Final_Script_AWS_scaled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d616b802",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying:\n",
      "    closeExistingDaskConnection()\n",
      "Expecting nothing\n",
      "ok\n",
      "1 items had no tests:\n",
      "    __main__\n",
      "1 items passed all tests:\n",
      "   1 tests in __main__.closeExistingDaskConnection\n",
      "1 tests in 2 items.\n",
      "1 passed and 0 failed.\n",
      "Test passed.\n"
     ]
    }
   ],
   "source": [
    "#####IMPORT HOUSING DATA FROM S3#####\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import s3fs\n",
    "import dask as dd\n",
    "from dask.dataframe import DataFrame\n",
    "from dask.distributed import Client, progress\n",
    "import dask.array as dask\n",
    "from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler\n",
    "%matplotlib inline\n",
    "import doctest\n",
    "\n",
    "#close existing dask connection \n",
    "def closeExistingDaskConnection():   \n",
    "    \"\"\"Closes an existing Dask connection for you, so you don't have resource leaks\n",
    "\n",
    "    If called with an existing connection, from prior Jupyter runs, it will close it.\n",
    "    >>> closeExistingDaskConnection()\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    #temp variable for client\n",
    "    closed = None\n",
    "    \n",
    "    try:\n",
    "        #if the client exists from prior runs\n",
    "        if client is not None:\n",
    "            #close it\n",
    "            client.close()\n",
    "            \n",
    "            #print(\"closed existing connection, \",client)\n",
    "            #set closed = client to get state\n",
    "            closed = client\n",
    "    #if the client variable did not exist (first run) then a name exception is thrown which we can catch\n",
    "    except Exception as e:\n",
    "        if str(e)!= \"name 'client' is not defined\":\n",
    "            raise(e)\n",
    "        #print(e)\n",
    "    \n",
    "    return closed\n",
    "\n",
    "#call because we need to close existing connections\n",
    "client = closeExistingDaskConnection()\n",
    "if __name__ == \"__main__\":\n",
    "    doctest.testmod(verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "745b0a1b-520c-4758-8a22-f136f9c13ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\lauren\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\distributed\\client.py:1265: VersionMismatchWarning: Mismatched versions found\n",
      "\n",
      "+---------+---------------+----------------+----------------+\n",
      "| Package | client        | scheduler      | workers        |\n",
      "+---------+---------------+----------------+----------------+\n",
      "| python  | 3.7.1.final.0 | 3.7.10.final.0 | 3.7.10.final.0 |\n",
      "+---------+---------------+----------------+----------------+\n",
      "  warnings.warn(version_module.VersionMismatchWarning(msg[0][\"warning\"]))\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-4fc87424-d58b-11ec-b108-28d0eacec16e</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Direct</td>\n",
       "            <td style=\"text-align: left;\"></td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"http://ec2-34-207-112-108.compute-1.amazonaws.com:8787/status\" target=\"_blank\">http://ec2-34-207-112-108.compute-1.amazonaws.com:8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-856a0917-a27d-4613-8c7f-4f70e0a5747a</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://172.31.20.60:8786\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 2\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://172.31.20.60:8787/status\" target=\"_blank\">http://172.31.20.60:8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> 33 minutes ago\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 29.78 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: tcp://172.31.29.231:33211</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://172.31.29.231:33211\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://172.31.29.231:36985/status\" target=\"_blank\">http://172.31.29.231:36985/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 14.89 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://172.31.29.231:38909\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/ec2-user/dask-worker-space/worker-972g2ssq\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks executing: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in memory: </strong> 0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks ready: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in flight: </strong>0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>CPU usage:</strong> 0.0%\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Last seen: </strong> 23 hours ago\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory usage: </strong> 546.71 MiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Spilled bytes: </strong> 0 B\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Read bytes: </strong> 0.0 B\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Write bytes: </strong> 0.0 B\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: tcp://172.31.31.22:41445</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://172.31.31.22:41445\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://172.31.31.22:37483/status\" target=\"_blank\">http://172.31.31.22:37483/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 14.89 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://172.31.31.22:45513\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /home/ec2-user/dask-worker-space/worker-ygueikcd\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks executing: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in memory: </strong> 0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks ready: </strong> 0\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Tasks in flight: </strong>0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>CPU usage:</strong> 2.0%\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Last seen: </strong> 23 hours ago\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory usage: </strong> 551.25 MiB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Spilled bytes: </strong> 0 B\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Read bytes: </strong> 706.4557774862952 B\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Write bytes: </strong> 1.23 kiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
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      ],
      "text/plain": [
       "<Client: 'tcp://172.31.20.60:8786' processes=2 threads=4, memory=29.78 GiB>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#new client connected to LK Dask Scheduler\n",
    "client = Client(address=\"ec2-34-207-112-108.compute-1.amazonaws.com:8786\",timeout=\"60000s\",)\n",
    "display(client)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2b3c20d-d6b8-4a08-8a25-d490b05522c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162846"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#declare the states being used for the analysis\n",
    "#States with Data = ['AZ', 'CA', 'CT', 'FL', 'GA', 'IL', 'IA', 'MD', 'MA', 'MI', 'MN', 'MO', 'NE', 'NV', 'NJ', 'NC', 'OH', 'OK', 'OR', 'PA', 'SC', 'TN', 'TX', 'VA']\n",
    "#States = ['AZ', 'CA', 'CT', 'FL', 'GA', 'IL', 'IA', 'MD', 'MA', 'MI', 'MN', 'MO', 'NE', 'NV', 'NJ', 'NC', 'OH', 'OK', 'OR', 'PA', 'SC', 'TN', 'TX', 'VA']\n",
    "States = ['DC']\n",
    "df_all = pd.DataFrame()\n",
    "df_all = dd.dataframe.from_pandas(df_all, npartitions=10)\n",
    "\n",
    "#pull chosen state's data from S3 and append to dask dataframe\n",
    "for State in States:\n",
    "    try:\n",
    "        bucket = 'housingdata123'\n",
    "        data_key = 'HousingData_Dolt_{}.csv'.format(State)\n",
    "        data_location = 's3://{}/{}'.format(bucket,data_key)\n",
    "\n",
    "        df_all = dd.dataframe.read_csv(data_location, storage_options={'key': 'AKIA5OMWNVBKXKF5SI7O',\n",
    "                                       'secret': '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'}, \n",
    "                                   dtype={'zip5': 'object', 'physical_address': 'object','property_id': \n",
    "                                          'object', 'sale_date':'object', 'year_built': 'object','book': 'object',\n",
    "                                          'buyer_name': 'object','page': 'object','property_type': 'object',\n",
    "                                          'sale_type': 'object','seller_name': 'object','city': 'object', \n",
    "                                          'num_units': 'object','county': 'object'})\n",
    "       \n",
    "        #Append is deprecated and should now be concat.  Decided to read the CSV directly into df_all since otherwise it was blank (LK)\n",
    "        #df_all = df_all.append(df)\n",
    "        \n",
    "    except:\n",
    "        print(\"Import failed for the following state: {}\".format(State))\n",
    "\n",
    "#df_all.head(10)\n",
    "len(df_all.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ba9c905",
   "metadata": {},
   "outputs": [
    {
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       "\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>state</th>\n",
       "      <th>zip5</th>\n",
       "      <th>physical_address</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>property_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 102</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2020-11-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 103</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2003-01-28 00:00:00</td>\n",
       "      <td>191050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 104</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2015-03-24 00:00:00</td>\n",
       "      <td>390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 201</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2003-01-31 00:00:00</td>\n",
       "      <td>198350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 203</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2013-06-06 00:00:00</td>\n",
       "      <td>352500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DC</td>\n",
       "      <td>20002</td>\n",
       "      <td>1 16TH ST NE</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>75999</td>\n",
       "      <td>2021-04-08 00:00:00</td>\n",
       "      <td>900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 204</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2009-05-13 00:00:00</td>\n",
       "      <td>320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 301</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2021-11-23 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 302</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2018-02-22 00:00:00</td>\n",
       "      <td>451000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DC</td>\n",
       "      <td>20003</td>\n",
       "      <td>1 18TH ST SE # 304</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>76000</td>\n",
       "      <td>2012-08-06 00:00:00</td>\n",
       "      <td>362000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state   zip5    physical_address        city      county property_id  \\\n",
       "0    DC  20003  1 18TH ST SE # 102  WASHINGTON  WASHINGTON       76000   \n",
       "1    DC  20003  1 18TH ST SE # 103  WASHINGTON  WASHINGTON       76000   \n",
       "2    DC  20003  1 18TH ST SE # 104  WASHINGTON  WASHINGTON       76000   \n",
       "3    DC  20003  1 18TH ST SE # 201  WASHINGTON  WASHINGTON       76000   \n",
       "4    DC  20003  1 18TH ST SE # 203  WASHINGTON  WASHINGTON       76000   \n",
       "5    DC  20002        1 16TH ST NE  WASHINGTON  WASHINGTON       75999   \n",
       "6    DC  20003  1 18TH ST SE # 204  WASHINGTON  WASHINGTON       76000   \n",
       "7    DC  20003  1 18TH ST SE # 301  WASHINGTON  WASHINGTON       76000   \n",
       "8    DC  20003  1 18TH ST SE # 302  WASHINGTON  WASHINGTON       76000   \n",
       "9    DC  20003  1 18TH ST SE # 304  WASHINGTON  WASHINGTON       76000   \n",
       "\n",
       "             sale_date  sale_price  \n",
       "0  2020-11-24 00:00:00           0  \n",
       "1  2003-01-28 00:00:00      191050  \n",
       "2  2015-03-24 00:00:00      390000  \n",
       "3  2003-01-31 00:00:00      198350  \n",
       "4  2013-06-06 00:00:00      352500  \n",
       "5  2021-04-08 00:00:00      900000  \n",
       "6  2009-05-13 00:00:00      320000  \n",
       "7  2021-11-23 00:00:00           0  \n",
       "8  2018-02-22 00:00:00      451000  \n",
       "9  2012-08-06 00:00:00      362000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#####CLEANUP TIME#####\n",
    "\n",
    "#choose years for analysis; if you want all years, type 'all'\n",
    "Years = ['2016','2017','2018','2019']\n",
    "#Years= ['all']\n",
    "\n",
    "#drop extraneous columns\n",
    "#missing df_all = df_all so column drop was not working; Removed drop of 'sale_price\" as this is referenced later and needed (LK)\n",
    "df_all = df_all.drop(columns=['property_type','seller_name','buyer_name','num_units','year_built','source_url','book','page','sale_type'])\n",
    "df_all.compute()\n",
    "df_all.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3547ab83-0b06-4cb0-893c-a6e38dff07c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162846"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_all.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebb9065b-57e7-4430-895c-b7a97d3172e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop rows with 0 for their sale price\n",
    "df_all = df_all[df_all.sale_price != 0]\n",
    "df_all.compute()\n",
    "#df_all.isna().sum().sum()\n",
    "len(df_all.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "baffbf4b-0011-4642-aa2d-e07f8540c3a4",
   "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>zip5</th>\n",
       "      <th>sale_date</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20001</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.454690e+06</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20001</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.466283e+06</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20001</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.271792e+06</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20001</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.194899e+06</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20002</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.461908e+06</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20002</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.258494e+06</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20002</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.287787e+06</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20002</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.496683e+06</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20003</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.112682e+06</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20003</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.080292e+06</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20003</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.060349e+06</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20003</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.068744e+06</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20004</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.960241e+05</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20004</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.135991e+06</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20004</td>\n",
       "      <td>2018</td>\n",
       "      <td>8.803072e+06</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20004</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.719506e+06</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20005</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.904078e+06</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20005</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.484323e+06</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20005</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.786607e+06</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20005</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.181332e+06</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20006</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.226107e+06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20006</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.935500e+07</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20006</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.461697e+07</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20006</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.688725e+07</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20007</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.220986e+06</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20007</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.757897e+06</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20007</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.968370e+06</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20007</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.783415e+06</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20008</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.129182e+06</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20008</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.294756e+06</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     zip5 sale_date          mean  count\n",
