_model_pop.to_csv('model_pop_loyalty.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance_bins = [-1,.1, 1.1, 5.1, 10.1, 25.1, 50.1, 100.1, 2500.1]\n",
    "distance_labels = [1,2,3,4,5,6,7,8]\n",
    "data['distance_binned'] = pd.cut(data['customer_distance_to_dealer'], bins=distance_bins, labels=distance_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2878d8ed708>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cJOnu6P17Xq1rpuwmPADsqhDPUDHaseM4ddDIaULGI3dvwk7bVEmTzOzuTJ3RBG/hd5rZPEk1AwXX7AwkHWJm50g6pqocgJxwZo7jVNFgndISj1wASRMJKQPuztT5DHCqmc0DMLPc2KDL3GMdDVc8DdcoOGoSvRBvk/SH+N2FSk5f0WB35CKP3CxbAVtJ+pukKZLG1rpozZGBmf0yDkteMLOf1HOnORxFSKCyZvx+LnBI/HweQaj0i/j9RjPbL2tcY1hUESpNVEjHdkTmWo7TEaTMEiQdScgeVmFC9KBdUiXHrHpldCjBe3cPgjPejdH797midutaQIwuxfvXU7caSZsC/wackbmeC5WcvkIJ/5nZBDPbKXNMqLpckUdudZ0rzGyhmT0EzCZ0DoWk7Cb8XdIpknaX9PbKUYfdT4GvkRMJq11CpT9fem4dt+04jaPBuwlTiR65cVo8jpAyIMvlwHsAokv/VsCDg100xc+g4n14QqbMCH+Zc5G0H/BUTIiyR06VtgiVzpk214VKTktppJ+BmeV65Eo6Abg1+gJdDbxP0t0Eb+GvxkjihaRsLb6nxH2/E9g/vtirAGtKOsfMDnGhktNPDDTY6yjPI9fMjs98NkKKwmOok5RIR+tJOlnSdEnTJP1M0no1bvjrZrapmY0kDGX+HDuCilBpfLVQKa4DUCVUyh0WxQeuCJXAhUpOh9IN8QxS1gwmAk8DHyG8fE8DF5Rs93RgQ4JQKbuFeCBB3nw7cDJRqBT/6leGRbMIgqesUOkYSfcT1hBcqOR0HA3eWmwKKWsG65rZdzPfvyep7pV7M7seuD5+dqGS01fU62bcTlI6g+skjQMujN8PBP7Y+FtqPs+/uqh2pQwvLUgXKj26ID3Bx9brD0+qv9ZK6f5Vz776WrLNky+mP8vc19PbeeiZNOHRTpuvntzGhqu1xyet87uCtM7gs4TFiLPj9yHAS9FN2cxszUJLx+lz2jn8r5eU3YRBXY8lbZuZxzuOk6ELZglpSVRqcHbtKo7Tn3TDAmIjO4PCp8gRKt2YESM9LunyWO5CJacn6YatxUbmWhzMq28ZoZKZ7V45IekSlvUNcKGS03N0w25C04O55wmVMufWILgzX17jMi5UcrqafpsmFO0/FQqVCCnbrs26IdMCodJfr6gZAcpxGooSjnaR4o4sSYdU5vGSNo8uwwCY2a45NkuESgWXHQ+cn/leESptD/ycpSOGhgiVKpLQdx3wsYLbcZzmMCDVfbTtHhPqngbsRniBAV4kzOMHoyJUmkMY2u8p6RwIWgfC8H+J45KZvWAxUWv0OBxWr1CpqtxxOopuWEBM6Qx2MbMvAq8CxNhqtfIm5AqV4umPAn8wsyVuZy5UcnqVblgzSNlNWBhX9StZmEeQvw5QL+OAE6vKPKOS05N0w25CSmdwMnAZsIGk7xNe3G/Va5wVKsXve+TUcaGS05N0gTdykjvyuZKmAXsRFu4+aGazmnZnTSQ1Q9BGa6T7MW28WproCODR+S8n1b/xkXnJbeyz5YhkmxsefD7ZZtxbN0q2eeParyTVTxWcATz/WrpNI+gGbULKbsKbgIfM7FTgLmBvSWs37c4cp4cYSDjaRUrblwCLJW1JcCAaRQhz7jhODbphATGlM3g9Ovd8GPiZmf0HIeX6oEiaI+nOqDW4NZb9UNI9ku6QdFllhCFppKRXMtqE0zPXGROvc79C+LXKrsO6kiZHbcJkSeuk/AAcpxUMHaj/aBcpTS+UNB44FPhDLKs31e57zGwHM9spfp8MbGdmbwPuJeSEq/BArLuDmX0uU/4LQmKJ0fGoZIg5juDFOBq4Nn53nI6i10YGnyQ4HX3fzB6SNAo4p0yjZnZNxoV4CjWSqEjaGFjTzG6KW42/Y6kG4QCCJgFcm+B0KAOq/2jbPdZb0czuNrN/N7Pz4/eHzKzaTyDXFLhGIaLykTnnPwVclfk+SkHufIOkirpxE4IXYoWsBmFDM3si3tMTQM1ss47TanrKA1HSaEkXK+R7f7By1GH6TjN7O7AP8EVJ785c85vAIkLuRYAngM3NbEdCiLXzJK1JggZhkPtfIlT6+6Tzaxs4TgPpNW3Cbwjz9kWEtE2/o47oRmb2ePz3KYLT0s4Akg4D9gM+Hof+mNlrlawvUdz0ACEt1FyWnUpkNQhPxmlEZTqRm3o6K1R6x/7j86o4TtPota3F4WZ2LSAze9jMvsMgqdUAJK2mELMASasB7yPkRRhLcCPe38xeztQfEV2ekbQFYaHwwTj8f1HSrnEX4VCWahAmETQJ4NoEp0NpcK7FppDijvyqpAHgvqgTeIza8/MNgcviCulQ4Dwz+5NCwpOVgcnx3JS4c/Bu4ISoTVgMfM7M/hmv9Xngt8BwwhpDZZ3hROBCSUcAjxAEUI7TUXSBA2JSZ3A0sCrw78B3CaOCwwYziLqB7XPKtyyofwnBuSnv3K3AdjnlzxJcpB2nY+kCnVKSNmFq/DifsM3oOE6dtHNhsF7q7gwkbQV8FXhj1s7MBl036ETOvn5OUv0zD9updqUqJkybW7tSFaPXXyWp/sPPpgl7AL4/595km+3fmO7U+fQradmRANZYKS0+702PvJjcxps3SPsZN4ou6AuSpgkXERKm/oown3ccp056apoALDIzD0HuOCUY0gVDg5qdgaR148ffS/oCwVdgSVbNzGp/kf0cQrzExYQOZSdJFwBbxyprEyIc7yBpJCG/wux4rrLLgKQxLN1NuBI4ysws3t8FwEhgDnBQDMnmOB1Dr4wMprFsJOKvZs4ZsEUd13iPmT2zxMjs4MpnST8GstEzHjCzHXKuUREqTSF0BmMJ24sVodKJCpmWjiP4MDhOx9ANwU1qdgZmNqpZjUcHooOo7by0RKgUv1eESlcRhEp7xKpnEUKreWfgdBTdMDJI0SZ8MRvZSNI6cdpQi8GESrsDT5rZfZkyFyo5PUejhUoqyD2aU+9ASSap5pZYijvyZ8zsucqXOC//TB12hUIllk+i0hKh0jO3/qG2geM0kKEDqvuohZbmHt0H2AYYL2mbnHprEJwEb67nHlM6g4FKdKHMDdWMFDqIUGkoIWrSBZm6LREqrb/TfnlVHKdpNHhkkJt7NKfed4GTiLlOapHSGVxD0ADsJWlPwl/0Pw1mUCRUiqffC9xjZnMz9V2o5PQkA6juIzuKjUf19Loo9+gSJO0IbGZmdQ+DU/wMvkZYzf88Ydh+DTmZlavIFSrFc+NYdooALlRyepSUzQQzmwBMGOxyeWZL29IA8BPg8PpbrbMziH+tz4qp0U6vVX/J3RUIleK5w3PKXKjk9CQN3k0oyj1aYQ3Cu3J9/EO8ETBJ0v7xPcqlrs7AzBbHIfxKcY7iOE4CDRYqLck9SgglMA5YklrczJ4H1q98l3Q98J+DdQSQNk2YA/xN0iTgpUzD/5twjY7gmH1HJ9UvIzp6zxbp+WV+dFWaiOi08Tsmt7Hv965JtrnsM7ukt3PaTck2b9ks7Wf24w+8JbmNL11yV+1KTaCRQUvMLDf3qKQTgFvNbFKZ66Z0Bo/HY4AwDHEcp04a7YCYl3vUzI4vqLtHPddMiWfwX/XWdRxnWdoZ27BeUuIZXEeOo0+teAbRa/EMwoKGEUKjfxj4ALCA4EvwSTN7zoVKTq/SDdqElA7rPwkipa8C/w+YAQy6IBH5GfAnM3szYWdhFp5RyekzlHC0i5RpwrSqor9JumEwm+hK/G7ifmfciVhA8FGoMAU4sMZ1XKjkdDXdEM8gRai0buZYP4Y736iG2RbA08BvovjojOiJmKUlGZWyXl1/vvTcvCqO0zS6IaNSym5CJa4BhEQqc4Aj6rj+24Evm9nNkn5GGMb/Pxg0o9KzcY3gcknb0gChUtar69xpc5NsHWdF6YY1g5TOYBvgC8C7CC/ijdReM5gLzDWzimrqYuKcXkszKu2VzahEjKJkZtMk1S1UMrMnBhMqOU476YbdhJR7PAt4C3Ay8PP4edD0amb2D+BRSZUQZ3sBd8szKjl9hrogJXvKyGBrM8vqDK6TdHsddl8GzpW0EvAgIefCVDyjktNHdP4kIa0zuE3SrmY2BUDSLsDfahmZ2QygOsqKZ1Ry+opu2E1I6Qx2AQ6V9Ej8vjkwS9KdgEWfga7guVcXJdXfZoPh3P1UWsKS6x58rnalKubPT9OA/Xr6Y8ltTPzP9Jw3X/n9rGSbp56an2zz8stpz/+V5BZgjy3TNSONoNcWEMfWrtK7nPqhNFHMFy9Lf4Gc3qXzu4I0p6OHm3kjjtPLdMHAIGlk4DhOSQa6YGzQ9O1PSWtLuljSPZJmSdpN0gWSZsRjjqQZse5ISa9kzp2euc4YSXcqhIY+uRKcNXpETpZ0X/w3PUuo4zSZbvBAbIUvxHJCJTM7uCJGIuweXJqp70Ilp+cYkOo+2naPzbx4Rqj0awhCpWzuhfjX/SCWD4xafZ0lQqXorVgRKkEQKp0VP5+VKXecjiElOnL77rG51BIqtSyjUlao9NcrzmvAozlO/fg0YalQ6RcxS9JLLDuMb1lGpWwSlXcd8LHaBo7TQLwzyBcqvR1an1HJcdqJEv5rF03tDIqESvGzZ1Ry+oYhUt1Hu2iFn0GeUAk8o5LTR7jTEYVCJc+o5PQV7Rz+10tfeiAescvIdt9CLv86ZtPalTKUSdSy65vWTba56O4nk21SnwVg2uy05Z4ySVTKiK4OGZNsshwNTq/WFPqyM2gFqcImcHFTL+MjA8dxgO5YM2i2B+LWGZ3BDEkvSDpa0g+jVuEOSZcpJFpxbYLTs3TDbkKztxZnZzQIY4CXgcvwJCpOn9ENSVRaGbR1L8KL/rCZXWNmlXBDU1jWoWg5XJvgdD1d0Bu0sjPI8yuAFiVRcZx20vceiBWiw9H+wEVV5UVJVBquTcgKlX79qwmpj+A4K0Q3aBNatZuwDzDdzJZsWKvFSVSyGZVeXZTWkTjOitIFmwktmyYso070JCpOv9FrSVRKIWlVYG/gs5niU/AkKk4f0Q1+Bq3QJrwMrFdV5klUnL6i0X1BHF3/DBgCnGFmJ1adPwb4NGFN7mngU7UinHdDPkjH6X4auLUYp9KnEtbitgHGS9qmqtptwE7Rl+di4KRa1+1Ld+R1/uVLyTav3HZKUv3EpE0AnHnCqWn105vg7N9+M9mmVYKgMVtvkCxWSuWDbx7R1OsX0eAtw52B+83sQQBJEwn+NpVYIZjZdZn6U4BDal20LzsDp3OZcty/1l23TIfbLhqsWtwEeDTzfS4h/WERR7CsL08u3hk4TitI6AwkHUlwva8wIW6ND3a13O1ySYcQ4onU7GWb2hnEcGcXZIq2AI4HdgMqodDWBp4zsx0kjQRmAbPjucouA5LGsHQ34UrgKDMzSevGNkYCc4CDzGxe0x7KcUqQMk3I+sQUMBfYLPM963eztE3pvcA3gX+NPjyD0hahkidRcfqNBnsgTgVGSxoVvXvHEfxtMu1pR+CXBF+euhZi2iJUqhR4EhWnX2ikTimK/L4EXE0YSV9oZjMlnSBp/1jth8DqwEUxHMCkgsstoZVrBnlCpcIkKsALwLfM7EYShEqSXKjkdB4NdjQwsysJ0+