geochunk/notebook/ZipDistribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zip code distribution analysis for \"geochunk\" algorithm\n",
    "\n",
    "Our goal is to group zip codes by variable length prefixes in such a way that the chunk sizes tend to be roughly equal, with no giant, leftover chunks.\n",
    "\n",
    "But first, we need to import some standard Python data libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some zip code census data from [this blog post](https://blog.splitwise.com/2013/09/18/the-2010-us-census-population-by-zip-code-totally-free/) that we want to load and examine. We need to do this `np.object_` business to force the CSV parser to treat zip codes as strings, and not as integers (thus losing the leading zeroes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Code ZCTA</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01001</td>\n",
       "      <td>16769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01002</td>\n",
       "      <td>29049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01003</td>\n",
       "      <td>10372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01005</td>\n",
       "      <td>5079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01007</td>\n",
       "      <td>14649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01008</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01009</td>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01010</td>\n",
       "      <td>3609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01011</td>\n",
       "      <td>1370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01012</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Zip Code ZCTA  2010 Census Population\n",
       "0         01001                   16769\n",
       "1         01002                   29049\n",
       "2         01003                   10372\n",
       "3         01005                    5079\n",
       "4         01007                   14649\n",
       "5         01008                    1263\n",
       "6         01009                     741\n",
       "7         01010                    3609\n",
       "8         01011                    1370\n",
       "9         01012                     661"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zippop = pd.read_csv('../src/zip2010.csv', dtype={'Zip Code ZCTA': np.object_, '2010 Census Population': np.int32})\n",
    "zippop[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "308739931"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop = zippop['2010 Census Population']\n",
    "pop.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the distribution of population by zip code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fead3553fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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LJU3MxRt+bSUNknSFpIclLZK0T3+4VpK+kP77e0DSpZI2a7VrJWmWpJWSHsjF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fead30cdef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pop, bins=50)\n",
    "plt.title(\"Population per ZIP code\")\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"# of ZIP codes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Code ZCTA</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20640</th>\n",
       "      <td>60629</td>\n",
       "      <td>113916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27727</th>\n",
       "      <td>79936</td>\n",
       "      <td>111086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2748</th>\n",
       "      <td>11368</td>\n",
       "      <td>109931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30109</th>\n",
       "      <td>90650</td>\n",
       "      <td>105549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29986</th>\n",
       "      <td>90011</td>\n",
       "      <td>103892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30192</th>\n",
       "      <td>91331</td>\n",
       "      <td>103689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2720</th>\n",
       "      <td>11226</td>\n",
       "      <td>101572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30045</th>\n",
       "      <td>90201</td>\n",
       "      <td>101279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2753</th>\n",
       "      <td>11373</td>\n",
       "      <td>100820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>11220</td>\n",
       "      <td>99598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>11385</td>\n",
       "      <td>98592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>10467</td>\n",
       "      <td>97060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30460</th>\n",
       "      <td>92335</td>\n",
       "      <td>95397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26671</th>\n",
       "      <td>77084</td>\n",
       "      <td>95137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>10025</td>\n",
       "      <td>94600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2702</th>\n",
       "      <td>11208</td>\n",
       "      <td>94469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30075</th>\n",
       "      <td>90280</td>\n",
       "      <td>94396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26773</th>\n",
       "      <td>77449</td>\n",
       "      <td>94382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2729</th>\n",
       "      <td>11236</td>\n",
       "      <td>93877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27192</th>\n",
       "      <td>78521</td>\n",
       "      <td>93818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Zip Code ZCTA  2010 Census Population\n",
       "20640         60629                  113916\n",
       "27727         79936                  111086\n",
       "2748          11368                  109931\n",
       "30109         90650                  105549\n",
       "29986         90011                  103892\n",
       "30192         91331                  103689\n",
       "2720          11226                  101572\n",
       "30045         90201                  101279\n",
       "2753          11373                  100820\n",
       "2714          11220                   99598\n",
       "2759          11385                   98592\n",
       "2527          10467                   97060\n",
       "30460         92335                   95397\n",
       "26671         77084                   95137\n",
       "2451          10025                   94600\n",
       "2702          11208                   94469\n",
       "30075         90280                   94396\n",
       "26773         77449                   94382\n",
       "2729          11236                   93877\n",
       "27192         78521                   93818"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zippop.nlargest(20, '2010 Census Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Code ZCTA</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>01199</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>02203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>05481</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1558</th>\n",
       "      <td>06020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1580</th>\n",
       "      <td>06059</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Zip Code ZCTA  2010 Census Population\n",
       "75           01199                       0\n",
       "383          02203                       0\n",
       "1423         05481                       0\n",
       "1558         06020                       0\n",
       "1580         06059                       0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zippop.nsmallest(5, '2010 Census Population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at 4-digit zip code prefixes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Prefix</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>1121</td>\n",
       "      <td>710148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>1122</td>\n",