       "0   20001      2016  1.454690e+06    481\n",
       "1   20001      2017  1.466283e+06    652\n",
       "2   20001      2018  1.271792e+06    715\n",
       "3   20001      2019  1.194899e+06    766\n",
       "4   20002      2016  1.461908e+06    790\n",
       "5   20002      2017  1.258494e+06    868\n",
       "6   20002      2018  1.287787e+06   1016\n",
       "7   20002      2019  1.496683e+06   1043\n",
       "8   20003      2016  1.112682e+06    296\n",
       "9   20003      2017  1.080292e+06    404\n",
       "10  20003      2018  1.060349e+06    535\n",
       "11  20003      2019  2.068744e+06    560\n",
       "12  20004      2016  8.960241e+05     41\n",
       "13  20004      2017  1.135991e+06     50\n",
       "14  20004      2018  8.803072e+06     64\n",
       "15  20004      2019  3.719506e+06     55\n",
       "16  20005      2016  5.904078e+06    161\n",
       "17  20005      2017  5.484323e+06    243\n",
       "18  20005      2018  5.786607e+06    273\n",
       "19  20005      2019  3.181332e+06    281\n",
       "20  20006      2016  5.226107e+06     14\n",
       "21  20006      2017  2.935500e+07     20\n",
       "22  20006      2018  2.461697e+07     12\n",
       "23  20006      2019  3.688725e+07     12\n",
       "24  20007      2016  2.220986e+06    456\n",
       "25  20007      2017  1.757897e+06    448\n",
       "26  20007      2018  2.968370e+06    504\n",
       "27  20007      2019  1.783415e+06    555\n",
       "28  20008      2016  1.129182e+06    261\n",
       "29  20008      2017  1.294756e+06    305"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reformat the sales dates to only include the year\n",
    "df_all['sale_date'] = df_all['sale_date'].str[0:4]\n",
    "df_all['sale_date'] = df_all['sale_date'].astype(str)\n",
    "\n",
    "if 'all' not in Years:\n",
    "    \n",
    "    #drop rows that aren't in the desired year range\n",
    "    df_all = df_all[df_all.sale_date.isin(Years)]\n",
    "\n",
    "Years = list(set(df_all['sale_date']))\n",
    "\n",
    "#group and average housing prices by zip code\n",
    "house_price_avg = df_all.groupby(['zip5','sale_date']).agg({'sale_price': ['mean','count']}).compute()\n",
    "house_price_avg = house_price_avg.droplevel(level = 0,axis=1)\n",
    "house_price_avg = house_price_avg.reset_index()\n",
    "house_price_avg.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd2ec63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#house_price_avg.isna().sum().sum()\n",
    "len(house_price_avg.index)\n",
    "#running a time series analysis on the housing data\n",
    "#parallelizing model prediction for several parameters: feature number, categorical variable buckets\n",
    "#comparing the time series forecasting to the census bureau modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0cd5fcc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table: DP05, 2019, Completed\n",
      "Table: DP05, 2017, Completed\n",
      "Table: DP05, 2016, Completed\n",
      "Table: DP05, 2018, Completed\n"
     ]
    }
   ],
   "source": [
    "#####IMPORT CHOSEN CENSUS BUREAU DATA#####\n",
    "\n",
    "#####melt the dataframes, add a year column, append to consolidated dataframe, edit, and then unmelt\n",
    "\n",
    "import requests # request http, api\n",
    "import pandas as pd # tabluar data\n",
    "import dask as dd\n",
    "from dask.dataframe import DataFrame\n",
    "\n",
    "#create a state code dictionary for the census bureau API\n",
    "api_states= ['AL','AK','AZ','AR','CA','CO','CT','DE','DC','FL','GA','HI','ID','IL','IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT','NE','NV','NH','NJ','NM','NY','NC','ND','OH','OK','OR','PA','RI','SC','SD','TN','TX','UT','VT','VA','WA','WV','WI','WY']\n",
    "api_codes= [1,2,4,5,6,8,9,10,11,12,13,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,46,47,48,49,50,51,53,54,55,56]\n",
    "zip_iterator = zip(api_states, api_codes)\n",
    "API_State_Codes = dict(zip_iterator)\n",
    "\n",
    "#create list of codes associated with original state query\n",
    "API_List = ''\n",
    "for state in States:\n",
    "    API_List = API_List + str(API_State_Codes[state]) + ','\n",
    "\n",
    "API_List = API_List[0:-1]\n",
    "\n",
    "#declare census api key\n",
    "census_api_key = \"35bea501f0c96a696cc609f9cb32b27e6541fbae\"\n",
    "\n",
    "#declare tables and years\n",
    "Tables = ['DP05']\n",
    "Consol_Zip_Table = pd.DataFrame()\n",
    "Consol_Zip_Table = dd.dataframe.from_pandas(Consol_Zip_Table, npartitions=10)\n",
    "        \n",
    "for Year in Years:\n",
    "    for ID in Tables:\n",
    "        \n",
    "        #subject tables\n",
    "        try:\n",
    "            if str(ID[0]) == 'S':\n",
    "\n",
    "                url = \"https://api.census.gov/data/{0}/acs/acs5/subject?get=NAME,group({1})&for=zip%20code%20tabulation%20area:*&in=state:{2}&key={3}\"\\\n",
    "                .format(Year, ID ,census_api_key)\n",
    "\n",
    "                response = requests.request(\"GET\", url)\n",
    "\n",
    "        except:\n",
    "                print('Subject Table: ' + str(ID) + ', ' + str(Year) + ', Failed')\n",
    "                continue\n",
    "\n",
    "        #data profile tables\n",
    "        try:\n",
    "            if str(ID[0]) == 'D':\n",
    "\n",
    "                url = \"https://api.census.gov/data/{0}/acs/acs5/profile?get=NAME,group({1})&for=zip%20code%20tabulation%20area:*&in=state:{2}&key={3}\"\\\n",
    "                .format(Year, ID , API_List, census_api_key)\n",
    "\n",
    "                response = requests.request(\"GET\", url)\n",
    "                \n",
    "        except:\n",
    "                print('Data Profile Table: ' + str(ID) + ', ' + str(Year) + ', Failed')\n",
    "                continue\n",
    "\n",
    "        #detailed tables\n",
    "        try:\n",
    "            if str(ID[0]) == 'B':\n",
    "\n",
    "                url = \"https://api.census.gov/data/{0}/acs/acs5?get=NAME,group({1})&for=zip%20code%20tabulation%20area:*&in=state:{2}&key={3}\"\\\n",
    "                .format(Year, ID ,census_api_key)\n",
    "\n",
    "                response = requests.request(\"GET\", url)\n",
    "                \n",
    "        except:\n",
    "                print('Detailed Table: ' + str(ID) + ', ' + str(Year) + ', Failed')\n",
    "                continue\n",
    "                \n",
    "                \n",
    "        #create pandas dataframe, remove duplicate columns\n",
    "        df = pd.DataFrame(response.json()[1:], columns=response.json()[0])\n",
    "        df = df.loc[:,~df.columns.duplicated()]\n",
    "                \n",
    "        #add year column, melt into a long form\n",
    "        Remove_Col = ['NAME','GEO_ID','state','zip code tabulation area'] \n",
    "        Col = list(df.columns)\n",
    "        Col = [ele for ele in Col if ele not in Remove_Col]\n",
    "        \n",
    "        df_melt = df.melt(id_vars=['zip code tabulation area'], value_vars= Col, ignore_index=False)\n",
    "        df_melt['YEAR'] = Year\n",
    "        \n",
    "        #remove margin of error variables\n",
    "        df_melt = df_melt[df_melt['variable'].str.contains(\"M\")==False]\n",
    "        #remove annotation variables\n",
    "        df_melt = df_melt[df_melt['variable'].str.endswith(\"A\")==False]\n",
    "        #remove percent estimate variables\n",
    "        df_melt = df_melt[df_melt['variable'].str.endswith(\"PE\")==False]\n",
    "\n",
    "        #append to consolidated dataframe\n",
    "        #append deprecated and resulting in blank table.  Setting direct assignment (LK)\n",
    "        Consol_Zip_Table = Consol_Zip_Table.append(df_melt)\n",
    "        #Consol_Zip_Table = df_melt\n",
    "        \n",
    "        print('Table: ' + str(ID) + ', ' + str(Year) + ', Completed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14143e54-f7d1-487a-8540-95ca16e39835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip code tabulation area    object\n",
       "variable                    object\n",
       "value                       object\n",
       "YEAR                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Consol_Zip_Table.head(80)\n",
    "#Consol_Zip_Table.dtypes\n",
    "#type(Consol_Zip_Table)\n",
    "#Consol_Zip_Table.isna().sum().sum()\n",
    "#len(Consol_Zip_Table.index)\n",
    "\n",
    "#Consol_Zip_Table.compute()\n",
    "#Consol_Zip_Table.head()\n",
    "#df_melt.head()\n",
    "#len(Consol_Zip_Table.index)\n",
    "Consol_Zip_Table.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8ff09cb-b2a5-4ce9-8448-6e7e3ba870a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correct the datatypes in preparation for the join\n",
    "#Consol_Zip_Table = Consol_Zip_Table.compute()\n",
    "\n",
    "#ddf[\"nums\"] = dd.to_numeric(ddf[\"nums\"], errors=\"coerce\")\n",
    "Consol_Zip_Table[\"YEAR\"] = Consol_Zip_Table[\"YEAR\"].astype(str)\n",
    "Consol_Zip_Table[\"zip code tabulation area\"] = Consol_Zip_Table[\"zip code tabulation area\"].astype(str)\n",
    "Consol_Zip_Table[\"variable\"] = Consol_Zip_Table[\"variable\"].astype(str)\n",
    "Consol_Zip_Table.compute()\n",
    "\n",
    "#Consol_Zip_Table[\"value\"] = Consol_Zip_Table[\"value\"].astype(float)\n",
    "#Consol_Zip_Table.dtypes\n",
    "Consol_Zip_Table[\"value\"] = dd.dataframe.to_numeric(Consol_Zip_Table[\"value\"], errors=\"coerce\")\n",
    "#Consol_Zip_Table.value = dd.dataframe.to_numeric(Consol_Zip_Table.value, errors=\"coerce\")\n",
    "\n",
    "#Consol_Zip_Table.compute()\n",
    "#Consol_Zip_Table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59f83917-85cc-43d8-81b3-b48dd06f3876",
   "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 code tabulation area</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20012</td>\n",
       "      <td>DP05_0001E</td>\n",
       "      <td>17576.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20012</td>\n",
       "      <td>DP05_0008E</td>\n",
       "      <td>867.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20012</td>\n",
       "      <td>DP05_0037E</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20012</td>\n",
       "      <td>DP05_0054E</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20012</td>\n",
       "      <td>DP05_0082E</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2019</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>52</th>\n",
       "      <td>20593</td>\n",
       "      <td>DP05_0056E</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>20593</td>\n",
       "      <td>DP05_0015E</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>20593</td>\n",
       "      <td>DP05_0047E</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>20593</td>\n",
       "      <td>DP05_0023E</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>20593</td>\n",
       "      <td>DP05_0031E</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18603 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip code tabulation area    variable    value  YEAR\n",
       "0                     20012  DP05_0001E  17576.0  2019\n",
       "0                     20012  DP05_0008E    867.0  2019\n",
       "0                     20012  DP05_0037E   4995.0  2019\n",
       "0                     20012  DP05_0054E      0.0  2019\n",
       "0                     20012  DP05_0082E    191.0  2019\n",
       "..                      ...         ...      ...   ...\n",
       "52                    20593  DP05_0056E      0.0  2018\n",
       "52                    20593  DP05_0015E      0.0  2018\n",
       "52                    20593  DP05_0047E      0.0  2018\n",
       "52                    20593  DP05_0023E      0.0  2018\n",
       "52                    20593  DP05_0031E      0.0  2018\n",
       "\n",
       "[18603 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Consol_Zip_Table.compute()\n",
    "#Consol_Zip_Table[\"value\"] = Consol_Zip_Table[\"value\"].fillna(0)\n",
    "#Consol_Zip_Table[\"value\"] = dd.dataframe.to_numeric(Consol_Zip_Table[\"value\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80d16eb8-7cc7-4ebe-a8d4-5ac55ca8b2f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip code tabulation area    object\n",
       "variable                    object\n",
       "value                        int64\n",
       "YEAR                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Consol_Zip_Table.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "57778bc7-e3b8-4f91-94ef-bf2a6fdf174e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Final_Table = pd.pivot_table(pandas_df, index=['YEAR','zip code tabulation area'], columns=['variable'], values=['value'],aggfunc=np.sum,fill_value=0)\n",
    "test = Consol_Zip_Table.compute()\n",
    "Final_Table = pd.pivot_table(test, index=['YEAR','zip code tabulation area'], columns=['variable'], values=['value'],aggfunc=np.sum,fill_value=0)\n",
    "Final_Table = Final_Table.droplevel(level = 0,axis=1)\n",
    "Final_Table = Final_Table.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7068d4e-7690-4a78-b6a6-72a484c7fa1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final_Table.isna().sum().sum()\n",
    "len(Final_Table.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de45c37c-6075-45cc-ab41-fe61e024721b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(Final_Table)\n",
    "#Consol_Zip_Table = Consol_Zip_Table.dropna()\n",
    "#Consol_Zip_Table.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3063bfbb-449a-4543-80d7-cf39df6e1518",
   "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>zip5</th>\n",
       "      <th>sale_date</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20001</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.454690e+06</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20001</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.466283e+06</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20001</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.271792e+06</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20001</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.194899e+06</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20002</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.461908e+06</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20002</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.258494e+06</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20002</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.287787e+06</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20002</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.496683e+06</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20003</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.112682e+06</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20003</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.080292e+06</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20003</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.060349e+06</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20003</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.068744e+06</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20004</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.960241e+05</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20004</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.135991e+06</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20004</td>\n",
       "      <td>2018</td>\n",
       "      <td>8.803072e+06</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20004</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.719506e+06</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20005</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.904078e+06</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20005</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.484323e+06</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20005</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.786607e+06</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20005</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.181332e+06</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20006</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.226107e+06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20006</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.935500e+07</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20006</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.461697e+07</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20006</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.688725e+07</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20007</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.220986e+06</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20007</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.757897e+06</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20007</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.968370e+06</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20007</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.783415e+06</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20008</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.129182e+06</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20008</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.294756e+06</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>20008</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.302044e+07</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>20008</td>\n",
       "      <td>2019</td>\n",
       "      <td>9.815546e+05</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>20009</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.359884e+06</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>20009</td>\n",