Vs2fGZz+9NvWZbhUq0MIlKVqi06JmZKaaOs8L0fa7FDHlCpZYmUclmVBq6/rYNezDHqYcuCGfQHqFSxJOoOP1DF/QG7RIqgSdRcfoIj45MvlAplh+eU+ZCJacncdWi4zhAdwQ38c6gg5g39ZRSIqpe4cwTTuXME9Js5k1NE5A9v2BhWgMNop1BS+qlLzuDVAViK+nke0uhTEap1I4AYJUu+Q3ugr6gPzsDx2k1XdAXNH9rUdJ/SJop6S5J50taRdK5kmbHsjMlDYt195D0fCaj0vGZ64yNNvdLOi5TPkrSzTGj0gXRwclxOosu2Fpsdnq1TYB/J0Rc2Y4QomkcITT6m4G3ErYKP50xuzEjVDohXmewyC4/AH4ShUrzCNptx+koPG9CYCgwPHocrgo8bmZXWgS4hRoZlchEdjGzBcBE4IDogLQnIawTuFDJ6VAGVP/Rtnts5sXN7DHgRwRnoCeA583smsr5OD34BPCnjNlukm6XdJWkit9wXmSXTQj+C89lUrVlBUyO0zn4NEHrEDC9LAsAABNJSURBVCTGo4A3AKvFyCsVTgP+EpWJANOBN5rZ9sDPgcsrl8q5vA1SnncvS4RKEyZ4RiWntXTDNKHZuwnvBR4ys6cBJF0KvAM4R9K3gRFk3JTN7IXM5yslnSZpfYojuzwDrC1paBwd5EZ8idfLRo/xjEpOS+mGrcVmrxk8AuwqadU4v98LmCXp08D7gfFm9nqlsqSNYj0k7Rzv71kKIrvENYfrgAPjJVyo5HQkXTBLaO7IwMxulnQxYfi/iBDLfQLwEvAwcFN89y+NOwcHAp+PQqVXgHHxhV8kqRLZZQhwpplVghIcC0yU9L14/V8385kcpwzdMDJohVDp28C362nXzE4hpF7LO7dcZJdY/iBht8FxOhZ3R3aSaJUuoRUJYVrBvKmnJN/bk/NrBgluCp3fFXhn0DS6xWe+k5g39ZSkn1undlJ5dMHAwDsDx2kFHtzEcZxA5/cFbRMq3ZgRIz0u6fJY14VKTk/SDVuLbREqmdnumYxKN7FsRiUXKjk9h4dKDywnVKqckLQGQWh0eYFtBRcqOd1NFwwN2ipUAj5EyJP4QqasKUIl1yY47aQL+oKmZ2HOCpWeI+R9O8TMzolVxgNnZEwqQqX5kvYljBhG0wChkmsTnHbSDVuLzZ4mLBEqmdlCwtrAOwAkrUcY/v+xUtnMXjCz+fHzlcCweoVKVeWO01F0g2qxLUKleO6jwB/M7NVKZRcqOb1Kg1OyN4VmrxncTFjcmw7cGdurDNXzMiodCNwl6XbgZKJQqSgFdbQ5FjhG0v2ENQQXKjkdRzd0Bu0SKmFme+SUuVDJ6UncA9HpSIbvmC6ISk1W4ixLNywgKky7+46+fGgo1xFA7yR3KckKv8ovvvp63b9za6zSnrCoPjJwnFbQBSMD7wwcpwW00824XlohVDoqipRmSjo6lv1Q0j2S7pB0maS1Y/lISa9khEqnZ64zRtKdUah0cmYLcl1Jk6NQaXJ0dHKcjqLRHohFwr3M+ZWjcO/+KOQbWeuazRYqbQd8hrDavz2wn6TRwGRgOzN7G3Av8PWM2QMZodLnMuW/AI4keCSOBsbG8uMILs2jgWvjd8fpLBrYG9QQ7lU4AphnZlsCPyEI+gal2SODtwBTzOzl6CtwA/AhM7smoyeYQo2MSpI2BtY0s5uio9HvWCpIOoAgUAIXKjkdSoM9EHOFe1V1su/FxcBeldF0IWbWtIPQGdxLcAZalSBX/nlVnd8Dh8TPIwmRk28jdBy7x/KdgP/L2OxO8F6EIFTKXm9ewb0cCdwajyOL6pR4xqbbdOp9+fM356j6XV3u95XgnHdG5vsngFOq6twFbJr5/gCw/mDtNtsDcRZheDKZkELtdkLIdAAkfTN+PzcWPQFsbmY7AscA50lakwRB0iD3MsHMdopHkWzxyJRrttCmU++rVTadel9Noep3Ne/3tZ73IfmdafoCopn92szebmbvBv4J3Acg6TBgP+DjFrsuM3vNzJ6Nn6cRerOtCEKl7FQiK0h6Mk4jKtOJp5r9TI7TZoqEe7l1opBvLcL7V0grdhM2iP9uDnwYOF/SWIKmYH8zezlTd0RcHEHSFoSFwgfN7AngRUm7xnnPoSwVJE0iCJTAhUpOf5Ar3Kuqk30vDgT+XPmjW0gL5j83AncTpgh7xbL7CcFKZsTj9Fj+EWBmrDsd+EDmOjsR5kEPEPQLFe/J9Qi7CPfFf9ddkblaJ9p06n3587fvAPYlrMc9AHwzlp1A+AMLsApwUXzXbgG2qHXNfnVHdhynilbEQHQcpwvwzsBxHMA7A8dxIt4ZOH1P1Lf0vabFFxATkfRmgqvnJgQnjscJ8RhnDWLzfoKbdNbmCjP7UyPql7XJ2K4LmJnNq1V3RSjTTqpNvfXjVvdJhLiczxGcdNYE/gwcZ2Zz6r3HXqGvO4MSL+mxhPDuEwlOHRAcPsYBE83sxBybnxIcp35XZXMocJ+ZHbUi9VfAJvllkLQWQVT2QWBELH6K4Ntxopk916B2kmxKtnET8FPgYjNbHMuGEAL1Hm1mu1bb9Dzt3i9t4z7tTwkxFccB74rHuFj2swKbe4FhOeUrEV66XJuCcuXZpNZfAZubgIOBIZmyIfFnMKXA5mqCs9hGmbKNYtnkBraTZFOyjdyfS61zvXy0/Qba9uDlXqB7CEleqsvfCMwusLkD2DmnfGfgzhWtvwI2yS9D0TMOdq5kO0k2JduYCJwG7AK8IR67xLILW/372AlHP0c6elXSzmZ2S1X5vwCv5hkARwPXSrqPpeneNge2JIRyz+Nw4Bcxr2RlCL8Z8EI8t6L1y9pMk3QaQeb6aMbmMIJqNI+HJX0NOMvMngSQtGFs49ECmzLtpNqUaeNQgub/vwjTRBF+dpPo03D7fbtmIOnthIApeS/QFywIpfLsBgh/cbO/QFMtzjsHaW+jrI2Z/aOR9VNtok/7ESxdDF3mZTCz13Js1iEEjzkA2DAW/yPa/MDMlhPClGwnyaZMG87y9G1nUKHESyqWdgaVRcdbbJAfZFx4G1tlc7XlLLiVqV/Wpp+JSr4jyFlAJnQgC9t4e22hrzuDEi/p+whzyvuAx2LxpoRpwhds2QzTFZtDCUlkrqmy2Rv4LzP73YrUXwGbUi9DiR2Y5HZSbUq2cT5h5+Eslt2BOYwgdjs473l6mb7tDEq+QLOAfWz5ra1RwJVm9pYcm9nALtUdTBxy32xmW61I/RWwSX4ZSm5hlmknyaZkG7PNbOvq8nju3ryfWc/T7hXMdh3AbGDtnPJ1KN5puA8YmlO+EiEmXZ7NvcBaOeVrUbC1mFJ/BWwG2xkoev4yOzBl2kmyKdnGFIJPwUCmbICwRXlzO34n2330826CyA8D9TrFMWrPBKZKmsiyq9bjKF6B/j4wXdI1LLsDsTfw3QbUL2szT9JHgUvM7HVYsjj6UaDIe6/MDkyZdlJtyrQxjhCS7zRJ8wj/z9cmOCqNK7Dpafp5mnAYcDxhmrDcC2Rmvy2w2wbYn6pVazO7e5C21gHeX2VztRW4zKbWL9nGSMLLsCfhhcm+DMeZ2UM5Nsk7MCXbSbIp00aV/XqEd+GZwer1On3bGUC5ly5jm+ozvyGZxS2L+/SNql/WJtolvQxltj3LtFPy3uquX6AzucLM7qn3/nqJvu4MIO0FyvjA7wk8H4vXYnAf+B2A02O9uYQXaFPCgtcXzGz6itQvaxPtkl+GktueZdpJsilRP1ln0uv0bWdQ8qVLFrdImgF81sxurirfFfilmW2/IvVXwKaM6KrMDkyZdpJsSrZxL7CtLb9NuRIw00KGrv6i3SuY7ToIgVh3ySnfFbi9wKbRfvbL7UCk1l8BmzKiqzI7MKXEXSk2JdtI1pn0+tHPuwmrWdVfUgAzmyJptQKbMj7wV0n6I2FvPmtzKCGxzIrWL2vzOkGc83BV+cbxXB5ldmDKtJNqU6aNMjqTnqafpwknA28i/wV6yMyW+4Uo6wMvaZ88GzO7shH1S7YxlhByPvdlsByPwjI7MCXbSbIp00a0K6Uz6VX6tjOAci9dL1HmZSi57VmmnSSbkm0k60x6mX6eJmBmVwFX1Vu/pA98JTrQAcAGsbgwOlBq/bI2Ecscr2f+LcTM5km6jmV3YGptrya3U8Imqf5gOhNJuTqTXqdvRwYlX7oyPvBXE7Yez7K4Hx/36Q8nZJjae0Xqr4BNGdFVmR2YMu0k2ZRsI1ln0vO0ewWzXQfFIbyOoziEV6P97Jc7l1p/BWxmASNzykcBswpsyuzAlGknyaZkG8k6k14/+jlU+kgz+4FlvOfM7B8W9qQ3L7CZJ+mjcX4KhLmqpIMp9oF/WNLXonNTxWbDuDeeFx0otX5Zm6EsHd1keQwYVmBTuAMDFO3AlGkn1aZMGxWdybGSPhaPY4Gb6dNIR/28ZvCw0kN4VcQtp0qqTCPWBq6jWNxyMGG0cUO8vgFPEnYgDmpA/azN9ZkOoZZNGdFVmS3MMu2k2iS3YWb/I+kKgs5kN5YuOn7cBtGZ9DL9vGaQDeFVWTOovEAnWrHAZxfCC/oA8BbCEPluq3MHQtLuLA1UmjeX/XfgMjMr6pCKrrsl8CHCS7CI4Ihzvpk9P4jNW8jfTRlMdLUv+UKtwbY9y7STZKMSAjJnWfq2MxgMSZ80s9/klH8b2IcwoppMeKlvAN5L2F77fo7NLWa2c/z8aeCLwOXA+4Df2/Kutc8DLxE6m/OAi6yG6CZ2IPsBfyGk6p5BmLZ8iLCAdn3dD98nSFqTsIC8KWHB8PzMudPM7Attu7l20e5Fi048gEcKyu8kxONflSDbXTOWDwfuKLC5LfN5KjAifl6N/FDptxGCbLyPMMR9mjAEPwxYY7D7ip9XBa6PnzfPtp/w/FcVlG9EkDCfCqwHfIcQpv1CYOMCm7GZz2sBZ0Sb84ANC2ymA98CtqjzflcHTgBmEgRkTxOClxw+iM0lwImEbeJJ8fvKlfbb/TvYjqNv1wwk3VF0iqWRf6tZZMGJ5WVJD5jZCwBm9oqkoj3tgTglGSCMxJ6ONi9JWpRT3ywE6LgGuEbSMMJoZDzwI5ZmMqpmKLAYWJkQbwAzeyTaL/+QITZB7ilgh4JzvwX+SOjIrgPOJYxIDiBsOR6QY/PfLF1P+DEhmvIHgA8DvyS8jNWsQ1iLuV7SP4DzgQvM7PGC+zoXuIzgDHVQvL+JwLckbWVm38ixeZOZfSR+vlzSN4E/S9q/oI3ep929UbsOwvrADgRhSvYYSXCkybO5GVg1fs6Gy1qLgr8mwBzgQeCh+O9GsXx1YEZO/cK/5MDwgvKjCH9tJxAEOJ+M5SOAvxTYLCb4JlyXc7xSYJMd5TxSdW65Z4nl04vq1GmzO8GH4B/x3o7MqX971feplf9HwD0FbczK/j+MZYcRRhcPt/v3sx1H22+gbQ8ehuDvKjh3XkH5ygXl6wNvTWx/VWBUTvlWJZ9nW+BA4M111r8LGF1w7tGC8tszn79Xda4oc9Nc4BjgK7EzVOZc0dRquY6VMD0bC/wm59zfK/8vCaOOqzPnivwsTgLem1M+lj5Nr+YLiH2KpAMJL/DsnHMfNLPLc8pPAE4ys/lV5VsSdmAOzLH5dlXRaWb2dPSQPMnMDs2xmWhmdcchlPQ2wlrEVoRO7lNmdq+kEcB4Mzu5wO7NhN2Hm7PPJGkfC67qfYV3Bs5yFO2mdKPNIDtDXyZIlWcRpotHmdkV8dx0MytaU+lZvDNwlkPSI2ZW5IXZVTZF9SXdCexmZvMVAqpeDJxtZj+TdJuZ7ZhyX71A3+4m9DtldlM61abkztCQytTAzOZI2gO4WNIbKQ7U0tN4Z9C/bEjYiqv2tBRhQa6bbMq08Q9JO5jZDIA4QtiP4Nr81gKbnsY7g/7lD8DqlZchi6Tru8ymTBuHEty2l2Bmi4BDJf2ywKan8TUDx3EA+lrC7DhOBu8MHMcBvDNwHCfinUGHIylPZNNyJL1B0sXtvg+nefgCYocjab6Zrd7C9obGVXWnz/CRQZORdKikOyTdLulsSb+NuoDK+fnx340l/UXSDEl3Sdpd0onA8Fh2bqx3TDx/l6SjY9lISfdIOiOWnyvpvZL+Juk+SZXgKqtJOlPSVEm3SToglh8u6SJJvydIp/OeY6SkuzL1L5X0p3j9kzL1xkqaHp/32li2rqTL489hStQSIOk7ks6SdI2kOZI+LOkkSXfGaw+L9cZIukHSNElXS9q40f+fHPpXtdiKg6AknA2sH7+vS4gJcGCmzvz471eAb8bPQ4iBTCrn4+cxhEAmqxEk0DOBHQmy60UEZ5kBYBrBeUaEGAOXR/v/Bg6Jn9cmhEZbjRD3cS4h3HvRs4wE7oqfDycoENcCViGkNduMIJl+lKjGrFwP+Dnw7fh5T6J0mRAc5a+EoKXbAy8TwpdDiE/wwXju7ywNCnMwcGa7/9/24uFOR81lT0LG5mcAzOyfUqGn61TgzPjX8HLLcaAB3kWIj/gSgKRLCXr/SYSUcHfG8pnAtWZm0Qd/ZLR/H7C/pP+M31dhaSToyWb2z4Rnu9ZifEVJdxNiQaxDiJ/wUOV5M/f9kVj2Z0nrKeStgBBVaWG8zyEsDYRSue+tge2AyfFnNwR4IuE+nTrxzqC55CUqXUScnin8dq8EYGZ/kfRu4N+AsyX90JZPcT6Yz3w2z+Prme+vs/T/s4CPWJVsWSHI60t1PVF+e4tjG0WJWfPuu1LvNQAze13SQot//jP3LUKK9N0S789JxNcMmsu1wEGS1oMwdyZEPhoTzx9AjOsfBTJPmdmvCIFXKhLahVoauuwvwAclraqQKfpDwI0J93M18OXYCSGp0cq8m4B/VchKVHleCPf98Vi2B/CMxZBxdTAbGCFpt2g/TNK2Db1rB/CRQVMxs5mSvk/IgbCYEOz0WOAKSbcQOovKX+Q9gK9KWgjMJ/jOQwhldoeCxv7jkn4L3BLPnWFmtylIcOvhu8BP4/VE6Jj2K/+Ey2IhaMmRwKUKiWaeImRo/g7wm6gufJkQXqzeay6IC64nx6nF0PgMMxt1307AtxYdxwF8muA4TsSnCc4ySHorcHZV8Wtmtks77sdpHT5NcBwH8GmC4zgR7wwcxwG8M3AcJ+KdgeM4gHcGjuNE/j/AOCMDxZEvNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=pd.pivot_table(data=income.loc[(income['purchase_price'] != '?')], index='purchase_price', columns=['customer_income'], values='actual', aggfunc='mean'),cmap='Blues',square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2878d9d2608>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(data=pd.pivot_table(data=income.loc[income['purchase_vehicle_year'] >= 1990], index='purchase_vehicle_year', columns=['customer_income'], values='counter', aggfunc='count'),cmap='Blues',square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>insert_num</th>\n",
       "      <th>purchase_make</th>\n",
       "      <th>purchase_model</th>\n",
       "      <th>purchase_vehicle_year</th>\n",
       "      <th>purchase_price</th>\n",
       "      <th>trade_in</th>\n",
       "      <th>vehicle_financing</th>\n",
       "      <th>customer_age</th>\n",
       "      <th>customer_income</th>\n",
       "      <th>customer_gender</th>\n",
       "      <th>customer_previous_purchase</th>\n",
       "      <th>customer_distance_to_dealer</th>\n",
       "      <th>post_purchase_satisfaction</th>\n",
       "      <th>vehicle_warranty_used</th>\n",
       "      <th>subsequent_purchases</th>\n",
       "      <th>actual</th>\n",
       "      <th>counter</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>81690</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>CHARGER</td>\n",
       "      <td>2010</td>\n",
       "      <td>12500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>30000</td>\n",
       "      <td>U</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>109994</td>\n",
       "      <td>FORD</td>\n",
       "      <td>EXPLORER SPORT TRAC</td>\n",
       "      <td>2007</td>\n",
       "      <td>17500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>30000</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11399</td>\n",