       "      <td>621986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1046</td>\n",
       "      <td>605938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3773</th>\n",
       "      <td>6062</td>\n",
       "      <td>605797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>1123</td>\n",
       "      <td>588379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>1120</td>\n",
       "      <td>584187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1045</td>\n",
       "      <td>560238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3772</th>\n",
       "      <td>6061</td>\n",
       "      <td>543914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>1002</td>\n",
       "      <td>505290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3774</th>\n",
       "      <td>6063</td>\n",
       "      <td>497137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Zip Prefix  2010 Census Population\n",
       "533        1121                  710148\n",
       "534        1122                  621986\n",
       "498        1046                  605938\n",
       "3773       6062                  605797\n",
       "535        1123                  588379\n",
       "532        1120                  584187\n",
       "497        1045                  560238\n",
       "3772       6061                  543914\n",
       "481        1002                  505290\n",
       "3774       6063                  497137"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum_by_zip_prefix(zippop, length):\n",
    "    prefixes = zippop['Zip Code ZCTA'].str[:length].rename('Zip Prefix')\n",
    "    df = pd.concat([prefixes, zippop['2010 Census Population']], axis=1)\n",
    "    return df.groupby('Zip Prefix').sum().reset_index()\n",
    "prefix_4 = sum_by_zip_prefix(zippop, 4)\n",
    "prefix_4.nlargest(10, '2010 Census Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7feacf8499e8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R8ad6BmZmZs2r1m6r05TZVNLPgO/XOzAzM2tetSSP9wPDgXuBh4BngH3rGZSZmTW3WpLH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\nCvv/3Fm3sH/MEGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fead34c2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(prefix_4['2010 Census Population'], bins=50)\n",
    "plt.title(\"Population per 4-digit ZIP prefix\")\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"# of Prefixes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And various shorter prefix lengths. We tried to use 3-digit prefixes for the first Pachynorm run, which as you can, gave us some huge chunks with just under 3 million people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Prefix</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>770</td>\n",
       "      <td>2906700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>606</td>\n",
       "      <td>2680484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>112</td>\n",
       "      <td>2504700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>900</td>\n",
       "      <td>2404395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>945</td>\n",
       "      <td>2274102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>300</td>\n",
       "      <td>2177710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>750</td>\n",
       "      <td>2079278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>917</td>\n",
       "      <td>1924074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>331</td>\n",
       "      <td>1885845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>070</td>\n",
       "      <td>1683417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Zip Prefix  2010 Census Population\n",
       "699        770                 2906700\n",
       "555        606                 2680484\n",
       "91         112                 2504700\n",
       "803        900                 2404395\n",
       "843        945                 2274102\n",
       "276        300                 2177710\n",
       "679        750                 2079278\n",
       "818        917                 1924074\n",
       "306        331                 1885845\n",
       "59         070                 1683417"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prefix_3 = sum_by_zip_prefix(zippop, 3)\n",
    "prefix_3.nlargest(10, '2010 Census Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7feacf6cd3c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSeUjwMeA/SVdXmhUZmbWlmq5nfBhEdGdphcCe0o6sMCYzMysTVVMKpLeFxEvAY9LGt1n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\nqZiZWW6cVMzMLDf/H7nT5MpRysfwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feacf6d0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(prefix_3['2010 Census Population'], bins=50)\n",
    "plt.title(\"Population per 3-digit ZIP prefix\")\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"# of Prefixes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Prefix</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>33</td>\n",
       "      <td>9285786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>92</td>\n",
       "      <td>9033701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>60</td>\n",
       "      <td>8783167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>7579159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>7254703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>77</td>\n",
       "      <td>7012758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>95</td>\n",
       "      <td>6786586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>48</td>\n",
       "      <td>6634334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>78</td>\n",
       "      <td>6358782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32</td>\n",
       "      <td>6263654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Zip Prefix  2010 Census Population\n",
       "31         33                 9285786\n",
       "90         92                 9033701\n",
       "58         60                 8783167\n",
       "9          11                 7579159\n",
       "28         30                 7254703\n",
       "75         77                 7012758\n",
       "93         95                 6786586\n",
       "46         48                 6634334\n",
       "76         78                 6358782\n",
       "30         32                 6263654"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prefix_2 = sum_by_zip_prefix(zippop, 2)\n",
    "prefix_2.nlargest(10, '2010 Census Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Prefix</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>69</td>\n",
       "      <td>184566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>51</td>\n",
       "      <td>486400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>82</td>\n",
       "      <td>520260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>88</td>\n",
       "      <td>620867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>625741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>86</td>\n",
       "      <td>644295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>58</td>\n",
       "      <td>671781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>795935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>57</td>\n",
       "      <td>813464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>81</td>\n",
       "      <td>832537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Zip Prefix  2010 Census Population\n",
       "67         69                  184566\n",
       "49         51                  486400\n",
       "80         82                  520260\n",
       "86         88                  620867\n",
       "4          05                  625741\n",
       "84         86                  644295\n",
       "56         58                  671781\n",
       "24         26                  795935\n",
       "55         57                  813464\n",