       "      <td>2017</td>\n",
       "      <td>8.741012e+05</td>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>20009</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.248639e+06</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>20009</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.405689e+05</td>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>20010</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.699310e+05</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>20010</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.221939e+05</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20010</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.201979e+05</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>20010</td>\n",
       "      <td>2019</td>\n",
       "      <td>7.271982e+05</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20011</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.900579e+05</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20011</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.153370e+05</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20011</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.679880e+05</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>20011</td>\n",
       "      <td>2019</td>\n",
       "      <td>7.650013e+05</td>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>20012</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.124332e+05</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>20012</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.333025e+06</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>20012</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.386142e+06</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>20012</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.073632e+05</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>20015</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.531458e+06</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>20015</td>\n",
       "      <td>2017</td>\n",
       "      <td>9.453638e+05</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     zip5 sale_date          mean  count\n",
       "0   20001      2016  1.454690e+06    481\n",
       "1   20001      2017  1.466283e+06    652\n",
       "2   20001      2018  1.271792e+06    715\n",
       "3   20001      2019  1.194899e+06    766\n",
       "4   20002      2016  1.461908e+06    790\n",
       "5   20002      2017  1.258494e+06    868\n",
       "6   20002      2018  1.287787e+06   1016\n",
       "7   20002      2019  1.496683e+06   1043\n",
       "8   20003      2016  1.112682e+06    296\n",
       "9   20003      2017  1.080292e+06    404\n",
       "10  20003      2018  1.060349e+06    535\n",
       "11  20003      2019  2.068744e+06    560\n",
       "12  20004      2016  8.960241e+05     41\n",
       "13  20004      2017  1.135991e+06     50\n",
       "14  20004      2018  8.803072e+06     64\n",
       "15  20004      2019  3.719506e+06     55\n",
       "16  20005      2016  5.904078e+06    161\n",
       "17  20005      2017  5.484323e+06    243\n",
       "18  20005      2018  5.786607e+06    273\n",
       "19  20005      2019  3.181332e+06    281\n",
       "20  20006      2016  5.226107e+06     14\n",
       "21  20006      2017  2.935500e+07     20\n",
       "22  20006      2018  2.461697e+07     12\n",
       "23  20006      2019  3.688725e+07     12\n",
       "24  20007      2016  2.220986e+06    456\n",
       "25  20007      2017  1.757897e+06    448\n",
       "26  20007      2018  2.968370e+06    504\n",
       "27  20007      2019  1.783415e+06    555\n",
       "28  20008      2016  1.129182e+06    261\n",
       "29  20008      2017  1.294756e+06    305\n",
       "30  20008      2018  4.302044e+07    531\n",
       "31  20008      2019  9.815546e+05    393\n",
       "32  20009      2016  1.359884e+06    770\n",
       "33  20009      2017  8.741012e+05    802\n",
       "34  20009      2018  1.248639e+06    822\n",
       "35  20009      2019  8.405689e+05    967\n",
       "36  20010      2016  7.699310e+05    302\n",
       "37  20010      2017  7.221939e+05    333\n",
       "38  20010      2018  7.201979e+05    421\n",
       "39  20010      2019  7.271982e+05    463\n",
       "40  20011      2016  7.900579e+05    567\n",
       "41  20011      2017  6.153370e+05    709\n",
       "42  20011      2018  6.679880e+05    718\n",
       "43  20011      2019  7.650013e+05    921\n",
       "44  20012      2016  7.124332e+05    112\n",
       "45  20012      2017  1.333025e+06    138\n",
       "46  20012      2018  1.386142e+06    155\n",
       "47  20012      2019  8.073632e+05    196\n",
       "48  20015      2016  1.531458e+06    171\n",
       "49  20015      2017  9.453638e+05    198"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#join the housing data and census data\n",
    "house_price_avg[\"sale_date\"] = house_price_avg[\"sale_date\"].astype(str)\n",
    "house_price_avg[\"zip5\"] = house_price_avg[\"zip5\"].astype(str)\n",
    "\n",
    "house_price_avg.head(50)\n",
    "#house_price_avg.isna().sum().sum()\n",
    "#Final_Table.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05bcd6dc-ee11-4f52-95e4-5d88c69c8b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#house_price_avg.isna().sum().sum()\n",
    "len(Final_Table.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8aa7054",
   "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>YEAR</th>\n",
       "      <th>zip code tabulation area</th>\n",
       "      <th>DP05_0001E</th>\n",
       "      <th>DP05_0002E</th>\n",
       "      <th>DP05_0003E</th>\n",
       "      <th>DP05_0004E</th>\n",
       "      <th>DP05_0005E</th>\n",
       "      <th>DP05_0006E</th>\n",
       "      <th>DP05_0007E</th>\n",
       "      <th>DP05_0008E</th>\n",
       "      <th>...</th>\n",
       "      <th>DP05_0084E</th>\n",
       "      <th>DP05_0085E</th>\n",
       "      <th>DP05_0086E</th>\n",
       "      <th>DP05_0087E</th>\n",
       "      <th>DP05_0088E</th>\n",
       "      <th>DP05_0089E</th>\n",
       "      <th>zip5</th>\n",
       "      <th>sale_date</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>20001</td>\n",
       "      <td>44998</td>\n",
       "      <td>21903</td>\n",
       "      <td>23095</td>\n",
       "      <td>1769.0</td>\n",
       "      <td>1422</td>\n",
       "      <td>1043</td>\n",
       "      <td>2634</td>\n",
       "      <td>6592</td>\n",
       "      <td>...</td>\n",
       "      <td>18989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20001</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.454690e+06</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>20002</td>\n",
       "      <td>59659</td>\n",
       "      <td>28845</td>\n",
       "      <td>30814</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>2209</td>\n",
       "      <td>1998</td>\n",
       "      <td>2250</td>\n",
       "      <td>4514</td>\n",
       "      <td>...</td>\n",
       "      <td>25522</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20002</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.461908e+06</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>20003</td>\n",
       "      <td>29071</td>\n",
       "      <td>15335</td>\n",
       "      <td>13736</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>637</td>\n",
       "      <td>794</td>\n",
       "      <td>1871</td>\n",
       "      <td>...</td>\n",
       "      <td>11480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20003</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.112682e+06</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>20004</td>\n",
       "      <td>1708</td>\n",
       "      <td>923</td>\n",
       "      <td>785</td>\n",
       "      <td>73.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20004</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.960241e+05</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>20005</td>\n",
       "      <td>13063</td>\n",
       "      <td>6626</td>\n",
       "      <td>6437</td>\n",
       "      <td>486.0</td>\n",
       "      <td>128</td>\n",
       "      <td>114</td>\n",
       "      <td>113</td>\n",
       "      <td>888</td>\n",
       "      <td>...</td>\n",
       "      <td>4891</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20005</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.904078e+06</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>20006</td>\n",
       "      <td>2920</td>\n",
       "      <td>1475</td>\n",
       "      <td>1445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>924</td>\n",
       "      <td>...</td>\n",
       "      <td>1267</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20006</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.226107e+06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>20007</td>\n",
       "      <td>26415</td>\n",
       "      <td>12589</td>\n",
       "      <td>13826</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>894</td>\n",
       "      <td>751</td>\n",
       "      <td>1125</td>\n",
       "      <td>3845</td>\n",
       "      <td>...</td>\n",
       "      <td>10595</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20007</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.220986e+06</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>20008</td>\n",
       "      <td>28261</td>\n",
       "      <td>12455</td>\n",
       "      <td>15806</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>1279</td>\n",
       "      <td>555</td>\n",
       "      <td>469</td>\n",
       "      <td>1871</td>\n",
       "      <td>...</td>\n",
       "      <td>12350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20008</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.129182e+06</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>20009</td>\n",
       "      <td>51508</td>\n",
       "      <td>26945</td>\n",
       "      <td>24563</td>\n",
       "      <td>2085.0</td>\n",
       "      <td>1423</td>\n",
       "      <td>784</td>\n",
       "      <td>936</td>\n",
       "      <td>3379</td>\n",
       "      <td>...</td>\n",
       "      <td>19557</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20009</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.359884e+06</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016</td>\n",
       "      <td>20010</td>\n",
       "      <td>32421</td>\n",
       "      <td>16331</td>\n",
       "      <td>16090</td>\n",
       "      <td>2449.0</td>\n",
       "      <td>1292</td>\n",
       "      <td>937</td>\n",
       "      <td>1071</td>\n",
       "      <td>2175</td>\n",
       "      <td>...</td>\n",
       "      <td>11212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20010</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.699310e+05</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016</td>\n",
       "      <td>20011</td>\n",
       "      <td>67349</td>\n",
       "      <td>32335</td>\n",
       "      <td>35014</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>4052</td>\n",
       "      <td>2889</td>\n",
       "      <td>3061</td>\n",
       "      <td>3527</td>\n",
       "      <td>...</td>\n",
       "      <td>23763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20011</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.900579e+05</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016</td>\n",
       "      <td>20012</td>\n",
       "      <td>14457</td>\n",
       "      <td>6928</td>\n",
       "      <td>7529</td>\n",
       "      <td>802.0</td>\n",
       "      <td>766</td>\n",
       "      <td>643</td>\n",
       "      <td>584</td>\n",
       "      <td>730</td>\n",
       "      <td>...</td>\n",
       "      <td>5710</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20012</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.124332e+05</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016</td>\n",
       "      <td>20015</td>\n",
       "      <td>16323</td>\n",
       "      <td>7933</td>\n",
       "      <td>8390</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1215</td>\n",
       "      <td>1289</td>\n",
       "      <td>757</td>\n",
       "      <td>455</td>\n",
       "      <td>...</td>\n",
       "      <td>5929</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20015</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.531458e+06</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016</td>\n",
       "      <td>20016</td>\n",
       "      <td>35192</td>\n",
       "      <td>15966</td>\n",
       "      <td>19226</td>\n",
       "      <td>1826.0</td>\n",
       "      <td>1991</td>\n",
       "      <td>1526</td>\n",
       "      <td>3875</td>\n",
       "      <td>3100</td>\n",
       "      <td>...</td>\n",
       "      <td>14374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.129660e+06</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>20017</td>\n",
       "      <td>20371</td>\n",
       "      <td>9183</td>\n",
       "      <td>11188</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>609</td>\n",
       "      <td>874</td>\n",
       "      <td>1498</td>\n",
       "      <td>...</td>\n",
       "      <td>8993</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20017</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.180954e+06</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016</td>\n",
       "      <td>20018</td>\n",
       "      <td>20363</td>\n",
       "      <td>9392</td>\n",
       "      <td>10971</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>927</td>\n",
       "      <td>845</td>\n",
       "      <td>1042</td>\n",
       "      <td>1280</td>\n",
       "      <td>...</td>\n",
       "      <td>8478</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20018</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.560603e+05</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>20019</td>\n",
       "      <td>57962</td>\n",
       "      <td>25232</td>\n",
       "      <td>32730</td>\n",
       "      <td>5092.0</td>\n",
       "      <td>4146</td>\n",
       "      <td>3380</td>\n",
       "      <td>4323</td>\n",
       "      <td>4799</td>\n",
       "      <td>...</td>\n",
       "      <td>24376</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20019</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.680264e+05</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016</td>\n",
       "      <td>20020</td>\n",
       "      <td>54955</td>\n",
       "      <td>24526</td>\n",
       "      <td>30429</td>\n",
       "      <td>4540.0</td>\n",
       "      <td>4498</td>\n",
       "      <td>3782</td>\n",
       "      <td>3902</td>\n",
       "      <td>3956</td>\n",
       "      <td>...</td>\n",
       "      <td>22228</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20020</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.237930e+05</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016</td>\n",
       "      <td>20024</td>\n",
       "      <td>12183</td>\n",
       "      <td>5582</td>\n",
       "      <td>6601</td>\n",
       "      <td>500.0</td>\n",
       "      <td>306</td>\n",
       "      <td>280</td>\n",
       "      <td>272</td>\n",
       "      <td>871</td>\n",
       "      <td>...</td>\n",
       "      <td>5616</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20024</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.381499e+05</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016</td>\n",
       "      <td>20032</td>\n",
       "      <td>41232</td>\n",
       "      <td>18460</td>\n",
       "      <td>22772</td>\n",
       "      <td>4208.0</td>\n",
       "      <td>3696</td>\n",
       "      <td>2686</td>\n",
       "      <td>2778</td>\n",
       "      <td>4199</td>\n",
       "      <td>...</td>\n",
       "      <td>16172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20032</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.006602e+05</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016</td>\n",
       "      <td>20036</td>\n",
       "      <td>5655</td>\n",
       "      <td>2619</td>\n",
       "      <td>3036</td>\n",
       "      <td>208.0</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>418</td>\n",
       "      <td>...</td>\n",
       "      <td>2494</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20036</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.624033e+06</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>20037</td>\n",
       "      <td>15285</td>\n",
       "      <td>6969</td>\n",
       "      <td>8316</td>\n",
       "      <td>276.0</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>1828</td>\n",
       "      <td>4923</td>\n",
       "      <td>...</td>\n",
       "      <td>7022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20037</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.914406e+06</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>20045</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20045</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.555000e+08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016</td>\n",
       "      <td>20260</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20260</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.225000e+07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017</td>\n",
       "      <td>20001</td>\n",
       "      <td>45925</td>\n",
       "      <td>22060</td>\n",
       "      <td>23865</td>\n",
       "      <td>92.4</td>\n",
       "      <td>1822</td>\n",
       "      <td>1324</td>\n",
       "      <td>873</td>\n",