       "      <td>BMW</td>\n",
       "      <td>328</td>\n",
       "      <td>2010</td>\n",
       "      <td>22500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>90000</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>36685</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>CRUZE</td>\n",
       "      <td>2012</td>\n",
       "      <td>12500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256143</td>\n",
       "      <td>MITSUBISHI</td>\n",
       "      <td>OUTLANDER SPORT</td>\n",
       "      <td>2011</td>\n",
       "      <td>17500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>312463</td>\n",
       "      <td>195314</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>GRAND CHEROKEE</td>\n",
       "      <td>2005</td>\n",
       "      <td>17500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>70000</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>312464</td>\n",
       "      <td>58219</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>SILVERADO 1500</td>\n",
       "      <td>2013</td>\n",
       "      <td>27500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>110000</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>312465</td>\n",
       "      <td>207386</td>\n",
       "      <td>KIA</td>\n",
       "      <td>OPTIMA</td>\n",
       "      <td>2012</td>\n",
       "      <td>17500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>50000</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>312466</td>\n",
       "      <td>252391</td>\n",
       "      <td>MINI</td>\n",
       "      <td>COOPER</td>\n",
       "      <td>2010</td>\n",
       "      <td>17500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>50000</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>312467</td>\n",
       "      <td>52652</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>MALIBU</td>\n",
       "      <td>2012</td>\n",
       "      <td>17500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>50000</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>312468 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        insert_num purchase_make       purchase_model  purchase_vehicle_year  \\\n",
       "0            81690         DODGE              CHARGER                   2010   \n",
       "1           109994          FORD  EXPLORER SPORT TRAC                   2007   \n",
       "2            11399           BMW                  328                   2010   \n",
       "3            36685     CHEVROLET                CRUZE                   2012   \n",
       "4           256143    MITSUBISHI      OUTLANDER SPORT                   2011   \n",
       "...            ...           ...                  ...                    ...   \n",
       "312463      195314          JEEP       GRAND CHEROKEE                   2005   \n",
       "312464       58219     CHEVROLET       SILVERADO 1500                   2013   \n",
