zipcode_build_df.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import Dependencies\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>complaint count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78745</td>\n",
       "      <td>31381</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78704</td>\n",
       "      <td>29674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78702</td>\n",
       "      <td>25203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78744</td>\n",
       "      <td>22108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78758</td>\n",
       "      <td>21741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  complaint count\n",
       "0     78745            31381\n",
       "1     78704            29674\n",
       "2     78702            25203\n",
       "3     78744            22108\n",
       "4     78758            21741"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#main dataframe: get the total complaints by zipcode from cleaned 311 data from master\n",
    "complaints_df = pd.read_csv('raw_data/zipcode_complaints.csv')\n",
    "del complaints_df[\"Unnamed: 0\"]\n",
    "complaints_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>crime reports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78741.0</td>\n",
       "      <td>3121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78753.0</td>\n",
       "      <td>2904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78704.0</td>\n",
       "      <td>2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78758.0</td>\n",
       "      <td>2281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78745.0</td>\n",
       "      <td>2108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   zip_code  crime reports\n",
       "0   78741.0           3121\n",
       "1   78753.0           2904\n",
       "2   78704.0           2380\n",
       "3   78758.0           2281\n",
       "4   78745.0           2108"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read data for crime by zipcode\n",
    "crime_df = pd.read_csv('raw_data/crime.csv')\n",
    "del crime_df[\"Unnamed: 0\"]\n",
    "crime_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>complaint count</th>\n",
       "      <th>crime reports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78745</td>\n",
       "      <td>31381</td>\n",
       "      <td>2108.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78704</td>\n",
       "      <td>29674</td>\n",
       "      <td>2380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78702</td>\n",
       "      <td>25203</td>\n",
       "      <td>1497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78744</td>\n",
       "      <td>22108</td>\n",
       "      <td>1807.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78758</td>\n",
       "      <td>21741</td>\n",
       "      <td>2281.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zip_code  complaint count  crime reports\n",
       "0    78745            31381         2108.0\n",
       "1    78704            29674         2380.0\n",
       "2    78702            25203         1497.0\n",
       "3    78744            22108         1807.0\n",
       "4    78758            21741         2281.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first merge\n",
    "zipmain_df = pd.merge(complaints_df, crime_df, on = \"zip_code\", how = \"outer\")\n",
    "zipmain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>complaint_count</th>\n",
       "      <th>crime_reports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>78745</td>\n",
       "      <td>31381</td>\n",
       "      <td>2108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78704</td>\n",
       "      <td>29674</td>\n",
       "      <td>2380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78702</td>\n",
       "      <td>25203</td>\n",
       "      <td>1497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78744</td>\n",
       "      <td>22108</td>\n",
       "      <td>1807.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78758</td>\n",
       "      <td>21741</td>\n",
       "      <td>2281.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  zip_code  complaint_count  crime_reports\n",
       "0    78745            31381         2108.0\n",
       "1    78704            29674         2380.0\n",
       "2    78702            25203         1497.0\n",
       "3    78744            22108         1807.0\n",
       "4    78758            21741         2281.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipmain_df = zipmain_df.rename(columns={\"complaint count\":\"complaint_count\", \"crime reports\":\"crime_reports\"})\n",
    "zipmain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>median_household_income($)</th>\n",
       "      <th>population_below_poverty_level(%)</th>\n",
       "      <th>unemployment(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78726.0</td>\n",
       "      <td>66096</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78724.0</td>\n",
       "      <td>35711</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78701.0</td>\n",
       "      <td>68152</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78702.0</td>\n",
       "      <td>34734</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78703.0</td>\n",
       "      <td>92606</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  median_household_income($)  population_below_poverty_level(%)  \\\n",
       "0   78726.0                       66096                                  9   \n",
       "1   78724.0                       35711                                 38   \n",
       "2   78701.0                       68152                                 20   \n",
       "3   78702.0                       34734                                 33   \n",
       "4   78703.0                       92606                                 10   \n",
       "\n",
       "   unemployment(%)  \n",
       "0                4  \n",
       "1                8  \n",
       "2                9  \n",
       "3               11  \n",
       "4                4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read data from 2014 household dataset\n",
    "df_2014 = pd.read_csv('raw_data/2014_household_dataset.csv')\n",
    "del df_2014[\"Unnamed: 0\"]\n",
