RealEstateAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from RealEstateAnalysis.Data import DataCsv\n",
    "from decimal import Decimal\n",
    "from pprint import pprint as pp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RealEstateAnalysis/SampleData/EBR_Building_Permits.csv\n"
     ]
    }
   ],
   "source": [
    "fileName = \"RealEstateAnalysis/SampleData/EBR_Building_Permits.csv\"\n",
    "print(fileName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseGeo(coords):\n",
    "    parsed = coords.split(\"\\n\")[-1].replace(\"(\", \"\").replace(\")\", \"\").split(\",\")\n",
    "    if len(parsed) == 2:\n",
    "        return [float(p) for p in parsed]\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseMoney(money):\n",
    "    return float(money.replace(\"$\", \"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['internal_id',\n",
      " 'permit_number',\n",
      " 'permit_type',\n",
      " 'project_description',\n",
      " 'lot_number',\n",
      " 'square_footage',\n",
      " 'project_value',\n",
      " 'permit_fee',\n",
      " 'creation_date',\n",
      " 'issued_date',\n",
      " 'address',\n",
      " 'street_address',\n",
      " 'city',\n",
      " 'state',\n",
      " 'zip',\n",
      " 'subdivision',\n",
      " 'parish_name',\n",
      " 'owner_name',\n",
      " 'applicant_name',\n",
      " 'contractor_name',\n",
      " 'contractor_address',\n",
      " 'geolocation']\n"
     ]
    }
   ],
   "source": [
    "data = DataCsv.new(fileName)\n",
    "pp([b for a in data.load().sample(1).data for b in a.keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geolocation</th>\n",
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       "      <th>9</th>\n",
       "      <td>[30.367206, -91.124658]</td>\n",
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       "      <th>10</th>\n",
       "      <td>[30.442629, -91.159963]</td>\n",
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       "      <th>11</th>\n",
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       "      <th>13</th>\n",
       "      <td>[30.49662, -91.082209]</td>\n",
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       "      <th>14</th>\n",
       "      <td>[30.415026, -91.060568]</td>\n",
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       "      <th>15</th>\n",
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       "      <td>[30.389028, -91.10515]</td>\n",
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       "      <td>[30.462457, -91.152943]</td>\n",
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       "      <th>19</th>\n",
       "      <td>[30.387931, -91.18289]</td>\n",
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       "      <th>20</th>\n",
       "      <td>[30.469381, -91.159607]</td>\n",
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       "      <td>None</td>\n",
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       "      <th>22</th>\n",
       "      <td>[30.409019, -91.049055]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[30.482105, -91.063148]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[30.461341, -91.018688]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[30.422417, -91.010119]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[30.394636, -91.044718]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[30.490747, -91.051212]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63753</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63754</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63755</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63756</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63757</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63758</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63759</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63760</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63761</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63762</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63763</th>\n",
       "      <td>[30.512246, -91.125615]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63764</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63765</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63766</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63767</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63768</th>\n",
       "      <td>[30.390347, -91.123802]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63769</th>\n",
       "      <td>[30.508836, -91.127854]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63770</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63771</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63772</th>\n",
       "      <td>[30.488563, -91.065333]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63773</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63774</th>\n",
       "      <td>[30.428428, -91.179887]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63775</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63776</th>\n",
       "      <td>[30.356795, -91.12613]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63777</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63778</th>\n",
       "      <td>[30.392218, -91.030166]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63779</th>\n",
       "      <td>[30.481418, -91.157648]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63780</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63781</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63782</th>\n",
       "      <td>[30.49504, -91.1303]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                   geolocation\n",
       "0      [30.511097, -91.116758]\n",
       "1                         None\n",
       "2      [30.380736, -91.014381]\n",
       "3      [30.402395, -90.998373]\n",
       "4      [30.505111, -91.116621]\n",
       "5                         None\n",
       "6      [30.395607, -91.072878]\n",
       "7      [30.411741, -91.056567]\n",
       "8      [30.480904, -91.115949]\n",
       "9      [30.367206, -91.124658]\n",
       "10     [30.442629, -91.159963]\n",
       "11     [30.523823, -91.176778]\n",
       "12                        None\n",
       "13      [30.49662, -91.082209]\n",
       "14     [30.415026, -91.060568]\n",
       "15      [30.43589, -91.002016]\n",
       "16      [30.389028, -91.10515]\n",
       "17      [30.424839, -91.05517]\n",
       "18     [30.462457, -91.152943]\n",
       "19      [30.387931, -91.18289]\n",
       "20     [30.469381, -91.159607]\n",
       "21                        None\n",
       "22     [30.409019, -91.049055]\n",
       "23                        None\n",
       "24     [30.482105, -91.063148]\n",
       "25                        None\n",
       "26     [30.461341, -91.018688]\n",
       "27     [30.422417, -91.010119]\n",
       "28     [30.394636, -91.044718]\n",
       "29     [30.490747, -91.051212]\n",
       "...                        ...\n",
