EXL Round 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "File = pd.ExcelFile(r\"C:\\Users\\welcome\\Desktop\\EXL CASES\\ROUND 2\\Data.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'Demographics',\n",
       " u'Store Mapping',\n",
       " u'Category Mapping',\n",
       " u'Ping_Information',\n",
       " u'Variable Descriptions']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "File.sheet_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Shopper_Id</th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Owns_Home</th>\n",
       "      <th>Number_of_Children_under_18_years_of_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>831b9564-99d7-3b57-a9c0-3ac1f31560d5</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>D</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_2</td>\n",
       "      <td>1bd5093b-5a76-3ccc-b0d2-37695a858a95</td>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_3</td>\n",
       "      <td>d08139f8-c50d-3472-adf8-6dcc4724164c</td>\n",
       "      <td>51</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_4</td>\n",
       "      <td>29b285af-f631-3100-9a4e-5de09df7f974</td>\n",
       "      <td>35</td>\n",
       "      <td>F</td>\n",
       "      <td>D</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_5</td>\n",
       "      <td>63fc3248-9900-3074-b887-c6ed6d73d0e3</td>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shopper_6</td>\n",
       "      <td>3fe4135c-06c1-395c-923d-f10f3552dd75</td>\n",
       "      <td>50</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shopper_7</td>\n",
       "      <td>725954b6-5227-3d88-b6bf-8a6b822dd8d4</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shopper_8</td>\n",
       "      <td>c0b4b558-84e1-3400-8f6f-18db12478292</td>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shopper_9</td>\n",
       "      <td>eca4c7c4-7b92-3648-ba55-f84020ced575</td>\n",
       "      <td>36</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>39a86f65-f08d-330b-8d37-57d082631adf</td>\n",
       "      <td>56</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shopper_11</td>\n",
       "      <td>178732a0-709f-37de-8188-becd7d4f859f</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shopper_12</td>\n",
       "      <td>b78feb44-2b0b-3131-9935-f0802c08db3f</td>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shopper_13</td>\n",
       "      <td>9cc20ae7-6b44-3c97-9f51-76789bbcf275</td>\n",
       "      <td>76</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shopper_14</td>\n",
       "      <td>43eefca9-9f82-3adc-8efe-decc03100807</td>\n",
       "      <td>50</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shopper_15</td>\n",
       "      <td>00d93a11-9304-345c-917a-c9be3ff4e999</td>\n",
       "      <td>25</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Shopper_16</td>\n",
       "      <td>e4b8f1dd-cd15-3477-8b38-f12b22c1e00d</td>\n",
       "      <td>71</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shopper_17</td>\n",
       "      <td>1d367279-918a-3a87-9fe4-148b0b57fbaa</td>\n",
       "      <td>66</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shopper_18</td>\n",
       "      <td>96d2a6dc-0fc6-3539-a56d-3753a9cf7d95</td>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Shopper_19</td>\n",
       "      <td>6b98b21d-5de8-342b-a74f-2e22b17d567a</td>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Shopper_20</td>\n",
       "      <td>0e2bda2d-9447-3963-ac56-c18b34892350</td>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Shopper_21</td>\n",
       "      <td>23d3ea98-d569-39f7-bf85-3a8c6804779e</td>\n",
       "      <td>39</td>\n",
       "      <td>F</td>\n",
       "      <td>D</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shopper_22</td>\n",
       "      <td>8a3fed0d-2b89-3c23-81b3-e574d09cff1c</td>\n",
       "      <td>63</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shopper_23</td>\n",
       "      <td>9595b3cd-9cca-358a-9332-beb3fc866adf</td>\n",
       "      <td>77</td>\n",
       "      <td>M</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shopper_24</td>\n",
       "      <td>59081e1d-5dcc-3611-906e-eb9bce76f678</td>\n",
       "      <td>56</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shopper_25</td>\n",
       "      <td>18dd5301-f972-3a99-b2bf-00e76e43eecd</td>\n",
       "      <td>80</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shopper_26</td>\n",
       "      <td>d3b1c83b-e81d-39b5-b05f-5e56e788118a</td>\n",
       "      <td>64</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Shopper_27</td>\n",
       "      <td>9240bfdc-f217-3f15-8a25-56275b126406</td>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Shopper_28</td>\n",
       "      <td>bc811f9d-e05a-33bf-ac77-f2885989841f</td>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Shopper_29</td>\n",
       "      <td>8fa6c30a-ae6f-39fb-9e19-76689d8235d9</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Shopper_30</td>\n",
       "      <td>92a3e8dd-449c-3e91-ab67-345909ace019</td>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>537</th>\n",
       "      <td>Shopper_538</td>\n",
       "      <td>09fb9cf4-2b53-39d0-ba0d-d1bab7a4588f</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Shopper_539</td>\n",
       "      <td>3ebdd444-6192-3dbd-9731-91563c743057</td>\n",
       "      <td>63</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>Shopper_540</td>\n",
       "      <td>0093f844-f100-3ff0-8d87-38cea990e4fd</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>Shopper_541</td>\n",
       "      <td>752f68b4-12e2-3d3e-a009-23fc7a7e2545</td>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Shopper_542</td>\n",
       "      <td>32db0e93-9276-3ff3-9666-4223d7b96490</td>\n",
       "      <td>58</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Shopper_543</td>\n",
       "      <td>2aeddf52-6dc6-39d6-becb-877e717d0e23</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Shopper_544</td>\n",
       "      <td>c7c8b32c-055f-3dd1-9bbf-b89ea7ec4c44</td>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Shopper_545</td>\n",
       "      <td>853923ef-9e74-3bd7-b1db-078e63bfc0ca</td>\n",
       "      <td>55</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Shopper_546</td>\n",
       "      <td>c4298718-00e8-349b-bc68-daa2e68f6287</td>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Shopper_547</td>\n",
       "      <td>dc2410cb-3c2e-3672-bf58-5cad8ebd80fd</td>\n",
       "      <td>72</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>Shopper_548</td>\n",
       "      <td>d7cff28f-89f8-3c95-bee8-05306fe437b9</td>\n",
       "      <td>62</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>Shopper_549</td>\n",
       "      <td>4a1aeea8-bf02-31ce-ae80-054d0d71663f</td>\n",
       "      <td>56</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Shopper_550</td>\n",
       "      <td>e6d9272b-b5ab-33a1-b334-da4de6b2d352</td>\n",
       "      <td>68</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Shopper_551</td>\n",
       "      <td>662e858c-a7a8-3aa0-a028-82f2cc9d7108</td>\n",
       "      <td>77</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Shopper_552</td>\n",
       "      <td>7444d133-1a7a-33e9-b6d0-e5838ad5ec7a</td>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Shopper_553</td>\n",
       "      <td>77d9cc00-f1b6-38dd-a887-572f663c11c4</td>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>Shopper_554</td>\n",
       "      <td>7254bebe-09b0-3359-9d5d-d55d00353e25</td>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>Shopper_555</td>\n",
       "      <td>61f76c1f-54db-3542-99d9-bed6bb1441bd</td>\n",
       "      <td>71</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>0998e032-56c2-3579-8518-5f9260d17dec</td>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>Shopper_557</td>\n",
       "      <td>c1757a9e-2f29-3a9d-9ffb-cbb9fd20f69b</td>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>Shopper_558</td>\n",
       "      <td>5b262605-4c8a-32ff-b917-bcf23c986cae</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>Shopper_559</td>\n",
       "      <td>299782a2-5b9b-3233-836a-57fb2c7b0874</td>\n",
       "      <td>62</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>Shopper_560</td>\n",
       "      <td>c18719bc-d3f3-3db1-9220-c9261e928ea4</td>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>Shopper_561</td>\n",
       "      <td>432d56d7-c301-34b8-bd4a-69bd3e5e125d</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>Shopper_562</td>\n",
       "      <td>3ba1d8a5-4150-3e03-a52a-ae08393f17dd</td>\n",
       "      <td>64</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>Shopper_563</td>\n",
       "      <td>131b6acc-649d-3d5b-bd84-0385c4e360a5</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Shopper_564</td>\n",
       "      <td>24761403-2ff6-3b82-8861-1f8c91f9b8be</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>Shopper_565</td>\n",
       "      <td>72b79daa-a4c1-375a-9303-f4f5580a6096</td>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>Shopper_566</td>\n",
       "      <td>fe03a8f8-2624-3440-9ff5-45037d4afcdc</td>\n",
       "      <td>93</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>Shopper_567</td>\n",
       "      <td>ec58077c-fc04-34a4-af31-d951098e224d</td>\n",
       "      <td>48</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>567 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Shopper_Id                                    ID  Age Gender  \\\n",
       "0      Shopper_1  831b9564-99d7-3b57-a9c0-3ac1f31560d5   23      F   \n",
       "1      Shopper_2  1bd5093b-5a76-3ccc-b0d2-37695a858a95   42      F   \n",
       "2      Shopper_3  d08139f8-c50d-3472-adf8-6dcc4724164c   51      F   \n",
       "3      Shopper_4  29b285af-f631-3100-9a4e-5de09df7f974   35      F   \n",
       "4      Shopper_5  63fc3248-9900-3074-b887-c6ed6d73d0e3   42      M   \n",
       "5      Shopper_6  3fe4135c-06c1-395c-923d-f10f3552dd75   50      M   \n",
       "6      Shopper_7  725954b6-5227-3d88-b6bf-8a6b822dd8d4   40      M   \n",
       "7      Shopper_8  c0b4b558-84e1-3400-8f6f-18db12478292   58      M   \n",
       "8      Shopper_9  eca4c7c4-7b92-3648-ba55-f84020ced575   36      F   \n",
       "9     Shopper_10  39a86f65-f08d-330b-8d37-57d082631adf   56      F   \n",
       "10    Shopper_11  178732a0-709f-37de-8188-becd7d4f859f   23      F   \n",
       "11    Shopper_12  b78feb44-2b0b-3131-9935-f0802c08db3f   61      M   \n",
       "12    Shopper_13  9cc20ae7-6b44-3c97-9f51-76789bbcf275   76      M   \n",
       "13    Shopper_14  43eefca9-9f82-3adc-8efe-decc03100807   50      M   \n",
       "14    Shopper_15  00d93a11-9304-345c-917a-c9be3ff4e999   25      F   \n",
       "15    Shopper_16  e4b8f1dd-cd15-3477-8b38-f12b22c1e00d   71      M   \n",
       "16    Shopper_17  1d367279-918a-3a87-9fe4-148b0b57fbaa   66      F   \n",
       "17    Shopper_18  96d2a6dc-0fc6-3539-a56d-3753a9cf7d95   28      F   \n",
       "18    Shopper_19  6b98b21d-5de8-342b-a74f-2e22b17d567a   36      M   \n",
       "19    Shopper_20  0e2bda2d-9447-3963-ac56-c18b34892350   61      F   \n",
       "20    Shopper_21  23d3ea98-d569-39f7-bf85-3a8c6804779e   39      F   \n",
       "21    Shopper_22  8a3fed0d-2b89-3c23-81b3-e574d09cff1c   63      M   \n",
       "22    Shopper_23  9595b3cd-9cca-358a-9332-beb3fc866adf   77      M   \n",
       "23    Shopper_24  59081e1d-5dcc-3611-906e-eb9bce76f678   56      M   \n",
       "24    Shopper_25  18dd5301-f972-3a99-b2bf-00e76e43eecd   80      F   \n",
       "25    Shopper_26  d3b1c83b-e81d-39b5-b05f-5e56e788118a   64      F   \n",
       "26    Shopper_27  9240bfdc-f217-3f15-8a25-56275b126406   62      M   \n",
       "27    Shopper_28  bc811f9d-e05a-33bf-ac77-f2885989841f   46      F   \n",
       "28    Shopper_29  8fa6c30a-ae6f-39fb-9e19-76689d8235d9   23      F   \n",
       "29    Shopper_30  92a3e8dd-449c-3e91-ab67-345909ace019   46      F   \n",
       "..           ...                                   ...  ...    ...   \n",
       "537  Shopper_538  09fb9cf4-2b53-39d0-ba0d-d1bab7a4588f   24      F   \n",
       "538  Shopper_539  3ebdd444-6192-3dbd-9731-91563c743057   63      M   \n",
       "539  Shopper_540  0093f844-f100-3ff0-8d87-38cea990e4fd   45      M   \n",
       "540  Shopper_541  752f68b4-12e2-3d3e-a009-23fc7a7e2545   31      F   \n",
       "541  Shopper_542  32db0e93-9276-3ff3-9666-4223d7b96490   58      F   \n",
       "542  Shopper_543  2aeddf52-6dc6-39d6-becb-877e717d0e23   49      F   \n",
       "543  Shopper_544  c7c8b32c-055f-3dd1-9bbf-b89ea7ec4c44   68      M   \n",
       "544  Shopper_545  853923ef-9e74-3bd7-b1db-078e63bfc0ca   55      M   \n",
       "545  Shopper_546  c4298718-00e8-349b-bc68-daa2e68f6287   61      F   \n",
       "546  Shopper_547  dc2410cb-3c2e-3672-bf58-5cad8ebd80fd   72      M   \n",
       "547  Shopper_548  d7cff28f-89f8-3c95-bee8-05306fe437b9   62      F   \n",
       "548  Shopper_549  4a1aeea8-bf02-31ce-ae80-054d0d71663f   56      M   \n",
       "549  Shopper_550  e6d9272b-b5ab-33a1-b334-da4de6b2d352   68      F   \n",
       "550  Shopper_551  662e858c-a7a8-3aa0-a028-82f2cc9d7108   77      M   \n",
       "551  Shopper_552  7444d133-1a7a-33e9-b6d0-e5838ad5ec7a   42      M   \n",
       "552  Shopper_553  77d9cc00-f1b6-38dd-a887-572f663c11c4   67      M   \n",
       "553  Shopper_554  7254bebe-09b0-3359-9d5d-d55d00353e25   61      M   \n",
       "554  Shopper_555  61f76c1f-54db-3542-99d9-bed6bb1441bd   71      M   \n",
       "555  Shopper_556  0998e032-56c2-3579-8518-5f9260d17dec   45      F   \n",
       "556  Shopper_557  c1757a9e-2f29-3a9d-9ffb-cbb9fd20f69b   44      F   \n",
       "557  Shopper_558  5b262605-4c8a-32ff-b917-bcf23c986cae   22      M   \n",
       "558  Shopper_559  299782a2-5b9b-3233-836a-57fb2c7b0874   62      F   \n",
       "559  Shopper_560  c18719bc-d3f3-3db1-9220-c9261e928ea4   49      M   \n",
       "560  Shopper_561  432d56d7-c301-34b8-bd4a-69bd3e5e125d   27      F   \n",
       "561  Shopper_562  3ba1d8a5-4150-3e03-a52a-ae08393f17dd   64      F   \n",
       "562  Shopper_563  131b6acc-649d-3d5b-bd84-0385c4e360a5   27      F   \n",
       "563  Shopper_564  24761403-2ff6-3b82-8861-1f8c91f9b8be   60      M   \n",
       "564  Shopper_565  72b79daa-a4c1-375a-9303-f4f5580a6096   45      F   \n",
       "565  Shopper_566  fe03a8f8-2624-3440-9ff5-45037d4afcdc   93      M   \n",
       "566  Shopper_567  ec58077c-fc04-34a4-af31-d951098e224d   48      M   \n",
       "\n",
       "    Marital_Status Owns_Home  Number_of_Children_under_18_years_of_age  \n",
       "0                D         N                                         1  \n",
       "1                M         N                                         1  \n",
       "2                M         N                                         1  \n",
       "3                D         N                                         2  \n",
       "4                S         N                                         0  \n",
       "5                M         Y                                         1  \n",
       "6                M         Y                                         0  \n",
       "7                M         Y                                         0  \n",
       "8                M         N                                         2  \n",
       "9                M         Y                                         1  \n",
       "10               S         N                                         0  \n",
       "11               M         Y                                         0  \n",
       "12               M         Y                                         2  \n",
       "13               M         Y                                         2  \n",
       "14               M         Y                                         0  \n",
       "15               M         Y                                         0  \n",
       "16               M         N                                         0  \n",
       "17               M         Y                                         2  \n",
       "18               S         N                                         0  \n",
       "19               M         Y                                         0  \n",
       "20               D         N                                         1  \n",
       "21               M         N                                         0  \n",
       "22               D         Y                                         1  \n",
       "23               S         Y                                         0  \n",
       "24               S         Y                                         0  \n",
       "25               S         Y                                         0  \n",
       "26               S         Y                                         0  \n",
       "27               M         Y                                         3  \n",
       "28               M         N                                         0  \n",
       "29               M         Y                                         0  \n",
       "..             ...       ...                                       ...  \n",
       "537              S         Y                                         0  \n",
       "538              M         Y                                         0  \n",
       "539              M         N                                         2  \n",
       "540              M         N                                         0  \n",
       "541              S         N                                         0  \n",
       "542              M         Y                                         1  \n",
       "543              M         Y                                         0  \n",
       "544              M         Y                                         0  \n",
       "545              M         Y                                         0  \n",
       "546              M         Y                                         1  \n",
       "547              S         N                                         0  \n",
       "548              S         Y                                         0  \n",
       "549              S         N                                         0  \n",
       "550              M         N                                         0  \n",
       "551              S         N                                         0  \n",
       "552              M         N                                         0  \n",
       "553              M         Y                                         0  \n",
       "554              S         Y                                         0  \n",
       "555              M         Y                                         4  \n",
       "556              S         N                                         0  \n",
       "557              S         Y                                         0  \n",
       "558              M         Y                                         0  \n",
       "559              M         Y                                         3  \n",
       "560              S         Y                                         0  \n",
       "561              M         Y                                         0  \n",
       "562              S         Y                                         0  \n",
       "563              M         Y                                         1  \n",
       "564              M         Y                                         1  \n",
       "565              M         Y                                         0  \n",
       "566              M         Y                                         0  \n",
       "\n",
       "[567 rows x 7 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demographics= pd.read_excel(File, 'Demographics' )\n",
    "demographics.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store_Name</th>\n",
       "      <th>Floor_name</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jamba Juice</td>\n",
       "      <td>Concourse Level</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784091</td>\n",
       "      <td>-122.406813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coriander Gourmet Thai</td>\n",
       "      <td>Concourse Level</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784596</td>\n",
       "      <td>-122.406210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NYS Collection</td>\n",
       "      <td>Concourse Level</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784396</td>\n",
       "      <td>-122.406587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hallmark</td>\n",
       "      <td>Concourse Level</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783771</td>\n",
       "      <td>-122.407377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sorabol Korean BBQ &amp; Asian Noodles</td>\n",
       "      <td>Concourse Level</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784601</td>\n",
       "      <td>-122.406328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Store_Name       Floor_name  Floor_Index  \\\n",
       "0                         Jamba Juice  Concourse Level            0   \n",
       "1              Coriander Gourmet Thai  Concourse Level            0   \n",
       "2                      NYS Collection  Concourse Level            0   \n",
       "3                            Hallmark  Concourse Level            0   \n",
       "4  Sorabol Korean BBQ & Asian Noodles  Concourse Level            0   \n",
       "\n",
       "    latitude   longitude  \n",
       "0  37.784091 -122.406813  \n",
       "1  37.784596 -122.406210  \n",
       "2  37.784396 -122.406587  \n",
       "3  37.783771 -122.407377  \n",
       "4  37.784601 -122.406328  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storemapping = pd.read_excel(File, \"Store Mapping\")\n",
    "storemapping.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "storemapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Store_Name</th>\n",
       "      <th>Broad_Category</th>\n",
       "      <th>Fine_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amazon Pop-Up</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bespoke Events</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bespoke Coworking</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Specialty Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dyson</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Penhaligonï¿½Ûªs</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Store_Name Broad_Category    Fine_Category\n",
       "0      Amazon Pop-Up         Retail            Misc.\n",
       "1     Bespoke Events         Retail            Misc.\n",
       "2  Bespoke Coworking         Retail  Specialty Store\n",
       "3              Dyson         Retail            Misc.\n",
       "4   Penhaligonï¿½Ûªs         Retail            Misc."
