clustering_design.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
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      "text/plain": [
       "  age00_04 age05_09 age10_14 age15_19 age20_24 age25_29 age30_34 age35_39  \\\n",
       "0     1049     1005      829      811      851     1004     1369     1593   \n",
       "1     4492     3749     3869     3986     4085     4005     4285     4410   \n",
       "2     3622     3025     2836     2761     2833     2869     3346     3687   \n",
       "3      388      385      439      612      451      397      316      291   \n",
       "4      891      859      898     1162      819      753      756      831   \n",
       "\n",
       "  age40_44 age45_49   ...   trwhome trwmoto trwother trwpublic trwself  \\\n",
       "0     1444     1382   ...        70       0       54       586     350   \n",
       "1     4253     4385   ...       396       6       51      4834    2176   \n",
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       "3      408      559   ...        83       0       22       106      35   \n",
       "4      976     1117   ...       367       0       28       229     198   \n",
       "\n",
       "  two_bed_county two_bedindex vph1 vphgt1 vphnone  \n",
       "0           1654           90   40     51       9  \n",
       "1           1519           85   46     32      22  \n",
       "2           1288          112   42     48      10  \n",
       "3           1288          121   24     70       7  \n",
       "4           1288           95   27     66       7  \n",
       "\n",
       "[5 rows x 387 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "from pprint import pprint\n",
    "import json\n",
    "import pandas as pd\n",
    "import pymongo\n",
    "import ast\n",
    "import os\n",
    "\n",
    " # Create connection variable for Project3 database in MLab\n",
    "connMLAB = \"mongodb://jonathan:Biomed#101@ds137003.mlab.com:37003/project3\"\n",
    "\n",
    "\n",
    "# Pass connection to the pymongo instance.\n",
    "client = pymongo.MongoClient(connMLAB)\n",
    "\n",
    "# Connect to a database. Will create one if not already available.\n",
    "db = client.project3\n",
    "posts = db.zip_community_test5.find({\"response.result\":{'$exists':True}})\n",
    "community = {}\n",
    "keys = []\n",
    "for post in posts:\n",
    "    dict_temp = post[\"response\"][\"result\"][\"package\"][\"item\"][0]\n",
    "    \n",
    "    for key, value in dict_temp.items():\n",
    "        if key not in keys:\n",
    "            community.update([(key, [value])])\n",
    "            keys.append(key)\n",
    "            \n",
    "        else:\n",
    "            community[key].append(value)\n",
    "\n",
    "community_df = pd.DataFrame(community)\n",
    "community_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    zip_codes inccymedd medsaleprice trwpublic avg_prop_tax\n",
      "0       07001     72883       226000       586         7092\n",
      "1       07002     57787       260000      4834         9585\n",
      "2       07003     73824       265000      2851        10330\n",
      "3       07004     98000       411000       106         8913\n",
      "4       07005     96533       389500       229        10787\n",
      "5       07006    103740       490000       597        11883\n",
      "6       07008     63809       210000       559         6410\n",
      "7       07009    106214       416000       430        10049\n",
      "8       07010     61125       400000      2090         8095\n",
      "9       07012     81550       315000       392         9508\n",
      "10      07011     59924       255000      1134         8261\n",
      "11      07013     82080       315000       710         9843\n",
      "12      07014     88513       276000       153         8739\n",
      "13      07016    115262       426800      1284        10507\n",
      "14      07017     38314        85000      3814         8559\n",
      "15      07018     37911        95500      3546         8064\n",
      "16      07020    106084       545000      1036         7687\n",
      "17      07021    161680       793210        84        18817\n",
      "18      07022     53379       339000      1111         8680\n",
      "19      07023    120766       415000       394        11151\n",
      "20      07024     70538       450000      2958        10211\n",
      "21      07026     49138       297350       647         7394\n",
      "22      07027     78145       355000       161         9442\n",
      "23      07028    161848       653000       820        18414\n",
      "24      07029     57240       290000      1326         9636\n",
      "25      07030    112910       591000     14466         7651\n",
      "26      07031     73885       315000       635         9521\n",
      "27      07032     63557       260000      2418         9772\n",
      "28      07033     99484       300000       102         8294\n",
      "29      07034     80436       238000       190         5856\n",
      "..        ...       ...          ...       ...          ...\n",
      "569     08840    108175       348000      1245         9563\n",
      "570     08844    114664       350000       424         8725\n",
      "571     08846     81540       232000       213         7569\n",
      "572     08848     92566       271000        47         7681\n",
      "573     08850     97832       322000       134         9097\n",
      "574     08852     99520       374215       464         8761\n",
      "575     08853    121415       500000        64        10687\n",
      "576     08854     88557       265000      1550         7231\n",
      "577     08857     88602       315000      2175         7305\n",
      "578     08858    152146                      0        11813\n",
      "579     08859     81376       245196      1089         6708\n",
      "580     08861     47519       160000      1353         6925\n",
      "581     08863     78736       226000       466         7767\n",
      "582     08865     56276       134900       160         5171\n",