       "79         81                  832537"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prefix_2.nsmallest(10, '2010 Census Population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7feacf5d1f60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pmwgkLQX0R8R2adD6pSLi+WpCMzOzKrRtGkrjDhyRns93EjAz6z5lzhFcIekLktaStOrC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\nf18naXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feacf5e1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(prefix_2['2010 Census Population'], bins=20)\n",
    "plt.title(\"Population per 2-digit ZIP prefix\")\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"# of Prefixes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Prefix</th>\n",
       "      <th>2010 Census Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>23236989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>32978141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30390138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>42575790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>32249466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>16513346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>23499661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>36345281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>21075552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>49875567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Zip Prefix  2010 Census Population\n",
       "0          0                23236989\n",
       "1          1                32978141\n",
       "2          2                30390138\n",
       "3          3                42575790\n",
       "4          4                32249466\n",
       "5          5                16513346\n",
       "6          6                23499661\n",
       "7          7                36345281\n",
       "8          8                21075552\n",
       "9          9                49875567"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_by_zip_prefix(zippop, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable length chunks\n",
    "\n",
    "Now that we've explored the data, we're ready to experiment with variable length chunks.  First, we want to build a table that will allow us to quickly look up the population of any prefix from length 0 (the entire country) to length 5 (a single zip)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2906700, 10538]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prefix_populations = [ sum_by_zip_prefix(zippop, prefix).set_index('Zip Prefix') for prefix in range(0,6) ]\n",
    "def prefix_population(prefix):\n",
    "    return prefix_populations[len(prefix)]['2010 Census Population'].get(prefix, 0)\n",
    "[prefix_population('770'), prefix_population('03755')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9020',\n",
       " '9021',\n",
       " '9022',\n",
       " '9023',\n",
       " '9024',\n",
       " '9025',\n",
       " '9026',\n",
       " '9027',\n",
       " '9028',\n",
       " '9029']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sub_prefixes(prefix):\n",
    "    return [ prefix + str(n) for n in range(0,10) ]\n",
    "sub_prefixes('902')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>2010 Census Population</th>\n",
       "      <th>Prefixes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chunk</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>010_0</th>\n",
       "      <td>77922</td>\n",
       "      <td>[0100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_1</th>\n",
       "      <td>96062</td>\n",
       "      <td>[0101, 0102]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_2</th>\n",
       "      <td>76753</td>\n",
       "      <td>[0103, 0104]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_3</th>\n",
       "      <td>73002</td>\n",
       "      <td>[0105, 0106]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_4</th>\n",
       "      <td>37535</td>\n",
       "      <td>[0107]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_5</th>\n",
       "      <td>88283</td>\n",
       "      <td>[0108]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_6</th>\n",
       "      <td>19818</td>\n",
       "      <td>[0109]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0110_0</th>\n",
       "      <td>92014</td>\n",
       "      <td>[01100, 01101, 01102, 01103, 01104, 01105, 011...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0110_1</th>\n",
       "      <td>30250</td>\n",
       "      <td>[01109]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011_0</th>\n",
       "      <td>46571</td>\n",
       "      <td>[0111, 0112, 0113, 0114, 0115, 0116, 0117, 011...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>012_0</th>\n",
       "      <td>89837</td>\n",
       "      <td>[0120, 0121, 0122, 0123]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>012_1</th>\n",
       "      <td>40852</td>\n",
       "      <td>[0124, 0125, 0126, 0127, 0128, 0129]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>014_0</th>\n",
       "      <td>82626</td>\n",
       "      <td>[0140, 0141, 0142, 0143, 0144]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>014_1</th>\n",
       "      <td>60658</td>\n",
       "      <td>[0145]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>014_2</th>\n",
       "      <td>68528</td>\n",
       "      <td>[0146, 0147, 0148, 0149]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015_0</th>\n",
       "      <td>82551</td>\n",
       "      <td>[0150, 0151]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015_1</th>\n",
       "      <td>85393</td>\n",
       "      <td>[0152, 0153]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015_2</th>\n",
       "      <td>76897</td>\n",
       "      <td>[0154, 0155]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015_3</th>\n",
       "      <td>79853</td>\n",
       "      <td>[0156, 0157]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015_4</th>\n",
       "      <td>49025</td>\n",
       "      <td>[0158, 0159]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160_0</th>\n",
       "      <td>77412</td>\n",
       "      <td>[01600, 01601, 01602, 01603, 01604]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160_1</th>\n",
       "      <td>79958</td>\n",
       "      <td>[01605, 01606, 01607, 01608, 01609]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>016_0</th>\n",
       "      <td>31032</td>\n",
       "      <td>[0161, 0162, 0163, 0164, 0165, 0166, 0167, 016...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017_0</th>\n",
       "      <td>73888</td>\n",
       "      <td>[0170, 0171]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017_1</th>\n",
       "      <td>52571</td>\n",
       "      <td>[0172, 0173]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017_2</th>\n",
       "      <td>81318</td>\n",
       "      <td>[0174]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017_3</th>\n",
       "      <td>82505</td>\n",
       "      <td>[0175]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017_4</th>\n",
       "      <td>88745</td>\n",
       "      <td>[0176, 0177, 0178, 0179]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184_0</th>\n",
       "      <td>76383</td>\n",
       "      <td>[01840, 01841, 01842, 01843]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0184_1</th>\n",
       "      <td>75601</td>\n",
       "      <td>[01844, 01845, 01846, 01847, 01848, 01849]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988_1</th>\n",
       "      <td>68451</td>\n",
       "      <td>[9882, 9883]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988_2</th>\n",
       "      <td>58751</td>\n",
       "      <td>[9884, 9885, 9886, 9887, 9888, 9889]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9890_0</th>\n",
       "      <td>91008</td>\n",
       "      <td>[98900, 98901, 98902, 98903, 98904, 98905, 989...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9890_1</th>\n",
       "      <td>35240</td>\n",