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorymapping = pd.read_excel(File, \"Category Mapping\")\n",
    "categorymapping.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "categorymapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Shopper_ID</th>\n",
       "      <th>floor</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>seenTimestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>-2934.558251</td>\n",
       "      <td>-16169.722290</td>\n",
       "      <td>2016-11-22T02:16:06.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>117.262475</td>\n",
       "      <td>151.823812</td>\n",
       "      <td>2016-12-23T22:01:25.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>156.125087</td>\n",
       "      <td>171.357200</td>\n",
       "      <td>2016-11-22T01:42:28.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>Concourse</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>134.499673</td>\n",
       "      <td>167.674401</td>\n",
       "      <td>2016-10-13T18:27:38.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>Floor 2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>94.981693</td>\n",
       "      <td>172.269243</td>\n",
       "      <td>2016-10-12T21:36:18.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shopper_ID      floor        lat         lng            x             y  \\\n",
       "0  Shopper_556    Floor 4  37.661463 -122.298673 -2934.558251 -16169.722290   \n",
       "1  Shopper_556    Floor 1  37.784062 -122.406943   117.262475    151.823812   \n",
       "2  Shopper_556    Floor 4  37.784361 -122.406710   156.125087    171.357200   \n",
       "3  Shopper_360  Concourse  37.784341 -122.406891   134.499673    167.674401   \n",
       "4  Shopper_360    Floor 2  37.784017 -122.407307    94.981693    172.269243   \n",
       "\n",
       "              seenTimestamp  \n",
       "0  2016-11-22T02:16:06.000Z  \n",
       "1  2016-12-23T22:01:25.000Z  \n",
       "2  2016-11-22T01:42:28.000Z  \n",
       "3  2016-10-13T18:27:38.000Z  \n",
       "4  2016-10-12T21:36:18.000Z  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginformation = pd.read_excel(File, \"Ping_Information\")\n",
    "pinginformation.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "pinginformation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 567 entries, 0 to 566\n",
      "Data columns (total 7 columns):\n",
      "Shopper_Id                                  567 non-null object\n",
      "ID                                          567 non-null object\n",
      "Age                                         567 non-null int64\n",
      "Gender                                      567 non-null object\n",
      "Marital_Status                              567 non-null object\n",
      "Owns_Home                                   567 non-null object\n",
      "Number_of_Children_under_18_years_of_age    567 non-null int64\n",
      "dtypes: int64(2), object(5)\n",
      "memory usage: 31.1+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 166 entries, 0 to 165\n",
      "Data columns (total 5 columns):\n",
      "Store_Name     166 non-null object\n",
      "Floor_name     166 non-null object\n",
      "Floor_Index    166 non-null int64\n",
      "latitude       166 non-null float64\n",
      "longitude      166 non-null float64\n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 6.6+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 154 entries, 0 to 153\n",
      "Data columns (total 3 columns):\n",
      "Store_Name        154 non-null object\n",
      "Broad_Category    154 non-null object\n",
      "Fine_Category     154 non-null object\n",
      "dtypes: object(3)\n",
      "memory usage: 3.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 107635 entries, 0 to 107634\n",
      "Data columns (total 7 columns):\n",
      "Shopper_ID       107635 non-null object\n",
      "floor            107635 non-null object\n",
      "lat              107635 non-null float64\n",
      "lng              107635 non-null float64\n",
      "x                107635 non-null float64\n",
      "y                107635 non-null float64\n",
      "seenTimestamp    107635 non-null object\n",
      "dtypes: float64(4), object(3)\n",
      "memory usage: 5.7+ MB\n",
      "(None, None, None, None)\n"
     ]
    }
   ],
   "source": [
    "print(demographics.info(), storemapping.info(), categorymapping.info(), pinginformation.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Store_Name</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jamba Juice</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784091</td>\n",
       "      <td>-122.406813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coriander Gourmet Thai</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784596</td>\n",
       "      <td>-122.406210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NYS Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784396</td>\n",
       "      <td>-122.406587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hallmark</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783771</td>\n",
       "      <td>-122.407377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sorabol Korean BBQ &amp; Asian Noodles</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784601</td>\n",
       "      <td>-122.406328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Store_Name  Floor_Index   latitude   longitude\n",
       "0                         Jamba Juice            0  37.784091 -122.406813\n",
       "1              Coriander Gourmet Thai            0  37.784596 -122.406210\n",
       "2                      NYS Collection            0  37.784396 -122.406587\n",
       "3                            Hallmark            0  37.783771 -122.407377\n",
       "4  Sorabol Korean BBQ & Asian Noodles            0  37.784601 -122.406328"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storemap = storemapping.drop(['Floor_name'], axis=1)\n",
    "storemap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>Shopper_ID</th>\n",
       "      <th>floor</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>Concourse</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>Floor 2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shopper_ID      floor        lat         lng\n",
       "0  Shopper_556    Floor 4  37.661463 -122.298673\n",
       "1  Shopper_556    Floor 1  37.784062 -122.406943\n",
       "2  Shopper_556    Floor 4  37.784361 -122.406710\n",
       "3  Shopper_360  Concourse  37.784341 -122.406891\n",
       "4  Shopper_360    Floor 2  37.784017 -122.407307"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo=pinginformation.drop(['x', 'y', 'seenTimestamp'], axis=1)\n",
    "pinginfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'Concourse', u'Floor 1', u'Floor 2', u'Floor 3', u'Floor 4']"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floors= sorted(pinginfo['floor'].unique())\n",
    "floors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'Concourse': 0, u'Floor 1': 1, u'Floor 2': 2, u'Floor 3': 3, u'Floor 4': 4}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floor_mapping= dict(zip(floors, range(0, len(floors) + 1)))\n",
    "floor_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Shopper_ID</th>\n",
       "      <th>floor</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>Floor_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>Floor 4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>Concourse</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>Floor 2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shopper_ID      floor        lat         lng  Floor_Index\n",
       "0  Shopper_556    Floor 4  37.661463 -122.298673            4\n",
       "1  Shopper_556    Floor 1  37.784062 -122.406943            1\n",
       "2  Shopper_556    Floor 4  37.784361 -122.406710            4\n",
       "3  Shopper_360  Concourse  37.784341 -122.406891            0\n",
       "4  Shopper_360    Floor 2  37.784017 -122.407307            2"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo['Floor_Index']= pinginfo['floor'].map(floor_mapping).astype(int)\n",
    "pinginfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shopper_ID</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shopper_ID        lat         lng  Floor_Index   latitude   longitude\n",
       "0  Shopper_556  37.661463 -122.298673            4  37.661463 -122.298673\n",
       "1  Shopper_556  37.784062 -122.406943            1  37.784062 -122.406943\n",
       "2  Shopper_556  37.784361 -122.406710            4  37.784361 -122.406710\n",
       "3  Shopper_360  37.784341 -122.406891            0  37.784341 -122.406891\n",
       "4  Shopper_360  37.784017 -122.407307            2  37.784017 -122.407307"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo.drop(['floor'], axis=1, inplace= True)\n",
    "pinginfo['latitude']= pinginfo['lat']\n",
    "pinginfo['longitude']= pinginfo['lng']\n",
    "pinginfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Shopper_ID</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shopper_ID  Floor_Index   latitude   longitude\n",
       "0  Shopper_556            4  37.661463 -122.298673\n",
       "1  Shopper_556            1  37.784062 -122.406943\n",
       "2  Shopper_556            4  37.784361 -122.406710\n",
       "3  Shopper_360            0  37.784341 -122.406891\n",
       "4  Shopper_360            2  37.784017 -122.407307"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo.drop(['lat', 'lng'], axis=1, inplace= True)\n",
    "pinginfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0.      ,   37.784091, -122.406813],\n",
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       "       [   0.      ,   37.783944, -122.407537],\n",
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       "       [   0.      ,   37.784569, -122.406141],\n",
       "       [   0.      ,   37.78467 , -122.406679],\n",
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       "       [   0.      ,   37.784159, -122.40635 ],\n",
       "       [   0.      ,   37.783959, -122.40691 ],\n",
       "       [   0.      ,   37.784411, -122.406709],\n",
       "       [   0.      ,   37.784511, -122.405956],\n",
       "       [   0.      ,   37.784071, -122.406885],\n",
       "       [   0.      ,   37.784591, -122.406782],\n",
       "       [   0.      ,   37.783778, -122.407161],\n",
       "       [   0.      ,   37.784053, -122.407307],\n",
       "       [   0.      ,   37.783893, -122.407389],\n",
       "       [   0.      ,   37.784222, -122.406978],\n",
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       "       [   0.      ,   37.784157, -122.405925],\n",
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       "       [   0.      ,   37.783791, -122.406984],\n",
       "       [   1.      ,   37.78379 , -122.40747 ],\n",
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       "       [   1.      ,   37.784033, -122.406896],\n",
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       "       [   1.      ,   37.784296, -122.406519],\n",
       "       [   1.      ,   37.784464, -122.406181],\n",
       "       [   1.      ,   37.784195, -122.40696 ],\n",
       "       [   1.      ,   37.784221, -122.406652],\n",
       "       [   1.      ,   37.784776, -122.406643],\n",
       "       [   1.      ,   37.784527, -122.406296],\n",
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       "       [   1.      ,   37.783731, -122.407122],\n",
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       "       [   1.      ,   37.784613, -122.406467],\n",
       "       [   1.      ,   37.78398 , -122.407454],\n",
       "       [   1.      ,   37.784714, -122.406476],\n",
       "       [   1.      ,   37.78409 , -122.406835],\n",
       "       [   1.      ,   37.784503, -122.406813],\n",
       "       [   1.      ,   37.784112, -122.406949],\n",
       "       [   1.      ,   37.784884, -122.406486],\n",
       "       [   1.      ,   37.78461 , -122.406886],\n",
       "       [   1.      ,   37.784466, -122.406961],\n",
       "       [   1.      ,   37.78447 , -122.40676 ],\n",
       "       [   1.      ,   37.78373 , -122.407061],\n",
       "       [   1.      ,   37.783909, -122.407393],\n",
       "       [   1.      ,   37.78381 , -122.407336],\n",
       "       [   1.      ,   37.783864, -122.406836],\n",
       "       [   1.      ,   37.784188, -122.407159],\n",
       "       [   1.      ,   37.783827, -122.407024],\n",
       "       [   1.      ,   37.784047, -122.407035],\n",
       "       [   1.      ,   37.784397, -122.406148],\n",
       "       [   1.      ,   37.784185, -122.406379],\n",
       "       [   1.      ,   37.784396, -122.406726],\n",
       "       [   2.      ,   37.784579, -122.406476],\n",
       "       [   2.      ,   37.783744, -122.407323],\n",
       "       [   2.      ,   37.784515, -122.406218],\n",
       "       [   2.      ,   37.784024, -122.407272],\n",
       "       [   2.      ,   37.784139, -122.407183],\n",
       "       [   2.      ,   37.784341, -122.406785],\n",
       "       [   2.      ,   37.784559, -122.40627 ],\n",
       "       [   2.      ,   37.784136, -122.405805],\n",
       "       [   2.      ,   37.784705, -122.406568],\n",
       "       [   2.      ,   37.784142, -122.40742 ],\n",
       "       [   2.      ,   37.784267, -122.406528],\n",
       "       [   2.      ,   37.784046, -122.406884],\n",
       "       [   2.      ,   37.784252, -122.406942],\n",
       "       [   2.      ,   37.784387, -122.406732],\n",
       "       [   2.      ,   37.783739, -122.407271],\n",
       "       [   2.      ,   37.783745, -122.407149],\n",
       "       [   2.      ,   37.784385, -122.406276],\n",
       "       [   2.      ,   37.784195, -122.407081],\n",
       "       [   2.      ,   37.784433, -122.40608 ],\n",
       "       [   2.      ,   37.784031, -122.406181],\n",
       "       [   2.      ,   37.783852, -122.405805],\n",
       "       [   2.      ,   37.783931, -122.406086],\n",
       "       [   2.      ,   37.784484, -122.406502],\n",
       "       [   2.      ,   37.783724, -122.406893],\n",
       "       [   2.      ,   37.784555, -122.406814],\n",
       "       [   2.      ,   37.783832, -122.407021],\n",
       "       [   2.      ,   37.783954, -122.407507],\n",
       "       [   2.      ,   37.784185, -122.406432],\n",
       "       [   2.      ,   37.783864, -122.406924],\n",
       "       [   2.      ,   37.783946, -122.406796],\n",
       "       [   2.      ,   37.784316, -122.40656 ],\n",
       "       [   2.      ,   37.784172, -122.406733],\n",
       "       [   2.      ,   37.784271, -122.406619],\n",
       "       [   2.      ,   37.784286, -122.406001],\n",
       "       [   3.      ,   37.784042, -122.406901],\n",
       "       [   3.      ,   37.783629, -122.407154],\n",
       "       [   3.      ,   37.783741, -122.406902],\n",
       "       [   3.      ,   37.784628, -122.406187],\n",
       "       [   3.      ,   37.78435 , -122.406064],\n",
       "       [   3.      ,   37.784105, -122.406326],\n",
       "       [   3.      ,   37.784615, -122.406862],\n",
       "       [   3.      ,   37.784428, -122.406643],\n",
       "       [   3.      ,   37.784447, -122.406809],\n",
       "       [   3.      ,   37.783986, -122.406922],\n",
       "       [   3.      ,   37.783839, -122.406836],\n",
       "       [   3.      ,   37.783812, -122.406021],\n",
       "       [   3.      ,   37.784133, -122.407419],\n",
       "       [   3.      ,   37.783752, -122.407371],\n",
       "       [   3.      ,   37.78429 , -122.406561],\n",
       "       [   3.      ,   37.783737, -122.407253],\n",
       "       [   3.      ,   37.78424 , -122.406455],\n",
       "       [   3.      ,   37.784101, -122.406854],\n",
       "       [   3.      ,   37.783871, -122.407073],\n",
       "       [   3.      ,   37.784153, -122.405871],\n",
       "       [   3.      ,   37.783745, -122.405601],\n",
       "       [   3.      ,   37.784191, -122.407111],\n",
       "       [   3.      ,   37.784267, -122.405933],\n",
       "       [   3.      ,   37.784386, -122.406589],\n",
       "       [   3.      ,   37.78454 , -122.406132],\n",
       "       [   3.      ,   37.783912, -122.407474],\n",
       "       [   3.      ,   37.784663, -122.406313],\n",
       "       [   3.      ,   37.784135, -122.406406],\n",
       "       [   3.      ,   37.783957, -122.406249],\n",
       "       [   3.      ,   37.783814, -122.407399],\n",
       "       [   3.      ,   37.784197, -122.40698 ],\n",
       "       [   3.      ,   37.784701, -122.406758],\n",
       "       [   3.      ,   37.78436 , -122.406786],\n",
       "       [   4.      ,   37.78442 , -122.406832],\n",
       "       [   4.      ,   37.784702, -122.406248],\n",
       "       [   4.      ,   37.784206, -122.406528],\n",
       "       [   4.      ,   37.784178, -122.405776],\n",
       "       [   4.      ,   37.783859, -122.40635 ],\n",
       "       [   4.      ,   37.784539, -122.406081],\n",
       "       [   4.      ,   37.78382 , -122.407311],\n",
       "       [   4.      ,   37.783802, -122.405736],\n",
       "       [   5.      ,   37.784076, -122.407307],\n",
       "       [   5.      ,   37.78389 , -122.405736],\n",
       "       [   5.      ,   37.784204, -122.406574],\n",
       "       [   6.      ,   37.78409 , -122.407309],\n",
       "       [   7.      ,   37.784073, -122.407289],\n",
       "       [   8.      ,   37.784087, -122.407307]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training:\n",
    "X = storemap.drop(['Store_Name'],axis=1).as_matrix().astype(np.float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elements = np.array(X)\\nelements'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''elements = np.array(X)\n",
    "elements'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mean = np.mean(elements, axis=0)\\nsd = np.std(elements, axis=0)'"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''mean = np.mean(elements, axis=0)\n",
    "sd = np.std(elements, axis=0)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'final_list = [x for x in X if (np.all(x) > mean - 2 * sd)]\\nfinal_list = [x for x in final_list if (np.all(x) < mean + 2 * sd)]\\n'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''final_list = [x for x in X if (np.all(x) > mean - 2 * sd)]\n",
    "final_list = [x for x in final_list if (np.all(x) < mean + 2 * sd)]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.08827864e+00, -3.44149111e-01, -2.21927721e-01],\n",
       "       [-1.08827864e+00,  1.40158609e+00,  1.04342435e+00],\n",
       "       [-1.08827864e+00,  7.10205813e-01,  2.52317003e-01],\n",
       "       [-1.08827864e+00, -1.45035756e+00, -1.40544110e+00],\n",
       "       [-1.08827864e+00,  1.41887060e+00,  7.95809851e-01],\n",
       "       [-1.08827864e+00,  1.18725820e+00,  1.43163353e+00],\n",
       "       [-1.08827864e+00, -8.52313615e-01, -1.74118958e+00],\n",
       "       [-1.08827864e+00,  8.86507784e-01,  1.01404636e+00],\n",
       "       [-1.08827864e+00, -1.02166013e-01, -4.04490955e-01],\n",
       "       [-1.08827864e+00,  2.13444919e+00,  4.32781810e-01],\n",
       "       [-1.08827864e+00,  1.30824975e+00,  1.18821588e+00],\n",
       "       [-1.08827864e+00,  1.65739679e+00,  5.92616288e-02],\n",
       "       [-1.08827864e+00, -2.23157562e-01, -7.42337861e-01],\n",
       "       [-1.08827864e+00, -1.09079816e-01,  7.49644435e-01],\n",
       "       [-1.08827864e+00, -8.00460094e-01, -4.25475235e-01],\n",
       "       [-1.08827864e+00,  7.62059334e-01, -3.69121068e-03],\n",
       "       [-1.08827864e+00,  1.10774947e+00,  1.57642506e+00],\n",
       "       [-1.08827864e+00, -4.13287138e-01, -3.73014536e-01],\n",
       "       [-1.08827864e+00,  1.38430158e+00, -1.56876453e-01],\n",
       "       [-1.08827864e+00, -1.42615925e+00, -9.52180659e-01],\n",
       "       [-1.08827864e+00, -4.75511363e-01, -1.25855114e+00],\n",
       "       [-1.08827864e+00, -1.02861559e+00, -1.43062224e+00],\n",
       "       [-1.08827864e+00,  1.08704971e-01, -5.68168338e-01],\n",
       "       [-1.08827864e+00, -8.21201502e-01, -1.28163385e+00],\n",
       "       [-1.08827864e+00,  1.08700806e+00,  3.19466699e-01],\n",
       "       [-1.08827864e+00, -1.46418516e+00, -7.61223712e-01],\n",
       "       [-1.08827864e+00, -2.92295590e-01, -1.01093664e+00],\n",
       "       [-1.08827864e+00,  3.50688069e-01,  1.46310995e+00],\n",
       "       [-1.08827864e+00,  8.93421587e-01,  8.86396206e-02],\n",
       "       [-1.08827864e+00,  5.61559053e-01, -1.37990602e-01],\n",
       "       [-1.08827864e+00,  3.57601871e-01,  1.13995204e+00],\n",
       "       [-1.08827864e+00,  4.47481308e-01,  1.07490077e+00],\n",
       "       [-1.08827864e+00, -8.52313615e-01, -9.81558651e-01],\n",
       "       [-1.08827864e+00, -1.24294347e+00, -4.90526503e-01],\n",
       "       [-1.08827864e+00,  4.19826096e-01,  1.85761441e+00],\n",
       "       [-1.08827864e+00, -1.04935699e+00,  1.93945310e+00],\n",
       "       [-1.08827864e+00,  6.03083520e-02, -1.51665779e+00],\n",
       "       [-1.08827864e+00,  1.08700806e+00,  6.46821464e-01],\n",
       "       [-1.08827864e+00,  1.50183623e+00,  6.09049760e-01],\n",
       "       [-1.08827864e+00, -2.33528266e-01, -5.09412354e-01],\n",
       "       [-1.08827864e+00, -2.09329956e-01, -1.39914582e+00],\n",
       "       [-1.08827864e+00, -5.30821786e-01, -4.82132791e-01],\n",
       "       [-1.08827864e+00,  9.21076798e-01, -2.34518289e-01],\n",
       "       [-1.08827864e+00,  7.75886940e-01,  1.80725214e+00],\n",
       "       [-1.08827864e+00, -2.57726576e-01, -8.74538823e-01],\n",
       "       [-1.08827864e+00, -1.15993619e-01,  1.64147633e+00],\n",
       "       [-1.08827864e+00, -3.33778406e-01, -1.92585124e+00],\n",
       "       [-1.08827864e+00, -1.38121953e+00, -5.80758906e-01],\n",
       "       [-4.24108586e-01, -1.38467643e+00, -1.60059491e+00],\n",
       "       [-4.24108586e-01,  1.46726722e+00,  7.87416139e-01],\n",
       "       [-4.24108586e-01, -5.89589109e-01,  1.55753921e+00],\n",
       "       [-4.24108586e-01, -3.99417883e-02, -3.31045976e-01],\n",
       "       [-4.24108586e-01, -1.67847140e-01, -6.98270873e-01],\n",
       "       [-4.24108586e-01,  9.07249192e-01,  5.54490633e-01],\n",
       "       [-4.24108586e-01, -5.44649391e-01, -3.96097243e-01],\n",
       "       [-4.24108586e-01,  2.64265534e-01,  2.69104427e-01],\n",
       "       [-4.24108586e-01,  3.64515674e-01,  3.95010106e-01],\n",
       "       [-4.24108586e-01,  9.45275108e-01,  1.10427876e+00],\n",
       "       [-4.24108586e-01,  1.53686339e-02, -5.30396634e-01],\n",
       "       [-4.24108586e-01,  1.05248070e-01,  1.15919184e-01],\n",
       "       [-4.24108586e-01,  2.02382834e+00,  1.34805036e-01],\n",
       "       [-4.24108586e-01,  1.16305990e+00,  8.62959546e-01],\n",
       "       [-4.24108586e-01, -1.79950460e+00, -1.27114171e+00],\n",
       "       [-4.24108586e-01, -1.58863361e+00, -8.70341967e-01],\n",
       "       [-4.24108586e-01, -7.41692770e-01, -4.17081523e-01],\n",
       "       [-4.24108586e-01,  1.46035341e+00,  5.04128361e-01],\n",
       "       [-4.24108586e-01, -7.27865165e-01, -1.56702006e+00],\n",
       "       [-4.24108586e-01,  1.80950046e+00,  4.85242509e-01],\n",
       "       [-4.24108586e-01, -3.47606012e-01, -2.68093136e-01],\n",
       "       [-4.24108586e-01,  1.08009426e+00, -2.21927721e-01],\n",