      "583     08867    124748       416500        39        10514\n",
      "584     08869     75262       272000        70         7161\n",
      "585     08872     80403       243000       579         6910\n",
      "586     08873     89194       279000      1307         7170\n",
      "587     08876     87756       307550       326         8669\n",
      "588     08879     71645       217500       711         6575\n",
      "589     08880     73379       215000        35         7735\n",
      "590     08882     64556       213000       319         5932\n",
      "591     08884     76556       256700       157         7497\n",
      "592     08886    107129       254500        29         7874\n",
      "593     08887     79734       170000        28         4671\n",
      "594     08889    124908       355950       110         9951\n",
      "595     08890     91230                      0             \n",
      "596     08901     44528       200500      2608         6025\n",
      "597     08902     81889       255000      1453         8258\n",
      "598     08904     75054       355000       812        10428\n",
      "\n",
      "[599 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "community_df.rename(columns={'geo_code':'zip_codes'}, inplace=True)\n",
    "community_input = community_df.loc[:,['zip_codes', 'inccymedd', 'medsaleprice', 'trwpublic', 'avg_prop_tax']]\n",
    "print(community_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "posts = db.poi_3.find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "poi = {}\n",
    "keys = []\n",
    "for post in posts:\n",
    "    dict_temp = post\n",
    "    \n",
    "    for key, value in dict_temp.items():\n",
    "        if key not in keys:\n",
    "            poi.update([(key, [value])])\n",
    "            keys.append(key)\n",
    "            \n",
    "        else:\n",
    "            poi[key].append(value)\n",
    "\n",
    "poi_df = pd.DataFrame(poi)\n",
    "poi_grouped = poi_df.groupby(['zip_code', 'business_category']).count()\n",
    "poi_unstacked = poi_grouped.unstack(level=-1, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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'07421', '07421', '07421', '07421', '07421', '07480', '07480', '07421', '07480', '07421', '07421', '07421', '07421', '07421', '07421', '07421', '07421', '07421', '07421', '07421', '07480', '07480', '07480', '07480', '07480', '07421', '07480', '07480', '07480', '07480', '07480', '07421', '07480', '07480', '10925', '07480', '07480', '07480', '07480', '07480', '07480', '07480', '07480', '10925', '07480', '10925', '07480', '07421', '07421', '07480', '07480', '07480', '07480', '07421', '07480', '07480', '07421', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07422', '07462', '07462', '07422', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07422', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07462', '07421', '07462', '07462', '07462', '07462', '07421', '07421', '07421', '10990', '07462', '10990', '10990', '07421', '10990', '10990', '10990', '07423', '07423', '07423', '07423', '07423', '07423', '07450', '07423', '07423', '07423', '07450', '07450', '07423', '07450', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07450', '07450', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07450', '07423', '07423', '07423', '07450', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07450', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07423', '07450', '07450', '07423', '07423', '07423', '07450', '07450', '07450', '07450', '07423', '07450', '07450', '07450', '07450', '07450', '07450', '07450', '07450', '07458', '07450', '07450', '07450', '07423', '07450', '07458', '07450', '07450', '07423', '07423', '07423', '07450', '07450', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07424', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07430', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07481', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07450', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07432', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07438', '07438', '07435', '07435', '07435', '07435', '07435', '07435', '07435', '07438', '07438', '07438', '07435', '07438', '07438', '07435', '07435', '07435', '07435', '07435', '07435', '07438', '07438', '07438', '07480', '07438', '07438', '07438', '07480', '07435', '07438', '07438', '07438', '07480', '07480', '07480', '07480', '07438', '07435', '07480', '07438', '07438']\n"
     ]
    }
   ],
   "source": [
    "print(list(poi_df['zip_code']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_codes</th>\n",
       "      <th>recreation</th>\n",
       "      <th>eating-drinking</th>\n",
       "      <th>shopping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07001</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07002</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07003</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07004</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07005</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>07006</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07008</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>07009</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>07010</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>07011</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>07012</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>07013</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>07014</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>07016</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>07017</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>07018</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>07020</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>07021</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>07022</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>07023</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>07024</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>07026</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>07027</