       "      <td>2528</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>1040</td>\n",
       "      <td>20982</td>\n",
       "      <td>37707</td>\n",
       "      <td>17766</td>\n",
       "      <td>19941</td>\n",
       "      <td>20001</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.466283e+06</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017</td>\n",
       "      <td>20002</td>\n",
       "      <td>61787</td>\n",
       "      <td>29602</td>\n",
       "      <td>32185</td>\n",
       "      <td>92.0</td>\n",
       "      <td>4416</td>\n",
       "      <td>2339</td>\n",
       "      <td>1880</td>\n",
       "      <td>2131</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>1284</td>\n",
       "      <td>29425</td>\n",
       "      <td>49749</td>\n",
       "      <td>23402</td>\n",
       "      <td>26347</td>\n",
       "      <td>20002</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.258494e+06</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017</td>\n",
       "      <td>20003</td>\n",
       "      <td>30259</td>\n",
       "      <td>15633</td>\n",
       "      <td>14626</td>\n",
       "      <td>106.9</td>\n",
       "      <td>1899</td>\n",
       "      <td>1205</td>\n",
       "      <td>749</td>\n",
       "      <td>755</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>870</td>\n",
       "      <td>13718</td>\n",
       "      <td>25074</td>\n",
       "      <td>13047</td>\n",
       "      <td>12027</td>\n",
       "      <td>20003</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.080292e+06</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017</td>\n",
       "      <td>20004</td>\n",
       "      <td>1717</td>\n",
       "      <td>914</td>\n",
       "      <td>803</td>\n",
       "      <td>113.8</td>\n",
       "      <td>83</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1560</td>\n",
       "      <td>1464</td>\n",
       "      <td>771</td>\n",
       "      <td>693</td>\n",
       "      <td>20004</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.135991e+06</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017</td>\n",
       "      <td>20005</td>\n",
       "      <td>13234</td>\n",
       "      <td>6740</td>\n",
       "      <td>6494</td>\n",
       "      <td>103.8</td>\n",
       "      <td>582</td>\n",
       "      <td>221</td>\n",
       "      <td>120</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>437</td>\n",
       "      <td>8917</td>\n",
       "      <td>10144</td>\n",
       "      <td>5184</td>\n",
       "      <td>4960</td>\n",
       "      <td>20005</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.484323e+06</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017</td>\n",
       "      <td>20006</td>\n",
       "      <td>2780</td>\n",
       "      <td>1332</td>\n",
       "      <td>1448</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1528</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>116</td>\n",
       "      <td>732</td>\n",
       "      <td>2360</td>\n",
       "      <td>1100</td>\n",
       "      <td>1260</td>\n",
       "      <td>20006</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.935500e+07</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR zip code tabulation area  DP05_0001E  DP05_0002E  DP05_0003E  \\\n",
       "0   2016                    20001       44998       21903       23095   \n",
       "1   2016                    20002       59659       28845       30814   \n",
       "2   2016                    20003       29071       15335       13736   \n",
       "3   2016                    20004        1708         923         785   \n",
       "4   2016                    20005       13063        6626        6437   \n",
       "5   2016                    20006        2920        1475        1445   \n",
       "6   2016                    20007       26415       12589       13826   \n",
       "7   2016                    20008       28261       12455       15806   \n",
       "8   2016                    20009       51508       26945       24563   \n",
       "9   2016                    20010       32421       16331       16090   \n",
       "10  2016                    20011       67349       32335       35014   \n",
       "11  2016                    20012       14457        6928        7529   \n",
       "12  2016                    20015       16323        7933        8390   \n",
       "13  2016                    20016       35192       15966       19226   \n",
       "14  2016                    20017       20371        9183       11188   \n",
       "15  2016                    20018       20363        9392       10971   \n",
       "16  2016                    20019       57962       25232       32730   \n",
       "17  2016                    20020       54955       24526       30429   \n",
       "18  2016                    20024       12183        5582        6601   \n",
       "19  2016                    20032       41232       18460       22772   \n",
       "20  2016                    20036        5655        2619        3036   \n",
       "21  2016                    20037       15285        6969        8316   \n",
       "22  2016                    20045           0           0           0   \n",
       "23  2016                    20260           0           0           0   \n",
       "24  2017                    20001       45925       22060       23865   \n",
       "25  2017                    20002       61787       29602       32185   \n",
       "26  2017                    20003       30259       15633       14626   \n",
       "27  2017                    20004        1717         914         803   \n",
       "28  2017                    20005       13234        6740        6494   \n",
       "29  2017                    20006        2780        1332        1448   \n",
       "\n",
       "    DP05_0004E  DP05_0005E  DP05_0006E  DP05_0007E  DP05_0008E  ...  \\\n",
       "0       1769.0        1422        1043        2634        6592  ...   \n",
       "1       3923.0        2209        1998        2250        4514  ...   \n",
       "2       1574.0        1019         637         794        1871  ...   \n",
       "3         73.0           9           7           0          39  ...   \n",
       "4        486.0         128         114         113         888  ...   \n",
       "5          0.0           0           0        1500         924  ...   \n",
       "6       1435.0         894         751        1125        3845  ...   \n",
       "7       1107.0        1279         555         469        1871  ...   \n",
       "8       2085.0        1423         784         936        3379  ...   \n",
       "9       2449.0        1292         937        1071        2175  ...   \n",
       "10      4899.0        4052        2889        3061        3527  ...   \n",
       "11       802.0         766         643         584         730  ...   \n",
       "12      1000.0        1215        1289         757         455  ...   \n",
       "13      1826.0        1991        1526        3875        3100  ...   \n",
       "14      1790.0        1016         609         874        1498  ...   \n",
       "15      1913.0         927         845        1042        1280  ...   \n",
       "16      5092.0        4146        3380        4323        4799  ...   \n",
       "17      4540.0        4498        3782        3902        3956  ...   \n",
       "18       500.0         306         280         272         871  ...   \n",
       "19      4208.0        3696        2686        2778        4199  ...   \n",
       "20       208.0          39           4          12         418  ...   \n",
       "21       276.0          95           0        1828        4923  ...   \n",
       "22         0.0           0           0           0           0  ...   \n",
       "23         0.0           0           0           0           0  ...   \n",
       "24        92.4        1822        1324         873        2528  ...   \n",
       "25        92.0        4416        2339        1880        2131  ...   \n",
       "26       106.9        1899        1205         749         755  ...   \n",
       "27       113.8          83           8           8           8  ...   \n",
       "28       103.8         582         221         120          68  ...   \n",
       "29        92.0           0           0           0        1528  ...   \n",
       "\n",
       "    DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  DP05_0089E  \\\n",
       "0        18989           0           0           0           0           0   \n",
       "1        25522           0           0           0           0           0   \n",
       "2        11480           0           0           0           0           0   \n",
       "3          701           0           0           0           0           0   \n",
       "4         4891           0           0           0           0           0   \n",
       "5         1267           0           0           0           0           0   \n",
       "6        10595           0           0           0           0           0   \n",
       "7        12350           0           0           0           0           0   \n",
       "8        19557           0           0           0           0           0   \n",
       "9        11212           0           0           0           0           0   \n",
       "10       23763           0           0           0           0           0   \n",
       "11        5710           0           0           0           0           0   \n",
       "12        5929           0           0           0           0           0   \n",
       "13       14374           0           0           0           0           0   \n",
       "14        8993           0           0           0           0           0   \n",
       "15        8478           0           0           0           0           0   \n",
       "16       24376           0           0           0           0           0   \n",
       "17       22228           0           0           0           0           0   \n",
       "18        5616           0           0           0           0           0   \n",
       "19       16172           0           0           0           0           0   \n",
       "20        2494           0           0           0           0           0   \n",
       "21        7022           0           0           0           0           0   \n",
       "22           0           0           0           0           0           0   \n",
       "23           0           0           0           0           0           0   \n",
       "24         103        1040       20982       37707       17766       19941   \n",
       "25          58        1284       29425       49749       23402       26347   \n",
       "26          15         870       13718       25074       13047       12027   \n",
       "27           0          96        1560        1464         771         693   \n",
       "28          30         437        8917       10144        5184        4960   \n",
       "29           6         116         732        2360        1100        1260   \n",
       "\n",
       "     zip5  sale_date          mean  count  \n",
       "0   20001       2016  1.454690e+06    481  \n",
       "1   20002       2016  1.461908e+06    790  \n",
       "2   20003       2016  1.112682e+06    296  \n",
       "3   20004       2016  8.960241e+05     41  \n",
       "4   20005       2016  5.904078e+06    161  \n",
       "5   20006       2016  5.226107e+06     14  \n",
       "6   20007       2016  2.220986e+06    456  \n",
       "7   20008       2016  1.129182e+06    261  \n",
       "8   20009       2016  1.359884e+06    770  \n",
       "9   20010       2016  7.699310e+05    302  \n",
       "10  20011       2016  7.900579e+05    567  \n",
       "11  20012       2016  7.124332e+05    112  \n",
       "12  20015       2016  1.531458e+06    171  \n",
       "13  20016       2016  1.129660e+06    324  \n",
       "14  20017       2016  1.180954e+06    234  \n",
       "15  20018       2016  6.560603e+05    189  \n",
       "16  20019       2016  4.680264e+05    426  \n",
       "17  20020       2016  3.237930e+05    319  \n",
       "18  20024       2016  8.381499e+05    179  \n",
       "19  20032       2016  4.006602e+05    162  \n",
       "20  20036       2016  5.624033e+06     99  \n",
       "21  20037       2016  1.914406e+06    136  \n",
       "22  20045       2016  1.555000e+08      1  \n",
       "23  20260       2016  1.225000e+07      1  \n",
       "24  20001       2017  1.466283e+06    652  \n",
       "25  20002       2017  1.258494e+06    868  \n",
       "26  20003       2017  1.080292e+06    404  \n",
       "27  20004       2017  1.135991e+06     50  \n",
       "28  20005       2017  5.484323e+06    243  \n",
       "29  20006       2017  2.935500e+07     20  \n",
       "\n",
       "[30 rows x 95 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new_df = pd.merge(A_df, B_df,  how='left', left_on=['A_c1','c2'], right_on = ['B_c1','c2'])\n",
    "Merged_Matrix = pd.merge(Final_Table, house_price_avg, how='inner', left_on=['zip code tabulation area','YEAR'], right_on=['zip5','sale_date'])\n",
    "#Merged_Matrix = Final_Table.join(house_price_avg, lsuffix=['zip code tabulation area','YEAR'], rsuffix=['zip5','sale_date'])  \n",
    "Merged_Matrix.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eccb495f-15d0-4990-916b-98f214b270a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Merged_Matrix.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3baa830-1cab-49a7-a9f9-5c0e0f3f40d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type(Merged_Matrix)\n",
    "len(Merged_Matrix.index)\n",
    "#Merged_Matrix = Merged_Matrix.dropna(subset=['mean'])\n",
    "#Merged_Matrix.dtypes\n",
    "#Merged_Matrix['mean']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b25e92ae-0ed0-42cb-bbd9-55152de0c317",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask_ml.datasets\n",
    "import dask_ml.cluster\n",
    "#from dask_ml.cluster import KMeans\n",
    "\n",
    "#clf = dask_ml.cluster.KMeans()\n",
    "#type(clf)\n",
    "#clf.fit(Merged_Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "761475f0-4b51-4347-a7f1-b978cbef0b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>YEAR</th>\n",
       "      <th>zip code tabulation area</th>\n",
       "      <th>DP05_0001E</th>\n",
       "      <th>DP05_0002E</th>\n",
       "      <th>DP05_0003E</th>\n",
       "      <th>DP05_0004E</th>\n",
       "      <th>DP05_0005E</th>\n",
       "      <th>DP05_0006E</th>\n",
       "      <th>DP05_0007E</th>\n",
       "      <th>DP05_0008E</th>\n",
       "      <th>...</th>\n",
       "      <th>DP05_0084E</th>\n",
       "      <th>DP05_0085E</th>\n",
       "      <th>DP05_0086E</th>\n",
       "      <th>DP05_0087E</th>\n",
       "      <th>DP05_0088E</th>\n",
       "      <th>DP05_0089E</th>\n",
       "      <th>zip5</th>\n",
       "      <th>sale_date</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>20001</td>\n",
       "      <td>44998</td>\n",
       "      <td>21903</td>\n",
       "      <td>23095</td>\n",
       "      <td>1769.0</td>\n",
       "      <td>1422</td>\n",
       "      <td>1043</td>\n",
       "      <td>2634</td>\n",
       "      <td>6592</td>\n",
       "      <td>...</td>\n",
       "      <td>18989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20001</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.454690e+06</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>20002</td>\n",
       "      <td>59659</td>\n",
       "      <td>28845</td>\n",
       "      <td>30814</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>2209</td>\n",
       "      <td>1998</td>\n",
       "      <td>2250</td>\n",
       "      <td>4514</td>\n",
       "      <td>...</td>\n",
       "      <td>25522</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20002</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.461908e+06</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>20003</td>\n",
       "      <td>29071</td>\n",
       "      <td>15335</td>\n",
       "      <td>13736</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>637</td>\n",
       "      <td>794</td>\n",
       "      <td>1871</td>\n",
       "      <td>...</td>\n",
       "      <td>11480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20003</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.112682e+06</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>20004</td>\n",
       "      <td>1708</td>\n",
       "      <td>923</td>\n",
       "      <td>785</td>\n",
       "      <td>73.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20004</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.960241e+05</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>20005</td>\n",
       "      <td>13063</td>\n",
       "      <td>6626</td>\n",
       "      <td>6437</td>\n",
       "      <td>486.0</td>\n",
       "      <td>128</td>\n",
       "      <td>114</td>\n",
       "      <td>113</td>\n",
       "      <td>888</td>\n",
       "      <td>...</td>\n",
       "      <td>4891</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20005</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.904078e+06</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR zip code tabulation area  DP05_0001E  DP05_0002E  DP05_0003E  \\\n",
       "0  2016                    20001       44998       21903       23095   \n",
       "1  2016                    20002       59659       28845       30814   \n",
       "2  2016                    20003       29071       15335       13736   \n",
       "3  2016                    20004        1708         923         785   \n",
       "4  2016                    20005       13063        6626        6437   \n",