       "       [-4.24108586e-01, -2.71554181e-01, -5.07313926e-01],\n",
       "       [-4.24108586e-01,  2.39717369e+00,  4.64258229e-01],\n",
       "       [-4.24108586e-01,  1.44998271e+00, -3.75112964e-01],\n",
       "       [-4.24108586e-01,  9.52188910e-01, -5.32495062e-01],\n",
       "       [-4.24108586e-01,  9.66016516e-01, -1.10711038e-01],\n",
       "       [-4.24108586e-01, -1.59209051e+00, -7.42337861e-01],\n",
       "       [-4.24108586e-01, -9.73305163e-01, -1.43901595e+00],\n",
       "       [-4.24108586e-01, -1.31553840e+00, -1.31940556e+00],\n",
       "       [-4.24108586e-01, -1.12886573e+00, -2.70191564e-01],\n",
       "       [-4.24108586e-01, -8.82967582e-03, -9.47983803e-01],\n",
       "       [-4.24108586e-01, -1.25677108e+00, -6.64696025e-01],\n",
       "       [-4.24108586e-01, -4.96252772e-01, -6.87778733e-01],\n",
       "       [-4.24108586e-01,  7.13662715e-01,  1.17352689e+00],\n",
       "       [-4.24108586e-01, -1.92003800e-02,  6.88790024e-01],\n",
       "       [-4.24108586e-01,  7.10205813e-01, -3.93644864e-02],\n",
       "       [ 2.40061464e-01,  1.34281877e+00,  4.85242509e-01],\n",
       "       [ 2.40061464e-01, -1.54369389e+00, -1.29212599e+00],\n",
       "       [ 2.40061464e-01,  1.12157708e+00,  1.02663693e+00],\n",
       "       [ 2.40061464e-01, -5.75761504e-01, -1.18510616e+00],\n",
       "       [ 2.40061464e-01, -1.78217844e-01, -9.98346074e-01],\n",
       "       [ 2.40061464e-01,  5.20076237e-01, -1.63171737e-01],\n",
       "       [ 2.40061464e-01,  1.27368074e+00,  9.17518674e-01],\n",
       "       [ 2.40061464e-01, -1.88588548e-01,  1.89328769e+00],\n",
       "       [ 2.40061464e-01,  1.77838834e+00,  2.92187135e-01],\n",
       "       [ 2.40061464e-01, -1.67847140e-01, -1.49567351e+00],\n",
       "       [ 2.40061464e-01,  2.64265534e-01,  3.76124254e-01],\n",
       "       [ 2.40061464e-01, -4.99709673e-01, -3.70916108e-01],\n",
       "       [ 2.40061464e-01,  2.12412013e-01, -4.92624931e-01],\n",
       "       [ 2.40061464e-01,  6.79093701e-01, -5.19550543e-02],\n",
       "       [ 2.40061464e-01, -1.56097840e+00, -1.18300774e+00],\n",
       "       [ 2.40061464e-01, -1.54023699e+00, -9.26999523e-01],\n",
       "       [ 2.40061464e-01,  6.72179898e-01,  9.04928106e-01],\n",
       "       [ 2.40061464e-01,  1.53686339e-02, -7.84306420e-01],\n",
       "       [ 2.40061464e-01,  8.38111165e-01,  1.31621999e+00],\n",
       "       [ 2.40061464e-01, -5.51563194e-01,  1.10427876e+00],\n",
       "       [ 2.40061464e-01, -1.17034854e+00,  1.89328769e+00],\n",
       "       [ 2.40061464e-01, -8.97253333e-01,  1.30362942e+00],\n",
       "       [ 2.40061464e-01,  1.01441314e+00,  4.30683382e-01],\n",
       "       [ 2.40061464e-01, -1.61283192e+00, -3.89801959e-01],\n",
       "       [ 2.40061464e-01,  1.25985313e+00, -2.24026149e-01],\n",
       "       [ 2.40061464e-01, -1.23948657e+00, -6.58400741e-01],\n",
       "       [ 2.40061464e-01, -8.17744601e-01, -1.67823674e+00],\n",
       "       [ 2.40061464e-01, -1.92003800e-02,  5.77573341e-01],\n",
       "       [ 2.40061464e-01, -1.12886573e+00, -4.54853227e-01],\n",
       "       [ 2.40061464e-01, -8.45399812e-01, -1.86254445e-01],\n",
       "       [ 2.40061464e-01,  4.33653702e-01,  3.08974559e-01],\n",
       "       [ 2.40061464e-01, -6.41400981e-02, -5.40534823e-02],\n",
       "       [ 2.40061464e-01,  2.78093139e-01,  1.85167308e-01],\n",
       "       [ 2.40061464e-01,  3.29946660e-01,  1.48199580e+00],\n",
       "       [ 9.04231513e-01, -5.13537279e-01, -4.06589383e-01],\n",
       "       [ 9.04231513e-01, -1.94123755e+00, -9.37491663e-01],\n",
       "       [ 9.04231513e-01, -1.55406460e+00, -4.08687811e-01],\n",
       "       [ 9.04231513e-01,  1.51220694e+00,  1.09168820e+00],\n",
       "       [ 9.04231513e-01,  5.51188349e-01,  1.34979484e+00],\n",
       "       [ 9.04231513e-01, -2.95752491e-01,  8.00006707e-01],\n",
       "       [ 9.04231513e-01,  1.46726722e+00, -3.24750692e-01],\n",
       "       [ 9.04231513e-01,  8.20826658e-01,  1.34805036e-01],\n",
       "       [ 9.04231513e-01,  8.86507784e-01, -2.13534009e-01],\n",
       "       [ 9.04231513e-01, -7.07123756e-01, -4.50656371e-01],\n",
       "       [ 9.04231513e-01, -1.21528826e+00, -2.70191564e-01],\n",
       "       [ 9.04231513e-01, -1.30862460e+00,  1.44002724e+00],\n",
       "       [ 9.04231513e-01, -1.98959252e-01, -1.49357508e+00],\n",
       "       [ 9.04231513e-01, -1.51603868e+00, -1.39285054e+00],\n",
       "       [ 9.04231513e-01,  3.43774266e-01,  3.06876131e-01],\n",
       "       [ 9.04231513e-01, -1.56789220e+00, -1.14523603e+00],\n",
       "       [ 9.04231513e-01,  1.70929196e-01,  5.29309497e-01],\n",
       "       [ 9.04231513e-01, -3.09580097e-01, -3.07963268e-01],\n",
       "       [ 9.04231513e-01, -1.10466742e+00, -7.67518996e-01],\n",
       "       [ 9.04231513e-01, -1.29821224e-01,  1.75479144e+00],\n",
       "       [ 9.04231513e-01, -1.54023699e+00,  2.32136699e+00],\n",
       "       [ 9.04231513e-01,  1.54102834e-03, -8.47259260e-01],\n",
       "       [ 9.04231513e-01,  2.64265534e-01,  1.62468890e+00],\n",
       "       [ 9.04231513e-01,  6.75636799e-01,  2.48120147e-01],\n",
       "       [ 9.04231513e-01,  1.20799961e+00,  1.20710174e+00],\n",
       "       [ 9.04231513e-01, -9.62934459e-01, -1.60898862e+00],\n",
       "       [ 9.04231513e-01,  1.63319848e+00,  8.27286270e-01],\n",
       "       [ 9.04231513e-01, -1.92045449e-01,  6.32132468e-01],\n",
       "       [ 9.04231513e-01, -8.07373897e-01,  9.61585661e-01],\n",
       "       [ 9.04231513e-01, -1.30171080e+00, -1.45160652e+00],\n",
       "       [ 9.04231513e-01,  2.22824367e-02, -5.72365194e-01],\n",
       "       [ 9.04231513e-01,  1.76456074e+00, -1.06514182e-01],\n",
       "       [ 9.04231513e-01,  5.85757363e-01, -1.65270165e-01],\n",
       "       [ 1.56840156e+00,  7.93171447e-01, -2.61797853e-01],\n",
       "       [ 1.56840156e+00,  1.76801764e+00,  9.63684089e-01],\n",
       "       [ 1.56840156e+00,  5.33945492e-02,  3.76124254e-01],\n",
       "       [ 1.56840156e+00, -4.33986897e-02,  1.95414210e+00],\n",
       "       [ 1.56840156e+00, -1.14615023e+00,  7.49644435e-01],\n",
       "       [ 1.56840156e+00,  1.20454271e+00,  1.31412156e+00],\n",
       "       [ 1.56840156e+00, -1.28096939e+00, -1.26694486e+00],\n",
       "       [ 1.56840156e+00, -1.34319361e+00,  2.03807922e+00],\n",
       "       [ 2.23257161e+00, -3.96002631e-01, -1.25855114e+00],\n",
       "       [ 2.23257161e+00, -1.03898629e+00,  2.03807922e+00],\n",
       "       [ 2.23257161e+00,  4.64807464e-02,  2.79596567e-01],\n",
       "       [ 2.89674166e+00, -3.47606012e-01, -1.26274800e+00],\n",
       "       [ 3.56091171e+00, -4.06373336e-01, -1.22077944e+00],\n",
       "       [ 4.22508176e+00, -3.57976716e-01, -1.25855114e+00]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert into matrix having mean= 0 and std dev= 1\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'Jamba Juice', u'Coriander Gourmet Thai', u'NYS Collection',\n",
       "       u'Hallmark', u'Sorabol Korean BBQ & Asian Noodles', u'Pasta Moto',\n",
       "       u'Abercrombie & Fitch', u'Fire of Brazil', u'ProActiv Solutions ',\n",
       "       u'Corner W', u'Buckhorn Grill', u'Cako Bakery',\n",
       "       u\"Claire's Boutique\", u'Bristol Farms', u'PacSun', u'DAVIDsTEA',\n",
       "       u'Andale Mexican Restaurant', u'Sunglass Hut ',\n",
       "       u\"Peet's Coffee & Tea\", u'Panda Express', u'GNC', u'New Era',\n",
       "       u'LEGO', u'Mrs. Fields', u\"Beard Papa's Cream Puffs\",\n",
       "       u'Ajisen Ramen', u'Shoe Palace ', u'Loving Hut Vegan Cuisine ',\n",
       "       u'Ghirardelli Ice Cream and Chocolate Shop ',\n",
       "       u'Marbles: The Brain Store', u'Caffe Central', u'Pressed Juicery',\n",
       "       u'Cafe Bellini', u'Sarku Japan', u'Kichi Grill', u\"Bloomingdale's\",\n",
       "       u'Warriors Team Store', u'Lobster Me',\n",
       "       u'Amoura Cafe Fresh Mediterranean', u\"Wetzel's Pretzels\",\n",
       "       u'Shoe Wiz', u'Haagen Dazs', u'Sur La Table',\n",
       "       u\"Charley's Grilled Subs\", u'Champs Sports',\n",
       "       u'San Francisco Soup Company', u'Starbucks', u'Chipotle', u'Razer',\n",
       "       u'Madewell', u\"Bloomingdale's\", u'Swatch', u\"Kiehl's\",\n",
       "       u'Amazon Pop-Up', u'Janie And Jack', u'The Art of Shaving',\n",
       "       u'RED Valentino', u'Burberry', u'Swarovski',\n",
       "       u'Brighton Collectibles', u'Michael Kors', u'Karen Millen',\n",
       "       u'Aritzia', u'Tumi', u'Kate Spade New York',\n",
       "       u'Penhaligon\\xef\\xbf\\xbd\\xdb\\xaas', u'Abercrombie & Fitch',\n",
       "       u'Tory Burch ', u\"L'Occitane en Provence\", u'Rolex',\n",
       "       u'Sunglass Hut', u'Timberland', u'Tourneau', u'Adidas',\n",
       "       u'Superdry ', u'Origins', u'Club Monaco Men',\n",
       "       u'Currency Exchange International ', u'Club Monaco Women',\n",
       "       u'American Eagle Outfitters', u'Nordstrom E-Bar', u\"Kiehl's\",\n",
       "       u'Tiffany & Co.', u'BOSS', u'Michael Kors Men',\n",
       "       u'lululemon athletica', u'Coach', u'bareMinerals',\n",
       "       u'Forever Flawless ', u'OAK+FORT', u\"Auntie Anne's Pretzels\",\n",
       "       u'Max & Co.', u'Onassis', u'Zara', u'bebe', u'ECCO',\n",
       "       u'Lady Foot Locker', u'Cotton On', u'Pandora', u'The Body Shop',\n",
       "       u'Express', u'T-Mobile', u'Brookstone', u'BCBGMAXAZRIA',\n",
       "       u'H&M Man', u\"Bloomingdale's\", u'49ers Team Store',\n",
       "       u'Studio Manhattan', u\"Francesca's Collection \", u'Adidas',\n",
       "       u'Dyson', u'Hollister Co.', u'Microsoft', u'Samsonite', u'GUESS ',\n",
       "       u'Aveda', u'Camper', u'Yoppi Frozen Yogurt ', u'J.Crew',\n",
       "       u'The Walking Company', u'Express', u'Papyrus',\n",
       "       u'San Francisco Luggage Co.', u'Banana Republic', u'Vera Bradley',\n",
       "       u'Eddie Bauer', u'Godiva Chocolatier', u'SF Sports', u'Aldo',\n",
       "       u'House of Hoops by Foot Locker', u'abercrombie kids',\n",
       "       u\"Victoria's Secret\", u'Perfumania', u'Kay Jewelers',\n",
       "       u'Amiri Salon', u'Clarks', u'Lids', u'iPlay n Talk',\n",
       "       u'Go! Toys and Games', u\"Bloomingdale's\", u'Fossil', u'Vans',\n",
       "       u'Eyebrow Plus', u'True Religion Jeans', u'Finish Line',\n",
       "       u'Lucky Brand Jeans', u'Bath & Body Works', u'H&M', u'Teavana',\n",
       "       u'Steve Madden', u'Forever Flawless', u'Solstice', u'TAP 415',\n",
       "       u'Optica', u'M.Y. China', u'Bespoke Coworking', u'Bespoke Events',\n",
       "       u'Bose', u'Nordstrom', u\"Bloomingdale's\", u'Nordstrom',\n",
       "       u'Century San Francisco Centre 9', u'Burke Williams Day Spa',\n",
       "       u'Nordstrom', u'Nordstrom', u'Nordstrom'], dtype=object)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = storemap['Store_Name'].as_matrix().astype(np.object)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CV:\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import cross_validation\n",
    "from sklearn import tree\n",
    "from sklearn import svm\n",
    "from sklearn import ensemble\n",
    "from sklearn import neighbors\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stratified_cv(X, y, clf_class, shuffle=True, n_folds=2, **kwargs):\n",
    "    stratified_k_fold = cross_validation.StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle)\n",
    "    y_pred = y.copy()\n",
    "    for ii, jj in stratified_k_fold:\n",
    "        X_train, X_test = X[ii], X[jj]\n",
    "        y_train = y[ii]\n",
    "        clf = clf_class(**kwargs)\n",
    "        clf.fit(X_train,y_train)\n",
    "        y_pred[jj] = clf.predict(X_test)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passive Aggressive Classifier: 0.01\n",
      "Gradient Boosting Classifier:  0.04\n",
      "Support vector machine(SVM):   0.04\n",
      "Random Forest Classifier:      0.05\n",
      "K Nearest Neighbor Classifier: 0.07\n",
      "Logistic Regression:           0.07\n"
     ]
    }
   ],
   "source": [
    "print('Passive Aggressive Classifier: {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, linear_model.PassiveAggressiveClassifier))))\n",
    "print('Gradient Boosting Classifier:  {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, ensemble.GradientBoostingClassifier))))\n",
    "print('Support vector machine(SVM):   {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, svm.SVC))))\n",
    "print('Random Forest Classifier:      {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, ensemble.RandomForestClassifier))))\n",
    "print('K Nearest Neighbor Classifier: {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, neighbors.KNeighborsClassifier))))\n",
    "print('Logistic Regression:           {:.2f}'.format(metrics.accuracy_score(y, stratified_cv(X, y, linear_model.LogisticRegression))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   4.        ,   37.66146302, -122.2986728 ],\n",
       "       [   1.        ,   37.78406211, -122.4069432 ],\n",
       "       [   4.        ,   37.78436125, -122.4067095 ],\n",
       "       ...,\n",
       "       [   2.        ,   36.0179502 , -116.704627  ],\n",
       "       [   1.        ,   37.78404745, -122.4069997 ],\n",
       "       [   0.        ,   37.78407819, -122.4070869 ]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FITTING TO LOGREG:\n",
    "X_test = pinginfo.drop(['Shopper_ID'], axis=1).as_matrix().astype(np.float)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.83464372,  0.36209925, -0.40275272],\n",
       "       [-0.32798594,  0.42248019, -0.41922354],\n",
       "       [ 1.83464372,  0.42262752, -0.41918799],\n",
       "       ...,\n",
       "       [ 0.39289061, -0.44734271,  0.44825139],\n",
       "       [-0.32798594,  0.42247297, -0.41923214],\n",
       "       [-1.04886249,  0.42248811, -0.4192454 ]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "test_data = scaler.fit_transform(X_test)\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mean accuracy: 0.849397590361'"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "clf= linear_model.LogisticRegression(penalty = 'l2', solver ='lbfgs', C=100000, random_state =0)\n",
    "train_features = X\n",
    "\n",
    "train_target = y\n",
    "\n",
    "# Fit the model to our training data\n",
    "clf = clf.fit(train_features, train_target)\n",
    "score = clf.score(train_features, train_target)\n",
    "\"Mean accuracy: {0}\".format(score)F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mean accuracy of SVM: 1.0'"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from sklearn.svm import SVC\n",
    "\n",
    "clf = svm.SVC(kernel='rbf', gamma=100, C=1)\n",
    "clf.fit(X, y)\n",
    "\n",
    "\n",
    "# Training data features\n",
    "train_features = X\n",
    "\n",
    "# target column values\n",
    "train_target = y\n",
    "\n",
    "# Fit the model to our training data\n",
    "clf = clf.fit(train_features, train_target)\n",
    "score = clf.score(train_features, train_target)\n",
    "\"Mean accuracy of SVM: {0}\".format(score)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_y= clf.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>Shopper_ID</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Store_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shopper_420</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661624</td>\n",
       "      <td>-122.298459</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shopper_420</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783856</td>\n",
       "      <td>-122.407092</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shopper_245</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783970</td>\n",
       "      <td>-122.407194</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shopper_334</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783828</td>\n",
       "      <td>-122.406986</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shopper_205</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784219</td>\n",
       "      <td>-122.406896</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shopper_205</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784638</td>\n",
       "      <td>-122.406411</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shopper_530</td>\n",
       "      <td>1</td>\n",
       "      <td>35.085149</td>\n",
       "      <td>-113.663734</td>\n",
       "      <td>49ers Team Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shopper_530</td>\n",
       "      <td>0</td>\n",
       "      <td>36.541271</td>\n",
       "      <td>-118.435462</td>\n",
       "      <td>Jamba Juice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shopper_359</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783961</td>\n",
       "      <td>-122.407034</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>35.239976</td>\n",
       "      <td>-114.220990</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784049</td>\n",
       "      <td>-122.407055</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784084</td>\n",
       "      <td>-122.407044</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shopper_481</td>\n",
       "      <td>2</td>\n",
       "      <td>35.265054</td>\n",
       "      <td>-114.246672</td>\n",
       "      <td>H&amp;M Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784287</td>\n",
       "      <td>-122.406954</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>1</td>\n",
       "      <td>36.980523</td>\n",
       "      <td>-119.811229</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>4</td>\n",
       "      <td>37.759561</td>\n",
       "      <td>-122.385293</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shopper_287</td>\n",
       "      <td>0</td>\n",
       "      <td>37.656648</td>\n",
       "      <td>-122.006913</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shopper_536</td>\n",
       "      <td>2</td>\n",
       "      <td>34.031987</td>\n",
       "      <td>-110.253652</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shopper_344</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784028</td>\n",
       "      <td>-122.407106</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shopper_41</td>\n",
       "      <td>2</td>\n",
       "      <td>33.504061</td>\n",
       "      <td>-108.734063</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shopper_41</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783814</td>\n",
       "      <td>-122.407127</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Shopper_122</td>\n",
       "      <td>4</td>\n",
       "      <td>36.066776</td>\n",
       "      <td>-116.842850</td>\n",
       "      <td>M.Y. China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Shopper_51</td>\n",
       "      <td>0</td>\n",
       "      <td>37.720868</td>\n",
       "      <td>-122.343518</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Shopper_51</td>\n",
       "      <td>1</td>\n",
       "      <td>35.265134</td>\n",
       "      <td>-114.246611</td>\n",
       "      <td>49ers Team Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Shopper_68</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783840</td>\n",
       "      <td>-122.407236</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Shopper_180</td>\n",
       "      <td>2</td>\n",
       "      <td>36.468473</td>\n",
       "      <td>-118.174922</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Shopper_449</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783913</td>\n",
       "      <td>-122.407134</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Shopper_449</td>\n",
       "      <td>4</td>\n",
       "      <td>37.722828</td>\n",
       "      <td>-122.352689</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Shopper_561</td>\n",
       "      <td>1</td>\n",
       "      <td>36.879668</td>\n",
       "      <td>-119.478708</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Shopper_475</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783922</td>\n",
       "      <td>-122.407186</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Shopper_557</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784189</td>\n",
       "      <td>-122.406847</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784015</td>\n",
       "      <td>-122.407044</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784161</td>\n",
       "      <td>-122.406898</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>3</td>\n",
       "      <td>37.784055</td>\n",
       "      <td>-122.407036</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Shopper_268</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783936</td>\n",
       "      <td>-122.407100</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Shopper_268</td>\n",
       "      <td>4</td>\n",
       "      <td>29.082109</td>\n",
       "      <td>-94.277709</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Shopper_550</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784015</td>\n",
       "      <td>-122.407177</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Shopper_550</td>\n",
       "      <td>1</td>\n",
       "      <td>37.714475</td>\n",
       "      <td>-122.182005</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Shopper_550</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783980</td>\n",
       "      <td>-122.407110</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Shopper_27</td>\n",
       "      <td>0</td>\n",
       "      <td>37.710393</td>\n",
       "      <td>-122.342140</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Shopper_27</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784047</td>\n",
       "      <td>-122.407153</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Shopper_212</td>\n",
       "      <td>4</td>\n",
       "      <td>28.338228</td>\n",
       "      <td>-91.804871</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Shopper_212</td>\n",
       "      <td>2</td>\n",
       "      <td>33.544888</td>\n",
       "      <td>-108.770188</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Shopper_212</td>\n",
       "      <td>0</td>\n",
       "      <td>34.315595</td>\n",
       "      <td>-111.249673</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Shopper_405</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784146</td>\n",
       "      <td>-122.407033</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Shopper_405</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784104</td>\n",
       "      <td>-122.407331</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Shopper_405</td>\n",
       "      <td>4</td>\n",
       "      <td>35.756871</td>\n",
       "      <td>-115.930030</td>\n",
       "      <td>M.Y. China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Shopper_60</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784251</td>\n",
       "      <td>-122.406806</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Shopper_8</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783982</td>\n",
       "      <td>-122.406909</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Shopper_8</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783863</td>\n",
       "      <td>-122.407156</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Shopper_8</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784020</td>\n",