       "      <td>[98908, 98909]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989_0</th>\n",
       "      <td>77532</td>\n",
       "      <td>[9891, 9892, 9893]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989_1</th>\n",
       "      <td>80875</td>\n",
       "      <td>[9894, 9895, 9896, 9897, 9898, 9899]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990_0</th>\n",
       "      <td>82533</td>\n",
       "      <td>[9900, 9901]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990_1</th>\n",
       "      <td>59475</td>\n",
       "      <td>[9902, 9903, 9904, 9905, 9906, 9907, 9908, 9909]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991_0</th>\n",
       "      <td>67655</td>\n",
       "      <td>[9910, 9911, 9912, 9913, 9914, 9915]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991_1</th>\n",
       "      <td>47583</td>\n",
       "      <td>[9916, 9917, 9918, 9919]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9920_0</th>\n",
       "      <td>61795</td>\n",
       "      <td>[99200, 99201, 99202, 99203, 99204]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9920_1</th>\n",
       "      <td>76939</td>\n",
       "      <td>[99205, 99206]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9920_2</th>\n",
       "      <td>80047</td>\n",
       "      <td>[99207, 99208, 99209]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992_0</th>\n",
       "      <td>76513</td>\n",
       "      <td>[9921]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992_1</th>\n",
       "      <td>47562</td>\n",
       "      <td>[9922, 9923, 9924, 9925, 9926, 9927, 9928, 9929]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993_0</th>\n",
       "      <td>99875</td>\n",
       "      <td>[9930, 9931, 9932]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993_1</th>\n",
       "      <td>91450</td>\n",
       "      <td>[9933]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993_2</th>\n",
       "      <td>33446</td>\n",
       "      <td>[9934]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993_3</th>\n",
       "      <td>79378</td>\n",
       "      <td>[9935]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993_4</th>\n",
       "      <td>44835</td>\n",
       "      <td>[9936, 9937, 9938, 9939]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9950_0</th>\n",
       "      <td>97248</td>\n",
       "      <td>[99500, 99501, 99502, 99503, 99504]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9950_1</th>\n",
       "      <td>87630</td>\n",
       "      <td>[99505, 99506, 99507, 99508, 99509]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995_0</th>\n",
       "      <td>97276</td>\n",
       "      <td>[9951, 9952, 9953, 9954, 9955, 9956]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995_1</th>\n",
       "      <td>38506</td>\n",
       "      <td>[9957, 9958, 9959]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996_0</th>\n",
       "      <td>78544</td>\n",
       "      <td>[9960, 9961, 9962, 9963, 9964]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996_1</th>\n",
       "      <td>89277</td>\n",
       "      <td>[9965, 9966, 9967]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996_2</th>\n",
       "      <td>16262</td>\n",
       "      <td>[9968, 9969]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997_0</th>\n",
       "      <td>96393</td>\n",
       "      <td>[9970, 9971]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997_1</th>\n",
       "      <td>37796</td>\n",
       "      <td>[9972, 9973, 9974, 9975, 9976, 9977, 9978, 9979]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_0</th>\n",
       "      <td>92694</td>\n",
       "      <td>[994, 998, 999]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4444 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        2010 Census Population  \\\n",
       "Chunk                            \n",
       "010_0                    77922   \n",
       "010_1                    96062   \n",
       "010_2                    76753   \n",
       "010_3                    73002   \n",
       "010_4                    37535   \n",
       "010_5                    88283   \n",
       "010_6                    19818   \n",
       "0110_0                   92014   \n",
       "0110_1                   30250   \n",
       "011_0                    46571   \n",
       "012_0                    89837   \n",
       "012_1                    40852   \n",
       "014_0                    82626   \n",
       "014_1                    60658   \n",
       "014_2                    68528   \n",
       "015_0                    82551   \n",
       "015_1                    85393   \n",
       "015_2                    76897   \n",
       "015_3                    79853   \n",
       "015_4                    49025   \n",
       "0160_0                   77412   \n",
       "0160_1                   79958   \n",
       "016_0                    31032   \n",
       "017_0                    73888   \n",
       "017_1                    52571   \n",
       "017_2                    81318   \n",
       "017_3                    82505   \n",
       "017_4                    88745   \n",
       "0184_0                   76383   \n",
       "0184_1                   75601   \n",
       "...                        ...   \n",
       "988_1                    68451   \n",
       "988_2                    58751   \n",
       "9890_0                   91008   \n",
       "9890_1                   35240   \n",
       "989_0                    77532   \n",
       "989_1                    80875   \n",
       "990_0                    82533   \n",
       "990_1                    59475   \n",
       "991_0                    67655   \n",
       "991_1                    47583   \n",
       "9920_0                   61795   \n",
       "9920_1                   76939   \n",
       "9920_2                   80047   \n",
       "992_0                    76513   \n",
       "992_1                    47562   \n",
       "993_0                    99875   \n",
       "993_1                    91450   \n",
       "993_2                    33446   \n",
       "993_3                    79378   \n",
       "993_4                    44835   \n",
       "9950_0                   97248   \n",
       "9950_1                   87630   \n",
       "995_0                    97276   \n",
       "995_1                    38506   \n",
       "996_0                    78544   \n",
       "996_1                    89277   \n",
       "996_2                    16262   \n",
       "997_0                    96393   \n",
       "997_1                    37796   \n",
       "99_0                     92694   \n",
       "\n",
       "                                                 Prefixes  \n",
       "Chunk                                                      \n",
       "010_0                                              [0100]  \n",
       "010_1                                        [0101, 0102]  \n",
       "010_2                                        [0103, 0104]  \n",
       "010_3                                        [0105, 0106]  \n",
       "010_4                                              [0107]  \n",
       "010_5                                              [0108]  \n",
       "010_6                                              [0109]  \n",
       "0110_0  [01100, 01101, 01102, 01103, 01104, 01105, 011...  \n",
       "0110_1                                            [01109]  \n",
       "011_0   [0111, 0112, 0113, 0114, 0115, 0116, 0117, 011...  \n",
       "012_0                            [0120, 0121, 0122, 0123]  \n",
       "012_1                [0124, 0125, 0126, 0127, 0128, 0129]  \n",
       "014_0                      [0140, 0141, 0142, 0143, 0144]  \n",
       "014_1                                              [0145]  \n",
       "014_2                            [0146, 0147, 0148, 0149]  \n",
       "015_0                                        [0150, 0151]  \n",