       "\n",
       "   DP05_0004E  DP05_0005E  DP05_0006E  DP05_0007E  DP05_0008E  ...  \\\n",
       "0      1769.0        1422        1043        2634        6592  ...   \n",
       "1      3923.0        2209        1998        2250        4514  ...   \n",
       "2      1574.0        1019         637         794        1871  ...   \n",
       "3        73.0           9           7           0          39  ...   \n",
       "4       486.0         128         114         113         888  ...   \n",
       "\n",
       "   DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  DP05_0089E  \\\n",
       "0       18989           0           0           0           0           0   \n",
       "1       25522           0           0           0           0           0   \n",
       "2       11480           0           0           0           0           0   \n",
       "3         701           0           0           0           0           0   \n",
       "4        4891           0           0           0           0           0   \n",
       "\n",
       "    zip5  sale_date          mean  count  \n",
       "0  20001       2016  1.454690e+06    481  \n",
       "1  20002       2016  1.461908e+06    790  \n",
       "2  20003       2016  1.112682e+06    296  \n",
       "3  20004       2016  8.960241e+05     41  \n",
       "4  20005       2016  5.904078e+06    161  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Values pre-scaled\n",
    "Merged_Matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0e13538-e11e-41b5-8f25-8cf8ed54125e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>zip code tabulation area</th>\n",
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       "      <th>zip5</th>\n",
       "      <th>sale_date</th>\n",
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       "      <th>count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.454690e+06</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>20002</td>\n",
       "      <td>59659</td>\n",
       "      <td>28845</td>\n",
       "      <td>30814</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>2209</td>\n",
       "      <td>1998</td>\n",
       "      <td>2250</td>\n",
       "      <td>4514</td>\n",
       "      <td>...</td>\n",
       "      <td>25522</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2016</td>\n",
       "      <td>1.461908e+06</td>\n",
       "      <td>790</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>20003</td>\n",
       "      <td>29071</td>\n",
       "      <td>15335</td>\n",
       "      <td>13736</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>637</td>\n",
       "      <td>794</td>\n",
       "      <td>1871</td>\n",
       "      <td>...</td>\n",
       "      <td>11480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20003</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.112682e+06</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>20004</td>\n",
       "      <td>1708</td>\n",
       "      <td>923</td>\n",
       "      <td>785</td>\n",
       "      <td>73.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20004</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.960241e+05</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>20005</td>\n",
       "      <td>13063</td>\n",
       "      <td>6626</td>\n",
       "      <td>6437</td>\n",
       "      <td>486.0</td>\n",
       "      <td>128</td>\n",
       "      <td>114</td>\n",
       "      <td>113</td>\n",
       "      <td>888</td>\n",
       "      <td>...</td>\n",
       "      <td>4891</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20005</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.904078e+06</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR zip code tabulation area  DP05_0001E  DP05_0002E  DP05_0003E  \\\n",
       "0  2016                    20001       44998       21903       23095   \n",
       "1  2016                    20002       59659       28845       30814   \n",
       "2  2016                    20003       29071       15335       13736   \n",
       "3  2016                    20004        1708         923         785   \n",
       "4  2016                    20005       13063        6626        6437   \n",
       "\n",
       "   DP05_0004E  DP05_0005E  DP05_0006E  DP05_0007E  DP05_0008E  ...  \\\n",
       "0      1769.0        1422        1043        2634        6592  ...   \n",
       "1      3923.0        2209        1998        2250        4514  ...   \n",
       "2      1574.0        1019         637         794        1871  ...   \n",
       "3        73.0           9           7           0          39  ...   \n",
       "4       486.0         128         114         113         888  ...   \n",
       "\n",
       "   DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  DP05_0089E  \\\n",
       "0       18989           0           0           0           0           0   \n",
       "1       25522           0           0           0           0           0   \n",
       "2       11480           0           0           0           0           0   \n",
       "3         701           0           0           0           0           0   \n",
       "4        4891           0           0           0           0           0   \n",
       "\n",
       "    zip5  sale_date          mean  count  \n",
       "0  20001       2016  1.454690e+06    481  \n",
       "1  20002       2016  1.461908e+06    790  \n",
       "2  20003       2016  1.112682e+06    296  \n",
       "3  20004       2016  8.960241e+05     41  \n",
       "4  20005       2016  5.904078e+06    161  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Merged_Matrix_NotScaled = Merged_Matrix\n",
    "Merged_Matrix_NotScaled.head()\n",
    "#Merged_Matrix.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d83c3f90-5250-447d-abd6-ec4f921d489a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DP05_0001E</th>\n",
       "      <th>DP05_0002E</th>\n",
       "      <th>DP05_0003E</th>\n",
       "      <th>DP05_0004E</th>\n",
       "      <th>DP05_0005E</th>\n",
       "      <th>DP05_0006E</th>\n",
       "      <th>DP05_0007E</th>\n",
       "      <th>DP05_0008E</th>\n",
       "      <th>DP05_0009E</th>\n",
       "      <th>DP05_0010E</th>\n",
       "      <th>...</th>\n",
       "      <th>DP05_0082E</th>\n",
       "      <th>DP05_0083E</th>\n",
       "      <th>DP05_0084E</th>\n",
       "      <th>DP05_0085E</th>\n",
       "      <th>DP05_0086E</th>\n",
       "      <th>DP05_0087E</th>\n",
       "      <th>DP05_0088E</th>\n",
       "      <th>DP05_0089E</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44998</td>\n",
       "      <td>21903</td>\n",
       "      <td>23095</td>\n",
       "      <td>1769.0</td>\n",
       "      <td>1422</td>\n",
       "      <td>1043</td>\n",
       "      <td>2634</td>\n",
       "      <td>6592</td>\n",
       "      <td>14120</td>\n",
       "      <td>6464</td>\n",
       "      <td>...</td>\n",
       "      <td>36438</td>\n",
       "      <td>17449</td>\n",
       "      <td>18989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.454690e+06</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59659</td>\n",
       "      <td>28845</td>\n",
       "      <td>30814</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>2209</td>\n",
       "      <td>1998</td>\n",
       "      <td>2250</td>\n",
       "      <td>4514</td>\n",
       "      <td>16642</td>\n",
       "      <td>9582</td>\n",
       "      <td>...</td>\n",
       "      <td>48063</td>\n",
       "      <td>22541</td>\n",
       "      <td>25522</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.461908e+06</td>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29071</td>\n",
       "      <td>15335</td>\n",
       "      <td>13736</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>637</td>\n",
       "      <td>794</td>\n",
       "      <td>1871</td>\n",
       "      <td>8798</td>\n",
       "      <td>5190</td>\n",
       "      <td>...</td>\n",
       "      <td>24409</td>\n",
       "      <td>12929</td>\n",
       "      <td>11480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.112682e+06</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1708</td>\n",
       "      <td>923</td>\n",
       "      <td>785</td>\n",
       "      <td>73.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>454</td>\n",
       "      <td>396</td>\n",
       "      <td>...</td>\n",
       "      <td>1481</td>\n",
       "      <td>780</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.960241e+05</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13063</td>\n",
       "      <td>6626</td>\n",
       "      <td>6437</td>\n",
       "      <td>486.0</td>\n",
       "      <td>128</td>\n",
       "      <td>114</td>\n",
       "      <td>113</td>\n",
       "      <td>888</td>\n",
       "      <td>5318</td>\n",
       "      <td>2285</td>\n",
       "      <td>...</td>\n",
       "      <td>10152</td>\n",
       "      <td>5261</td>\n",
       "      <td>4891</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.904078e+06</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DP05_0001E  DP05_0002E  DP05_0003E  DP05_0004E  DP05_0005E  DP05_0006E  \\\n",
       "0       44998       21903       23095      1769.0        1422        1043   \n",
       "1       59659       28845       30814      3923.0        2209        1998   \n",
       "2       29071       15335       13736      1574.0        1019         637   \n",
       "3        1708         923         785        73.0           9           7   \n",
       "4       13063        6626        6437       486.0         128         114   \n",
       "\n",
       "   DP05_0007E  DP05_0008E  DP05_0009E  DP05_0010E  ...  DP05_0082E  \\\n",
       "0        2634        6592       14120        6464  ...       36438   \n",
       "1        2250        4514       16642        9582  ...       48063   \n",
       "2         794        1871        8798        5190  ...       24409   \n",
       "3           0          39         454         396  ...        1481   \n",
       "4         113         888        5318        2285  ...       10152   \n",
       "\n",
       "   DP05_0083E  DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  \\\n",
       "0       17449       18989           0           0           0           0   \n",
       "1       22541       25522           0           0           0           0   \n",
       "2       12929       11480           0           0           0           0   \n",
       "3         780         701           0           0           0           0   \n",
       "4        5261        4891           0           0           0           0   \n",
       "\n",
       "   DP05_0089E          mean  count  \n",
       "0           0  1.454690e+06    481  \n",
       "1           0  1.461908e+06    790  \n",
       "2           0  1.112682e+06    296  \n",
       "3           0  8.960241e+05     41  \n",
       "4           0  5.904078e+06    161  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = Merged_Matrix[[\"YEAR\",\"zip code tabulation area\",\"zip5\",\"sale_date\"]]\n",
    "discrete_data = selected_columns.copy()\n",
    "\n",
    "#Drop Discrete columns\n",
    "Merged_Matrix = Merged_Matrix.drop(columns=['YEAR','zip code tabulation area','zip5','sale_date'])\n",
    "Merged_Matrix.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8efc55f2-1abe-4f21-8dbe-d6a2b85f827b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "09d56944-8c8a-4b61-9c40-4708c0aae9e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DP05_0001E</th>\n",
       "      <th>DP05_0002E</th>\n",
       "      <th>DP05_0003E</th>\n",
       "      <th>DP05_0004E</th>\n",
       "      <th>DP05_0005E</th>\n",
       "      <th>DP05_0006E</th>\n",
       "      <th>DP05_0007E</th>\n",
       "      <th>DP05_0008E</th>\n",
       "      <th>DP05_0009E</th>\n",
       "      <th>DP05_0010E</th>\n",
       "      <th>...</th>\n",
       "      <th>DP05_0082E</th>\n",
       "      <th>DP05_0083E</th>\n",
       "      <th>DP05_0084E</th>\n",
       "      <th>DP05_0085E</th>\n",
       "      <th>DP05_0086E</th>\n",
       "      <th>DP05_0087E</th>\n",
       "      <th>DP05_0088E</th>\n",
       "      <th>DP05_0089E</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.645503</td>\n",
       "      <td>0.640963</td>\n",
       "      <td>0.649539</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.254110</td>\n",
       "      <td>0.220507</td>\n",
       "      <td>0.609299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.691411</td>\n",
       "      <td>0.316661</td>\n",
       "      <td>...</td>\n",
       "      <td>0.758130</td>\n",
       "      <td>0.774101</td>\n",
       "      <td>0.744025</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",
       "      <td>0.007288</td>\n",
       "      <td>0.460653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.855817</td>\n",
       "      <td>0.844112</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.394746</td>\n",
       "      <td>0.422410</td>\n",
       "      <td>0.520472</td>\n",
       "      <td>0.684769</td>\n",
       "      <td>0.814905</td>\n",
       "      <td>0.469407</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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",
       "      <td>0.007334</td>\n",
       "      <td>0.757198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.417028</td>\n",
       "      <td>0.448759</td>\n",
       "      <td>0.386320</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.182094</td>\n",
       "      <td>0.134672</td>\n",
       "      <td>0.183669</td>\n",
       "      <td>0.283829</td>\n",
       "      <td>0.430810</td>\n",
       "      <td>0.254250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507854</td>\n",
       "      <td>0.573577</td>\n",
       "      <td>0.449808</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",
       "      <td>0.005084</td>\n",
       "      <td>0.283109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024502</td>\n",
       "      <td>0.027010</td>\n",
       "      <td>0.022078</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005916</td>\n",
       "      <td>0.022231</td>\n",
       "      <td>0.019399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030814</td>\n",
       "      <td>0.034604</td>\n",
       "      <td>0.027466</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",
       "      <td>0.003688</td>\n",
       "      <td>0.038388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.187391</td>\n",
       "      <td>0.193901</td>\n",
       "      <td>0.181038</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.022873</td>\n",
       "      <td>0.024101</td>\n",
       "      <td>0.026139</td>\n",
       "      <td>0.134709</td>\n",
       "      <td>0.260405</td>\n",
       "      <td>0.111938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.211223</td>\n",
       "      <td>0.233397</td>\n",
       "      <td>0.191639</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",
       "      <td>0.035961</td>\n",
       "      <td>0.153551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DP05_0001E  DP05_0002E  DP05_0003E  DP05_0004E  DP05_0005E  DP05_0006E  \\\n",
       "0    0.645503    0.640963    0.649539    0.999995    0.254110    0.220507   \n",
       "1    0.855817    0.844112    0.866633    0.999998    0.394746    0.422410   \n",
       "2    0.417028    0.448759    0.386320    0.999995    0.182094    0.134672   \n",
       "3    0.024502    0.027010    0.022078    0.999992    0.001608    0.001480   \n",
       "4    0.187391    0.193901    0.181038    0.999993    0.022873    0.024101   \n",
       "\n",
       "   DP05_0007E  DP05_0008E  DP05_0009E  DP05_0010E  ...  DP05_0082E  \\\n",
       "0    0.609299    1.000000    0.691411    0.316661  ...    0.758130   \n",
       "1    0.520472    0.684769    0.814905    0.469407  ...    1.000000   \n",
       "2    0.183669    0.283829    0.430810    0.254250  ...    0.507854   \n",
       "3    0.000000    0.005916    0.022231    0.019399  ...    0.030814   \n",
       "4    0.026139    0.134709    0.260405    0.111938  ...    0.211223   \n",
       "\n",
       "   DP05_0083E  DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  \\\n",
       "0    0.774101    0.744025         0.0         0.0         0.0         0.0   \n",
       "1    1.000000    1.000000         0.0         0.0         0.0         0.0   \n",
       "2    0.573577    0.449808         0.0         0.0         0.0         0.0   \n",
       "3    0.034604    0.027466         0.0         0.0         0.0         0.0   \n",
       "4    0.233397    0.191639         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   DP05_0089E      mean     count  \n",
       "0         0.0  0.007288  0.460653  \n",
       "1         0.0  0.007334  0.757198  \n",
       "2         0.0  0.005084  0.283109  \n",
       "3         0.0  0.003688  0.038388  \n",
       "4         0.0  0.035961  0.153551  \n",
       "\n",
       "[5 rows x 91 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score as ss\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns\n",
    "  \n",
    "scaler = MinMaxScaler()\n",
    "  \n",
    "# Scaling the Price column of the created dataFrame and storing\n",
    "# the result in ScaledPrice Column\n",