    "df_2014 = df_2014.rename(columns={\"Zip Code\":\"zip_code\", \"Median household income($)\":\"median_household_income($)\",\n",
    "                                    \"Population below poverty level(%)\":\"population_below_poverty_level(%)\",\n",
    "                                    \"Unemployment(%)\":\"unemployment(%)\"})\n",
    "df_2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>complaint_count</th>\n",
       "      <th>crime_reports</th>\n",
       "      <th>median_household_income($)</th>\n",
       "      <th>population_below_poverty_level(%)</th>\n",
       "      <th>unemployment(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78745</td>\n",
       "      <td>31381</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>49243.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78704</td>\n",
       "      <td>29674</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>50248.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78702</td>\n",
       "      <td>25203</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>34734.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78744</td>\n",
       "      <td>22108</td>\n",
       "      <td>1807.0</td>\n",
       "      <td>41056.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78758</td>\n",
       "      <td>21741</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>41792.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zip_code  complaint_count  crime_reports  median_household_income($)  \\\n",
       "0    78745            31381         2108.0                     49243.0   \n",
       "1    78704            29674         2380.0                     50248.0   \n",
       "2    78702            25203         1497.0                     34734.0   \n",
       "3    78744            22108         1807.0                     41056.0   \n",
       "4    78758            21741         2281.0                     41792.0   \n",
       "\n",
       "   population_below_poverty_level(%)  unemployment(%)  \n",
       "0                               16.0              7.0  \n",
       "1                               21.0              7.0  \n",
       "2                               33.0             11.0  \n",
       "3                               26.0              9.0  \n",
       "4                               24.0             10.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#second merge\n",
    "zipmain_df = pd.merge(zipmain_df, df_2014, on = \"zip_code\", how = \"outer\")\n",
    "zipmain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>zip_code</th>\n",
       "      <th>total_population</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78602</td>\n",
       "      <td>25,160</td>\n",
       "      <td>13,344</td>\n",
       "      <td>11,816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78605</td>\n",
       "      <td>4,952</td>\n",
       "      <td>2,474</td>\n",
       "      <td>2,478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78606</td>\n",
       "      <td>5,205</td>\n",
       "      <td>2,594</td>\n",
       "      <td>2,611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78607</td>\n",
       "      <td>166</td>\n",
       "      <td>84</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78608</td>\n",
       "      <td>434</td>\n",
       "      <td>223</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code total_population    male  female\n",
       "0     78602           25,160  13,344  11,816\n",
       "1     78605            4,952   2,474   2,478\n",
       "2     78606            5,205   2,594   2,611\n",
       "3     78607              166      84      82\n",
       "4     78608              434     223     211"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read data for population by zipcode\n",
    "pop_df = pd.read_csv('raw_data/ZIPs_2010_population.csv')\n",
    "pop_df = pop_df.rename(columns={\" male\":\"male\", \" female\":\"female\", \"total_population_x\":\"total_population\"})\n",
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>total_population</th>\n",
       "      <th>male(%)</th>\n",
       "      <th>female(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78602</td>\n",
       "      <td>25160.0</td>\n",
       "      <td>53.036566</td>\n",
       "      <td>46.963434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78605</td>\n",
       "      <td>4952.0</td>\n",
       "      <td>49.959612</td>\n",
       "      <td>50.040388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78606</td>\n",
       "      <td>5205.0</td>\n",
       "      <td>49.836695</td>\n",
       "      <td>50.163305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78607</td>\n",
       "      <td>166.0</td>\n",
       "      <td>50.602410</td>\n",
       "      <td>49.397590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78608</td>\n",
       "      <td>434.0</td>\n",
       "      <td>51.382488</td>\n",
       "      <td>48.617512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  total_population    male(%)  female(%)\n",
       "0     78602           25160.0  53.036566  46.963434\n",
       "1     78605            4952.0  49.959612  50.040388\n",
       "2     78606            5205.0  49.836695  50.163305\n",
       "3     78607             166.0  50.602410  49.397590\n",
       "4     78608             434.0  51.382488  48.617512"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove commas & convert columns from string to float\n",
    "pop_df[\"male\"] = pop_df[\"male\"].str.replace(\",\",\"\").astype(float)\n",
    "pop_df[\"female\"] = pop_df[\"female\"].str.replace(\",\",\"\").astype(float)\n",
    "pop_df[\"total_population\"] = pop_df[\"total_population\"].str.replace(\",\",\"\").astype(float)\n",
    "\n",
    "#Calculate male/femae percentage\n",
    "pop_df[\"male(%)\"] = pop_df.apply(lambda x: (float(x[\"male\"]) / float(x[\"total_population\"])*100), axis=1)\n",
    "pop_df[\"female(%)\"] = pop_df.apply(lambda x: (float(x[\"female\"]) / float(x[\"total_population\"])*100), axis=1)\n",
    "\n",
    "#remove male/female column\n",
    "del pop_df[\"male\"]\n",
    "del pop_df[\"female\"]\n",
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#third merge\n",
    "zipmain_df = pd.merge(zipmain_df, pop_df, on = \"zip_code\", how = \"outer\")\n",
    "zipmain_df.to_csv(\"zip_code1.csv\")"
   ]
  }
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