       "015_1                                        [0152, 0153]  \n",
       "015_2                                        [0154, 0155]  \n",
       "015_3                                        [0156, 0157]  \n",
       "015_4                                        [0158, 0159]  \n",
       "0160_0                [01600, 01601, 01602, 01603, 01604]  \n",
       "0160_1                [01605, 01606, 01607, 01608, 01609]  \n",
       "016_0   [0161, 0162, 0163, 0164, 0165, 0166, 0167, 016...  \n",
       "017_0                                        [0170, 0171]  \n",
       "017_1                                        [0172, 0173]  \n",
       "017_2                                              [0174]  \n",
       "017_3                                              [0175]  \n",
       "017_4                            [0176, 0177, 0178, 0179]  \n",
       "0184_0                       [01840, 01841, 01842, 01843]  \n",
       "0184_1         [01844, 01845, 01846, 01847, 01848, 01849]  \n",
       "...                                                   ...  \n",
       "988_1                                        [9882, 9883]  \n",
       "988_2                [9884, 9885, 9886, 9887, 9888, 9889]  \n",
       "9890_0  [98900, 98901, 98902, 98903, 98904, 98905, 989...  \n",
       "9890_1                                     [98908, 98909]  \n",
       "989_0                                  [9891, 9892, 9893]  \n",
       "989_1                [9894, 9895, 9896, 9897, 9898, 9899]  \n",
       "990_0                                        [9900, 9901]  \n",
       "990_1    [9902, 9903, 9904, 9905, 9906, 9907, 9908, 9909]  \n",
       "991_0                [9910, 9911, 9912, 9913, 9914, 9915]  \n",
       "991_1                            [9916, 9917, 9918, 9919]  \n",
       "9920_0                [99200, 99201, 99202, 99203, 99204]  \n",
       "9920_1                                     [99205, 99206]  \n",
       "9920_2                              [99207, 99208, 99209]  \n",
       "992_0                                              [9921]  \n",
       "992_1    [9922, 9923, 9924, 9925, 9926, 9927, 9928, 9929]  \n",
       "993_0                                  [9930, 9931, 9932]  \n",
       "993_1                                              [9933]  \n",
       "993_2                                              [9934]  \n",
       "993_3                                              [9935]  \n",
       "993_4                            [9936, 9937, 9938, 9939]  \n",
       "9950_0                [99500, 99501, 99502, 99503, 99504]  \n",
       "9950_1                [99505, 99506, 99507, 99508, 99509]  \n",
       "995_0                [9951, 9952, 9953, 9954, 9955, 9956]  \n",
       "995_1                                  [9957, 9958, 9959]  \n",
       "996_0                      [9960, 9961, 9962, 9963, 9964]  \n",
       "996_1                                  [9965, 9966, 9967]  \n",
       "996_2                                        [9968, 9969]  \n",
       "997_0                                        [9970, 9971]  \n",
       "997_1    [9972, 9973, 9974, 9975, 9976, 9977, 9978, 9979]  \n",
       "99_0                                      [994, 998, 999]  \n",
       "\n",
       "[4444 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class ZipChunk:\n",
    "    \"A group of zip codes roughly equal in size to all other such chunks.\"\n",
    " \n",
    "    def __init__(self, id):\n",
    "        self.id = id\n",
    "        self.prefixes = []\n",
    "\n",
    "    def add_prefix(self, prefix):\n",
    "        self.prefixes.append(prefix)\n",
    "    \n",
    "    def population(self):\n",
    "        return sum([ prefix_population(prefix) for prefix in self.prefixes ])\n",
    "    \n",
    "def build_chunks_recursive(population, chunks, prefix):\n",
    "    lower_limit = population / 2\n",
    "    upper_limit = population\n",
    "    if lower_limit < prefix_population(prefix) <= upper_limit or len(prefix) >= 5:\n",
    "        chunk = ZipChunk(prefix)\n",
    "        chunk.add_prefix(prefix)\n",
    "        chunks.append(chunk)\n",
    "    else:\n",
    "        leftovers = []\n",
    "        for child in sub_prefixes(prefix): \n",
    "            if prefix_population(child) >= upper_limit:\n",
    "                build_chunks_recursive(population, chunks, child)\n",
    "            else:\n",
    "                leftovers.append(child)\n",
    "        i = 0\n",
    "        chunk = ZipChunk(\"%s_%i\" % (prefix, i))\n",
    "        while len(leftovers) > 0:\n",
    "            child = leftovers.pop(0)\n",
    "            assert(prefix_population(child) <= upper_limit)\n",
    "            if chunk.population() + prefix_population(child) > upper_limit:\n",
    "                i += 1\n",
    "                chunks.append(chunk)\n",
    "                chunk = ZipChunk(\"%s_%i\" % (prefix, i))\n",
    "            chunk.add_prefix(child)\n",
    "        if chunk.population() > 0:\n",
    "            chunks.append(chunk)\n",
    "    \n",
    "def build_chunks(population):\n",
    "    chunks = []\n",
    "    build_chunks_recursive(population, chunks, '')\n",
    "    chunks = sorted(chunks, key=lambda chunk: chunk.id)\n",
    "    result = pd.DataFrame([ (chunk.id, chunk.population(), chunk.prefixes) for chunk in chunks ])\n",
    "    result.columns = ['Chunk', '2010 Census Population', 'Prefixes']\n",
    "    result.set_index('Chunk', inplace=True)\n",
    "    return result\n",
    "\n",
    "chunks = build_chunks(100000)\n",
    "chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7feacf516b00>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3klyQ5Jb2OT/JXiOPTJI0VsO8cvSvgfcBv97mX9DKHPJb2gr2NWixGKYG8ZNV9ddVdVf7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feacf51d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(chunks['2010 Census Population'], bins=50)\n",
    "plt.title(\"Population per Chunk\")\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"# of Chunks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "308739931"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunks['2010 Census Population'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4444, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010 Census Population</th>\n",
       "      <th>Prefixes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chunk</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>010_0</th>\n",
       "      <td>210857</td>\n",
       "      <td>[0100, 0101, 0102, 0103]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_1</th>\n",
       "      <td>238700</td>\n",
       "      <td>[0104, 0105, 0106, 0107, 0108]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>010_2</th>\n",
       "      <td>19818</td>\n",
       "      <td>[0109]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015_0</th>\n",
       "      <td>244841</td>\n",
       "      <td>[0150, 0151, 0152, 0153, 0154, 0155]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>015_1</th>\n",
       "      <td>128878</td>\n",
       "      <td>[0156, 0157, 0158, 0159]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017_0</th>\n",
       "      <td>207777</td>\n",
       "      <td>[0170, 0171, 0172, 0173, 0174]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017_1</th>\n",
       "      <td>171250</td>\n",
       "      <td>[0175, 0176, 0177, 0178, 0179]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>018_0</th>\n",
       "      <td>184413</td>\n",
       "      <td>[0180, 0181, 0182]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>018_1</th>\n",
       "      <td>227505</td>\n",
       "      <td>[0183, 0184]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>018_2</th>\n",
       "      <td>210817</td>\n",
       "      <td>[0185, 0186, 0187]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>018_3</th>\n",
       "      <td>90391</td>\n",
       "      <td>[0188, 0189]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>019_0</th>\n",