       "      <td>-122.407079</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Shopper_213</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783982</td>\n",
       "      <td>-122.406909</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Shopper_59</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783816</td>\n",
       "      <td>-122.407319</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Shopper_35</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783982</td>\n",
       "      <td>-122.406909</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Shopper_197</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784137</td>\n",
       "      <td>-122.407002</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Shopper_ID  Floor_Index   latitude   longitude        Store_Name\n",
       "0   Shopper_556            4  37.661463 -122.298673           TAP 415\n",
       "1   Shopper_556            1  37.784062 -122.406943           Kiehl's\n",
       "2   Shopper_556            4  37.784361 -122.406710           TAP 415\n",
       "3   Shopper_360            0  37.784341 -122.406891           Kiehl's\n",
       "4   Shopper_360            2  37.784017 -122.407307         Cotton On\n",
       "5   Shopper_420            4  37.661624 -122.298459           TAP 415\n",
       "6   Shopper_420            1  37.783856 -122.407092           Kiehl's\n",
       "7   Shopper_245            2  37.783970 -122.407194         Cotton On\n",
       "8   Shopper_334            2  37.783828 -122.406986         Cotton On\n",
       "9   Shopper_205            1  37.784219 -122.406896           Kiehl's\n",
       "10  Shopper_205            0  37.784638 -122.406411           Kiehl's\n",
       "11  Shopper_530            1  35.085149 -113.663734  49ers Team Store\n",
       "12  Shopper_530            0  36.541271 -118.435462       Jamba Juice\n",
       "13  Shopper_359            0  37.783961 -122.407034           Kiehl's\n",
       "14  Shopper_189            0  35.239976 -114.220990    Bloomingdale's\n",
       "15  Shopper_189            0  37.784049 -122.407055           Kiehl's\n",
       "16  Shopper_189            0  37.784084 -122.407044           Kiehl's\n",
       "17  Shopper_481            2  35.265054 -114.246672           H&M Man\n",
       "18  Shopper_155            4  37.784287 -122.406954           TAP 415\n",
       "19  Shopper_155            1  36.980523 -119.811229           Kiehl's\n",
       "20  Shopper_155            4  37.759561 -122.385293           TAP 415\n",
       "21  Shopper_287            0  37.656648 -122.006913           Kiehl's\n",
       "22  Shopper_536            2  34.031987 -110.253652    Bloomingdale's\n",
       "23  Shopper_344            2  37.784028 -122.407106         Cotton On\n",
       "24   Shopper_41            2  33.504061 -108.734063    Bloomingdale's\n",
       "25   Shopper_41            3  37.783814 -122.407127          Solstice\n",
       "26  Shopper_122            4  36.066776 -116.842850        M.Y. China\n",
       "27   Shopper_51            0  37.720868 -122.343518           Kiehl's\n",
       "28   Shopper_51            1  35.265134 -114.246611  49ers Team Store\n",
       "29   Shopper_68            1  37.783840 -122.407236           Kiehl's\n",
       "..          ...          ...        ...         ...               ...\n",
       "70  Shopper_180            2  36.468473 -118.174922         Microsoft\n",
       "71  Shopper_449            1  37.783913 -122.407134           Kiehl's\n",
       "72  Shopper_449            4  37.722828 -122.352689           TAP 415\n",
       "73  Shopper_561            1  36.879668 -119.478708           Kiehl's\n",
       "74  Shopper_475            3  37.783922 -122.407186          Solstice\n",
       "75  Shopper_557            1  37.784189 -122.406847           Kiehl's\n",
       "76   Shopper_98            1  37.784015 -122.407044           Kiehl's\n",
       "77   Shopper_98            0  37.784161 -122.406898           Kiehl's\n",
       "78   Shopper_98            3  37.784055 -122.407036          Solstice\n",
       "79  Shopper_268            3  37.783936 -122.407100          Solstice\n",
       "80  Shopper_268            4  29.082109  -94.277709    Bloomingdale's\n",
       "81  Shopper_550            0  37.784015 -122.407177           Kiehl's\n",
       "82  Shopper_550            1  37.714475 -122.182005           Kiehl's\n",
       "83  Shopper_550            0  37.783980 -122.407110           Kiehl's\n",
       "84   Shopper_27            0  37.710393 -122.342140           Kiehl's\n",
       "85   Shopper_27            0  37.784047 -122.407153           Kiehl's\n",
       "86  Shopper_212            4  28.338228  -91.804871    Bloomingdale's\n",
       "87  Shopper_212            2  33.544888 -108.770188    Bloomingdale's\n",
       "88  Shopper_212            0  34.315595 -111.249673    Bloomingdale's\n",
       "89  Shopper_405            0  37.784146 -122.407033           Kiehl's\n",
       "90  Shopper_405            2  37.784104 -122.407331         Cotton On\n",
       "91  Shopper_405            4  35.756871 -115.930030        M.Y. China\n",
       "92   Shopper_60            4  37.784251 -122.406806           TAP 415\n",
       "93    Shopper_8            1  37.783982 -122.406909           Kiehl's\n",
       "94    Shopper_8            1  37.783863 -122.407156           Kiehl's\n",
       "95    Shopper_8            0  37.784020 -122.407079           Kiehl's\n",
       "96  Shopper_213            1  37.783982 -122.406909           Kiehl's\n",
       "97   Shopper_59            2  37.783816 -122.407319         Cotton On\n",
       "98   Shopper_35            1  37.783982 -122.406909           Kiehl's\n",
       "99  Shopper_197            1  37.784137 -122.407002           Kiehl's\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo['Store_Name'] = pred_y\n",
    "pinginfo.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pinginfo.sort_index(axis=0, level=None, ascending=True, inplace=True, kind='quicksort', na_position='last', sort_remaining=True, by='Shopper_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#shoppers= sorted(pinginfo['Shopper_ID'].unique(),key=lambda x: int(x[8:]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#shopper_list = pinginfo['Shopper_ID'].as_matrix().astype(np.object)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#shopper_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"lists=[]\\na='_'\\nfor shopper in shopper_list:\\n    new= shopper.split(a)\\n    lists.append(int(new[1]))\\nfoobar=[str(x) for x in sorted(lists)]\\n    \""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''lists=[]\n",
    "a='_'\n",
    "for shopper in shopper_list:\n",
    "    new= shopper.split(a)\n",
    "    lists.append(int(new[1]))\n",
    "foobar=[str(x) for x in sorted(lists)]\n",
    "    '''\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#foobar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>Shopper_ID</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Store_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shopper_420</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661624</td>\n",
       "      <td>-122.298459</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shopper_420</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783856</td>\n",
       "      <td>-122.407092</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shopper_245</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783970</td>\n",
       "      <td>-122.407194</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shopper_334</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783828</td>\n",
       "      <td>-122.406986</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shopper_205</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784219</td>\n",
       "      <td>-122.406896</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shopper_205</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784638</td>\n",
       "      <td>-122.406411</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shopper_530</td>\n",
       "      <td>1</td>\n",
       "      <td>35.085149</td>\n",
       "      <td>-113.663734</td>\n",
       "      <td>49ers Team Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shopper_530</td>\n",
       "      <td>0</td>\n",
       "      <td>36.541271</td>\n",
       "      <td>-118.435462</td>\n",
       "      <td>Jamba Juice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shopper_359</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783961</td>\n",
       "      <td>-122.407034</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>35.239976</td>\n",
       "      <td>-114.220990</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784049</td>\n",
       "      <td>-122.407055</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784084</td>\n",
       "      <td>-122.407044</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shopper_481</td>\n",
       "      <td>2</td>\n",
       "      <td>35.265054</td>\n",
       "      <td>-114.246672</td>\n",
       "      <td>H&amp;M Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784287</td>\n",
       "      <td>-122.406954</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>1</td>\n",
       "      <td>36.980523</td>\n",
       "      <td>-119.811229</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>4</td>\n",
       "      <td>37.759561</td>\n",
       "      <td>-122.385293</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shopper_287</td>\n",
       "      <td>0</td>\n",
       "      <td>37.656648</td>\n",
       "      <td>-122.006913</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shopper_536</td>\n",
       "      <td>2</td>\n",
       "      <td>34.031987</td>\n",
       "      <td>-110.253652</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shopper_344</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784028</td>\n",
       "      <td>-122.407106</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shopper_41</td>\n",
       "      <td>2</td>\n",
       "      <td>33.504061</td>\n",
       "      <td>-108.734063</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shopper_41</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783814</td>\n",
       "      <td>-122.407127</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Shopper_122</td>\n",
       "      <td>4</td>\n",
       "      <td>36.066776</td>\n",
       "      <td>-116.842850</td>\n",
       "      <td>M.Y. China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Shopper_51</td>\n",
       "      <td>0</td>\n",
       "      <td>37.720868</td>\n",
       "      <td>-122.343518</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Shopper_51</td>\n",
       "      <td>1</td>\n",
       "      <td>35.265134</td>\n",
       "      <td>-114.246611</td>\n",
       "      <td>49ers Team Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Shopper_68</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783840</td>\n",
       "      <td>-122.407236</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Shopper_180</td>\n",
       "      <td>2</td>\n",
       "      <td>36.468473</td>\n",
       "      <td>-118.174922</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Shopper_449</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783913</td>\n",
       "      <td>-122.407134</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Shopper_449</td>\n",
       "      <td>4</td>\n",
       "      <td>37.722828</td>\n",
       "      <td>-122.352689</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Shopper_561</td>\n",
       "      <td>1</td>\n",
       "      <td>36.879668</td>\n",
       "      <td>-119.478708</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Shopper_475</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783922</td>\n",
       "      <td>-122.407186</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Shopper_557</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784189</td>\n",
       "      <td>-122.406847</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784015</td>\n",
       "      <td>-122.407044</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784161</td>\n",
       "      <td>-122.406898</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>3</td>\n",
       "      <td>37.784055</td>\n",
       "      <td>-122.407036</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Shopper_268</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783936</td>\n",
       "      <td>-122.407100</td>\n",
       "      <td>Solstice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Shopper_268</td>\n",
       "      <td>4</td>\n",
       "      <td>29.082109</td>\n",
       "      <td>-94.277709</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Shopper_550</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784015</td>\n",
       "      <td>-122.407177</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Shopper_550</td>\n",
       "      <td>1</td>\n",
       "      <td>37.714475</td>\n",
       "      <td>-122.182005</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Shopper_550</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783980</td>\n",
       "      <td>-122.407110</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Shopper_27</td>\n",
       "      <td>0</td>\n",
       "      <td>37.710393</td>\n",
       "      <td>-122.342140</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Shopper_27</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784047</td>\n",
       "      <td>-122.407153</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Shopper_212</td>\n",
       "      <td>4</td>\n",
       "      <td>28.338228</td>\n",
       "      <td>-91.804871</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Shopper_212</td>\n",
       "      <td>2</td>\n",
       "      <td>33.544888</td>\n",
       "      <td>-108.770188</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Shopper_212</td>\n",
       "      <td>0</td>\n",
       "      <td>34.315595</td>\n",
       "      <td>-111.249673</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Shopper_405</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784146</td>\n",
       "      <td>-122.407033</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Shopper_405</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784104</td>\n",
       "      <td>-122.407331</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Shopper_405</td>\n",
       "      <td>4</td>\n",
       "      <td>35.756871</td>\n",
       "      <td>-115.930030</td>\n",
       "      <td>M.Y. China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Shopper_60</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784251</td>\n",
       "      <td>-122.406806</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Shopper_8</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783982</td>\n",
       "      <td>-122.406909</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Shopper_8</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783863</td>\n",
       "      <td>-122.407156</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Shopper_8</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784020</td>\n",
       "      <td>-122.407079</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Shopper_213</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783982</td>\n",
       "      <td>-122.406909</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Shopper_59</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783816</td>\n",
       "      <td>-122.407319</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Shopper_35</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783982</td>\n",
       "      <td>-122.406909</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Shopper_197</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784137</td>\n",
       "      <td>-122.407002</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Shopper_ID  Floor_Index   latitude   longitude        Store_Name\n",
       "0   Shopper_556            4  37.661463 -122.298673           TAP 415\n",
       "1   Shopper_556            1  37.784062 -122.406943           Kiehl's\n",
       "2   Shopper_556            4  37.784361 -122.406710           TAP 415\n",
       "3   Shopper_360            0  37.784341 -122.406891           Kiehl's\n",
       "4   Shopper_360            2  37.784017 -122.407307         Cotton On\n",
       "5   Shopper_420            4  37.661624 -122.298459           TAP 415\n",
       "6   Shopper_420            1  37.783856 -122.407092           Kiehl's\n",
       "7   Shopper_245            2  37.783970 -122.407194         Cotton On\n",
       "8   Shopper_334            2  37.783828 -122.406986         Cotton On\n",
       "9   Shopper_205            1  37.784219 -122.406896           Kiehl's\n",
       "10  Shopper_205            0  37.784638 -122.406411           Kiehl's\n",
       "11  Shopper_530            1  35.085149 -113.663734  49ers Team Store\n",
       "12  Shopper_530            0  36.541271 -118.435462       Jamba Juice\n",
       "13  Shopper_359            0  37.783961 -122.407034           Kiehl's\n",
       "14  Shopper_189            0  35.239976 -114.220990    Bloomingdale's\n",
       "15  Shopper_189            0  37.784049 -122.407055           Kiehl's\n",
       "16  Shopper_189            0  37.784084 -122.407044           Kiehl's\n",
       "17  Shopper_481            2  35.265054 -114.246672           H&M Man\n",
       "18  Shopper_155            4  37.784287 -122.406954           TAP 415\n",
       "19  Shopper_155            1  36.980523 -119.811229           Kiehl's\n",
       "20  Shopper_155            4  37.759561 -122.385293           TAP 415\n",
       "21  Shopper_287            0  37.656648 -122.006913           Kiehl's\n",
       "22  Shopper_536            2  34.031987 -110.253652    Bloomingdale's\n",
       "23  Shopper_344            2  37.784028 -122.407106         Cotton On\n",
       "24   Shopper_41            2  33.504061 -108.734063    Bloomingdale's\n",
       "25   Shopper_41            3  37.783814 -122.407127          Solstice\n",
       "26  Shopper_122            4  36.066776 -116.842850        M.Y. China\n",
       "27   Shopper_51            0  37.720868 -122.343518           Kiehl's\n",
       "28   Shopper_51            1  35.265134 -114.246611  49ers Team Store\n",
       "29   Shopper_68            1  37.783840 -122.407236           Kiehl's\n",
       "..          ...          ...        ...         ...               ...\n",
       "70  Shopper_180            2  36.468473 -118.174922         Microsoft\n",
       "71  Shopper_449            1  37.783913 -122.407134           Kiehl's\n",
       "72  Shopper_449            4  37.722828 -122.352689           TAP 415\n",
       "73  Shopper_561            1  36.879668 -119.478708           Kiehl's\n",
       "74  Shopper_475            3  37.783922 -122.407186          Solstice\n",
       "75  Shopper_557            1  37.784189 -122.406847           Kiehl's\n",
       "76   Shopper_98            1  37.784015 -122.407044           Kiehl's\n",
       "77   Shopper_98            0  37.784161 -122.406898           Kiehl's\n",
       "78   Shopper_98            3  37.784055 -122.407036          Solstice\n",
       "79  Shopper_268            3  37.783936 -122.407100          Solstice\n",
       "80  Shopper_268            4  29.082109  -94.277709    Bloomingdale's\n",
       "81  Shopper_550            0  37.784015 -122.407177           Kiehl's\n",
       "82  Shopper_550            1  37.714475 -122.182005           Kiehl's\n",
       "83  Shopper_550            0  37.783980 -122.407110           Kiehl's\n",
       "84   Shopper_27            0  37.710393 -122.342140           Kiehl's\n",
       "85   Shopper_27            0  37.784047 -122.407153           Kiehl's\n",
       "86  Shopper_212            4  28.338228  -91.804871    Bloomingdale's\n",
       "87  Shopper_212            2  33.544888 -108.770188    Bloomingdale's\n",
       "88  Shopper_212            0  34.315595 -111.249673    Bloomingdale's\n",
       "89  Shopper_405            0  37.784146 -122.407033           Kiehl's\n",
       "90  Shopper_405            2  37.784104 -122.407331         Cotton On\n",
       "91  Shopper_405            4  35.756871 -115.930030        M.Y. China\n",
       "92   Shopper_60            4  37.784251 -122.406806           TAP 415\n",
       "93    Shopper_8            1  37.783982 -122.406909           Kiehl's\n",
       "94    Shopper_8            1  37.783863 -122.407156           Kiehl's\n",
       "95    Shopper_8            0  37.784020 -122.407079           Kiehl's\n",
       "96  Shopper_213            1  37.783982 -122.406909           Kiehl's\n",
       "97   Shopper_59            2  37.783816 -122.407319         Cotton On\n",
       "98   Shopper_35            1  37.783982 -122.406909           Kiehl's\n",
       "99  Shopper_197            1  37.784137 -122.407002           Kiehl's\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_ping= pinginfo.groupby(['Shopper_ID', 'Store_Name','Floor_Index', 'latitude', 'longitude',' Store_Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "visiting_info = pinginfo.groupby(['Shopper_ID', 'Store_Name']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Floor_Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_ID</th>\n",
       "      <th>Store_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Shopper_1</th>\n",
       "      <th>49ers Team Store</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.534522</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOSS</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloomingdale's</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.119403</td>\n",
       "      <td>1.135146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Farms</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cotton On</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Floor_Index                                     \\\n",
       "                                  count      mean       std  min  25%  50%   \n",
       "Shopper_ID Store_Name                                                        \n",
       "Shopper_1  49ers Team Store         7.0  1.571429  0.534522  1.0  1.0  2.0   \n",
       "           BOSS                     3.0  1.000000  0.000000  1.0  1.0  1.0   \n",
       "           Bloomingdale's          67.0  1.119403  1.135146  0.0  0.0  1.0   \n",
       "           Bristol Farms            5.0  0.000000  0.000000  0.0  0.0  0.0   \n",
       "           Cotton On               12.0  2.000000  0.000000  2.0  2.0  2.0   \n",
       "\n",
       "                                       \n",
       "                             75%  max  \n",
       "Shopper_ID Store_Name                  \n",
       "Shopper_1  49ers Team Store  2.0  2.0  \n",
       "           BOSS              1.0  1.0  \n",
       "           Bloomingdale's    2.0  4.0  \n",
       "           Bristol Farms     0.0  0.0  \n",
       "           Cotton On         2.0  2.0  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visiting_info.drop(['latitude', 'longitude'], axis=1, inplace=True)\n",
    "visiting_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#visiting_info.to_csv(r\"C:\\Users\\welcome\\Desktop\\visitinginfo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shopper_ID</th>\n",
       "      <th>Store_Name</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>49ers Team Store</td>\n",