       "312465      207386           KIA               OPTIMA                   2012   \n",
       "312466      252391          MINI               COOPER                   2010   \n",
       "312467       52652     CHEVROLET               MALIBU                   2012   \n",
       "\n",
       "       purchase_price  trade_in  vehicle_financing customer_age  \\\n",
       "0               12500         0                  0           25   \n",
       "1               17500         0                  1           65   \n",
       "2               22500         1                  1           35   \n",
       "3               12500         1                  1           25   \n",
       "4               17500         0                  1           25   \n",
       "...               ...       ...                ...          ...   \n",
       "312463          17500         0                  1           35   \n",
       "312464          27500         0                  1           25   \n",
       "312465          17500         1                  1           45   \n",
       "312466          17500         1                  1           45   \n",
       "312467          17500         0                  1           35   \n",
       "\n",
       "        customer_income customer_gender  customer_previous_purchase  \\\n",
       "0                 30000               U                           0   \n",
       "1                 30000               M                           1   \n",
       "2                 90000               F                           1   \n",
       "3                 50000               M                           0   \n",
       "4                 50000               M                           0   \n",
       "...                 ...             ...                         ...   \n",
       "312463            70000               M                           0   \n",
       "312464           110000               F                           0   \n",
       "312465            50000               F                           0   \n",
       "312466            50000               M                           0   \n",
       "312467            50000               F                           0   \n",
       "\n",
       "       customer_distance_to_dealer post_purchase_satisfaction  \\\n",
       "0                                9                          ?   \n",
       "1                               15                          ?   \n",
       "2                               18                          ?   \n",
       "3                                2                          ?   \n",
       "4                               12                          ?   \n",
       "...                            ...                        ...   \n",
       "312463                           5                          ?   \n",
       "312464                           8                          ?   \n",
       "312465                           4                          ?   \n",