       "      <td>231079</td>\n",
       "      <td>[0190, 0191, 0192]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>019_1</th>\n",
       "      <td>245235</td>\n",
       "      <td>[0193, 0194, 0195, 0196, 0197, 0198, 0199]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_0</th>\n",
       "      <td>168835</td>\n",
       "      <td>[011]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_1</th>\n",
       "      <td>213800</td>\n",
       "      <td>[012, 013]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_2</th>\n",
       "      <td>211812</td>\n",
       "      <td>[014]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_3</th>\n",
       "      <td>188402</td>\n",
       "      <td>[016]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>020_0</th>\n",
       "      <td>184275</td>\n",
       "      <td>[0200, 0201, 0202, 0203, 0204]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>020_1</th>\n",
       "      <td>216096</td>\n",
       "      <td>[0205, 0206, 0207, 0208, 0209]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0212_0</th>\n",
       "      <td>243585</td>\n",
       "      <td>[02120, 02121, 02122, 02123, 02124, 02125, 021...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0212_1</th>\n",
       "      <td>16439</td>\n",
       "      <td>[02129]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0213_0</th>\n",
       "      <td>220173</td>\n",
       "      <td>[02130, 02131, 02132, 02133, 02134, 02135, 021...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0213_1</th>\n",
       "      <td>36349</td>\n",
       "      <td>[02139]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>021_0</th>\n",
       "      <td>136539</td>\n",
       "      <td>[0210, 0211]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>021_1</th>\n",
       "      <td>207555</td>\n",
       "      <td>[0214]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>021_2</th>\n",
       "      <td>220030</td>\n",
       "      <td>[0215, 0216]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>021_3</th>\n",
       "      <td>203272</td>\n",
       "      <td>[0217, 0218, 0219]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>023_0</th>\n",
       "      <td>241125</td>\n",
       "      <td>[0230, 0231, 0232, 0233, 0234]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>023_1</th>\n",
       "      <td>199938</td>\n",
       "      <td>[0235, 0236, 0237, 0238, 0239]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>024_0</th>\n",
       "      <td>242552</td>\n",
       "      <td>[0240, 0241, 0242, 0243, 0244, 0245, 0246]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981_3</th>\n",
       "      <td>207457</td>\n",
       "      <td>[9815, 9816, 9817, 9818, 9819]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982_0</th>\n",
       "      <td>168159</td>\n",
       "      <td>[9820, 9821]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982_1</th>\n",
       "      <td>230928</td>\n",
       "      <td>[9822, 9823]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982_2</th>\n",
       "      <td>138281</td>\n",
       "      <td>[9824, 9825, 9826]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982_3</th>\n",
       "      <td>249350</td>\n",
       "      <td>[9827, 9828]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982_4</th>\n",
       "      <td>90767</td>\n",
       "      <td>[9829]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983_0</th>\n",
       "      <td>240218</td>\n",
       "      <td>[9830, 9831, 9832, 9833, 9834]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983_1</th>\n",
       "      <td>148730</td>\n",
       "      <td>[9835, 9836]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983_2</th>\n",
       "      <td>162860</td>\n",
       "      <td>[9837]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983_3</th>\n",
       "      <td>161800</td>\n",
       "      <td>[9838, 9839]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984_0</th>\n",
       "      <td>214804</td>\n",
       "      <td>[9840, 9841, 9842, 9843]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984_1</th>\n",
       "      <td>185311</td>\n",
       "      <td>[9844, 9845, 9846, 9847, 9848, 9849]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985_0</th>\n",
       "      <td>241456</td>\n",
       "      <td>[9850, 9851, 9852]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985_1</th>\n",
       "      <td>234098</td>\n",
       "      <td>[9853, 9854, 9855, 9856, 9857, 9858, 9859]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986_0</th>\n",
       "      <td>227225</td>\n",
       "      <td>[9860, 9861, 9862, 9863, 9864, 9865]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986_1</th>\n",
       "      <td>191810</td>\n",
       "      <td>[9866, 9867]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986_2</th>\n",
       "      <td>154295</td>\n",
       "      <td>[9868, 9869]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989_0</th>\n",
       "      <td>203780</td>\n",
       "      <td>[9890, 9891, 9892, 9893]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989_1</th>\n",
       "      <td>80875</td>\n",
       "      <td>[9894, 9895, 9896, 9897, 9898, 9899]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98_0</th>\n",
       "      <td>219761</td>\n",
       "      <td>[987, 988]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992_0</th>\n",
       "      <td>218781</td>\n",
       "      <td>[9920]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992_1</th>\n",
       "      <td>124075</td>\n",
       "      <td>[9921, 9922, 9923, 9924, 9925, 9926, 9927, 992...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993_0</th>\n",
       "      <td>224771</td>\n",
       "      <td>[9930, 9931, 9932, 9933, 9934]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993_1</th>\n",
       "      <td>124213</td>\n",
       "      <td>[9935, 9936, 9937, 9938, 9939]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995_0</th>\n",
       "      <td>184878</td>\n",
       "      <td>[9950]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995_1</th>\n",
       "      <td>135782</td>\n",
       "      <td>[9951, 9952, 9953, 9954, 9955, 9956, 9957, 995...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_0</th>\n",
       "      <td>142008</td>\n",
       "      <td>[990]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_1</th>\n",
       "      <td>136934</td>\n",
       "      <td>[991, 994]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_2</th>\n",
       "      <td>184083</td>\n",
       "      <td>[996]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_3</th>\n",
       "      <td>205187</td>\n",
       "      <td>[997, 998, 999]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1780 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        2010 Census Population  \\\n",
       "Chunk                            \n",
       "010_0                   210857   \n",
       "010_1                   238700   \n",
       "010_2                    19818   \n",
       "015_0                   244841   \n",
       "015_1                   128878   \n",
       "017_0                   207777   \n",
       "017_1                   171250   \n",
       "018_0                   184413   \n",
       "018_1                   227505   \n",
       "018_2                   210817   \n",
       "018_3                    90391   \n",
       "019_0                   231079   \n",
       "019_1                   245235   \n",
       "01_0                    168835   \n",
       "01_1                    213800   \n",
       "01_2                    211812   \n",
       "01_3                    188402   \n",
       "020_0                   184275   \n",
       "020_1                   216096   \n",
       "0212_0                  243585   \n",
       "0212_1                   16439   \n",
       "0213_0                  220173   \n",
       "0213_1                   36349   \n",