       "      <td>7</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.534522</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>BOSS</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>67</td>\n",
       "      <td>1.119403</td>\n",
       "      <td>1.135146</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Bristol Farms</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>12</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>H&amp;M</td>\n",
       "      <td>11</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>H&amp;M Man</td>\n",
       "      <td>5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>47</td>\n",
       "      <td>0.404255</td>\n",
       "      <td>0.577083</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>M.Y. China</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>18</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>8</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Vera Bradley</td>\n",
       "      <td>2</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>abercrombie kids</td>\n",
       "      <td>2</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>49ers Team Store</td>\n",
       "      <td>6</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>BOSS</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Bespoke Events</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>4</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Bristol Farms</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>3</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>H&amp;M Man</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Jamba Juice</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>76</td>\n",
       "      <td>0.671053</td>\n",
       "      <td>0.640586</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>M.Y. China</td>\n",
       "      <td>13</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>75</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Shopper_100</td>\n",
       "      <td>49ers Team Store</td>\n",
       "      <td>5</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Shopper_100</td>\n",
       "      <td>BOSS</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Shopper_100</td>\n",
       "      <td>Bath &amp; Body Works</td>\n",
       "      <td>2</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Shopper_100</td>\n",
       "      <td>Bespoke Events</td>\n",
       "      <td>3</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>6147</th>\n",
       "      <td>Shopper_97</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>19</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6148</th>\n",
       "      <td>Shopper_97</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>9</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6149</th>\n",
       "      <td>Shopper_97</td>\n",
       "      <td>Vera Bradley</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6150</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>49ers Team Store</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6152</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>Bristol Farms</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6153</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>44</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>H&amp;M Man</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6155</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>115</td>\n",
       "      <td>0.426087</td>\n",
       "      <td>0.496671</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6156</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>M.Y. China</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6157</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>21</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6158</th>\n",
       "      <td>Shopper_98</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6159</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>49ers Team Store</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6160</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>BOSS</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6161</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Bath &amp; Body Works</td>\n",
       "      <td>2</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6162</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Bespoke Events</td>\n",
       "      <td>11</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6163</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>6</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.752773</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6164</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Bristol Farms</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6165</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Camper</td>\n",
       "      <td>5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6166</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>20</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6167</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>H&amp;M</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6168</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>H&amp;M Man</td>\n",
       "      <td>5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6169</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Jamba Juice</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6170</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>92</td>\n",
       "      <td>0.510870</td>\n",
       "      <td>0.620077</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6171</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>M.Y. China</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6172</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6173</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>8</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6174</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>Vera Bradley</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6176</th>\n",
       "      <td>Shopper_99</td>\n",
       "      <td>abercrombie kids</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6177 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Shopper_ID         Store_Name  count      mean       std  min   25%  \\\n",
       "0       Shopper_1   49ers Team Store      7  1.571429  0.534522    1  1.00   \n",
       "1       Shopper_1               BOSS      3  1.000000  0.000000    1  1.00   \n",
       "2       Shopper_1     Bloomingdale's     67  1.119403  1.135146    0  0.00   \n",
       "3       Shopper_1      Bristol Farms      5  0.000000  0.000000    0  0.00   \n",
       "4       Shopper_1          Cotton On     12  2.000000  0.000000    2  2.00   \n",
       "5       Shopper_1                H&M     11  3.000000  0.000000    3  3.00   \n",
       "6       Shopper_1            H&M Man      5  2.000000  0.000000    2  2.00   \n",
       "7       Shopper_1            Kiehl's     47  0.404255  0.577083    0  0.00   \n",
       "8       Shopper_1         M.Y. China      2  4.000000  0.000000    4  4.00   \n",
       "9       Shopper_1           Solstice     18  3.000000  0.000000    3  3.00   \n",
       "10      Shopper_1            TAP 415      8  4.000000  0.000000    4  4.00   \n",
       "11      Shopper_1       Vera Bradley      2  3.000000  0.000000    3  3.00   \n",
       "12      Shopper_1   abercrombie kids      2  3.000000  0.000000    3  3.00   \n",
       "13     Shopper_10   49ers Team Store      6  1.166667  0.408248    1  1.00   \n",
       "14     Shopper_10               BOSS      1  1.000000       NaN    1  1.00   \n",
       "15     Shopper_10     Bespoke Events      4  4.000000  0.000000    4  4.00   \n",
       "16     Shopper_10     Bloomingdale's      4  1.500000  1.732051    0  0.75   \n",
       "17     Shopper_10      Bristol Farms      1  0.000000       NaN    0  0.00   \n",
       "18     Shopper_10          Cotton On      3  2.000000  0.000000    2  2.00   \n",
       "19     Shopper_10            H&M Man      1  2.000000       NaN    2  2.00   \n",
       "20     Shopper_10        Jamba Juice      1  0.000000       NaN    0  0.00   \n",
       "21     Shopper_10            Kiehl's     76  0.671053  0.640586    0  0.00   \n",
       "22     Shopper_10         M.Y. China     13  4.000000  0.000000    4  4.00   \n",
       "23     Shopper_10          Microsoft      1  2.000000       NaN    2  2.00   \n",
       "24     Shopper_10           Solstice      1  4.000000       NaN    4  4.00   \n",
       "25     Shopper_10            TAP 415     75  4.000000  0.000000    4  4.00   \n",
       "26    Shopper_100   49ers Team Store      5  1.200000  0.447214    1  1.00   \n",
       "27    Shopper_100               BOSS      7  1.000000  0.000000    1  1.00   \n",
       "28    Shopper_100  Bath & Body Works      2  3.000000  0.000000    3  3.00   \n",
       "29    Shopper_100     Bespoke Events      3  4.000000  0.000000    4  4.00   \n",
       "...           ...                ...    ...       ...       ...  ...   ...   \n",
       "6147   Shopper_97           Solstice     19  3.000000  0.000000    3  3.00   \n",
       "6148   Shopper_97            TAP 415      9  4.000000  0.000000    4  4.00   \n",
       "6149   Shopper_97       Vera Bradley      1  3.000000       NaN    3  3.00   \n",
       "6150   Shopper_98   49ers Team Store      1  1.000000       NaN    1  1.00   \n",
       "6151   Shopper_98     Bloomingdale's      3  0.666667  0.577350    0  0.50   \n",
       "6152   Shopper_98      Bristol Farms      3  0.000000  0.000000    0  0.00   \n",
       "6153   Shopper_98          Cotton On     44  2.000000  0.000000    2  2.00   \n",
       "6154   Shopper_98            H&M Man      1  2.000000       NaN    2  2.00   \n",
       "6155   Shopper_98            Kiehl's    115  0.426087  0.496671    0  0.00   \n",
       "6156   Shopper_98         M.Y. China      1  4.000000       NaN    4  4.00   \n",
       "6157   Shopper_98           Solstice     21  3.000000  0.000000    3  3.00   \n",
       "6158   Shopper_98            TAP 415      1  4.000000       NaN    4  4.00   \n",
       "6159   Shopper_99   49ers Team Store      3  1.000000  0.000000    1  1.00   \n",
       "6160   Shopper_99               BOSS      6  1.000000  0.000000    1  1.00   \n",
       "6161   Shopper_99  Bath & Body Works      2  3.000000  0.000000    3  3.00   \n",
       "6162   Shopper_99     Bespoke Events     11  4.000000  0.000000    4  4.00   \n",
       "6163   Shopper_99     Bloomingdale's      6  1.166667  0.752773    0  1.00   \n",
       "6164   Shopper_99      Bristol Farms      4  0.000000  0.000000    0  0.00   \n",
       "6165   Shopper_99             Camper      5  2.000000  0.000000    2  2.00   \n",
       "6166   Shopper_99          Cotton On     20  2.000000  0.000000    2  2.00   \n",
       "6167   Shopper_99                H&M      1  3.000000       NaN    3  3.00   \n",
       "6168   Shopper_99            H&M Man      5  2.000000  0.000000    2  2.00   \n",
       "6169   Shopper_99        Jamba Juice      2  0.000000  0.000000    0  0.00   \n",
       "6170   Shopper_99            Kiehl's     92  0.510870  0.620077    0  0.00   \n",
       "6171   Shopper_99         M.Y. China      2  4.000000  0.000000    4  4.00   \n",
       "6172   Shopper_99          Microsoft      4  2.000000  0.000000    2  2.00   \n",
       "6173   Shopper_99           Solstice      8  3.000000  0.000000    3  3.00   \n",
       "6174   Shopper_99            TAP 415     17  4.000000  0.000000    4  4.00   \n",
       "6175   Shopper_99       Vera Bradley      1  4.000000       NaN    4  4.00   \n",
       "6176   Shopper_99   abercrombie kids      1  3.000000       NaN    3  3.00   \n",
       "\n",
       "      50%   75%  max  \n",
       "0     2.0  2.00    2  \n",
       "1     1.0  1.00    1  \n",
       "2     1.0  2.00    4  \n",
       "3     0.0  0.00    0  \n",
       "4     2.0  2.00    2  \n",
       "5     3.0  3.00    3  \n",
       "6     2.0  2.00    2  \n",
       "7     0.0  1.00    2  \n",
       "8     4.0  4.00    4  \n",
       "9     3.0  3.00    3  \n",
       "10    4.0  4.00    4  \n",
       "11    3.0  3.00    3  \n",
       "12    3.0  3.00    3  \n",
       "13    1.0  1.00    2  \n",
       "14    1.0  1.00    1  \n",
       "15    4.0  4.00    4  \n",
       "16    1.0  1.75    4  \n",
       "17    0.0  0.00    0  \n",
       "18    2.0  2.00    2  \n",
       "19    2.0  2.00    2  \n",
       "20    0.0  0.00    0  \n",
       "21    1.0  1.00    2  \n",
       "22    4.0  4.00    4  \n",
       "23    2.0  2.00    2  \n",
       "24    4.0  4.00    4  \n",
       "25    4.0  4.00    4  \n",
       "26    1.0  1.00    2  \n",
       "27    1.0  1.00    1  \n",
       "28    3.0  3.00    3  \n",
       "29    4.0  4.00    4  \n",
       "...   ...   ...  ...  \n",
       "6147  3.0  3.00    3  \n",
       "6148  4.0  4.00    4  \n",
       "6149  3.0  3.00    3  \n",
       "6150  1.0  1.00    1  \n",
       "6151  1.0  1.00    1  \n",
       "6152  0.0  0.00    0  \n",
       "6153  2.0  2.00    2  \n",
       "6154  2.0  2.00    2  \n",
       "6155  0.0  1.00    1  \n",
       "6156  4.0  4.00    4  \n",
       "6157  3.0  3.00    3  \n",
       "6158  4.0  4.00    4  \n",
       "6159  1.0  1.00    1  \n",
       "6160  1.0  1.00    1  \n",
       "6161  3.0  3.00    3  \n",
       "6162  4.0  4.00    4  \n",
       "6163  1.0  1.75    2  \n",
       "6164  0.0  0.00    0  \n",
       "6165  2.0  2.00    2  \n",
       "6166  2.0  2.00    2  \n",
       "6167  3.0  3.00    3  \n",
       "6168  2.0  2.00    2  \n",
       "6169  0.0  0.00    0  \n",
       "6170  0.0  1.00    2  \n",
       "6171  4.0  4.00    4  \n",
       "6172  2.0  2.00    2  \n",
       "6173  3.0  3.00    3  \n",
       "6174  4.0  4.00    4  \n",
       "6175  4.0  4.00    4  \n",
       "6176  3.0  3.00    3  \n",
       "\n",
       "[6177 rows x 10 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitinginfo = pd.read_csv(r\"C:\\Users\\welcome\\Desktop\\visitinginfo.csv\")\n",
    "visitinginfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shopper_ID\n",
       "Shopper_1      ([Bloomingdale's], [67])\n",
       "Shopper_10            ([Kiehl's], [76])\n",
       "Shopper_100           ([Kiehl's], [99])\n",
       "Shopper_101           ([Kiehl's], [99])\n",
       "Shopper_102          ([TAP 415], [125])\n",
       "Shopper_103           ([Kiehl's], [72])\n",
       "Shopper_104          ([Kiehl's], [118])\n",
       "Shopper_105          ([Kiehl's], [110])\n",
       "Shopper_106          ([Kiehl's], [123])\n",
       "Shopper_107           ([Kiehl's], [96])\n",
       "Shopper_108          ([Kiehl's], [166])\n",
       "Shopper_109           ([Kiehl's], [72])\n",
       "Shopper_11           ([Kiehl's], [107])\n",
       "Shopper_110           ([Kiehl's], [72])\n",
       "Shopper_111          ([Kiehl's], [103])\n",
       "Shopper_112          ([Kiehl's], [105])\n",
       "Shopper_113          ([Kiehl's], [137])\n",
       "Shopper_114           ([TAP 415], [89])\n",
       "Shopper_115           ([Kiehl's], [56])\n",
       "Shopper_116          ([TAP 415], [166])\n",
       "Shopper_117          ([Kiehl's], [154])\n",
       "Shopper_118          ([Kiehl's], [106])\n",
       "Shopper_119          ([Kiehl's], [144])\n",
       "Shopper_12            ([Kiehl's], [72])\n",
       "Shopper_120          ([Kiehl's], [111])\n",
       "Shopper_121           ([Kiehl's], [80])\n",
       "Shopper_122           ([Kiehl's], [72])\n",
       "Shopper_123          ([Kiehl's], [139])\n",
       "Shopper_124          ([Kiehl's], [126])\n",
       "Shopper_125           ([Kiehl's], [70])\n",
       "                         ...           \n",
       "Shopper_72            ([Kiehl's], [84])\n",
       "Shopper_73            ([Kiehl's], [85])\n",
       "Shopper_74           ([Kiehl's], [120])\n",
       "Shopper_75           ([Kiehl's], [109])\n",
       "Shopper_76           ([Kiehl's], [181])\n",
       "Shopper_77            ([Kiehl's], [64])\n",
       "Shopper_78            ([Kiehl's], [61])\n",
       "Shopper_79            ([Kiehl's], [73])\n",
       "Shopper_8             ([Kiehl's], [95])\n",
       "Shopper_80            ([Kiehl's], [90])\n",
       "Shopper_81            ([Kiehl's], [96])\n",
       "Shopper_82            ([Kiehl's], [73])\n",
       "Shopper_83           ([Kiehl's], [105])\n",
       "Shopper_84            ([TAP 415], [85])\n",
       "Shopper_85           ([Kiehl's], [141])\n",
       "Shopper_86            ([Kiehl's], [76])\n",
       "Shopper_87            ([Kiehl's], [62])\n",
       "Shopper_88            ([Kiehl's], [77])\n",
       "Shopper_89           ([Kiehl's], [138])\n",
       "Shopper_9            ([Kiehl's], [119])\n",
       "Shopper_90           ([Kiehl's], [106])\n",
       "Shopper_91           ([Kiehl's], [128])\n",
       "Shopper_92            ([Kiehl's], [98])\n",
       "Shopper_93            ([Kiehl's], [52])\n",
       "Shopper_94            ([Kiehl's], [93])\n",
       "Shopper_95           ([Kiehl's], [121])\n",
       "Shopper_96            ([Kiehl's], [91])\n",
       "Shopper_97     ([Bloomingdale's], [59])\n",
       "Shopper_98           ([Kiehl's], [115])\n",
       "Shopper_99            ([Kiehl's], [92])\n",
       "Name: Store_Name, Length: 567, dtype: object"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "df_freq = pinginfo.groupby(by='Shopper_ID').Store_Name.apply(stats.mode)\n",
    "df_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store_Name    ([Kiehl's], [76])\n",
       "Name: Shopper_10, dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freq= df_freq.to_frame()\n",
    "df_freq.iloc[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Bloomingdale's\",\n",
       "       u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'Cotton On', u\"Kiehl's\", u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'Cotton On', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\",\n",
       "       u\"Bloomingdale's\", u\"Kiehl's\", u\"Bloomingdale's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u'TAP 415', u'TAP 415', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'TAP 415', u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u'Cotton On', u\"Bloomingdale's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'Cotton On', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u'Cotton On', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Bloomingdale's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'Cotton On', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u'TAP 415', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'Cotton On',\n",
       "       u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\",\n",
       "       u'Cotton On', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'Cotton On', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u'Solstice', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'TAP 415', u'Solstice', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'Solstice', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'Solstice', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'Cotton On',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'Cotton On', u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u'Cotton On', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'Solstice',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'Cotton On', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'Solstice', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u'TAP 415', u'TAP 415', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\",\n",
       "       u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'Solstice', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\", u\"Kiehl's\",\n",
       "       u'Solstice', u\"Kiehl's\", u'TAP 415', u'Cotton On', u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u'TAP 415',\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u'TAP 415', u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Bloomingdale's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u'TAP 415', u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Kiehl's\", u\"Bloomingdale's\",\n",
       "       u\"Kiehl's\", u\"Kiehl's\"], dtype=object)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mostvisitedshoplist= []\n",
    "for shop in df_freq['Store_Name']:\n",
    "    mostvisitedshoplist.append(shop[0][0])\n",
    "mostvisitedshop_array= np.array(mostvisitedshoplist)\n",
    "mostvisitedshop_array.astype(object)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>Store_Name</th>\n",
       "      <th>New_Store</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shopper_1</th>\n",
       "      <td>([Bloomingdale's], [67])</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_10</th>\n",
       "      <td>([Kiehl's], [76])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_100</th>\n",
       "      <td>([Kiehl's], [99])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_101</th>\n",
       "      <td>([Kiehl's], [99])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_102</th>\n",
       "      <td>([TAP 415], [125])</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Store_Name       New_Store\n",
       "Shopper_ID                                           \n",
       "Shopper_1    ([Bloomingdale's], [67])  Bloomingdale's\n",
       "Shopper_10          ([Kiehl's], [76])         Kiehl's\n",
       "Shopper_100         ([Kiehl's], [99])         Kiehl's\n",
       "Shopper_101         ([Kiehl's], [99])         Kiehl's\n",
       "Shopper_102        ([TAP 415], [125])         TAP 415"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freq['New_Store']= mostvisitedshop_array\n",
    "df_freq.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freq.to_csv(r\"C:\\Users\\welcome\\Desktop\\exl__round1_.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>Shopper_ID</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Store_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shopper_ID  Floor_Index   latitude   longitude Store_Name\n",
       "0  Shopper_556            4  37.661463 -122.298673    TAP 415\n",
       "1  Shopper_556            1  37.784062 -122.406943    Kiehl's\n",
       "2  Shopper_556            4  37.784361 -122.406710    TAP 415\n",
       "3  Shopper_360            0  37.784341 -122.406891    Kiehl's\n",
       "4  Shopper_360            2  37.784017 -122.407307  Cotton On"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>Store_Name</th>\n",
       "      <th>Broad_Category</th>\n",
       "      <th>Fine_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amazon Pop-Up</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bespoke Events</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bespoke Coworking</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Specialty Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dyson</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Penhaligonï¿½Ûªs</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Misc.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Store_Name Broad_Category    Fine_Category\n",
       "0      Amazon Pop-Up         Retail            Misc.\n",
       "1     Bespoke Events         Retail            Misc.\n",
       "2  Bespoke Coworking         Retail  Specialty Store\n",
       "3              Dyson         Retail            Misc.\n",
       "4   Penhaligonï¿½Ûªs         Retail            Misc."