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>07028</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>07029</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>07030</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>07031</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>07032</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>07033</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>07034</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>07423</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>07424</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>07430</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>07432</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>07435</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>07438</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>07440</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>07442</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>07446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>07450</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>07458</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>07461</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>07462</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>07463</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>07465</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>07480</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>07481</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>07604</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>07608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>07643</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>07834</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>07901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>07920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>08812</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>08830</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>08854</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>10309</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>10925</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>10969</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>10990</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    zip_codes recreation eating-drinking shopping\n",
       "0       07001          1              29       27\n",
       "1       07002          5              28       27\n",
       "2       07003         14              29       19\n",
       "3       07004         14              20       39\n",
       "4       07005         13              24       31\n",
       "5       07006         12              30       39\n",
       "6       07008          5              27       27\n",
       "7       07009          9              24       21\n",
       "8       07010          1              36       20\n",
       "9       07011          5              20       26\n",
       "10      07012          6              35       31\n",
       "11      07013          5              16       24\n",
       "12      07014          2              31       35\n",
       "13      07016          3              37       17\n",
       "14      07017          2              34       24\n",
       "15      07018          2              42       24\n",
       "16      07020          7              38       22\n",
       "17      07021          4               0        0\n",
       "18      07022          4              42       22\n",
       "19      07023          8              23       19\n",
       "20      07024          6              36       21\n",
       "21      07026          6              35       26\n",
       "22      07027         17              25       16\n",
       "23      07028          2               5        8\n",
       "24      07029          6              38       21\n",
       "25      07030         10              32       23\n",
       "26      07031          3              28       21\n",
       "27      07032          3              26       19\n",
       "28      07033          4              30       16\n",
       "29      07034          6              25       15\n",
       "..        ...        ...             ...      ...\n",
       "121     07423          4              15       16\n",
       "122     07424         10              27       23\n",
       "123     07430         17              21       25\n",
       "124     07432          1              13       32\n",
       "125     07435          9               9        4\n",
       "126     07438          0               6        1\n",
       "127     07440          1               0        0\n",
       "128     07442          3              15        5\n",
       "129     07446          0               0        1\n",
       "130     07450          2               3        4\n",
       "131     07458          1               0        0\n",
       "132     07461          1               1        3\n",
       "133     07462          8              32       28\n",
       "134     07463         10               0        1\n",
       "135     07465          0               2        2\n",
       "136     07480          2              10       10\n",
       "137     07481          0               0        1\n",
       "138     07604          0               2        3\n",
       "139     07608          0               0        1\n",
       "140     07643          0               3        1\n",
       "141     07834          1               0        4\n",
       "142     07901          3               0        0\n",
       "143     07920          0               0        0\n",
       "144     08812          0               0        3\n",
       "145     08830          1               0        2\n",
       "146     08854          0               0        1\n",
       "147     10309          0               0        3\n",
       "148     10925          0               3        0\n",
       "149     10969          3               3        0\n",
       "150     10990          1               1        7\n",