       "021_0                   136539   \n",
       "021_1                   207555   \n",
       "021_2                   220030   \n",
       "021_3                   203272   \n",
       "023_0                   241125   \n",
       "023_1                   199938   \n",
       "024_0                   242552   \n",
       "...                        ...   \n",
       "981_3                   207457   \n",
       "982_0                   168159   \n",
       "982_1                   230928   \n",
       "982_2                   138281   \n",
       "982_3                   249350   \n",
       "982_4                    90767   \n",
       "983_0                   240218   \n",
       "983_1                   148730   \n",
       "983_2                   162860   \n",
       "983_3                   161800   \n",
       "984_0                   214804   \n",
       "984_1                   185311   \n",
       "985_0                   241456   \n",
       "985_1                   234098   \n",
       "986_0                   227225   \n",
       "986_1                   191810   \n",
       "986_2                   154295   \n",
       "989_0                   203780   \n",
       "989_1                    80875   \n",
       "98_0                    219761   \n",
       "992_0                   218781   \n",
       "992_1                   124075   \n",
       "993_0                   224771   \n",
       "993_1                   124213   \n",
       "995_0                   184878   \n",
       "995_1                   135782   \n",
       "99_0                    142008   \n",
       "99_1                    136934   \n",
       "99_2                    184083   \n",
       "99_3                    205187   \n",
       "\n",
       "                                                 Prefixes  \n",
       "Chunk                                                      \n",
       "010_0                            [0100, 0101, 0102, 0103]  \n",
       "010_1                      [0104, 0105, 0106, 0107, 0108]  \n",
       "010_2                                              [0109]  \n",
       "015_0                [0150, 0151, 0152, 0153, 0154, 0155]  \n",
       "015_1                            [0156, 0157, 0158, 0159]  \n",
       "017_0                      [0170, 0171, 0172, 0173, 0174]  \n",
       "017_1                      [0175, 0176, 0177, 0178, 0179]  \n",
       "018_0                                  [0180, 0181, 0182]  \n",
       "018_1                                        [0183, 0184]  \n",
       "018_2                                  [0185, 0186, 0187]  \n",
       "018_3                                        [0188, 0189]  \n",
       "019_0                                  [0190, 0191, 0192]  \n",
       "019_1          [0193, 0194, 0195, 0196, 0197, 0198, 0199]  \n",
       "01_0                                                [011]  \n",
       "01_1                                           [012, 013]  \n",
       "01_2                                                [014]  \n",
       "01_3                                                [016]  \n",
       "020_0                      [0200, 0201, 0202, 0203, 0204]  \n",
       "020_1                      [0205, 0206, 0207, 0208, 0209]  \n",
       "0212_0  [02120, 02121, 02122, 02123, 02124, 02125, 021...  \n",
       "0212_1                                            [02129]  \n",
       "0213_0  [02130, 02131, 02132, 02133, 02134, 02135, 021...  \n",
       "0213_1                                            [02139]  \n",
       "021_0                                        [0210, 0211]  \n",
       "021_1                                              [0214]  \n",
       "021_2                                        [0215, 0216]  \n",
       "021_3                                  [0217, 0218, 0219]  \n",
       "023_0                      [0230, 0231, 0232, 0233, 0234]  \n",
       "023_1                      [0235, 0236, 0237, 0238, 0239]  \n",
       "024_0          [0240, 0241, 0242, 0243, 0244, 0245, 0246]  \n",
       "...                                                   ...  \n",
       "981_3                      [9815, 9816, 9817, 9818, 9819]  \n",
       "982_0                                        [9820, 9821]  \n",
       "982_1                                        [9822, 9823]  \n",
       "982_2                                  [9824, 9825, 9826]  \n",
       "982_3                                        [9827, 9828]  \n",
       "982_4                                              [9829]  \n",
       "983_0                      [9830, 9831, 9832, 9833, 9834]  \n",
       "983_1                                        [9835, 9836]  \n",
       "983_2                                              [9837]  \n",
       "983_3                                        [9838, 9839]  \n",
       "984_0                            [9840, 9841, 9842, 9843]  \n",
       "984_1                [9844, 9845, 9846, 9847, 9848, 9849]  \n",
       "985_0                                  [9850, 9851, 9852]  \n",
       "985_1          [9853, 9854, 9855, 9856, 9857, 9858, 9859]  \n",
       "986_0                [9860, 9861, 9862, 9863, 9864, 9865]  \n",
       "986_1                                        [9866, 9867]  \n",
       "986_2                                        [9868, 9869]  \n",
       "989_0                            [9890, 9891, 9892, 9893]  \n",
       "989_1                [9894, 9895, 9896, 9897, 9898, 9899]  \n",
       "98_0                                           [987, 988]  \n",
       "992_0                                              [9920]  \n",
       "992_1   [9921, 9922, 9923, 9924, 9925, 9926, 9927, 992...  \n",
       "993_0                      [9930, 9931, 9932, 9933, 9934]  \n",
       "993_1                      [9935, 9936, 9937, 9938, 9939]  \n",
       "995_0                                              [9950]  \n",
       "995_1   [9951, 9952, 9953, 9954, 9955, 9956, 9957, 995...  \n",
       "99_0                                                [990]  \n",
       "99_1                                           [991, 994]  \n",
       "99_2                                                [996]  \n",
       "99_3                                      [997, 998, 999]  \n",
       "\n",
       "[1780 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunks_250000 = build_chunks(250000)\n",
    "chunks_250000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7feacf55c320>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JmZl1RLN7Ftd+USzgFEkvA6+m5+FfFpuZdYdmt6oc0WidmZl1j4ZDQ5L2kXRonfRDJH2k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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fead3277f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(chunks_250000['2010 Census Population'], bins=50)\n",
    "plt.title(\"Population per Chunk (250,000)\")\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"# of Chunks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chunks_250000.to_csv('geochunk_zip_250000.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010 Census Population</th>\n",
       "      <th>Prefixes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chunk</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>018_0</th>\n",
       "      <td>411918</td>\n",
       "      <td>[0180, 0181, 0182, 0183, 0184]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>018_1</th>\n",
       "      <td>301208</td>\n",
       "      <td>[0185, 0186, 0187, 0188, 0189]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_0</th>\n",
       "      <td>469375</td>\n",
       "      <td>[010]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_1</th>\n",
       "      <td>382635</td>\n",
       "      <td>[011, 012, 013]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_2</th>\n",
       "      <td>211812</td>\n",
       "      <td>[014]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_3</th>\n",
       "      <td>373719</td>\n",