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorymapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stores, category in zip(categorymapping['Store_Name'], categorymapping['Fine_Category']):\n",
    "    store_and_category_mapping = dict(zip(categorymapping['Store_Name'], categorymapping['Fine_Category']))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'49ers Team Store': u'Sports Stores',\n",
       " u'Abercrombie & Fitch': u'Apparel And Accessories',\n",
       " u'Adidas': u'Apparel And Accessories',\n",
       " u'Ajisen Ramen': u'Qsr Restaurants',\n",
       " u'Aldo': u'Shoe Stores',\n",
       " u'Amazon Pop-Up': u'Misc.',\n",
       " u'American Eagle Outfitters': u'Apparel And Accessories',\n",
       " u'Amiri Salon': u'Furniture And Decor',\n",
       " u'Amoura Cafe Fresh Mediterranean': u'Qsr Restaurants',\n",
       " u'Andale Mexican Restaurant': u'Qsr Restaurants',\n",
       " u'Aritzia': u'Apparel And Accessories',\n",
       " u\"Auntie Anne's Pretzels\": u'Qsr Restaurants',\n",
       " u'Aveda': u'Furniture And Decor',\n",
       " u'BCBGMAXAZRIA': u'Apparel And Accessories',\n",
       " u'BOSS': u'Apparel And Accessories',\n",
       " u'Banana Republic': u'Apparel And Accessories',\n",
       " u'Bath & Body Works': u'Furniture And Decor',\n",
       " u\"Beard Papa's Cream Puffs\": u'Qsr Restaurants',\n",
       " u'Bespoke Coworking': u'Specialty Store',\n",
       " u'Bespoke Events': u'Misc.',\n",
       " u\"Bloomingdale's\": u'Department Stores',\n",
       " u'Bose': u'Electronics Stores',\n",
       " u'Brighton Collectibles': u'Apparel And Accessories',\n",
       " u'Bristol Farms': u'Casual Restaurants',\n",
       " u'Brookstone': u'Department Stores',\n",
       " u'Buckhorn Grill': u'Qsr Restaurants',\n",
       " u'Burberry': u'Apparel And Accessories',\n",
       " u'Burke Williams Day Spa': u'Misc.',\n",
       " u'Cafe Bellini': u'Qsr Restaurants',\n",
       " u'Caffe Central': u'Qsr Restaurants',\n",
       " u'Cako Bakery': u'Qsr Restaurants',\n",
       " u'Camper': u'Shoe Stores',\n",
       " u'Century San Francisco Centre 9': u'Misc.',\n",
       " u'Champs Sports': u'Sports Stores',\n",
       " u\"Charley's Grilled Subs\": u'Qsr Restaurants',\n",
       " u'Chipotle': u'Qsr Restaurants',\n",
       " u\"Claire's Boutique\": u'Apparel And Accessories',\n",
       " u'Clarks': u'Shoe Stores',\n",
       " u'Club Monaco Men': u'Apparel And Accessories',\n",
       " u'Club Monaco Women': u'Apparel And Accessories',\n",
       " u'Coach': u'Apparel And Accessories',\n",
       " u'Coriander Gourmet Thai': u'Qsr Restaurants',\n",
       " u'Corner W': u'Shoe Stores',\n",
       " u'Cotton On': u'Apparel And Accessories',\n",
       " u'Currency Exchange International ': u'Misc.',\n",
       " u'DAVIDsTEA': u'Qsr Restaurants',\n",
       " u'Dyson': u'Misc.',\n",
       " u'ECCO': u'Shoe Stores',\n",
       " u'Eddie Bauer': u'Apparel And Accessories',\n",
       " u'Express': u'Apparel And Accessories',\n",
       " u'Eyebrow Plus': u'Furniture And Decor',\n",
       " u'Finish Line': u'Apparel And Accessories',\n",
       " u'Fire of Brazil': u'Qsr Restaurants',\n",
       " u'Forever Flawless': u'Furniture And Decor',\n",
       " u'Forever Flawless ': u'Furniture And Decor',\n",
       " u'Fossil': u'Apparel And Accessories',\n",
       " u\"Francesca's Collection \": u'Apparel And Accessories',\n",
       " u'GNC': u'Furniture And Decor',\n",
       " u'GUESS ': u'Apparel And Accessories',\n",
       " u'Ghirardelli Ice Cream and Chocolate Shop ': u'Qsr Restaurants',\n",
       " u'Go! Toys and Games': u'Toy Stores',\n",
       " u'Godiva Chocolatier': u'Qsr Restaurants',\n",
       " u'H&M': u'Apparel And Accessories',\n",
       " u'H&M Man': u'Apparel And Accessories',\n",
       " u'Haagen Dazs': u'Qsr Restaurants',\n",
       " u'Hallmark': u'Office Supply Stores',\n",
       " u'Hollister Co.': u'Apparel And Accessories',\n",
       " u'House of Hoops by Foot Locker': u'Sports Stores',\n",
       " u'J.Crew': u'Apparel And Accessories',\n",
       " u'Jamba Juice': u'Qsr Restaurants',\n",
       " u'Janie And Jack': u'Apparel And Accessories',\n",
       " u'Karen Millen': u'Apparel And Accessories',\n",
       " u'Kate Spade New York': u'Apparel And Accessories',\n",
       " u'Kay Jewelers': u'Misc.',\n",
       " u'Kichi Grill': u'Qsr Restaurants',\n",
       " u\"Kiehl's\": u'Furniture And Decor',\n",
       " u\"L'Occitane en Provence\": u'Furniture And Decor',\n",
       " u'LEGO': u'Toy Stores',\n",
       " u'Lady Foot Locker': u'Sports Stores',\n",
       " u'Lids': u'Apparel and Accessories',\n",
       " u'Lobster Me': u'Qsr Restaurants',\n",
       " u'Loving Hut Vegan Cuisine ': u'Qsr Restaurants',\n",
       " u'Lucky Brand Jeans': u'Apparel And Accessories',\n",
       " u'M.Y. China': u'Qsr Restaurants',\n",
       " u'Madewell': u'Apparel And Accessories',\n",
       " u'Marbles: The Brain Store': u'Toy Stores',\n",
       " u'Max & Co.': u'Apparel And Accessories',\n",
       " u'Michael Kors': u'Apparel And Accessories',\n",
       " u'Michael Kors Men': u'Apparel And Accessories',\n",
       " u'Microsoft': u'Electronics Stores',\n",
       " u'Mrs. Fields': u'Qsr Restaurants',\n",
       " u'NYS Collection': u'Apparel And Accessories',\n",
       " u'New Era': u'Sports Stores',\n",
       " u'Nordstrom': u'Department Stores',\n",
       " u'Nordstrom E-Bar': u'Qsr Restaurants',\n",
       " u'OAK+FORT': u'Apparel And Accessories',\n",
       " u'Onassis': u'Furniture And Decor',\n",
       " u'Optica': u'Furniture And Decor',\n",
       " u'Origins': u'Furniture And Decor',\n",
       " u'PacSun': u'Apparel And Accessories',\n",
       " u'Panda Express': u'Qsr Restaurants',\n",
       " u'Pandora': u'Misc.',\n",
       " u'Papyrus': u'Office Supply Stores',\n",
       " u'Pasta Moto': u'Qsr Restaurants',\n",
       " u\"Peet's Coffee & Tea\": u'Qsr Restaurants',\n",
       " u'Penhaligon\\xef\\xbf\\xbd\\xdb\\xaas': u'Misc.',\n",
       " u'Perfumania': u'Furniture And Decor',\n",
       " u'Pressed Juicery': u'Qsr Restaurants',\n",
       " u'ProActiv Solutions ': u'Misc.',\n",
       " u'RED Valentino': u'Apparel And Accessories',\n",
       " u'Razer': u'Specialty Store',\n",
       " u'Rolex': u'Misc.',\n",
       " u'SF Sports': u'Apparel And Accessories',\n",
       " u'Samsonite': u'Apparel and Accessories',\n",
       " u'San Francisco Luggage Co.': u'Apparel and Accessories',\n",
       " u'San Francisco Soup Company': u'Qsr Restaurants',\n",
       " u'Sarku Japan': u'Qsr Restaurants',\n",
       " u'Shoe Palace ': u'Shoe Stores',\n",
       " u'Shoe Wiz': u'Misc.',\n",
       " u'Solstice': u'Apparel And Accessories',\n",
       " u'Sorabol Korean BBQ & Asian Noodles': u'Qsr Restaurants',\n",
       " u'Starbucks': u'Qsr Restaurants',\n",
       " u'Steve Madden': u'Shoe Stores',\n",
       " u'Studio Manhattan': u'Apparel and Accessories',\n",
       " u'Sunglass Hut': u'Apparel And Accessories',\n",
       " u'Sunglass Hut ': u'Apparel And Accessories',\n",
       " u'Superdry ': u'Apparel And Accessories',\n",
       " u'Sur La Table': u'Furniture And Decor',\n",
       " u'Swarovski': u'Apparel and Accessories',\n",
       " u'Swatch': u'Misc.',\n",
       " u'T-Mobile': u'Cellular Phone Stores',\n",
       " u'TAP 415': u'Qsr Restaurants',\n",
       " u'Teavana': u'Qsr Restaurants',\n",
       " u'The Art of Shaving': u'Furniture And Decor',\n",
       " u'The Body Shop': u'Furniture And Decor',\n",
       " u'The Walking Company': u'Shoe Stores',\n",
       " u'Tiffany & Co.': u'Misc.',\n",
       " u'Timberland': u'Apparel And Accessories',\n",
       " u'Tory Burch ': u'Apparel And Accessories',\n",
       " u'Tourneau': u'Misc.',\n",
       " u'True Religion Jeans': u'Apparel And Accessories',\n",
       " u'Tumi': u'Apparel and Accessories',\n",
       " u'Vans': u'Shoe Stores',\n",
       " u'Vera Bradley': u'Apparel And Accessories',\n",
       " u\"Victoria's Secret\": u'Apparel And Accessories',\n",
       " u'Warriors Team Store': u'Sports Stores',\n",
       " u\"Wetzel's Pretzels\": u'Qsr Restaurants',\n",
       " u'Yoppi Frozen Yogurt ': u'Qsr Restaurants',\n",
       " u'Zara': u'Apparel And Accessories',\n",
       " u'abercrombie kids': u'Apparel And Accessories',\n",
       " u'bareMinerals': u'Furniture And Decor',\n",
       " u'bebe': u'Apparel And Accessories',\n",
       " u'iPlay n Talk': u'Electronics Stores',\n",
       " u'lululemon athletica': u'Apparel and Accessories'}"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_and_category_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>Store_Name</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Store_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jamba Juice</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784091</td>\n",
       "      <td>-122.406813</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coriander Gourmet Thai</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784596</td>\n",
       "      <td>-122.406210</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NYS Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784396</td>\n",
       "      <td>-122.406587</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hallmark</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783771</td>\n",
       "      <td>-122.407377</td>\n",
       "      <td>Office Supply Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sorabol Korean BBQ &amp; Asian Noodles</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784601</td>\n",
       "      <td>-122.406328</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Store_Name  Floor_Index   latitude   longitude  \\\n",
       "0                         Jamba Juice            0  37.784091 -122.406813   \n",
       "1              Coriander Gourmet Thai            0  37.784596 -122.406210   \n",
       "2                      NYS Collection            0  37.784396 -122.406587   \n",
       "3                            Hallmark            0  37.783771 -122.407377   \n",
       "4  Sorabol Korean BBQ & Asian Noodles            0  37.784601 -122.406328   \n",
       "\n",
       "            Store_Category  \n",
       "0          Qsr Restaurants  \n",
       "1          Qsr Restaurants  \n",
       "2  Apparel And Accessories  \n",
       "3     Office Supply Stores  \n",
       "4          Qsr Restaurants  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storemap['Store_Category'] = storemap['Store_Name'].map(store_and_category_mapping).astype(object)\n",
    "storemap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'Apparel And Accessories': 0,\n",
       " u'Apparel and Accessories': 1,\n",
       " u'Casual Restaurants': 2,\n",
       " u'Cellular Phone Stores': 3,\n",
       " u'Department Stores': 4,\n",
       " u'Electronics Stores': 5,\n",
       " u'Furniture And Decor': 6,\n",
       " u'Misc.': 7,\n",
       " u'Office Supply Stores': 8,\n",
       " u'Qsr Restaurants': 9,\n",
       " u'Shoe Stores': 10,\n",
       " u'Specialty Store': 11,\n",
       " u'Sports Stores': 12,\n",
       " u'Toy Stores': 13}"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Store_Categories= sorted(storemap['Store_Category'].unique())\n",
    "Store_Categories_mapping = dict(zip(Store_Categories, range(0, len(Store_Categories) + 1)))\n",
    "Store_Categories_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>Store_Name</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Store_Category</th>\n",
       "      <th>Category_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jamba Juice</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784091</td>\n",
       "      <td>-122.406813</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coriander Gourmet Thai</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784596</td>\n",
       "      <td>-122.406210</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NYS Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784396</td>\n",
       "      <td>-122.406587</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hallmark</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783771</td>\n",
       "      <td>-122.407377</td>\n",
       "      <td>Office Supply Stores</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sorabol Korean BBQ &amp; Asian Noodles</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784601</td>\n",
       "      <td>-122.406328</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Store_Name  Floor_Index   latitude   longitude  \\\n",
       "0                         Jamba Juice            0  37.784091 -122.406813   \n",
       "1              Coriander Gourmet Thai            0  37.784596 -122.406210   \n",
       "2                      NYS Collection            0  37.784396 -122.406587   \n",
       "3                            Hallmark            0  37.783771 -122.407377   \n",
       "4  Sorabol Korean BBQ & Asian Noodles            0  37.784601 -122.406328   \n",
       "\n",
       "            Store_Category  Category_Index  \n",
       "0          Qsr Restaurants               9  \n",
       "1          Qsr Restaurants               9  \n",
       "2  Apparel And Accessories               0  \n",
       "3     Office Supply Stores               8  \n",
       "4          Qsr Restaurants               9  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "storemap['Category_Index'] = storemap['Store_Category'].map(Store_Categories_mapping).astype(int)\n",
    "storemap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0.      ,   37.784091, -122.406813],\n",
       "       [   0.      ,   37.784596, -122.40621 ],\n",
       "       [   0.      ,   37.784396, -122.406587],\n",
       "       [   0.      ,   37.783771, -122.407377],\n",
       "       [   0.      ,   37.784601, -122.406328],\n",
       "       [   0.      ,   37.784534, -122.406025],\n",
       "       [   0.      ,   37.783944, -122.407537],\n",
       "       [   0.      ,   37.784447, -122.406224],\n",
       "       [   0.      ,   37.784161, -122.4069  ],\n",
       "       [   0.      ,   37.784808, -122.406501],\n",
       "       [   0.      ,   37.784569, -122.406141],\n",
       "       [   0.      ,   37.78467 , -122.406679],\n",
       "       [   0.      ,   37.784126, -122.407061],\n",
       "       [   0.      ,   37.784159, -122.40635 ],\n",
       "       [   0.      ,   37.783959, -122.40691 ],\n",
       "       [   0.      ,   37.784411, -122.406709],\n",
       "       [   0.      ,   37.784511, -122.405956],\n",
       "       [   0.      ,   37.784071, -122.406885],\n",
       "       [   0.      ,   37.784591, -122.406782],\n",
       "       [   0.      ,   37.783778, -122.407161],\n",
       "       [   0.      ,   37.784053, -122.407307],\n",
       "       [   0.      ,   37.783893, -122.407389],\n",
       "       [   0.      ,   37.784222, -122.406978],\n",
       "       [   0.      ,   37.783953, -122.407318],\n",
       "       [   0.      ,   37.784505, -122.406555],\n",
       "       [   0.      ,   37.783767, -122.40707 ],\n",
       "       [   0.      ,   37.784106, -122.407189],\n",
       "       [   0.      ,   37.784292, -122.40601 ],\n",
       "       [   0.      ,   37.784449, -122.406665],\n",
       "       [   0.      ,   37.784353, -122.406773],\n",
       "       [   0.      ,   37.784294, -122.406164],\n",
       "       [   0.      ,   37.78432 , -122.406195],\n",
       "       [   0.      ,   37.783944, -122.407175],\n",
       "       [   0.      ,   37.783831, -122.406941],\n",
       "       [   0.      ,   37.784312, -122.405822],\n",
       "       [   0.      ,   37.783887, -122.405783],\n",
       "       [   0.      ,   37.784208, -122.40743 ],\n",
       "       [   0.      ,   37.784505, -122.406399],\n",
       "       [   0.      ,   37.784625, -122.406417],\n",
       "       [   0.      ,   37.784123, -122.40695 ],\n",
       "       [   0.      ,   37.78413 , -122.407374],\n",
       "       [   0.      ,   37.784037, -122.406937],\n",
       "       [   0.      ,   37.784457, -122.406819],\n",
       "       [   0.      ,   37.784415, -122.405846],\n",
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       "       [   1.      ,   37.784466, -122.406961],\n",
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       "       [   1.      ,   37.78373 , -122.407061],\n",
       "       [   1.      ,   37.783909, -122.407393],\n",
       "       [   1.      ,   37.78381 , -122.407336],\n",
       "       [   1.      ,   37.783864, -122.406836],\n",
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       "       [   2.      ,   37.784559, -122.40627 ],\n",
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       "       [   2.      ,   37.784267, -122.406528],\n",
       "       [   2.      ,   37.784046, -122.406884],\n",
       "       [   2.      ,   37.784252, -122.406942],\n",
       "       [   2.      ,   37.784387, -122.406732],\n",
       "       [   2.      ,   37.783739, -122.407271],\n",
       "       [   2.      ,   37.783745, -122.407149],\n",
       "       [   2.      ,   37.784385, -122.406276],\n",
       "       [   2.      ,   37.784195, -122.407081],\n",
       "       [   2.      ,   37.784433, -122.40608 ],\n",
       "       [   2.      ,   37.784031, -122.406181],\n",
       "       [   2.      ,   37.783852, -122.405805],\n",
       "       [   2.      ,   37.783931, -122.406086],\n",
       "       [   2.      ,   37.784484, -122.406502],\n",
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       "       [   2.      ,   37.784555, -122.406814],\n",
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       "       [   2.      ,   37.784185, -122.406432],\n",
       "       [   2.      ,   37.783864, -122.406924],\n",
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       "       [   2.      ,   37.784316, -122.40656 ],\n",
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       "       [   3.      ,   37.784042, -122.406901],\n",
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       "       [   3.      ,   37.78435 , -122.406064],\n",
       "       [   3.      ,   37.784105, -122.406326],\n",
       "       [   3.      ,   37.784615, -122.406862],\n",
       "       [   3.      ,   37.784428, -122.406643],\n",
       "       [   3.      ,   37.784447, -122.406809],\n",
       "       [   3.      ,   37.783986, -122.406922],\n",
       "       [   3.      ,   37.783839, -122.406836],\n",
       "       [   3.      ,   37.783812, -122.406021],\n",
       "       [   3.      ,   37.784133, -122.407419],\n",
       "       [   3.      ,   37.783752, -122.407371],\n",
       "       [   3.      ,   37.78429 , -122.406561],\n",
       "       [   3.      ,   37.783737, -122.407253],\n",
       "       [   3.      ,   37.78424 , -122.406455],\n",
       "       [   3.      ,   37.784101, -122.406854],\n",
       "       [   3.      ,   37.783871, -122.407073],\n",
       "       [   3.      ,   37.784153, -122.405871],\n",
       "       [   3.      ,   37.783745, -122.405601],\n",
       "       [   3.      ,   37.784191, -122.407111],\n",
       "       [   3.      ,   37.784267, -122.405933],\n",
       "       [   3.      ,   37.784386, -122.406589],\n",
       "       [   3.      ,   37.78454 , -122.406132],\n",
       "       [   3.      ,   37.783912, -122.407474],\n",
       "       [   3.      ,   37.784663, -122.406313],\n",
       "       [   3.      ,   37.784135, -122.406406],\n",
       "       [   3.      ,   37.783957, -122.406249],\n",
       "       [   3.      ,   37.783814, -122.407399],\n",
       "       [   3.      ,   37.784197, -122.40698 ],\n",
       "       [   3.      ,   37.784701, -122.406758],\n",
       "       [   3.      ,   37.78436 , -122.406786],\n",
       "       [   4.      ,   37.78442 , -122.406832],\n",
       "       [   4.      ,   37.784702, -122.406248],\n",
       "       [   4.      ,   37.784206, -122.406528],\n",
       "       [   4.      ,   37.784178, -122.405776],\n",
       "       [   4.      ,   37.783859, -122.40635 ],\n",
       "       [   4.      ,   37.784539, -122.406081],\n",
       "       [   4.      ,   37.78382 , -122.407311],\n",
       "       [   4.      ,   37.783802, -122.405736],\n",
       "       [   5.      ,   37.784076, -122.407307],\n",
       "       [   5.      ,   37.78389 , -122.405736],\n",
       "       [   5.      ,   37.784204, -122.406574],\n",
       "       [   6.      ,   37.78409 , -122.407309],\n",
       "       [   7.      ,   37.784073, -122.407289],\n",
       "       [   8.      ,   37.784087, -122.407307]])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = storemap.drop(['Store_Name', 'Store_Category' ,'Category_Index'],axis=1).as_matrix().astype(np.float)\n",
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [-4.24108586e-01,  1.08009426e+00, -2.21927721e-01],\n",
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       "       [-4.24108586e-01,  1.44998271e+00, -3.75112964e-01],\n",
       "       [-4.24108586e-01,  9.52188910e-01, -5.32495062e-01],\n",
       "       [-4.24108586e-01,  9.66016516e-01, -1.10711038e-01],\n",
       "       [-4.24108586e-01, -1.59209051e+00, -7.42337861e-01],\n",
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       "       [-4.24108586e-01, -1.31553840e+00, -1.31940556e+00],\n",
       "       [-4.24108586e-01, -1.12886573e+00, -2.70191564e-01],\n",
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       "       [-4.24108586e-01, -1.25677108e+00, -6.64696025e-01],\n",
       "       [-4.24108586e-01, -4.96252772e-01, -6.87778733e-01],\n",
       "       [-4.24108586e-01,  7.13662715e-01,  1.17352689e+00],\n",
       "       [-4.24108586e-01, -1.92003800e-02,  6.88790024e-01],\n",
       "       [-4.24108586e-01,  7.10205813e-01, -3.93644864e-02],\n",
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       "       [ 2.40061464e-01, -1.54369389e+00, -1.29212599e+00],\n",
       "       [ 2.40061464e-01,  1.12157708e+00,  1.02663693e+00],\n",
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       "       [ 2.40061464e-01, -1.54023699e+00, -9.26999523e-01],\n",
       "       [ 2.40061464e-01,  6.72179898e-01,  9.04928106e-01],\n",
       "       [ 2.40061464e-01,  1.53686339e-02, -7.84306420e-01],\n",
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       "       [ 2.40061464e-01, -5.51563194e-01,  1.10427876e+00],\n",
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       "       [ 2.40061464e-01, -8.97253333e-01,  1.30362942e+00],\n",
       "       [ 2.40061464e-01,  1.01441314e+00,  4.30683382e-01],\n",
       "       [ 2.40061464e-01, -1.61283192e+00, -3.89801959e-01],\n",
       "       [ 2.40061464e-01,  1.25985313e+00, -2.24026149e-01],\n",
       "       [ 2.40061464e-01, -1.23948657e+00, -6.58400741e-01],\n",
       "       [ 2.40061464e-01, -8.17744601e-01, -1.67823674e+00],\n",
       "       [ 2.40061464e-01, -1.92003800e-02,  5.77573341e-01],\n",
       "       [ 2.40061464e-01, -1.12886573e+00, -4.54853227e-01],\n",
       "       [ 2.40061464e-01, -8.45399812e-01, -1.86254445e-01],\n",
       "       [ 2.40061464e-01,  4.33653702e-01,  3.08974559e-01],\n",
       "       [ 2.40061464e-01, -6.41400981e-02, -5.40534823e-02],\n",
       "       [ 2.40061464e-01,  2.78093139e-01,  1.85167308e-01],\n",
       "       [ 2.40061464e-01,  3.29946660e-01,  1.48199580e+00],\n",
       "       [ 9.04231513e-01, -5.13537279e-01, -4.06589383e-01],\n",
       "       [ 9.04231513e-01, -1.94123755e+00, -9.37491663e-01],\n",
       "       [ 9.04231513e-01, -1.55406460e+00, -4.08687811e-01],\n",
       "       [ 9.04231513e-01,  1.51220694e+00,  1.09168820e+00],\n",
       "       [ 9.04231513e-01,  5.51188349e-01,  1.34979484e+00],\n",
       "       [ 9.04231513e-01, -2.95752491e-01,  8.00006707e-01],\n",
       "       [ 9.04231513e-01,  1.46726722e+00, -3.24750692e-01],\n",
       "       [ 9.04231513e-01,  8.20826658e-01,  1.34805036e-01],\n",
       "       [ 9.04231513e-01,  8.86507784e-01, -2.13534009e-01],\n",
       "       [ 9.04231513e-01, -7.07123756e-01, -4.50656371e-01],\n",
       "       [ 9.04231513e-01, -1.21528826e+00, -2.70191564e-01],\n",
       "       [ 9.04231513e-01, -1.30862460e+00,  1.44002724e+00],\n",
       "       [ 9.04231513e-01, -1.98959252e-01, -1.49357508e+00],\n",
       "       [ 9.04231513e-01, -1.51603868e+00, -1.39285054e+00],\n",
       "       [ 9.04231513e-01,  3.43774266e-01,  3.06876131e-01],\n",
       "       [ 9.04231513e-01, -1.56789220e+00, -1.14523603e+00],\n",
       "       [ 9.04231513e-01,  1.70929196e-01,  5.29309497e-01],\n",
       "       [ 9.04231513e-01, -3.09580097e-01, -3.07963268e-01],\n",