       "312466                           5                          ?   \n",
       "312467                           8                          ?   \n",
       "\n",
       "        vehicle_warranty_used  subsequent_purchases  actual  counter  index  \n",
       "0                           1                     0       0        1    NaN  \n",
       "1                           0                     0       0        1    NaN  \n",
       "2                           0                     9       1        1    NaN  \n",
       "3                           0                     0       0        1    NaN  \n",
       "4                           0                     0       0        1    NaN  \n",
       "...                       ...                   ...     ...      ...    ...  \n",
       "312463                      0                     0       0        1    NaN  \n",
       "312464                      0                     0       0        1    NaN  \n",
       "312465                      0                     0       0        1    NaN  \n",
       "312466                      1                     0       0        1    NaN  \n",
       "312467                      0                     0       0        1    NaN  \n",
       "\n",
       "[312468 rows x 18 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_percent = income.customer_income.value_counts().to_frame().reset_index()\n",
    "income_percent.rename({'customer_income':'percent_pop', 'index':'customer_income'}, inplace=True)\n",
    "income_pop = income.merge(income_percent, on='customer_income', how='left')\n",
    "income_pop\n",
    "#income_pop = income_pop.groupby(['purchase_price', 'customer_income', 'percent_pop'])['counter'].count()\n",
    "#income_pop\n",
    "#income_percent.percent_pop = income_percent.percent_pop/income_percent.percent_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5626785714285715\n",
      "0.7027027027027027\n",
      "0.33812395667642775\n"
     ]
    }
   ],
   "source": [
    "print(data.loc[(data['purchase_vehicle_year'] <= 2002) & (data['purchase_vehicle_year'] >= 1990)]['actual'].mean())\n",
    "print(data.loc[data['purchase_vehicle_year'] <= 1990]['actual'].mean())\n",
    "print(data.loc[data['purchase_vehicle_year'] > 2002]['actual'].mean())      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6653107344632768\n",
      "0.33702233632423717\n",
      "0.3760799593427071\n",
      "0.4036363636363636\n"
     ]
    }
   ],
   "source": [
    "price['purchase_price'].unique()\n",
    "\n",
    "print(price.loc[price['purchase_price'] <= 2500]['actual'].mean())\n",
    "print(price.loc[(price['purchase_price'] <= 37500) & (price['purchase_price'] > 2500)]['actual'].mean())\n",
    "print(price.loc[(price['purchase_price'] <= 62500) & (price['purchase_price'] > 37500)]['actual'].mean())\n",
    "print(price.loc[price['purchase_price'] > 62500]['actual'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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