       "63753                     None\n",
       "63754                     None\n",
       "63755                     None\n",
       "63756                     None\n",
       "63757                     None\n",
       "63758                     None\n",
       "63759                     None\n",
       "63760                     None\n",
       "63761                     None\n",
       "63762                     None\n",
       "63763  [30.512246, -91.125615]\n",
       "63764                     None\n",
       "63765                     None\n",
       "63766                     None\n",
       "63767                     None\n",
       "63768  [30.390347, -91.123802]\n",
       "63769  [30.508836, -91.127854]\n",
       "63770                     None\n",
       "63771                     None\n",
       "63772  [30.488563, -91.065333]\n",
       "63773                     None\n",
       "63774  [30.428428, -91.179887]\n",
       "63775                     None\n",
       "63776   [30.356795, -91.12613]\n",
       "63777                     None\n",
       "63778  [30.392218, -91.030166]\n",
       "63779  [30.481418, -91.157648]\n",
       "63780                     None\n",
       "63781                     None\n",
       "63782     [30.49504, -91.1303]\n",
       "\n",
       "[63783 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.select(geolocation = lambda x : parseGeo(x['geolocation'])) \\\n",
    "         .load() \\\n",
    "         .dataFrame()\n",
    "df\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-cb66b5150bd1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zip'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'project_value'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'square_footage'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m          \u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m          \u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'zip'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'project_value'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'square_footage'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m          \u001b[0;34m.\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/TensorFlow/RealEstateAnalysis/RealEstateAnalysis/Data/DataCsv.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     44\u001b[0m             \u001b[0mcsvreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDictReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsvfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m             self.data = [\n\u001b[0;32m---> 46\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcsvreader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             ]\n",
      "\u001b[0;32m~/Projects/TensorFlow/RealEstateAnalysis/RealEstateAnalysis/Data/DataCsv.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     45\u001b[0m             self.data = [\n\u001b[1;32m     46\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcsvreader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             ]\n\u001b[1;32m     49\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/TensorFlow/RealEstateAnalysis/RealEstateAnalysis/Data/DataModel.py\u001b[0m in \u001b[0;36mselector\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__select\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m                 \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__select\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-2beaeaaae670>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeolocation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mparseGeo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'geolocation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m          \u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m          \u001b[0;34m.\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-3-29cf378d6f4a>\u001b[0m in \u001b[0;36mparseGeo\u001b[0;34m(coords)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mparseGeo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mparsed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"(\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\")\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparsed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "df = data.select(['zip','project_value','square_footage']) \\\n",
    "         .load() \\\n",
    "         .export(['zip','project_value', 'square_footage']) \\\n",
    "         .dataFrame() \n",
    "df = df[df[1] > 0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11418c550>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11418cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "it = data.process(geolocation=parseGeo) \\\n",
    "         .process(project_value=lambda x: np.log(parseMoney(x))) \\\n",
    "         .where(state=\"LA\") \\\n",
    "         .where(lambda x: parseGeo(x['geolocation']) is not None) \\\n",
    "         .where(lambda x: parseMoney(x ['project_value']) > 0) \\\n",
    "         .load() \\\n",
    "         .export(['geolocation', 'project_value']) \n",
    "\n",
    "def inCityLimits(a, b):\n",
    "    return a < 31 and a > 30 and b > -91.5 and b < -90.5\n",
    "\n",
    "df = pandas.DataFrame([[loc[0],loc[1], val] for loc, val in it if inCityLimits(*loc)])\n",
    "df.plot(kind=\"scatter\", x=1, y=0,c=2, cmap=plt.get_cmap(\"jet\"), figsize=(10,7))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>project_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.502251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.693286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.210340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.372209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.580584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.723266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       project_value\n",
       "count   18183.000000\n",
       "mean       10.502251\n",
       "std         1.693286\n",
       "min         0.000000\n",
       "25%         9.210340\n",
       "50%        10.372209\n",
       "75%        11.580584\n",
       "max        20.723266"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "it = data.process(geolocation=parseGeo) \\\n",
    "         .process(project_value=lambda x : np.log(parseMoney(x))) \\\n",
    "         .where(state=\"LA\") \\\n",
    "         .where(lambda x: parseGeo(x['geolocation']) is not None) \\\n",
    "         .where(lambda x: parseMoney(x ['project_value']) > 0) \\\n",
    "         .load() \n",
    "df2 = pandas.DataFrame([item for item in it])\n",
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112eb0978>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111328780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newDf = df2.groupby('zip') \\\n",
    "           .agg('count') \\\n",
    "           .reset_index() \\\n",
    "           .iloc[:, 0:2] \\\n",
    "    \n",
    "newDf[newDf[\"internal_id\"] > 100].plot.bar(x=\"zip\", y=\"internal_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}