       "       [ 9.04231513e-01, -1.10466742e+00, -7.67518996e-01],\n",
       "       [ 9.04231513e-01, -1.29821224e-01,  1.75479144e+00],\n",
       "       [ 9.04231513e-01, -1.54023699e+00,  2.32136699e+00],\n",
       "       [ 9.04231513e-01,  1.54102834e-03, -8.47259260e-01],\n",
       "       [ 9.04231513e-01,  2.64265534e-01,  1.62468890e+00],\n",
       "       [ 9.04231513e-01,  6.75636799e-01,  2.48120147e-01],\n",
       "       [ 9.04231513e-01,  1.20799961e+00,  1.20710174e+00],\n",
       "       [ 9.04231513e-01, -9.62934459e-01, -1.60898862e+00],\n",
       "       [ 9.04231513e-01,  1.63319848e+00,  8.27286270e-01],\n",
       "       [ 9.04231513e-01, -1.92045449e-01,  6.32132468e-01],\n",
       "       [ 9.04231513e-01, -8.07373897e-01,  9.61585661e-01],\n",
       "       [ 9.04231513e-01, -1.30171080e+00, -1.45160652e+00],\n",
       "       [ 9.04231513e-01,  2.22824367e-02, -5.72365194e-01],\n",
       "       [ 9.04231513e-01,  1.76456074e+00, -1.06514182e-01],\n",
       "       [ 9.04231513e-01,  5.85757363e-01, -1.65270165e-01],\n",
       "       [ 1.56840156e+00,  7.93171447e-01, -2.61797853e-01],\n",
       "       [ 1.56840156e+00,  1.76801764e+00,  9.63684089e-01],\n",
       "       [ 1.56840156e+00,  5.33945492e-02,  3.76124254e-01],\n",
       "       [ 1.56840156e+00, -4.33986897e-02,  1.95414210e+00],\n",
       "       [ 1.56840156e+00, -1.14615023e+00,  7.49644435e-01],\n",
       "       [ 1.56840156e+00,  1.20454271e+00,  1.31412156e+00],\n",
       "       [ 1.56840156e+00, -1.28096939e+00, -1.26694486e+00],\n",
       "       [ 1.56840156e+00, -1.34319361e+00,  2.03807922e+00],\n",
       "       [ 2.23257161e+00, -3.96002631e-01, -1.25855114e+00],\n",
       "       [ 2.23257161e+00, -1.03898629e+00,  2.03807922e+00],\n",
       "       [ 2.23257161e+00,  4.64807464e-02,  2.79596567e-01],\n",
       "       [ 2.89674166e+00, -3.47606012e-01, -1.26274800e+00],\n",
       "       [ 3.56091171e+00, -4.06373336e-01, -1.22077944e+00],\n",
       "       [ 4.22508176e+00, -3.57976716e-01, -1.25855114e+00]])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert into matrix having mean= 0 and std dev= 1\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler()\n",
    "X2 = scaler.fit_transform(X2)\n",
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'Qsr Restaurants', u'Qsr Restaurants', u'Apparel And Accessories',\n",
       "       u'Office Supply Stores', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Apparel And Accessories', u'Qsr Restaurants', u'Misc.',\n",
       "       u'Shoe Stores', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Apparel And Accessories', u'Casual Restaurants',\n",
       "       u'Apparel And Accessories', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Apparel And Accessories', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Furniture And Decor', u'Sports Stores', u'Toy Stores',\n",
       "       u'Qsr Restaurants', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Shoe Stores', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Toy Stores', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Qsr Restaurants', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Department Stores', u'Sports Stores', u'Qsr Restaurants',\n",
       "       u'Qsr Restaurants', u'Qsr Restaurants', u'Misc.',\n",
       "       u'Qsr Restaurants', u'Furniture And Decor', u'Qsr Restaurants',\n",
       "       u'Sports Stores', u'Qsr Restaurants', u'Qsr Restaurants',\n",
       "       u'Qsr Restaurants', u'Specialty Store', u'Apparel And Accessories',\n",
       "       u'Department Stores', u'Misc.', u'Furniture And Decor', u'Misc.',\n",
       "       u'Apparel And Accessories', u'Furniture And Decor',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel and Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel and Accessories',\n",
       "       u'Apparel And Accessories', u'Misc.', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Furniture And Decor', u'Misc.',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories', u'Misc.',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Furniture And Decor', u'Apparel And Accessories', u'Misc.',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Qsr Restaurants', u'Furniture And Decor', u'Misc.',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel and Accessories', u'Apparel And Accessories',\n",
       "       u'Furniture And Decor', u'Furniture And Decor',\n",
       "       u'Apparel And Accessories', u'Qsr Restaurants',\n",
       "       u'Apparel And Accessories', u'Furniture And Decor',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Shoe Stores', u'Sports Stores', u'Apparel And Accessories',\n",
       "       u'Misc.', u'Furniture And Decor', u'Apparel And Accessories',\n",
       "       u'Cellular Phone Stores', u'Department Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Department Stores', u'Sports Stores', u'Apparel and Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories', u'Misc.',\n",
       "       u'Apparel And Accessories', u'Electronics Stores',\n",
       "       u'Apparel and Accessories', u'Apparel And Accessories',\n",
       "       u'Furniture And Decor', u'Shoe Stores', u'Qsr Restaurants',\n",
       "       u'Apparel And Accessories', u'Shoe Stores',\n",
       "       u'Apparel And Accessories', u'Office Supply Stores',\n",
       "       u'Apparel and Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Qsr Restaurants', u'Apparel And Accessories', u'Shoe Stores',\n",
       "       u'Sports Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Furniture And Decor', u'Misc.',\n",
       "       u'Furniture And Decor', u'Shoe Stores', u'Apparel and Accessories',\n",
       "       u'Electronics Stores', u'Toy Stores', u'Department Stores',\n",
       "       u'Apparel And Accessories', u'Shoe Stores', u'Furniture And Decor',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Furniture And Decor',\n",
       "       u'Apparel And Accessories', u'Qsr Restaurants', u'Shoe Stores',\n",
       "       u'Furniture And Decor', u'Apparel And Accessories',\n",
       "       u'Qsr Restaurants', u'Furniture And Decor', u'Qsr Restaurants',\n",
       "       u'Specialty Store', u'Misc.', u'Electronics Stores',\n",
       "       u'Department Stores', u'Department Stores', u'Department Stores',\n",
       "       u'Misc.', u'Misc.', u'Department Stores', u'Department Stores',\n",
       "       u'Department Stores'], dtype=object)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = storemap['Store_Category'].as_matrix().astype(object)\n",
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stratified_cv(X, y, clf_class, shuffle=True, n_folds=2, **kwargs):\n",
    "    stratified_k_fold = cross_validation.StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle)\n",
    "    y_pred = y.copy()\n",
    "    for ii, jj in stratified_k_fold:\n",
    "        X_train, X_test = X[ii], X[jj]\n",
    "        y_train = y[ii]\n",
    "        clf = clf_class(**kwargs)\n",
    "        clf.fit(X_train,y_train)\n",
    "        y_pred[jj] = clf.predict(X_test)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passive Aggressive Classifier: 0.30\n",
      "Gradient Boosting Classifier:  0.36\n",
      "Support vector machine(SVM):   0.48\n",
      "Random Forest Classifier:      0.35\n",
      "K Nearest Neighbor Classifier: 0.39\n",
      "Logistic Regression:           0.28\n"
     ]
    }
   ],
   "source": [
    "print('Passive Aggressive Classifier: {:.2f}'.format(metrics.accuracy_score(y2, stratified_cv(X2, y2, linear_model.PassiveAggressiveClassifier))))\n",
    "print('Gradient Boosting Classifier:  {:.2f}'.format(metrics.accuracy_score(y2, stratified_cv(X2, y2, ensemble.GradientBoostingClassifier))))\n",
    "print('Support vector machine(SVM):   {:.2f}'.format(metrics.accuracy_score(y2, stratified_cv(X2, y2, svm.SVC))))\n",
    "print('Random Forest Classifier:      {:.2f}'.format(metrics.accuracy_score(y2, stratified_cv(X2, y2, ensemble.RandomForestClassifier))))\n",
    "print('K Nearest Neighbor Classifier: {:.2f}'.format(metrics.accuracy_score(y2, stratified_cv(X2, y2, neighbors.KNeighborsClassifier))))\n",
    "print('Logistic Regression:           {:.2f}'.format(metrics.accuracy_score(y2, stratified_cv(X2, y2, linear_model.LogisticRegression))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mean accuracy of SVM: 1.0'"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SVM TRAINING AND FITTING:\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf2 = svm.SVC(kernel='rbf', C=100, gamma=10)\n",
    "clf2.fit(X2, y2)\n",
    "\n",
    "\n",
    "# Training data features, skip the first column 'Survived'\n",
    "train_features = X2\n",
    "\n",
    "# 'Survived' column values\n",
    "train_target = y2\n",
    "\n",
    "# Fit the model to our training data\n",
    "clf2 = clf2.fit(train_features, train_target)\n",
    "score = clf2.score(train_features, train_target)\n",
    "\"Mean accuracy of SVM: {0}\".format(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>Shopper_ID</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Store_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>Cotton On</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shopper_ID  Floor_Index   latitude   longitude Store_Name\n",
       "0  Shopper_556            4  37.661463 -122.298673    TAP 415\n",
       "1  Shopper_556            1  37.784062 -122.406943    Kiehl's\n",
       "2  Shopper_556            4  37.784361 -122.406710    TAP 415\n",
       "3  Shopper_360            0  37.784341 -122.406891    Kiehl's\n",
       "4  Shopper_360            2  37.784017 -122.407307  Cotton On"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   4.        ,   37.66146302, -122.2986728 ],\n",
       "       [   1.        ,   37.78406211, -122.4069432 ],\n",
       "       [   4.        ,   37.78436125, -122.4067095 ],\n",
       "       ...,\n",
       "       [   2.        ,   36.0179502 , -116.704627  ],\n",
       "       [   1.        ,   37.78404745, -122.4069997 ],\n",
       "       [   0.        ,   37.78407819, -122.4070869 ]])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test2 = pinginfo.drop(['Shopper_ID', 'Store_Name'], axis=1).as_matrix().astype(np.float)\n",
    "X_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.83464372,  0.36209925, -0.40275272],\n",
       "       [-0.32798594,  0.42248019, -0.41922354],\n",
       "       [ 1.83464372,  0.42262752, -0.41918799],\n",
       "       ...,\n",
       "       [ 0.39289061, -0.44734271,  0.44825139],\n",
       "       [-0.32798594,  0.42247297, -0.41923214],\n",
       "       [-1.04886249,  0.42248811, -0.4192454 ]])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "test_data2 = scaler.fit_transform(X_test2)\n",
    "test_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_y2 = clf2.predict(test_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>Shopper_ID</th>\n",
       "      <th>Floor_Index</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Store_Name</th>\n",
       "      <th>Store_Category_visited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661463</td>\n",
       "      <td>-122.298673</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784062</td>\n",
       "      <td>-122.406943</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_556</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784361</td>\n",
       "      <td>-122.406710</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784341</td>\n",
       "      <td>-122.406891</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_360</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784017</td>\n",
       "      <td>-122.407307</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shopper_420</td>\n",
       "      <td>4</td>\n",
       "      <td>37.661624</td>\n",
       "      <td>-122.298459</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shopper_420</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783856</td>\n",
       "      <td>-122.407092</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shopper_245</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783970</td>\n",
       "      <td>-122.407194</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shopper_334</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783828</td>\n",
       "      <td>-122.406986</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shopper_205</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784219</td>\n",
       "      <td>-122.406896</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shopper_205</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784638</td>\n",
       "      <td>-122.406411</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shopper_530</td>\n",
       "      <td>1</td>\n",
       "      <td>35.085149</td>\n",
       "      <td>-113.663734</td>\n",
       "      <td>49ers Team Store</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shopper_530</td>\n",
       "      <td>0</td>\n",
       "      <td>36.541271</td>\n",
       "      <td>-118.435462</td>\n",
       "      <td>Jamba Juice</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shopper_359</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783961</td>\n",
       "      <td>-122.407034</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>35.239976</td>\n",
       "      <td>-114.220990</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784049</td>\n",
       "      <td>-122.407055</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shopper_189</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784084</td>\n",
       "      <td>-122.407044</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shopper_481</td>\n",
       "      <td>2</td>\n",
       "      <td>35.265054</td>\n",
       "      <td>-114.246672</td>\n",
       "      <td>H&amp;M Man</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784287</td>\n",
       "      <td>-122.406954</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>1</td>\n",
       "      <td>36.980523</td>\n",
       "      <td>-119.811229</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Shopper_155</td>\n",
       "      <td>4</td>\n",
       "      <td>37.759561</td>\n",
       "      <td>-122.385293</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shopper_287</td>\n",
       "      <td>0</td>\n",
       "      <td>37.656648</td>\n",
       "      <td>-122.006913</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shopper_536</td>\n",
       "      <td>2</td>\n",
       "      <td>34.031987</td>\n",
       "      <td>-110.253652</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shopper_344</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784028</td>\n",
       "      <td>-122.407106</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shopper_41</td>\n",
       "      <td>2</td>\n",
       "      <td>33.504061</td>\n",
       "      <td>-108.734063</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shopper_41</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783814</td>\n",
       "      <td>-122.407127</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Shopper_122</td>\n",
       "      <td>4</td>\n",
       "      <td>36.066776</td>\n",
       "      <td>-116.842850</td>\n",
       "      <td>M.Y. China</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Shopper_51</td>\n",
       "      <td>0</td>\n",
       "      <td>37.720868</td>\n",
       "      <td>-122.343518</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Shopper_51</td>\n",
       "      <td>1</td>\n",
       "      <td>35.265134</td>\n",
       "      <td>-114.246611</td>\n",
       "      <td>49ers Team Store</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Shopper_68</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783840</td>\n",
       "      <td>-122.407236</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107605</th>\n",
       "      <td>Shopper_454</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783982</td>\n",
       "      <td>-122.406909</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107606</th>\n",
       "      <td>Shopper_278</td>\n",
       "      <td>3</td>\n",
       "      <td>30.227258</td>\n",
       "      <td>-97.925617</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107607</th>\n",
       "      <td>Shopper_318</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784039</td>\n",
       "      <td>-122.406977</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107608</th>\n",
       "      <td>Shopper_440</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784117</td>\n",
       "      <td>-122.407263</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107609</th>\n",
       "      <td>Shopper_363</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784069</td>\n",
       "      <td>-122.407386</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107610</th>\n",
       "      <td>Shopper_339</td>\n",
       "      <td>0</td>\n",
       "      <td>37.783989</td>\n",
       "      <td>-122.407159</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107611</th>\n",
       "      <td>Shopper_47</td>\n",
       "      <td>0</td>\n",
       "      <td>31.487016</td>\n",
       "      <td>-102.005697</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107612</th>\n",
       "      <td>Shopper_320</td>\n",
       "      <td>2</td>\n",
       "      <td>37.781113</td>\n",
       "      <td>-122.404167</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107613</th>\n",
       "      <td>Shopper_4</td>\n",
       "      <td>2</td>\n",
       "      <td>37.784046</td>\n",
       "      <td>-122.407011</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107614</th>\n",
       "      <td>Shopper_398</td>\n",
       "      <td>2</td>\n",
       "      <td>33.061211</td>\n",
       "      <td>-107.106029</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107615</th>\n",
       "      <td>Shopper_154</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784085</td>\n",
       "      <td>-122.406878</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107616</th>\n",
       "      <td>Shopper_459</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784052</td>\n",
       "      <td>-122.407107</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107617</th>\n",
       "      <td>Shopper_459</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784471</td>\n",
       "      <td>-122.406800</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107618</th>\n",
       "      <td>Shopper_419</td>\n",
       "      <td>2</td>\n",
       "      <td>37.743028</td>\n",
       "      <td>-122.371079</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107619</th>\n",
       "      <td>Shopper_319</td>\n",
       "      <td>3</td>\n",
       "      <td>34.320329</td>\n",
       "      <td>-111.206542</td>\n",
       "      <td>abercrombie kids</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107620</th>\n",
       "      <td>Shopper_319</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784294</td>\n",
       "      <td>-122.406786</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107621</th>\n",
       "      <td>Shopper_319</td>\n",
       "      <td>2</td>\n",
       "      <td>34.315878</td>\n",
       "      <td>-111.249350</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107622</th>\n",
       "      <td>Shopper_468</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783898</td>\n",
       "      <td>-122.407144</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107623</th>\n",
       "      <td>Shopper_244</td>\n",
       "      <td>3</td>\n",
       "      <td>37.781999</td>\n",
       "      <td>-122.405348</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107624</th>\n",
       "      <td>Shopper_373</td>\n",
       "      <td>3</td>\n",
       "      <td>37.783965</td>\n",
       "      <td>-122.407176</td>\n",
       "      <td>Solstice</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107625</th>\n",
       "      <td>Shopper_376</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784045</td>\n",
       "      <td>-122.407060</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107626</th>\n",
       "      <td>Shopper_100</td>\n",
       "      <td>0</td>\n",
       "      <td>35.219027</td>\n",
       "      <td>-114.115492</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107627</th>\n",
       "      <td>Shopper_100</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784057</td>\n",
       "      <td>-122.407213</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107628</th>\n",
       "      <td>Shopper_28</td>\n",
       "      <td>2</td>\n",
       "      <td>37.783934</td>\n",
       "      <td>-122.407144</td>\n",
       "      <td>Cotton On</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107629</th>\n",
       "      <td>Shopper_76</td>\n",
       "      <td>1</td>\n",
       "      <td>37.783984</td>\n",
       "      <td>-122.407237</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107630</th>\n",
       "      <td>Shopper_317</td>\n",
       "      <td>4</td>\n",
       "      <td>28.338261</td>\n",
       "      <td>-91.805123</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107631</th>\n",
       "      <td>Shopper_295</td>\n",
       "      <td>4</td>\n",
       "      <td>37.784115</td>\n",
       "      <td>-122.406981</td>\n",
       "      <td>TAP 415</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107632</th>\n",
       "      <td>Shopper_460</td>\n",
       "      <td>2</td>\n",
       "      <td>36.017950</td>\n",
       "      <td>-116.704627</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107633</th>\n",
       "      <td>Shopper_190</td>\n",
       "      <td>1</td>\n",
       "      <td>37.784047</td>\n",
       "      <td>-122.407000</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107634</th>\n",
       "      <td>Shopper_53</td>\n",
       "      <td>0</td>\n",
       "      <td>37.784078</td>\n",
       "      <td>-122.407087</td>\n",
       "      <td>Kiehl's</td>\n",
       "      <td>Toy Stores</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107635 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Shopper_ID  Floor_Index   latitude   longitude        Store_Name  \\\n",
       "0       Shopper_556            4  37.661463 -122.298673           TAP 415   \n",
       "1       Shopper_556            1  37.784062 -122.406943           Kiehl's   \n",
       "2       Shopper_556            4  37.784361 -122.406710           TAP 415   \n",
       "3       Shopper_360            0  37.784341 -122.406891           Kiehl's   \n",
       "4       Shopper_360            2  37.784017 -122.407307         Cotton On   \n",
       "5       Shopper_420            4  37.661624 -122.298459           TAP 415   \n",
       "6       Shopper_420            1  37.783856 -122.407092           Kiehl's   \n",
       "7       Shopper_245            2  37.783970 -122.407194         Cotton On   \n",
       "8       Shopper_334            2  37.783828 -122.406986         Cotton On   \n",
       "9       Shopper_205            1  37.784219 -122.406896           Kiehl's   \n",
       "10      Shopper_205            0  37.784638 -122.406411           Kiehl's   \n",
       "11      Shopper_530            1  35.085149 -113.663734  49ers Team Store   \n",
       "12      Shopper_530            0  36.541271 -118.435462       Jamba Juice   \n",
       "13      Shopper_359            0  37.783961 -122.407034           Kiehl's   \n",
       "14      Shopper_189            0  35.239976 -114.220990    Bloomingdale's   \n",
       "15      Shopper_189            0  37.784049 -122.407055           Kiehl's   \n",
       "16      Shopper_189            0  37.784084 -122.407044           Kiehl's   \n",
       "17      Shopper_481            2  35.265054 -114.246672           H&M Man   \n",
       "18      Shopper_155            4  37.784287 -122.406954           TAP 415   \n",
       "19      Shopper_155            1  36.980523 -119.811229           Kiehl's   \n",
       "20      Shopper_155            4  37.759561 -122.385293           TAP 415   \n",
       "21      Shopper_287            0  37.656648 -122.006913           Kiehl's   \n",
       "22      Shopper_536            2  34.031987 -110.253652    Bloomingdale's   \n",
       "23      Shopper_344            2  37.784028 -122.407106         Cotton On   \n",
       "24       Shopper_41            2  33.504061 -108.734063    Bloomingdale's   \n",
       "25       Shopper_41            3  37.783814 -122.407127          Solstice   \n",
       "26      Shopper_122            4  36.066776 -116.842850        M.Y. China   \n",
