cfg-dev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import requests\n",
    "from requests.auth import HTTPBasicAuth\n",
    "import ast\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counties = {\n",
    "'Barnstable': '001',\n",
    "'Berkshire': '003',\n",
    "'Bristol': '005',\n",
    "'Essex': '009',\n",
    "'Franklin': '011',\n",
    "'Hampden': '013',\n",
    "'Hampshire': '015',\n",
    "'Middlesex': '017',\n",
    "'Norfolk': '021',\n",
    "'Plymouth': '023',\n",
    "'Suffolk': '025',\n",
    "'Worcester': '027' \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What counties do you want to look at? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "county_list = \"Barnstable, Suffolk\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Variables to Fetch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "variables = {\n",
    "    \"B19001_002E\": \"Less than\\n10,000\",\n",
    "    \"B19001_003E\": \"10,000 to\\n14,999\",\n",
    "    \"B19001_004E\": \"15,000 to\\n19,999\",\n",
    "    \"B19001_005E\": \"20,000 to\\n24,999\",\n",
    "    \"B19001_006E\": \"25,000 to\\n29,999\",\n",
    "    \"B19001_007E\": \"30,000 to\\n34,999\",\n",
    "    \"B19001_008E\": \"35,000 to\\n39,999\",\n",
    "    \"B19001_009E\": \"40,000 to\\n44,999\",\n",
    "    \"B19001_010E\": \"45,000 to\\n49,999\",\n",
    "    \"B19001_011E\": \"50,000 to\\n59,999\",\n",
    "    \"B19001_012E\": \"60,000 to\\n74,999\",\n",
    "    \"B19001_013E\": \"75,000 to\\n99,999\",\n",
    "    \"B19001_014E\": \"100,000 to\\n124,999\",\n",
    "    \"B19001_015E\": \"125,000 to\\n149,999\",\n",
    "    \"B19001_016E\": \"150,000 to\\n199,999\",\n",
    "    \"B19001_017E\": \"Over\\n200,000\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "var_list = \",\".join(variables.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if county_list != \"*\":\n",
    "    county_list = county_list.split(\", \")\n",
    "    county_list = \",\".join(counties[c] for c in county_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "apikey = 'a5f4310141ca2f8f821d4f1a088c3d7e09381db7'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "req = \"http://api.census.gov/data/2015/acs1?get=NAME,\" + var_list + \"&for=county:\" + county_list + \"&in=state:25&key=\" + apikey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://api.census.gov/data/2015/acs1?get=NAME,B19001_005E,B19001_004E,B19001_014E,B19001_003E,B19001_002E,B19001_015E,B19001_010E,B19001_012E,B19001_013E,B19001_011E,B19001_016E,B19001_009E,B19001_008E,B19001_017E,B19001_007E,B19001_006E&for=county:001,025&in=state:25&key=a5f4310141ca2f8f821d4f1a088c3d7e09381db7'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "req"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r = requests.get(req)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = pd.DataFrame(ast.literal_eval(r.text))\n",
    "x.columns = x.iloc[0]\n",
    "x = x.reindex(x.index.drop(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Returned Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>B19001_005E</th>\n",
       "      <th>B19001_004E</th>\n",
       "      <th>B19001_014E</th>\n",
       "      <th>B19001_003E</th>\n",
       "      <th>B19001_002E</th>\n",
       "      <th>B19001_015E</th>\n",
       "      <th>B19001_010E</th>\n",
       "      <th>B19001_012E</th>\n",
       "      <th>B19001_013E</th>\n",
       "      <th>B19001_011E</th>\n",
       "      <th>B19001_016E</th>\n",
       "      <th>B19001_009E</th>\n",
       "      <th>B19001_008E</th>\n",
       "      <th>B19001_017E</th>\n",
       "      <th>B19001_007E</th>\n",
       "      <th>B19001_006E</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barnstable County, Massachusetts</td>\n",
       "      <td>4865</td>\n",
       "      <td>3673</td>\n",
       "      <td>9792</td>\n",
       "      <td>3317</td>\n",
       "      <td>4497</td>\n",
       "      <td>6489</td>\n",
       "      <td>3461</td>\n",
       "      <td>11972</td>\n",
       "      <td>14363</td>\n",
       "      <td>6272</td>\n",
       "      <td>5924</td>\n",
       "      <td>4016</td>\n",
       "      <td>3737</td>\n",
       "      <td>6142</td>\n",
       "      <td>4946</td>\n",
       "      <td>3985</td>\n",
       "      <td>25</td>\n",
       "      <td>001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suffolk County, Massachusetts</td>\n",
       "      <td>11799</td>\n",
       "      <td>14306</td>\n",
       "      <td>28050</td>\n",
       "      <td>21801</td>\n",
       "      <td>31907</td>\n",
       "      <td>17644</td>\n",
       "      <td>9509</td>\n",
       "      <td>23501</td>\n",
       "      <td>30829</td>\n",
       "      <td>20388</td>\n",
       "      <td>18853</td>\n",
       "      <td>11114</td>\n",
       "      <td>11138</td>\n",
       "      <td>25879</td>\n",
       "      <td>11244</td>\n",
       "      <td>12879</td>\n",
       "      <td>25</td>\n",
       "      <td>025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0                              NAME B19001_005E B19001_004E B19001_014E  \\\n",
       "1  Barnstable County, Massachusetts        4865        3673        9792   \n",
       "2     Suffolk County, Massachusetts       11799       14306       28050   \n",
       "\n",
       "0 B19001_003E B19001_002E B19001_015E B19001_010E B19001_012E B19001_013E  \\\n",
       "1        3317        4497        6489        3461       11972       14363   \n",
       "2       21801       31907       17644        9509       23501       30829   \n",
       "\n",
       "0 B19001_011E B19001_016E B19001_009E B19001_008E B19001_017E B19001_007E  \\\n",
       "1        6272        5924        4016        3737        6142        4946   \n",
       "2       20388       18853       11114       11138       25879       11244   \n",
       "\n",
       "0 B19001_006E state county  \n",
       "1        3985    25    001  \n",
       "2       12879    25    025  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tableau20 = [(31, 119, 180), (255, 127, 14), (255, 187, 120),    \n",
    "             (44, 160, 44), (152, 223, 138), (174, 199, 232), (214, 39, 40), (255, 152, 150),    \n",
    "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),    \n",
    "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),    \n",
    "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
    "\n",
    "for i in range(len(tableau20)):    \n",
    "    r, g, b = tableau20[i]    \n",
    "    tableau20[i] = (r / 255., g / 255., b / 255.)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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udVL8vC7aSs656XgjgvoCrwObObnQ/L/MbJGZFTrF2E5kXkTOYsGRHb1SbOvtb1vpnMto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\n+vbti5nx2GOP0bZtW9avXx9KgqQ3win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G5VeQ553VmrzMsHd7cCnqn7ioRCR806wI6Rbk/fySakg91N27Uzbq3JPsFteJjlY5kX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\nmQ3Q/wIfuVIwsBhGWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a8f950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import date2num\n",
    "import datetime\n",
    "\n",
    "percentage = True\n",
    "\n",
    "key = x['NAME']\n",
    "n_groups = len(variables.keys())\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "\n",
    "index = np.arange(1, n_groups*2+1, 2)\n",
    "bar_width = 1.5/len(key)\n",
    "\n",
    "opacity = 0.6\n",
    "\n",
    "i = 0\n",
    "for name in key:\n",
    "    cols = variables.keys()\n",
    "    data = x.loc[x['NAME']==name][cols].reindex_axis(sorted(cols), axis=1).as_matrix()[0]\n",
    "    data = [float(val) for val in data]\n",
    "    total = sum(data)\n",
    "    if percentage: data = [val/total for val in data]\n",
    "    indiv_plot = plt.bar(index + bar_width*i, data, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color=tableau20[i],\n",
    "                 linewidth = 0,\n",
    "                 label=name)\n",
    "    i += 1\n",
    "    \n",
    "plt.xlabel('Counties', fontsize=18)\n",
    "if percentage: \n",
    "    plt.ylabel('Percentage at income level', fontsize=18)\n",
    "else:\n",
    "    plt.ylabel('Number of people at income level', fontsize=18)\n",
    "plt.title('Income Level Distribution by County', fontsize=18)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(index + bar_width*len(key) / 2, [variables[k] for k in sorted(cols)], fontsize=10)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)    \n",
    "ax.spines[\"bottom\"].set_visible(False)    \n",
    "ax.spines[\"right\"].set_visible(False)    \n",
    "ax.spines[\"left\"].set_visible(False) \n",
    "ax.get_xaxis().tick_bottom()    \n",
    "ax.get_yaxis().tick_left() \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}