    "Merged_Matrix[[\"DP05_0001E\",\"DP05_0002E\",\"DP05_0003E\",\"DP05_0004E\",\"DP05_0005E\",\"DP05_0006E\",\"DP05_0007E\",\"DP05_0008E\",\"DP05_0009E\",\"DP05_0010E\",\"DP05_0011E\",\"DP05_0012E\",\"DP05_0013E\",\"DP05_0014E\",\"DP05_0015E\",\"DP05_0016E\",\"DP05_0017E\",\"DP05_0018E\",\"DP05_0019E\",\"DP05_0020E\",\"DP05_0021E\",\"DP05_0022E\",\"DP05_0023E\",\"DP05_0024E\",\"DP05_0025E\",\"DP05_0026E\",\"DP05_0027E\",\"DP05_0028E\",\"DP05_0029E\",\"DP05_0030E\",\"DP05_0031E\",\"DP05_0032E\",\"DP05_0033E\",\"DP05_0034E\",\"DP05_0035E\",\"DP05_0036E\",\"DP05_0037E\",\"DP05_0038E\",\"DP05_0039E\",\"DP05_0040E\",\"DP05_0041E\",\"DP05_0042E\",\"DP05_0043E\",\"DP05_0044E\",\"DP05_0045E\",\"DP05_0046E\",\"DP05_0047E\",\"DP05_0048E\",\"DP05_0049E\",\"DP05_0050E\",\"DP05_0051E\",\"DP05_0052E\",\"DP05_0053E\",\"DP05_0054E\",\"DP05_0055E\",\"DP05_0056E\",\"DP05_0057E\",\"DP05_0058E\",\"DP05_0059E\",\"DP05_0060E\",\"DP05_0061E\",\"DP05_0062E\",\"DP05_0063E\",\"DP05_0064E\",\"DP05_0065E\",\"DP05_0066E\",\"DP05_0067E\",\"DP05_0068E\",\"DP05_0069E\",\"DP05_0070E\",\"DP05_0071E\",\"DP05_0072E\",\"DP05_0073E\",\"DP05_0074E\",\"DP05_0075E\",\"DP05_0076E\",\"DP05_0077E\",\"DP05_0078E\",\"DP05_0079E\",\"DP05_0080E\",\"DP05_0081E\",\"DP05_0082E\",\"DP05_0083E\",\"DP05_0084E\",\"DP05_0085E\",\"DP05_0086E\",\"DP05_0087E\",\"DP05_0088E\",\"DP05_0089E\",\"mean\",\"count\"]] = scaler.fit_transform(Merged_Matrix[[\"DP05_0001E\",\"DP05_0002E\",\"DP05_0003E\",\"DP05_0004E\",\"DP05_0005E\",\"DP05_0006E\",\"DP05_0007E\",\"DP05_0008E\",\"DP05_0009E\",\"DP05_0010E\",\"DP05_0011E\",\"DP05_0012E\",\"DP05_0013E\",\"DP05_0014E\",\"DP05_0015E\",\"DP05_0016E\",\"DP05_0017E\",\"DP05_0018E\",\"DP05_0019E\",\"DP05_0020E\",\"DP05_0021E\",\"DP05_0022E\",\"DP05_0023E\",\"DP05_0024E\",\"DP05_0025E\",\"DP05_0026E\",\"DP05_0027E\",\"DP05_0028E\",\"DP05_0029E\",\"DP05_0030E\",\"DP05_0031E\",\"DP05_0032E\",\"DP05_0033E\",\"DP05_0034E\",\"DP05_0035E\",\"DP05_0036E\",\"DP05_0037E\",\"DP05_0038E\",\"DP05_0039E\",\"DP05_0040E\",\"DP05_0041E\",\"DP05_0042E\",\"DP05_0043E\",\"DP05_0044E\",\"DP05_0045E\",\"DP05_0046E\",\"DP05_0047E\",\"DP05_0048E\",\"DP05_0049E\",\"DP05_0050E\",\"DP05_0051E\",\"DP05_0052E\",\"DP05_0053E\",\"DP05_0054E\",\"DP05_0055E\",\"DP05_0056E\",\"DP05_0057E\",\"DP05_0058E\",\"DP05_0059E\",\"DP05_0060E\",\"DP05_0061E\",\"DP05_0062E\",\"DP05_0063E\",\"DP05_0064E\",\"DP05_0065E\",\"DP05_0066E\",\"DP05_0067E\",\"DP05_0068E\",\"DP05_0069E\",\"DP05_0070E\",\"DP05_0071E\",\"DP05_0072E\",\"DP05_0073E\",\"DP05_0074E\",\"DP05_0075E\",\"DP05_0076E\",\"DP05_0077E\",\"DP05_0078E\",\"DP05_0079E\",\"DP05_0080E\",\"DP05_0081E\",\"DP05_0082E\",\"DP05_0083E\",\"DP05_0084E\",\"DP05_0085E\",\"DP05_0086E\",\"DP05_0087E\",\"DP05_0088E\",\"DP05_0089E\",\"mean\",\"count\"]])\n",
    "Merged_Matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "03294fb4-1e1e-448d-b712-94c9286b19ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merged_Matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2c70e403-e18a-4686-a130-d7aea325c393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 49.1 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25273e8fba8>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "inertias = []\n",
    "di = pd.DataFrame(columns=[\"k\",\"inertia\",\"model\",\"sil\"])\n",
    "di = di.set_index('k')\n",
    "\n",
    "for r in range(2,9):\n",
    "#for r in range(2,4):\n",
    " \n",
    "    km = dask_ml.cluster.KMeans(n_clusters=r,random_state=1)\n",
    "    km.fit(Merged_Matrix)\n",
    "\n",
    "    #get score\n",
    "    score = ss(Merged_Matrix,km.labels_)\n",
    "\n",
    "    ap = {\"k\":[r],\"inertia\":[km.inertia_],\"model\":[km],\"sil\":score}\n",
    "    d2 = pd.DataFrame(ap)\n",
    "    d2 = d2.set_index('k')\n",
    "    \n",
    "    #append\n",
    "    di = pd.concat([di,d2])#,ignore_index=True)\n",
    "    inertias.append(km.inertia_)\n",
    "    \n",
    "plt.plot(inertias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "29599c3b-6440-44b7-bb72-f3ea4a4c9a89",
   "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>inertia</th>\n",
       "      <th>model</th>\n",
       "      <th>sil</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>291.669274</td>\n",
       "      <td>KMeans(n_clusters=2, random_state=1)</td>\n",
       "      <td>0.384735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>254.372458</td>\n",
       "      <td>KMeans(n_clusters=3, random_state=1)</td>\n",
       "      <td>0.286062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.181965</td>\n",
       "      <td>KMeans(n_clusters=4, random_state=1)</td>\n",
       "      <td>0.424697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>128.017096</td>\n",
       "      <td>KMeans(n_clusters=5, random_state=1)</td>\n",
       "      <td>0.434054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>117.191606</td>\n",
       "      <td>KMeans(n_clusters=6, random_state=1)</td>\n",
       "      <td>0.387198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.730365</td>\n",
       "      <td>KMeans(n_clusters=7, random_state=1)</td>\n",
       "      <td>0.388360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>95.471027</td>\n",
       "      <td>KMeans(random_state=1)</td>\n",
       "      <td>0.372182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      inertia                                 model       sil\n",
       "k                                                            \n",
       "2  291.669274  KMeans(n_clusters=2, random_state=1)  0.384735\n",
       "3  254.372458  KMeans(n_clusters=3, random_state=1)  0.286062\n",
       "4  151.181965  KMeans(n_clusters=4, random_state=1)  0.424697\n",
       "5  128.017096  KMeans(n_clusters=5, random_state=1)  0.434054\n",
       "6  117.191606  KMeans(n_clusters=6, random_state=1)  0.387198\n",
       "7  100.730365  KMeans(n_clusters=7, random_state=1)  0.388360\n",
       "8   95.471027                KMeans(random_state=1)  0.372182"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "di"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5ec0a847-90d0-4df1-914d-e119c30aa259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans(n_clusters=5, random_state=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1,\n",
       "       1, 1, 3, 0, 3, 1, 1, 1, 3, 3, 3, 3, 0, 1, 1, 3, 1, 1, 4, 4, 1, 4,\n",
       "       1, 1, 1, 3, 0, 3, 1, 1, 1, 3, 3, 3, 3, 0, 1, 1, 3, 1, 1, 4, 4, 1,\n",
       "       4, 1, 1, 3, 0, 3, 1, 1, 1, 3, 3, 3, 3, 0, 1, 1, 3, 1, 1, 4, 4, 1,\n",
       "       4, 1, 1])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = di.query(\"sil == sil.max()\")\n",
    "bestK = best.model.values[0]\n",
    "print(bestK)\n",
    "labels = bestK.labels_.compute()\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3abe7dc5-5397-4304-8460-fadd8c30de3f",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>DP05_0001E</th>\n",
       "      <th>DP05_0002E</th>\n",
       "      <th>DP05_0003E</th>\n",
       "      <th>DP05_0004E</th>\n",
       "      <th>DP05_0005E</th>\n",
       "      <th>DP05_0006E</th>\n",
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       "      <th>DP05_0009E</th>\n",
       "      <th>DP05_0010E</th>\n",
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       "      <th>DP05_0083E</th>\n",
       "      <th>DP05_0084E</th>\n",
       "      <th>DP05_0085E</th>\n",
       "      <th>DP05_0086E</th>\n",
       "      <th>DP05_0087E</th>\n",
       "      <th>DP05_0088E</th>\n",
       "      <th>DP05_0089E</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>clusters</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.645503</td>\n",
       "      <td>0.640963</td>\n",
       "      <td>0.649539</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.254110</td>\n",
       "      <td>0.220507</td>\n",
       "      <td>0.609299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.691411</td>\n",
       "      <td>0.316661</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774101</td>\n",
       "      <td>0.744025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>0.460653</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.855817</td>\n",
       "      <td>0.844112</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.394746</td>\n",
       "      <td>0.422410</td>\n",
       "      <td>0.520472</td>\n",
       "      <td>0.684769</td>\n",
       "      <td>0.814905</td>\n",
       "      <td>0.469407</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.757198</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.417028</td>\n",
       "      <td>0.448759</td>\n",
       "      <td>0.386320</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.182094</td>\n",
       "      <td>0.134672</td>\n",
       "      <td>0.183669</td>\n",
       "      <td>0.283829</td>\n",
       "      <td>0.430810</td>\n",
       "      <td>0.254250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.573577</td>\n",
       "      <td>0.449808</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005084</td>\n",
       "      <td>0.283109</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024502</td>\n",
       "      <td>0.027010</td>\n",
       "      <td>0.022078</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005916</td>\n",
       "      <td>0.022231</td>\n",
       "      <td>0.019399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034604</td>\n",
       "      <td>0.027466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003688</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.187391</td>\n",
       "      <td>0.193901</td>\n",
       "      <td>0.181038</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.022873</td>\n",
       "      <td>0.024101</td>\n",
       "      <td>0.026139</td>\n",
       "      <td>0.134709</td>\n",
       "      <td>0.260405</td>\n",
       "      <td>0.111938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233397</td>\n",
       "      <td>0.191639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035961</td>\n",
       "      <td>0.153551</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
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       "      <td>0.891636</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.982666</td>\n",
       "      <td>0.882664</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>0.504248</td>\n",
       "      <td>0.162570</td>\n",
       "      <td>0.475677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028437</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.760951</td>\n",
       "      <td>0.752229</td>\n",
       "      <td>0.682662</td>\n",
       "      <td>0.815511</td>\n",
       "      <td>0.007081</td>\n",
       "      <td>0.443378</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.208435</td>\n",
       "      <td>0.201510</td>\n",
       "      <td>0.214985</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.113117</td>\n",
       "      <td>0.048837</td>\n",
       "      <td>0.061300</td>\n",
       "      <td>0.054915</td>\n",
       "      <td>0.040398</td>\n",
       "      <td>0.215549</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022581</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.327542</td>\n",
       "      <td>0.276608</td>\n",
       "      <td>0.233343</td>\n",
       "      <td>0.230549</td>\n",
       "      <td>0.235884</td>\n",
       "      <td>0.020492</td>\n",
       "      <td>0.158349</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.613972</td>\n",
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       "      <td>0.660592</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.799678</td>\n",
       "      <td>0.873150</td>\n",
       "      <td>0.676382</td>\n",
       "      <td>0.421268</td>\n",
       "      <td>0.177260</td>\n",
       "      <td>0.359330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.572716</td>\n",
       "      <td>0.527679</td>\n",
       "      <td>0.541034</td>\n",
       "      <td>0.496998</td>\n",
       "      <td>0.581091</td>\n",
       "      <td>0.013477</td>\n",
       "      <td>0.275432</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.078253</td>\n",
       "      <td>0.078544</td>\n",
       "      <td>0.077933</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.008935</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002731</td>\n",
       "      <td>0.027372</td>\n",
       "      <td>0.121344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004836</td>\n",
       "      <td>0.000588</td>\n",
       "      <td>0.060489</td>\n",
       "      <td>0.134327</td>\n",
       "      <td>0.089263</td>\n",
       "      <td>0.094009</td>\n",
       "      <td>0.084945</td>\n",
       "      <td>0.023630</td>\n",
       "      <td>0.148752</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.217745</td>\n",
       "      <td>0.221965</td>\n",
       "      <td>0.213579</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.041637</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>0.323119</td>\n",
       "      <td>0.239790</td>\n",
       "      <td>0.148729</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015217</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.170528</td>\n",
       "      <td>0.242498</td>\n",
       "      <td>0.233268</td>\n",
       "      <td>0.242123</td>\n",
       "      <td>0.225212</td>\n",
       "      <td>0.016211</td>\n",
       "      <td>0.224568</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 92 columns</p>\n",
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       "    DP05_0001E  DP05_0002E  DP05_0003E  DP05_0004E  DP05_0005E  DP05_0006E  \\\n",
       "0     0.645503    0.640963    0.649539    0.999995    0.254110    0.220507   \n",
       "1     0.855817    0.844112    0.866633    0.999998    0.394746    0.422410   \n",
       "2     0.417028    0.448759    0.386320    0.999995    0.182094    0.134672   \n",
       "3     0.024502    0.027010    0.022078    0.999992    0.001608    0.001480   \n",
       "4     0.187391    0.193901    0.181038    0.999993    0.022873    0.024101   \n",
       "..         ...         ...         ...         ...         ...         ...   \n",
       "86    0.816196    0.737270    0.891636    0.999992    0.982666    0.882664   \n",
       "87    0.208435    0.201510    0.214985    0.999992    0.113117    0.048837   \n",
       "88    0.613972    0.565141    0.660592    0.999992    0.799678    0.873150   \n",
       "89    0.078253    0.078544    0.077933    0.999993    0.008935    0.012051   \n",
       "90    0.217745    0.221965    0.213579    0.999993    0.041637    0.003171   \n",
       "\n",
       "    DP05_0007E  DP05_0008E  DP05_0009E  DP05_0010E  ...  DP05_0083E  \\\n",
       "0     0.609299    1.000000    0.691411    0.316661  ...    0.774101   \n",
       "1     0.520472    0.684769    0.814905    0.469407  ...    1.000000   \n",
       "2     0.183669    0.283829    0.430810    0.254250  ...    0.573577   \n",
       "3     0.000000    0.005916    0.022231    0.019399  ...    0.034604   \n",
       "4     0.026139    0.134709    0.260405    0.111938  ...    0.233397   \n",
       "..         ...         ...         ...         ...  ...         ...   \n",
       "86    0.962757    0.504248    0.162570    0.475677  ...    0.028437   \n",
       "87    0.061300    0.054915    0.040398    0.215549  ...    0.022581   \n",
       "88    0.676382    0.421268    0.177260    0.359330  ...    0.039484   \n",
       "89    0.002082    0.002731    0.027372    0.121344  ...    0.004836   \n",
       "90    0.007634    0.323119    0.239790    0.148729  ...    0.015217   \n",
       "\n",
       "    DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  DP05_0089E  \\\n",
       "0     0.744025    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "1     1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "2     0.449808    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "3     0.027466    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "4     0.191639    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "..         ...         ...         ...         ...         ...         ...   \n",
       "86    0.003370    0.357143    0.760951    0.752229    0.682662    0.815511   \n",
       "87    0.000000    0.327542    0.276608    0.233343    0.230549    0.235884   \n",
       "88    0.000000    0.572716    0.527679    0.541034    0.496998    0.581091   \n",
       "89    0.000588    0.060489    0.134327    0.089263    0.094009    0.084945   \n",
       "90    0.003056    0.170528    0.242498    0.233268    0.242123    0.225212   \n",
       "\n",
       "        mean     count  clusters  \n",
       "0   0.007288  0.460653         2  \n",
       "1   0.007334  0.757198         2  \n",
       "2   0.005084  0.283109         2  \n",
       "3   0.003688  0.038388         1  \n",
       "4   0.035961  0.153551         1  \n",
       "..       ...       ...       ...  \n",
       "86  0.007081  0.443378         4  \n",
       "87  0.020492  0.158349         1  \n",
       "88  0.013477  0.275432         4  \n",
       "89  0.023630  0.148752         1  \n",
       "90  0.016211  0.224568         1  \n",
       "\n",
       "[91 rows x 92 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#join back clusters and records\n",
    "Merged_Matrix = pd.concat([Merged_Matrix,pd.Series(labels,name='clusters')],axis=1)\n",
    "Merged_Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51494e1a-ad6c-48a1-adda-9d0fc3af8363",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>zip code tabulation area</th>\n",
       "      <th>zip5</th>\n",
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       "      <th>DP05_0084E</th>\n",
       "      <th>DP05_0085E</th>\n",
       "      <th>DP05_0086E</th>\n",
       "      <th>DP05_0087E</th>\n",
       "      <th>DP05_0088E</th>\n",
       "      <th>DP05_0089E</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>20001</td>\n",
       "      <td>20001</td>\n",
       "      <td>2016</td>\n",
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       "      <td>0.640963</td>\n",
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       "      <td>0.999995</td>\n",
       "      <td>0.254110</td>\n",
       "      <td>0.220507</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>20002</td>\n",