       "27       Shopper_51            0  37.720868 -122.343518           Kiehl's   \n",
       "28       Shopper_51            1  35.265134 -114.246611  49ers Team Store   \n",
       "29       Shopper_68            1  37.783840 -122.407236           Kiehl's   \n",
       "...             ...          ...        ...         ...               ...   \n",
       "107605  Shopper_454            1  37.783982 -122.406909           Kiehl's   \n",
       "107606  Shopper_278            3  30.227258  -97.925617    Bloomingdale's   \n",
       "107607  Shopper_318            2  37.784039 -122.406977         Cotton On   \n",
       "107608  Shopper_440            0  37.784117 -122.407263           Kiehl's   \n",
       "107609  Shopper_363            0  37.784069 -122.407386           Kiehl's   \n",
       "107610  Shopper_339            0  37.783989 -122.407159           Kiehl's   \n",
       "107611   Shopper_47            0  31.487016 -102.005697    Bloomingdale's   \n",
       "107612  Shopper_320            2  37.781113 -122.404167           Kiehl's   \n",
       "107613    Shopper_4            2  37.784046 -122.407011         Cotton On   \n",
       "107614  Shopper_398            2  33.061211 -107.106029    Bloomingdale's   \n",
       "107615  Shopper_154            1  37.784085 -122.406878           Kiehl's   \n",
       "107616  Shopper_459            0  37.784052 -122.407107           Kiehl's   \n",
       "107617  Shopper_459            0  37.784471 -122.406800           Kiehl's   \n",
       "107618  Shopper_419            2  37.743028 -122.371079           Kiehl's   \n",
       "107619  Shopper_319            3  34.320329 -111.206542  abercrombie kids   \n",
       "107620  Shopper_319            0  37.784294 -122.406786           Kiehl's   \n",
       "107621  Shopper_319            2  34.315878 -111.249350    Bloomingdale's   \n",
       "107622  Shopper_468            2  37.783898 -122.407144         Cotton On   \n",
       "107623  Shopper_244            3  37.781999 -122.405348          Solstice   \n",
       "107624  Shopper_373            3  37.783965 -122.407176          Solstice   \n",
       "107625  Shopper_376            1  37.784045 -122.407060           Kiehl's   \n",
       "107626  Shopper_100            0  35.219027 -114.115492    Bloomingdale's   \n",
       "107627  Shopper_100            0  37.784057 -122.407213           Kiehl's   \n",
       "107628   Shopper_28            2  37.783934 -122.407144         Cotton On   \n",
       "107629   Shopper_76            1  37.783984 -122.407237           Kiehl's   \n",
       "107630  Shopper_317            4  28.338261  -91.805123    Bloomingdale's   \n",
       "107631  Shopper_295            4  37.784115 -122.406981           TAP 415   \n",
       "107632  Shopper_460            2  36.017950 -116.704627         Microsoft   \n",
       "107633  Shopper_190            1  37.784047 -122.407000           Kiehl's   \n",
       "107634   Shopper_53            0  37.784078 -122.407087           Kiehl's   \n",
       "\n",
       "         Store_Category_visited  \n",
       "0       Apparel And Accessories  \n",
       "1       Apparel And Accessories  \n",
       "2       Apparel And Accessories  \n",
       "3                    Toy Stores  \n",
       "4       Apparel And Accessories  \n",
       "5       Apparel And Accessories  \n",
       "6       Apparel And Accessories  \n",
       "7       Apparel And Accessories  \n",
       "8       Apparel And Accessories  \n",
       "9       Apparel And Accessories  \n",
       "10                   Toy Stores  \n",
       "11      Apparel And Accessories  \n",
       "12              Qsr Restaurants  \n",
       "13                   Toy Stores  \n",
       "14      Apparel And Accessories  \n",
       "15                   Toy Stores  \n",
       "16                   Toy Stores  \n",
       "17      Apparel And Accessories  \n",
       "18      Apparel And Accessories  \n",
       "19      Apparel And Accessories  \n",
       "20      Apparel And Accessories  \n",
       "21                   Toy Stores  \n",
       "22      Apparel And Accessories  \n",
       "23      Apparel And Accessories  \n",
       "24      Apparel And Accessories  \n",
       "25      Apparel And Accessories  \n",
       "26      Apparel And Accessories  \n",
       "27                   Toy Stores  \n",
       "28      Apparel And Accessories  \n",
       "29      Apparel And Accessories  \n",
       "...                         ...  \n",
       "107605  Apparel And Accessories  \n",
       "107606  Apparel And Accessories  \n",
       "107607  Apparel And Accessories  \n",
       "107608               Toy Stores  \n",
       "107609               Toy Stores  \n",
       "107610               Toy Stores  \n",
       "107611  Apparel And Accessories  \n",
       "107612  Apparel And Accessories  \n",
       "107613  Apparel And Accessories  \n",
       "107614  Apparel And Accessories  \n",
       "107615  Apparel And Accessories  \n",
       "107616               Toy Stores  \n",
       "107617               Toy Stores  \n",
       "107618  Apparel And Accessories  \n",
       "107619  Apparel And Accessories  \n",
       "107620               Toy Stores  \n",
       "107621  Apparel And Accessories  \n",
       "107622  Apparel And Accessories  \n",
       "107623  Apparel And Accessories  \n",
       "107624  Apparel And Accessories  \n",
       "107625  Apparel And Accessories  \n",
       "107626  Apparel And Accessories  \n",
       "107627               Toy Stores  \n",
       "107628  Apparel And Accessories  \n",
       "107629  Apparel And Accessories  \n",
       "107630  Apparel And Accessories  \n",
       "107631  Apparel And Accessories  \n",
       "107632  Apparel And Accessories  \n",
       "107633  Apparel And Accessories  \n",
       "107634               Toy Stores  \n",
       "\n",
       "[107635 rows x 6 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinginfo['Store_Category_visited'] = pred_y2\n",
    "pinginfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "visiting_info2 = pinginfo.groupby(['Shopper_ID', 'Store_Category_visited']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Floor_Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_ID</th>\n",
       "      <th>Store_Category_visited</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Shopper_1</th>\n",
       "      <th>Apparel And Accessories</th>\n",
       "      <td>159.0</td>\n",
       "      <td>1.767296</td>\n",
       "      <td>1.238593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy Stores</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Shopper_10</th>\n",
       "      <th>Apparel And Accessories</th>\n",
       "      <td>153.0</td>\n",
       "      <td>2.875817</td>\n",
       "      <td>1.465930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc.</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qsr Restaurants</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Floor_Index                                \\\n",
       "                                         count      mean       std  min  25%   \n",
       "Shopper_ID Store_Category_visited                                              \n",
       "Shopper_1  Apparel And Accessories       159.0  1.767296  1.238593  0.0  1.0   \n",
       "           Toy Stores                     30.0  0.000000  0.000000  0.0  0.0   \n",
       "Shopper_10 Apparel And Accessories       153.0  2.875817  1.465930  0.0  1.0   \n",
       "           Misc.                           5.0  1.000000  0.000000  1.0  1.0   \n",
       "           Qsr Restaurants                 1.0  0.000000       NaN  0.0  0.0   \n",
       "\n",
       "                                                   \n",
       "                                    50%  75%  max  \n",
       "Shopper_ID Store_Category_visited                  \n",
       "Shopper_1  Apparel And Accessories  2.0  3.0  4.0  \n",
       "           Toy Stores               0.0  0.0  0.0  \n",
       "Shopper_10 Apparel And Accessories  4.0  4.0  4.0  \n",
       "           Misc.                    1.0  1.0  1.0  \n",
       "           Qsr Restaurants          0.0  0.0  0.0  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visiting_info2.drop(['latitude', 'longitude'], axis=1, inplace=True)\n",
    "visiting_info2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#visiting_info2.to_csv(r\"C:\\Users\\welcome\\Desktop\\visitinginfo2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>Shopper_ID</th>\n",
       "      <th>Store_Category_visited</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "      <td>159</td>\n",
       "      <td>1.767296</td>\n",
       "      <td>1.238593</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shopper_1</td>\n",
       "      <td>Toy Stores</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "      <td>153</td>\n",
       "      <td>2.875817</td>\n",
       "      <td>1.465930</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Misc.</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shopper_10</td>\n",
       "      <td>Qsr Restaurants</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Shopper_ID   Store_Category_visited  count      mean       std  min  25%  \\\n",
       "0   Shopper_1  Apparel And Accessories    159  1.767296  1.238593    0  1.0   \n",
       "1   Shopper_1               Toy Stores     30  0.000000  0.000000    0  0.0   \n",
       "2  Shopper_10  Apparel And Accessories    153  2.875817  1.465930    0  1.0   \n",
       "3  Shopper_10                    Misc.      5  1.000000  0.000000    1  1.0   \n",
       "4  Shopper_10          Qsr Restaurants      1  0.000000       NaN    0  0.0   \n",
       "\n",
       "   50%  75%  max  \n",
       "0  2.0  3.0    4  \n",
       "1  0.0  0.0    0  \n",
       "2  4.0  4.0    4  \n",
       "3  1.0  1.0    1  \n",
       "4  0.0  0.0    0  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visitinginfo2 = pd.read_csv(r\"C:\\Users\\welcome\\Desktop\\visitinginfo2.csv\")\n",
    "visitinginfo2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "df_freq_category = pinginfo.groupby(by='Shopper_ID').Store_Category_visited.apply(stats.mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store_Category_visited    ([Apparel And Accessories], [153])\n",
       "Name: Shopper_10, dtype: object"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freq_category= df_freq_category.to_frame()\n",
    "df_freq_category.iloc[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
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       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
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       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
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       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
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       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
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       "       u'Toy Stores', u'Apparel And Accessories', u'Toy Stores',\n",
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       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
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       "       u'Apparel And Accessories', u'Toy Stores',\n",
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       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Toy Stores', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Toy Stores',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories', u'Apparel And Accessories',\n",
       "       u'Apparel And Accessories'], dtype='<U23')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mostvisitedcategorylist= []\n",
    "for category in df_freq_category['Store_Category_visited']:\n",
    "    mostvisitedcategorylist.append(category[0][0])\n",
    "    \n",
    "mostvisitedcategory_array= np.array(mostvisitedcategorylist)\n",
    "mostvisitedcategory_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>Store_Category_visited</th>\n",
       "      <th>New_Store_Category_visited</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shopper_1</th>\n",
       "      <td>([Apparel And Accessories], [159])</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_10</th>\n",
       "      <td>([Apparel And Accessories], [153])</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_100</th>\n",
       "      <td>([Apparel And Accessories], [118])</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_101</th>\n",
       "      <td>([Apparel And Accessories], [143])</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_102</th>\n",
       "      <td>([Apparel And Accessories], [174])</td>\n",
       "      <td>Apparel And Accessories</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Store_Category_visited New_Store_Category_visited\n",
       "Shopper_ID                                                                \n",
       "Shopper_1    ([Apparel And Accessories], [159])    Apparel And Accessories\n",
       "Shopper_10   ([Apparel And Accessories], [153])    Apparel And Accessories\n",
       "Shopper_100  ([Apparel And Accessories], [118])    Apparel And Accessories\n",
       "Shopper_101  ([Apparel And Accessories], [143])    Apparel And Accessories\n",
       "Shopper_102  ([Apparel And Accessories], [174])    Apparel And Accessories"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mostvisitedcategory_array.astype(object)\n",
    "df_freq_category['New_Store_Category_visited']= mostvisitedcategory_array\n",
    "df_freq_category.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freq_category.to_csv(r\"C:\\Users\\welcome\\Desktop\\exl__svm2ndpart2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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>Store_Name</th>\n",
       "      <th>New_Store</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Shopper_1</th>\n",
       "      <td>([Bloomingdale's], [67])</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_10</th>\n",
       "      <td>([Kiehl's], [76])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_100</th>\n",
       "      <td>([Kiehl's], [99])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_101</th>\n",
       "      <td>([Kiehl's], [99])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_102</th>\n",
       "      <td>([TAP 415], [125])</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_103</th>\n",
       "      <td>([Kiehl's], [72])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_104</th>\n",
       "      <td>([Kiehl's], [118])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_105</th>\n",
       "      <td>([Kiehl's], [110])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_106</th>\n",
       "      <td>([Kiehl's], [123])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_107</th>\n",
       "      <td>([Kiehl's], [96])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_108</th>\n",
       "      <td>([Kiehl's], [166])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_109</th>\n",
       "      <td>([Kiehl's], [72])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_11</th>\n",
       "      <td>([Kiehl's], [107])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_110</th>\n",
       "      <td>([Kiehl's], [72])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_111</th>\n",
       "      <td>([Kiehl's], [103])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_112</th>\n",
       "      <td>([Kiehl's], [105])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_113</th>\n",
       "      <td>([Kiehl's], [137])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_114</th>\n",
       "      <td>([TAP 415], [89])</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_115</th>\n",
       "      <td>([Kiehl's], [56])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_116</th>\n",
       "      <td>([TAP 415], [166])</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_117</th>\n",
       "      <td>([Kiehl's], [154])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_118</th>\n",
       "      <td>([Kiehl's], [106])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_119</th>\n",
       "      <td>([Kiehl's], [144])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_12</th>\n",
       "      <td>([Kiehl's], [72])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_120</th>\n",
       "      <td>([Kiehl's], [111])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_121</th>\n",
       "      <td>([Kiehl's], [80])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_122</th>\n",
       "      <td>([Kiehl's], [72])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_123</th>\n",
       "      <td>([Kiehl's], [139])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_124</th>\n",
       "      <td>([Kiehl's], [126])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_125</th>\n",
       "      <td>([Kiehl's], [70])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_72</th>\n",
       "      <td>([Kiehl's], [84])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_73</th>\n",
       "      <td>([Kiehl's], [85])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_74</th>\n",
       "      <td>([Kiehl's], [120])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_75</th>\n",
       "      <td>([Kiehl's], [109])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_76</th>\n",
       "      <td>([Kiehl's], [181])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_77</th>\n",
       "      <td>([Kiehl's], [64])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_78</th>\n",
       "      <td>([Kiehl's], [61])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_79</th>\n",
       "      <td>([Kiehl's], [73])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_8</th>\n",
       "      <td>([Kiehl's], [95])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_80</th>\n",
       "      <td>([Kiehl's], [90])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_81</th>\n",
       "      <td>([Kiehl's], [96])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_82</th>\n",
       "      <td>([Kiehl's], [73])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_83</th>\n",
       "      <td>([Kiehl's], [105])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_84</th>\n",
       "      <td>([TAP 415], [85])</td>\n",
       "      <td>TAP 415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_85</th>\n",
       "      <td>([Kiehl's], [141])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_86</th>\n",
       "      <td>([Kiehl's], [76])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_87</th>\n",
       "      <td>([Kiehl's], [62])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_88</th>\n",
       "      <td>([Kiehl's], [77])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_89</th>\n",
       "      <td>([Kiehl's], [138])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_9</th>\n",
       "      <td>([Kiehl's], [119])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_90</th>\n",
       "      <td>([Kiehl's], [106])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_91</th>\n",
       "      <td>([Kiehl's], [128])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_92</th>\n",
       "      <td>([Kiehl's], [98])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_93</th>\n",
       "      <td>([Kiehl's], [52])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_94</th>\n",
       "      <td>([Kiehl's], [93])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_95</th>\n",
       "      <td>([Kiehl's], [121])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_96</th>\n",
       "      <td>([Kiehl's], [91])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_97</th>\n",
       "      <td>([Bloomingdale's], [59])</td>\n",
       "      <td>Bloomingdale's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_98</th>\n",
       "      <td>([Kiehl's], [115])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopper_99</th>\n",
       "      <td>([Kiehl's], [92])</td>\n",
       "      <td>Kiehl's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>567 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Store_Name       New_Store\n",
       "Shopper_ID                                           \n",
       "Shopper_1    ([Bloomingdale's], [67])  Bloomingdale's\n",
       "Shopper_10          ([Kiehl's], [76])         Kiehl's\n",
       "Shopper_100         ([Kiehl's], [99])         Kiehl's\n",
       "Shopper_101         ([Kiehl's], [99])         Kiehl's\n",
       "Shopper_102        ([TAP 415], [125])         TAP 415\n",
       "Shopper_103         ([Kiehl's], [72])         Kiehl's\n",
       "Shopper_104        ([Kiehl's], [118])         Kiehl's\n",
       "Shopper_105        ([Kiehl's], [110])         Kiehl's\n",
       "Shopper_106        ([Kiehl's], [123])         Kiehl's\n",
       "Shopper_107         ([Kiehl's], [96])         Kiehl's\n",
       "Shopper_108        ([Kiehl's], [166])         Kiehl's\n",
       "Shopper_109         ([Kiehl's], [72])         Kiehl's\n",
       "Shopper_11         ([Kiehl's], [107])         Kiehl's\n",
       "Shopper_110         ([Kiehl's], [72])         Kiehl's\n",
       "Shopper_111        ([Kiehl's], [103])         Kiehl's\n",
       "Shopper_112        ([Kiehl's], [105])         Kiehl's\n",
       "Shopper_113        ([Kiehl's], [137])         Kiehl's\n",
       "Shopper_114         ([TAP 415], [89])         TAP 415\n",
       "Shopper_115         ([Kiehl's], [56])         Kiehl's\n",
       "Shopper_116        ([TAP 415], [166])         TAP 415\n",
       "Shopper_117        ([Kiehl's], [154])         Kiehl's\n",
       "Shopper_118        ([Kiehl's], [106])         Kiehl's\n",
       "Shopper_119        ([Kiehl's], [144])         Kiehl's\n",
       "Shopper_12          ([Kiehl's], [72])         Kiehl's\n",
       "Shopper_120        ([Kiehl's], [111])         Kiehl's\n",
       "Shopper_121         ([Kiehl's], [80])         Kiehl's\n",
       "Shopper_122         ([Kiehl's], [72])         Kiehl's\n",
       "Shopper_123        ([Kiehl's], [139])         Kiehl's\n",
       "Shopper_124        ([Kiehl's], [126])         Kiehl's\n",
       "Shopper_125         ([Kiehl's], [70])         Kiehl's\n",
       "...                               ...             ...\n",
       "Shopper_72          ([Kiehl's], [84])         Kiehl's\n",
       "Shopper_73          ([Kiehl's], [85])         Kiehl's\n",
       "Shopper_74         ([Kiehl's], [120])         Kiehl's\n",
       "Shopper_75         ([Kiehl's], [109])         Kiehl's\n",
       "Shopper_76         ([Kiehl's], [181])         Kiehl's\n",
       "Shopper_77          ([Kiehl's], [64])         Kiehl's\n",
       "Shopper_78          ([Kiehl's], [61])         Kiehl's\n",
       "Shopper_79          ([Kiehl's], [73])         Kiehl's\n",
       "Shopper_8           ([Kiehl's], [95])         Kiehl's\n",
       "Shopper_80          ([Kiehl's], [90])         Kiehl's\n",
       "Shopper_81          ([Kiehl's], [96])         Kiehl's\n",
       "Shopper_82          ([Kiehl's], [73])         Kiehl's\n",
       "Shopper_83         ([Kiehl's], [105])         Kiehl's\n",
       "Shopper_84          ([TAP 415], [85])         TAP 415\n",
       "Shopper_85         ([Kiehl's], [141])         Kiehl's\n",
       "Shopper_86          ([Kiehl's], [76])         Kiehl's\n",
       "Shopper_87          ([Kiehl's], [62])         Kiehl's\n",
       "Shopper_88          ([Kiehl's], [77])         Kiehl's\n",
       "Shopper_89         ([Kiehl's], [138])         Kiehl's\n",
       "Shopper_9          ([Kiehl's], [119])         Kiehl's\n",
       "Shopper_90         ([Kiehl's], [106])         Kiehl's\n",
       "Shopper_91         ([Kiehl's], [128])         Kiehl's\n",
       "Shopper_92          ([Kiehl's], [98])         Kiehl's\n",
       "Shopper_93          ([Kiehl's], [52])         Kiehl's\n",
       "Shopper_94          ([Kiehl's], [93])         Kiehl's\n",
       "Shopper_95         ([Kiehl's], [121])         Kiehl's\n",
       "Shopper_96          ([Kiehl's], [91])         Kiehl's\n",
       "Shopper_97   ([Bloomingdale's], [59])  Bloomingdale's\n",
       "Shopper_98         ([Kiehl's], [115])         Kiehl's\n",
       "Shopper_99          ([Kiehl's], [92])         Kiehl's\n",
       "\n",
       "[567 rows x 2 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [df_freq, df_freq_category]\n",
    "result = pd.concat(frames, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv(r\"C:\\Users\\welcome\\Desktop\\exl__final22.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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