       "\n",
       "[151 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poi_input = poi_unstacked.loc[:,[('_id', 'ATTRACTIONS - RECREATION'),('_id', 'EATING - DRINKING'), ('_id', 'SHOPPING')]]\n",
    "poi_input.reset_index(inplace=True)\n",
    "vals = poi_input.values\n",
    "poi_input_df = pd.DataFrame(vals,columns=['zip_codes', 'recreation', 'eating-drinking', 'shopping'])\n",
    "poi_input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['zip_codes', 'recreation', 'eating-drinking', 'shopping', 'inccymedd',\n",
       "       'medsaleprice', 'trwpublic', 'avg_prop_tax'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poi_input_df = poi_input_df.astype('int64')\n",
    "community_input = community_input.astype('U').replace('','0').astype('int64')\n",
    "input_df = poi_input_df.merge(community_input, how='inner', on='zip_codes')\n",
    "max_vals = input_df.max()\n",
    "max_vals\n",
    "input_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 7)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "input = input_df.iloc[:,1:8].values\n",
    "input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "input_std = scaler.fit_transform(input)\n",
    "distortions = []\n",
    "for i in range(1,20):\n",
    "    cluster = KMeans(n_clusters=i, random_state=1, n_jobs=-1)\n",
    "\n",
    "    model = cluster.fit(input_std)\n",
    "\n",
    "    distortions.append(model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,20), distortions, marker='o')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Distortions')\n",
    "plt.savefig('elbow.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 3, 3, 3, 4, 3, 0, 4, 1, 4, 0, 5, 0, 0, 1, 5, 0, 3, 1,\n",
       "       0, 6, 5, 0, 1, 0, 0, 2, 0, 6, 0, 3, 3, 3, 3, 3, 5, 6, 5, 0, 0, 3,\n",
       "       1, 0, 0, 5, 3, 1, 0, 2, 2, 0, 6, 6, 3, 5, 1, 1, 4, 6, 4, 2, 6, 2,\n",
       "       3, 3, 0, 5, 5, 1, 3, 0, 0, 3, 3, 0, 4, 1, 4, 0, 0, 0, 0, 0, 0, 0,\n",
       "       6, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 3, 2, 3, 0,\n",
       "       3, 0, 3, 2, 0, 2, 6, 2, 3, 1, 6, 4, 6, 2, 2, 2, 5, 5, 5, 2, 0, 6,\n",
       "       2, 2, 5, 2, 2, 2, 2, 5, 5, 2, 2, 2])"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parks = 7\n",
    "price = 100000\n",
    "tax = 8\n",
    "transportation = 7\n",
    "eats = 8\n",
    "income = 91000\n",
    "shop = 3\n",
    "\n",
    "parks = (parks/10)*max_vals['recreation']\n",
    "tax = ((10 - tax)/10)*max_vals['avg_prop_tax']\n",
    "transportation = (transportation/10)*max_vals['trwpublic']\n",
    "eats = (eats/10)*max_vals['eating-drinking']\n",
    "shop = (shop/10)*max_vals['shopping']\n",
    "input_list = [parks, eats, shop, income, price, transportation, tax]\n",
    "input_predict = scaler.transform([input_list])\n",
    "input_concat = np.concatenate((input_predict, input_std), axis=0)\n",
    "\n",
    "#cluster = KMeans(n_clusters=5, random_state=0, n_jobs=-1)\n",
    "cluster = AgglomerativeClustering(n_clusters=7, affinity='cosine', linkage='complete')\n",
    "#cluster = DBSCAN(n_jobs=-1)\n",
    "labels = cluster.fit_predict(input_concat)\n",
    "\n",
    "#labels = model.labels_\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zips': [7003,\n",
       "  7012,\n",
       "  7020,\n",
       "  7024,\n",
       "  7030,\n",
       "  7054,\n",
       "  7060,\n",
       "  7070,\n",
       "  7071,\n",
       "  7083,\n",
       "  7095,\n",
       "  7302,\n",
       "  7310,\n",
       "  7424]}"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins = input_df['zip_codes'].values\n",
    "zip_cluster_vals = pd.DataFrame([ins, labels[1:]],).transpose().values\n",
    "zip_cluster = pd.DataFrame(zip_cluster_vals, columns=['zip_code', 'label'])\n",
    "zips_predicted = zip_cluster.loc[zip_cluster['label'] == labels[0]]\n",
    "zips = zips_predicted['zip_code'].values\n",
    "zips_dict = {'zips': list(zips)}\n",
    "\n",
    "zips_dict\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'zip_codes': [7302,\n",
       "  7083,\n",
       "  7095,\n",
       "  7054,\n",
       "  7003,\n",
       "  7070,\n",
       "  7071,\n",
       "  7030,\n",
       "  7424,\n",
       "  7060,\n",
       "  7020,\n",
       "  7012,\n",
       "  7024,\n",
       "  7310]}"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import DistanceMetric\n",
    "\n",
    "input_vectors = input_df.loc[input_df['zip_codes'] == zips_dict['zips'][0]].values\n",
    "for i in range(len(zips_dict['zips'])-1):\n",
    "    vector = input_df.loc[input_df['zip_codes'] == zips_dict['zips'][i+1]].values\n",
    "    input_vectors = np.concatenate((input_vectors, vector), axis=0)\n",
    "input_vector_std = scaler.fit_transform(input_vectors[:,1:])\n",
    "input_vector_std = np.concatenate((input_predict, input_vector_std), axis=0)\n",
    "dist = DistanceMetric.get_metric('euclidean')\n",
    "distance = dist.pairwise(input_vector_std):\n",
    "distance = distance[0]\n",
    "distance_dict = {'distance': list(distance)[1:]}\n",
    "distance_dict\n",
    "zip_dist = {**zips_dict, **distance_dict}\n",
    "zip_dist_df = pd.DataFrame(zip_dist)\n",
    "zip_dist_df.sort_values('distance', axis=0, ascending=True, inplace=True)\n",
    "zip_codes = zip_dist_df['zips'].values\n",
    "zip_codes_dict = {'zip_codes': list(zip_codes)}\n",
    "zip_codes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump( model, open( \"model.p\", \"wb\" ) )\n",
    "pickle.dump( ins, open( \"ins.p\", \"wb\" ) )\n",
    "pickle.dump( max_vals, open( \"max_vals.p\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df.to_csv('zip_data.csv', index=False, index_label=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['zip_codes', 'recreation', 'eating-drinking', 'shopping', 'inccymedd',\n",
       "       'medsaleprice', 'trwpublic', 'avg_prop_tax'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df = pd.read_csv('zip_data.csv')\n",
    "input_df.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "input_df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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