       "      <td>20002</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.855817</td>\n",
       "      <td>0.844112</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.394746</td>\n",
       "      <td>0.422410</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007334</td>\n",
       "      <td>0.757198</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>20003</td>\n",
       "      <td>20003</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.417028</td>\n",
       "      <td>0.448759</td>\n",
       "      <td>0.386320</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.182094</td>\n",
       "      <td>0.134672</td>\n",
       "      <td>...</td>\n",
       "      <td>0.573577</td>\n",
       "      <td>0.449808</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005084</td>\n",
       "      <td>0.283109</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>20004</td>\n",
       "      <td>20004</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.024502</td>\n",
       "      <td>0.027010</td>\n",
       "      <td>0.022078</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034604</td>\n",
       "      <td>0.027466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003688</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>20005</td>\n",
       "      <td>20005</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.187391</td>\n",
       "      <td>0.193901</td>\n",
       "      <td>0.181038</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.022873</td>\n",
       "      <td>0.024101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233397</td>\n",
       "      <td>0.191639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035961</td>\n",
       "      <td>0.153551</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2019</td>\n",
       "      <td>20020</td>\n",
       "      <td>20020</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.816196</td>\n",
       "      <td>0.737270</td>\n",
       "      <td>0.891636</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.982666</td>\n",
       "      <td>0.882664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028437</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.760951</td>\n",
       "      <td>0.752229</td>\n",
       "      <td>0.682662</td>\n",
       "      <td>0.815511</td>\n",
       "      <td>0.007081</td>\n",
       "      <td>0.443378</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2019</td>\n",
       "      <td>20024</td>\n",
       "      <td>20024</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.208435</td>\n",
       "      <td>0.201510</td>\n",
       "      <td>0.214985</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.113117</td>\n",
       "      <td>0.048837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022581</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.327542</td>\n",
       "      <td>0.276608</td>\n",
       "      <td>0.233343</td>\n",
       "      <td>0.230549</td>\n",
       "      <td>0.235884</td>\n",
       "      <td>0.020492</td>\n",
       "      <td>0.158349</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2019</td>\n",
       "      <td>20032</td>\n",
       "      <td>20032</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.613972</td>\n",
       "      <td>0.565141</td>\n",
       "      <td>0.660592</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.799678</td>\n",
       "      <td>0.873150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.572716</td>\n",
       "      <td>0.527679</td>\n",
       "      <td>0.541034</td>\n",
       "      <td>0.496998</td>\n",
       "      <td>0.581091</td>\n",
       "      <td>0.013477</td>\n",
       "      <td>0.275432</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2019</td>\n",
       "      <td>20036</td>\n",
       "      <td>20036</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>0.078544</td>\n",
       "      <td>0.077933</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.008935</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004836</td>\n",
       "      <td>0.000588</td>\n",
       "      <td>0.060489</td>\n",
       "      <td>0.134327</td>\n",
       "      <td>0.089263</td>\n",
       "      <td>0.094009</td>\n",
       "      <td>0.084945</td>\n",
       "      <td>0.023630</td>\n",
       "      <td>0.148752</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2019</td>\n",
       "      <td>20037</td>\n",
       "      <td>20037</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.217745</td>\n",
       "      <td>0.221965</td>\n",
       "      <td>0.213579</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.041637</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015217</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.170528</td>\n",
       "      <td>0.242498</td>\n",
       "      <td>0.233268</td>\n",
       "      <td>0.242123</td>\n",
       "      <td>0.225212</td>\n",
       "      <td>0.016211</td>\n",
       "      <td>0.224568</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR zip code tabulation area   zip5 sale_date  DP05_0001E  DP05_0002E  \\\n",
       "0   2016                    20001  20001      2016    0.645503    0.640963   \n",
       "1   2016                    20002  20002      2016    0.855817    0.844112   \n",
       "2   2016                    20003  20003      2016    0.417028    0.448759   \n",
       "3   2016                    20004  20004      2016    0.024502    0.027010   \n",
       "4   2016                    20005  20005      2016    0.187391    0.193901   \n",
       "..   ...                      ...    ...       ...         ...         ...   \n",
       "86  2019                    20020  20020      2019    0.816196    0.737270   \n",
       "87  2019                    20024  20024      2019    0.208435    0.201510   \n",
       "88  2019                    20032  20032      2019    0.613972    0.565141   \n",
       "89  2019                    20036  20036      2019    0.078253    0.078544   \n",
       "90  2019                    20037  20037      2019    0.217745    0.221965   \n",
       "\n",
       "    DP05_0003E  DP05_0004E  DP05_0005E  DP05_0006E  ...  DP05_0083E  \\\n",
       "0     0.649539    0.999995    0.254110    0.220507  ...    0.774101   \n",
       "1     0.866633    0.999998    0.394746    0.422410  ...    1.000000   \n",
       "2     0.386320    0.999995    0.182094    0.134672  ...    0.573577   \n",
       "3     0.022078    0.999992    0.001608    0.001480  ...    0.034604   \n",
       "4     0.181038    0.999993    0.022873    0.024101  ...    0.233397   \n",
       "..         ...         ...         ...         ...  ...         ...   \n",
       "86    0.891636    0.999992    0.982666    0.882664  ...    0.028437   \n",
       "87    0.214985    0.999992    0.113117    0.048837  ...    0.022581   \n",
       "88    0.660592    0.999992    0.799678    0.873150  ...    0.039484   \n",
       "89    0.077933    0.999993    0.008935    0.012051  ...    0.004836   \n",
       "90    0.213579    0.999993    0.041637    0.003171  ...    0.015217   \n",
       "\n",
       "    DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  DP05_0089E  \\\n",
       "0     0.744025    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "1     1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "2     0.449808    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "3     0.027466    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "4     0.191639    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "..         ...         ...         ...         ...         ...         ...   \n",
       "86    0.003370    0.357143    0.760951    0.752229    0.682662    0.815511   \n",
       "87    0.000000    0.327542    0.276608    0.233343    0.230549    0.235884   \n",
       "88    0.000000    0.572716    0.527679    0.541034    0.496998    0.581091   \n",
       "89    0.000588    0.060489    0.134327    0.089263    0.094009    0.084945   \n",
       "90    0.003056    0.170528    0.242498    0.233268    0.242123    0.225212   \n",
       "\n",
       "        mean     count  clusters  \n",
       "0   0.007288  0.460653         2  \n",
       "1   0.007334  0.757198         2  \n",
       "2   0.005084  0.283109         2  \n",
       "3   0.003688  0.038388         1  \n",
       "4   0.035961  0.153551         1  \n",
       "..       ...       ...       ...  \n",
       "86  0.007081  0.443378         4  \n",
       "87  0.020492  0.158349         1  \n",
       "88  0.013477  0.275432         4  \n",
       "89  0.023630  0.148752         1  \n",
       "90  0.016211  0.224568         1  \n",
       "\n",
       "[91 rows x 96 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Merged_Matrix = pd.concat([discrete_data,Merged_Matrix],axis=1)\n",
    "Merged_Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4c521605-256c-422b-aaba-1de485e3503c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merged_Matrix\n",
    "#discrete_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cab1c5f3-7490-47d1-a6f2-0ff8e0922f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0', '1', '2', '3', '4']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "#get a tree\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "\n",
    "#get sorted labels for plot \n",
    "import numpy as np\n",
    "sorted = Merged_Matrix[\"clusters\"].unique()\n",
    "sorted = np.sort(sorted)\n",
    "sorted = list(map(str, sorted))\n",
    "sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "066bafa7-40e3-4991-87e3-16187b788bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='zip5', ylabel='clusters'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot - Not super interesting\n",
    "import seaborn as sns\n",
    "sns.scatterplot(data=Merged_Matrix, x=\"zip5\", y=\"clusters\", hue=\"YEAR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1122d2d-568b-46cc-adbb-d0a24181b4c8",
   "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>YEAR</th>\n",
       "      <th>zip code tabulation area</th>\n",
       "      <th>DP05_0001E</th>\n",
       "      <th>DP05_0002E</th>\n",
       "      <th>DP05_0003E</th>\n",
       "      <th>DP05_0004E</th>\n",
       "      <th>DP05_0005E</th>\n",
       "      <th>DP05_0006E</th>\n",
       "      <th>DP05_0007E</th>\n",
       "      <th>DP05_0008E</th>\n",
       "      <th>...</th>\n",
       "      <th>DP05_0084E</th>\n",
       "      <th>DP05_0085E</th>\n",
       "      <th>DP05_0086E</th>\n",
       "      <th>DP05_0087E</th>\n",
       "      <th>DP05_0088E</th>\n",
       "      <th>DP05_0089E</th>\n",
       "      <th>zip5</th>\n",
       "      <th>sale_date</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>20001</td>\n",
       "      <td>0.645503</td>\n",
       "      <td>0.640963</td>\n",
       "      <td>0.649539</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.254110</td>\n",
       "      <td>0.220507</td>\n",
       "      <td>0.609299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.744025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20001</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>0.460653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>20002</td>\n",
       "      <td>0.855817</td>\n",
       "      <td>0.844112</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.394746</td>\n",
       "      <td>0.422410</td>\n",
       "      <td>0.520472</td>\n",
       "      <td>0.684769</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20002</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.007334</td>\n",
       "      <td>0.757198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>20003</td>\n",
       "      <td>0.417028</td>\n",
       "      <td>0.448759</td>\n",
       "      <td>0.386320</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.182094</td>\n",
       "      <td>0.134672</td>\n",
       "      <td>0.183669</td>\n",
       "      <td>0.283829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.449808</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20003</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.005084</td>\n",
       "      <td>0.283109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>20004</td>\n",
       "      <td>0.024502</td>\n",
       "      <td>0.027010</td>\n",
       "      <td>0.022078</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005916</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20004</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.003688</td>\n",
       "      <td>0.038388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>20005</td>\n",
       "      <td>0.187391</td>\n",
       "      <td>0.193901</td>\n",
       "      <td>0.181038</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.022873</td>\n",
       "      <td>0.024101</td>\n",
       "      <td>0.026139</td>\n",
       "      <td>0.134709</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20005</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.035961</td>\n",
       "      <td>0.153551</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2019</td>\n",
       "      <td>20020</td>\n",
       "      <td>0.816196</td>\n",
       "      <td>0.737270</td>\n",
       "      <td>0.891636</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.982666</td>\n",
       "      <td>0.882664</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>0.504248</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.760951</td>\n",
       "      <td>0.752229</td>\n",
       "      <td>0.682662</td>\n",
       "      <td>0.815511</td>\n",
       "      <td>20020</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.007081</td>\n",
       "      <td>0.443378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2019</td>\n",
       "      <td>20024</td>\n",
       "      <td>0.208435</td>\n",
       "      <td>0.201510</td>\n",
       "      <td>0.214985</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.113117</td>\n",
       "      <td>0.048837</td>\n",
       "      <td>0.061300</td>\n",
       "      <td>0.054915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.327542</td>\n",
       "      <td>0.276608</td>\n",
       "      <td>0.233343</td>\n",
       "      <td>0.230549</td>\n",
       "      <td>0.235884</td>\n",
       "      <td>20024</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.020492</td>\n",
       "      <td>0.158349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2019</td>\n",
       "      <td>20032</td>\n",
       "      <td>0.613972</td>\n",
       "      <td>0.565141</td>\n",
       "      <td>0.660592</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.799678</td>\n",
       "      <td>0.873150</td>\n",
       "      <td>0.676382</td>\n",
       "      <td>0.421268</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.572716</td>\n",
       "      <td>0.527679</td>\n",
       "      <td>0.541034</td>\n",
       "      <td>0.496998</td>\n",
       "      <td>0.581091</td>\n",
       "      <td>20032</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.013477</td>\n",
       "      <td>0.275432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2019</td>\n",
       "      <td>20036</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>0.078544</td>\n",
       "      <td>0.077933</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.008935</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002731</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000588</td>\n",
       "      <td>0.060489</td>\n",
       "      <td>0.134327</td>\n",
       "      <td>0.089263</td>\n",
       "      <td>0.094009</td>\n",
       "      <td>0.084945</td>\n",
       "      <td>20036</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.023630</td>\n",
       "      <td>0.148752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2019</td>\n",
       "      <td>20037</td>\n",
       "      <td>0.217745</td>\n",
       "      <td>0.221965</td>\n",
       "      <td>0.213579</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.041637</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>0.323119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.170528</td>\n",
       "      <td>0.242498</td>\n",
       "      <td>0.233268</td>\n",
       "      <td>0.242123</td>\n",
       "      <td>0.225212</td>\n",
       "      <td>20037</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.016211</td>\n",
       "      <td>0.224568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR zip code tabulation area  DP05_0001E  DP05_0002E  DP05_0003E  \\\n",
       "0   2016                    20001    0.645503    0.640963    0.649539   \n",
       "1   2016                    20002    0.855817    0.844112    0.866633   \n",
       "2   2016                    20003    0.417028    0.448759    0.386320   \n",
       "3   2016                    20004    0.024502    0.027010    0.022078   \n",
       "4   2016                    20005    0.187391    0.193901    0.181038   \n",
       "..   ...                      ...         ...         ...         ...   \n",
       "86  2019                    20020    0.816196    0.737270    0.891636   \n",
       "87  2019                    20024    0.208435    0.201510    0.214985   \n",
       "88  2019                    20032    0.613972    0.565141    0.660592   \n",
       "89  2019                    20036    0.078253    0.078544    0.077933   \n",
       "90  2019                    20037    0.217745    0.221965    0.213579   \n",
       "\n",
       "    DP05_0004E  DP05_0005E  DP05_0006E  DP05_0007E  DP05_0008E  ...  \\\n",
       "0     0.999995    0.254110    0.220507    0.609299    1.000000  ...   \n",
       "1     0.999998    0.394746    0.422410    0.520472    0.684769  ...   \n",
       "2     0.999995    0.182094    0.134672    0.183669    0.283829  ...   \n",
       "3     0.999992    0.001608    0.001480    0.000000    0.005916  ...   \n",
       "4     0.999993    0.022873    0.024101    0.026139    0.134709  ...   \n",
       "..         ...         ...         ...         ...         ...  ...   \n",
       "86    0.999992    0.982666    0.882664    0.962757    0.504248  ...   \n",
       "87    0.999992    0.113117    0.048837    0.061300    0.054915  ...   \n",
       "88    0.999992    0.799678    0.873150    0.676382    0.421268  ...   \n",
       "89    0.999993    0.008935    0.012051    0.002082    0.002731  ...   \n",
       "90    0.999993    0.041637    0.003171    0.007634    0.323119  ...   \n",
       "\n",
       "    DP05_0084E  DP05_0085E  DP05_0086E  DP05_0087E  DP05_0088E  DP05_0089E  \\\n",
       "0     0.744025    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "1     1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "2     0.449808    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "3     0.027466    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "4     0.191639    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "..         ...         ...         ...         ...         ...         ...   \n",
       "86    0.003370    0.357143    0.760951    0.752229    0.682662    0.815511   \n",
       "87    0.000000    0.327542    0.276608    0.233343    0.230549    0.235884   \n",
       "88    0.000000    0.572716    0.527679    0.541034    0.496998    0.581091   \n",
       "89    0.000588    0.060489    0.134327    0.089263    0.094009    0.084945   \n",
       "90    0.003056    0.170528    0.242498    0.233268    0.242123    0.225212   \n",
       "\n",
       "     zip5  sale_date      mean     count  \n",
       "0   20001       2016  0.007288  0.460653  \n",
       "1   20002       2016  0.007334  0.757198  \n",
       "2   20003       2016  0.005084  0.283109  \n",
       "3   20004       2016  0.003688  0.038388  \n",
       "4   20005       2016  0.035961  0.153551  \n",
       "..    ...        ...       ...       ...  \n",
       "86  20020       2019  0.007081  0.443378  \n",
       "87  20024       2019  0.020492  0.158349  \n",
       "88  20032       2019  0.013477  0.275432  \n",
       "89  20036       2019  0.023630  0.148752  \n",
       "90  20037       2019  0.016211  0.224568  \n",
       "\n",
       "[91 rows x 95 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    clusters\n",
       "0          2\n",
       "1          2\n",
       "2          2\n",
       "3          1\n",
       "4          1\n",
       "..       ...\n",
       "86         4\n",
       "87         1\n",
       "88         4\n",
       "89         1\n",
       "90         1\n",
       "\n",
       "[91 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "#decide on x and y, let y be clusters and x be other data\n",
    "x = Merged_Matrix[[\"YEAR\",\"zip code tabulation area\",\"DP05_0001E\",\"DP05_0002E\",\"DP05_0003E\",\"DP05_0004E\",\"DP05_0005E\",\"DP05_0006E\",\"DP05_0007E\",\"DP05_0008E\",\"DP05_0009E\",\"DP05_0010E\",\"DP05_0011E\",\"DP05_0012E\",\"DP05_0013E\",\"DP05_0014E\",\"DP05_0015E\",\"DP05_0016E\",\"DP05_0017E\",\"DP05_0018E\",\"DP05_0019E\",\"DP05_0020E\",\"DP05_0021E\",\"DP05_0022E\",\"DP05_0023E\",\"DP05_0024E\",\"DP05_0025E\",\"DP05_0026E\",\"DP05_0027E\",\"DP05_0028E\",\"DP05_0029E\",\"DP05_0030E\",\"DP05_0031E\",\"DP05_0032E\",\"DP05_0033E\",\"DP05_0034E\",\"DP05_0035E\",\"DP05_0036E\",\"DP05_0037E\",\"DP05_0038E\",\"DP05_0039E\",\"DP05_0040E\",\"DP05_0041E\",\"DP05_0042E\",\"DP05_0043E\",\"DP05_0044E\",\"DP05_0045E\",\"DP05_0046E\",\"DP05_0047E\",\"DP05_0048E\",\"DP05_0049E\",\"DP05_0050E\",\"DP05_0051E\",\"DP05_0052E\",\"DP05_0053E\",\"DP05_0054E\",\"DP05_0055E\",\"DP05_0056E\",\"DP05_0057E\",\"DP05_0058E\",\"DP05_0059E\",\"DP05_0060E\",\"DP05_0061E\",\"DP05_0062E\",\"DP05_0063E\",\"DP05_0064E\",\"DP05_0065E\",\"DP05_0066E\",\"DP05_0067E\",\"DP05_0068E\",\"DP05_0069E\",\"DP05_0070E\",\"DP05_0071E\",\"DP05_0072E\",\"DP05_0073E\",\"DP05_0074E\",\"DP05_0075E\",\"DP05_0076E\",\"DP05_0077E\",\"DP05_0078E\",\"DP05_0079E\",\"DP05_0080E\",\"DP05_0081E\",\"DP05_0082E\",\"DP05_0083E\",\"DP05_0084E\",\"DP05_0085E\",\"DP05_0086E\",\"DP05_0087E\",\"DP05_0088E\",\"DP05_0089E\",\"zip5\",\"sale_date\",\"mean\",\"count\"]]\n",
    "y = Merged_Matrix[[\"clusters\"]]\n",
    "display(x)\n",
    "display(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "835d6417-311f-42c1-8ba8-92c94fff3b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grid search and cross validate\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'max_depth':np.arange(1, 20)}\n",
    "g = GridSearchCV(clf,parameters,refit=True,cv=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7dcd495b-0e2a-41c9-894f-7c52df883c15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5f608e1a-bcbe-4346-a7ef-bdf1ee3c34d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.015903</td>\n",
       "      <td>0.005724</td>\n",
       "      <td>0.001319</td>\n",
       "      <td>0.001319</td>\n",
       "      <td>6</td>\n",
       "      <td>{'max_depth': 6}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.988889</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.016717</td>\n",
       "      <td>0.003570</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>12</td>\n",
       "      <td>{'max_depth': 12}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.988889</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.015519</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>13</td>\n",
       "      <td>{'max_depth': 13}</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.007026</td>\n",
       "      <td>0.003306</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>7</td>\n",
       "      <td>{'max_depth': 7}</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967391</td>\n",
       "      <td>0.032609</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>0.009602</td>\n",
       "      <td>0.000643</td>\n",
       "      <td>8</td>\n",
       "      <td>{'max_depth': 8}</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>0.955556</td>\n",
       "      <td>0.966908</td>\n",
       "      <td>0.011353</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.008243</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': 4}</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.955556</td>\n",
       "      <td>0.956039</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007671</td>\n",
       "      <td>0.006908</td>\n",
       "      <td>0.011211</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>5</td>\n",
       "      <td>{'max_depth': 5}</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.955797</td>\n",
       "      <td>0.022464</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.010492</td>\n",
       "      <td>0.001097</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>17</td>\n",
       "      <td>{'max_depth': 17}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911111</td>\n",
       "      <td>0.955556</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.012164</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.005131</td>\n",
       "      <td>0.005131</td>\n",
       "      <td>18</td>\n",
       "      <td>{'max_depth': 18}</td>\n",
       "      <td>0.891304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.945652</td>\n",
       "      <td>0.054348</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.006177</td>\n",
       "      <td>0.005030</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>11</td>\n",
       "      <td>{'max_depth': 11}</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.944928</td>\n",
       "      <td>0.011594</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.011992</td>\n",
       "      <td>0.001234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 10}</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>0.911111</td>\n",
       "      <td>0.944686</td>\n",
       "      <td>0.033575</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.013700</td>\n",
       "      <td>0.004824</td>\n",
       "      <td>0.002807</td>\n",
       "      <td>0.002807</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 3}</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.923188</td>\n",
       "      <td>0.010145</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.011762</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>0.005469</td>\n",
       "      <td>0.005469</td>\n",
       "      <td>9</td>\n",
       "      <td>{'max_depth': 9}</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.922705</td>\n",
       "      <td>0.033816</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.010618</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.007152</td>\n",
       "      <td>0.007152</td>\n",
       "      <td>15</td>\n",
       "      <td>{'max_depth': 15}</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.012790</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.004912</td>\n",
       "      <td>0.004912</td>\n",
       "      <td>14</td>\n",
       "      <td>{'max_depth': 14}</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.858454</td>\n",
       "      <td>0.119324</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.008312</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>0.011438</td>\n",
       "      <td>0.002344</td>\n",
       "      <td>16</td>\n",
       "      <td>{'max_depth': 16}</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.152174</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.012131</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.005987</td>\n",
       "      <td>0.001180</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 2}</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.844444</td>\n",
       "      <td>0.835266</td>\n",
       "      <td>0.009179</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.013678</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.010435</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>19</td>\n",
       "      <td>{'max_depth': 19}</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.019286</td>\n",
       "      <td>0.010689</td>\n",
       "      <td>0.002352</td>\n",
       "      <td>0.002352</td>\n",
       "      <td>1</td>\n",
       "      <td>{'max_depth': 1}</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>0.711111</td>\n",
       "      <td>0.583816</td>\n",
       "      <td>0.127295</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "5        0.015903      0.005724         0.001319        0.001319   \n",
       "11       0.016717      0.003570         0.002268        0.002268   \n",
       "12       0.015519      0.004323         0.002143        0.002143   \n",
       "6        0.007026      0.003306         0.010858        0.000875   \n",
       "7        0.008230      0.001965         0.009602        0.000643   \n",
       "3        0.008243      0.002097         0.002541        0.002541   \n",
       "4        0.007671      0.006908         0.011211        0.001795   \n",
       "16       0.010492      0.001097         0.005859        0.005859   \n",
       "17       0.012164      0.000878         0.005131        0.005131   \n",
       "10       0.006177      0.005030         0.005875        0.005875   \n",
       "9        0.011992      0.001234         0.000000        0.000000   \n",
       "2        0.013700      0.004824         0.002807        0.002807   \n",
       "8        0.011762      0.001645         0.005469        0.005469   \n",
       "14       0.010618      0.000311         0.007152        0.007152   \n",
       "13       0.012790      0.001511         0.004912        0.004912   \n",
       "15       0.008312      0.001876         0.011438        0.002344   \n",
       "1        0.012131      0.000077         0.005987        0.001180   \n",
       "18       0.013678      0.000431         0.010435        0.000340   \n",
       "0        0.019286      0.010689         0.002352        0.002352   \n",
       "\n",
       "   param_max_depth             params  split0_test_score  split1_test_score  \\\n",
       "5                6   {'max_depth': 6}           1.000000           0.977778   \n",
       "11              12  {'max_depth': 12}           1.000000           0.977778   \n",
       "12              13  {'max_depth': 13}           0.956522           1.000000   \n",
       "6                7   {'max_depth': 7}           0.934783           1.000000   \n",
       "7                8   {'max_depth': 8}           0.978261           0.955556   \n",
       "3                4   {'max_depth': 4}           0.956522           0.955556   \n",
       "4                5   {'max_depth': 5}           0.978261           0.933333   \n",
       "16              17  {'max_depth': 17}           1.000000           0.911111   \n",
       "17              18  {'max_depth': 18}           0.891304           1.000000   \n",
       "10              11  {'max_depth': 11}           0.956522           0.933333   \n",
       "9               10  {'max_depth': 10}           0.978261           0.911111   \n",
       "2                3   {'max_depth': 3}           0.913043           0.933333   \n",
       "8                9   {'max_depth': 9}           0.956522           0.888889   \n",
       "14              15  {'max_depth': 15}           0.739130           1.000000   \n",
       "13              14  {'max_depth': 14}           0.739130           0.977778   \n",
       "15              16  {'max_depth': 16}           0.695652           1.000000   \n",
       "1                2   {'max_depth': 2}           0.826087           0.844444   \n",
       "18              19  {'max_depth': 19}           0.652174           1.000000   \n",
       "0                1   {'max_depth': 1}           0.456522           0.711111   \n",
       "\n",
       "    mean_test_score  std_test_score  rank_test_score  \n",
       "5          0.988889        0.011111                1  \n",
       "11         0.988889        0.011111                1  \n",
       "12         0.978261        0.021739                3  \n",
       "6          0.967391        0.032609                4  \n",
       "7          0.966908        0.011353                5  \n",
       "3          0.956039        0.000483                6  \n",
       "4          0.955797        0.022464                7  \n",
       "16         0.955556        0.044444                8  \n",
       "17         0.945652        0.054348                9  \n",
       "10         0.944928        0.011594               10  \n",
       "9          0.944686        0.033575               11  \n",
       "2          0.923188        0.010145               12  \n",
       "8          0.922705        0.033816               13  \n",
       "14         0.869565        0.130435               14  \n",
       "13         0.858454        0.119324               15  \n",
       "15         0.847826        0.152174               16  \n",
       "1          0.835266        0.009179               17  \n",
       "18         0.826087        0.173913               18  \n",
       "0          0.583816        0.127295               19  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#better results, rows & columns :)\n",
    "df = pd.DataFrame(g.cv_results_)\n",
    "df.sort_values(by=[\"mean_test_score\"],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a87a1630-438b-4d30-aa49-004755bca883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0', '1', '2', '3', '4']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get class names\n",
    "classes = pd.unique(y[\"clusters\"])\n",
    "classes.sort()\n",
    "classes = [str(i) for i in classes]\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "67bfa2ce-e2c9-4c57-b69c-eed199c36683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.2857142857142857, 0.9, 'DP05_0004E <= 0.323\\ngini = 0.692\\nsamples = 100.0%\\nvalue = [0.066, 0.473, 0.132, 0.231, 0.099]\\nclass = 1'),\n",
       " Text(0.14285714285714285, 0.7, 'gini = 0.0\\nsamples = 47.3%\\nvalue = [0.0, 1.0, 0.0, 0.0, 0.0]\\nclass = 1'),\n",
       " Text(0.42857142857142855, 0.7, 'DP05_0067E <= 0.349\\ngini = 0.695\\nsamples = 52.7%\\nvalue = [0.125, 0.0, 0.25, 0.438, 0.188]\\nclass = 3'),\n",
       " Text(0.2857142857142857, 0.5, 'gini = 0.0\\nsamples = 23.1%\\nvalue = [0.0, 0.0, 0.0, 1.0, 0.0]\\nclass = 3'),\n",
       " Text(0.5714285714285714, 0.5, 'DP05_0036E <= 0.313\\ngini = 0.642\\nsamples = 29.7%\\nvalue = [0.222, 0.0, 0.444, 0.0, 0.333]\\nclass = 2'),\n",
       " Text(0.42857142857142855, 0.3, 'gini = 0.0\\nsamples = 13.2%\\nvalue = [0.0, 0.0, 1.0, 0.0, 0.0]\\nclass = 2'),\n",
       " Text(0.7142857142857143, 0.3, 'DP05_0074E <= 0.03\\ngini = 0.48\\nsamples = 16.5%\\nvalue = [0.4, 0.0, 0.0, 0.0, 0.6]\\nclass = 4'),\n",
       " Text(0.5714285714285714, 0.1, 'gini = 0.0\\nsamples = 9.9%\\nvalue = [0.0, 0.0, 0.0, 0.0, 1.0]\\nclass = 4'),\n",
       " Text(0.8571428571428571, 0.1, 'gini = 0.0\\nsamples = 6.6%\\nvalue = [1.0, 0.0, 0.0, 0.0, 0.0]\\nclass = 0')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.client - ERROR - Failed to reconnect to scheduler after 60000.00 seconds, closing client\n"
     ]
    }
   ],
   "source": [
    "#plot\n",
    "# from matplotlib.pyplot import figure\n",
    "#width,length\n",
    "plt.figure(figsize=(20, 10), dpi=80)\n",
    "\n",
    "#label_font = {'size':'36'}\n",
    "#title_font = {'size':'21'}  # Adjust to fit\n",
    "plt.rcParams.update({'font.size': 55})\n",
    "#tree.plot_tree(g.best_estimator_,filled=True,feature_names=sorted(x.columns),class_names=classes,fontsize=10,rounded=True,proportion=True)\n",
    "tree.plot_tree(g.best_estimator_,filled=True,feature_names=x.columns.sort_values(),class_names=classes,fontsize=10,rounded=True,proportion=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd1efa85-4f22-447e-aeb2-2a88cc3192cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}