       "      <td>[015]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_4</th>\n",
       "      <td>188402</td>\n",
       "      <td>[016]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_5</th>\n",
       "      <td>379027</td>\n",
       "      <td>[017]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01_6</th>\n",
       "      <td>476314</td>\n",
       "      <td>[019]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>021_0</th>\n",
       "      <td>396563</td>\n",
       "      <td>[0210, 0211, 0212]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>021_1</th>\n",
       "      <td>464077</td>\n",
       "      <td>[0213, 0214]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>021_2</th>\n",
       "      <td>423302</td>\n",
       "      <td>[0215, 0216, 0217, 0218, 0219]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>027_0</th>\n",
       "      <td>461337</td>\n",
       "      <td>[0270, 0271, 0272, 0273, 0274, 0275, 0276, 0277]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>027_1</th>\n",
       "      <td>64999</td>\n",
       "      <td>[0278, 0279]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>028_0</th>\n",
       "      <td>451970</td>\n",
       "      <td>[0280, 0281, 0282, 0283, 0284, 0285, 0286, 0287]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>028_1</th>\n",
       "      <td>221007</td>\n",
       "      <td>[0288, 0289]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02_0</th>\n",
       "      <td>428586</td>\n",
       "      <td>[020, 022]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02_1</th>\n",
       "      <td>441063</td>\n",
       "      <td>[023]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02_2</th>\n",
       "      <td>408771</td>\n",
       "      <td>[024]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02_3</th>\n",
       "      <td>264504</td>\n",
       "      <td>[025, 026]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02_4</th>\n",
       "      <td>379604</td>\n",
       "      <td>[029]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03_0</th>\n",
       "      <td>377547</td>\n",
       "      <td>[030]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03_1</th>\n",
       "      <td>481457</td>\n",
       "      <td>[031, 032, 033, 034]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03_2</th>\n",
       "      <td>457569</td>\n",
       "      <td>[035, 036, 037, 038]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03_3</th>\n",
       "      <td>48241</td>\n",
       "      <td>[039]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04_0</th>\n",
       "      <td>449276</td>\n",
       "      <td>[040, 041]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04_1</th>\n",
       "      <td>484241</td>\n",
       "      <td>[042, 043, 044, 045]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04_2</th>\n",
       "      <td>346497</td>\n",
       "      <td>[046, 047, 048, 049]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05_0</th>\n",
       "      <td>480908</td>\n",
       "      <td>[050, 051, 052, 053, 054, 055, 056]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05_1</th>\n",
       "      <td>144833</td>\n",
       "      <td>[057, 058, 059]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972_0</th>\n",
       "      <td>392534</td>\n",
       "      <td>[9720, 9721]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972_1</th>\n",
       "      <td>449177</td>\n",
       "      <td>[9722, 9723, 9724, 9725, 9726, 9727, 9728, 9729]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973_0</th>\n",
       "      <td>462185</td>\n",
       "      <td>[9730, 9731, 9732, 9733, 9734, 9735]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973_1</th>\n",
       "      <td>135381</td>\n",
       "      <td>[9736, 9737, 9738, 9739]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974_0</th>\n",
       "      <td>394715</td>\n",
       "      <td>[9740, 9741, 9742, 9743, 9744, 9745, 9746]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974_1</th>\n",
       "      <td>161028</td>\n",
       "      <td>[9747, 9748, 9749]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97_0</th>\n",
       "      <td>319301</td>\n",
       "      <td>[971]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97_1</th>\n",
       "      <td>355650</td>\n",
       "      <td>[975, 976]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97_2</th>\n",
       "      <td>389914</td>\n",
       "      <td>[977, 978, 979]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980_0</th>\n",
       "      <td>341896</td>\n",
       "      <td>[9800, 9801]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980_1</th>\n",
       "      <td>226002</td>\n",
       "      <td>[9802]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980_2</th>\n",
       "      <td>382521</td>\n",
       "      <td>[9803, 9804]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980_3</th>\n",
       "      <td>398103</td>\n",
       "      <td>[9805, 9806, 9807, 9808, 9809]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981_0</th>\n",
       "      <td>428717</td>\n",
       "      <td>[9810, 9811]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981_1</th>\n",
       "      <td>432100</td>\n",
       "      <td>[9812, 9813, 9814, 9815, 9816, 9817, 9818, 9819]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982_0</th>\n",
       "      <td>447430</td>\n",
       "      <td>[9820, 9821, 9822, 9823, 9824]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982_1</th>\n",
       "      <td>430055</td>\n",
       "      <td>[9825, 9826, 9827, 9828, 9829]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983_0</th>\n",
       "      <td>388948</td>\n",
       "      <td>[9830, 9831, 9832, 9833, 9834, 9835, 9836]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983_1</th>\n",
       "      <td>324660</td>\n",
       "      <td>[9837, 9838, 9839]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986_0</th>\n",
       "      <td>419035</td>\n",
       "      <td>[9860, 9861, 9862, 9863, 9864, 9865, 9866, 9867]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986_1</th>\n",
       "      <td>154295</td>\n",
       "      <td>[9868, 9869]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98_0</th>\n",
       "      <td>400115</td>\n",
       "      <td>[984]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98_1</th>\n",
       "      <td>475554</td>\n",
       "      <td>[985, 987]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98_2</th>\n",
       "      <td>219761</td>\n",
       "      <td>[988]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98_3</th>\n",
       "      <td>284655</td>\n",
       "      <td>[989]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_0</th>\n",
       "      <td>257246</td>\n",
       "      <td>[990, 991]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_1</th>\n",
       "      <td>342856</td>\n",
       "      <td>[992]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_2</th>\n",
       "      <td>370680</td>\n",
       "      <td>[993, 994]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_3</th>\n",
       "      <td>320660</td>\n",
       "      <td>[995]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99_4</th>\n",
       "      <td>389270</td>\n",
       "      <td>[996, 997, 998, 999]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>875 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       2010 Census Population  \\\n",
       "Chunk                           \n",
       "018_0                  411918   \n",
       "018_1                  301208   \n",
       "01_0                   469375   \n",
       "01_1                   382635   \n",
       "01_2                   211812   \n",
       "01_3                   373719   \n",
       "01_4                   188402   \n",
       "01_5                   379027   \n",
       "01_6                   476314   \n",
       "021_0                  396563   \n",
       "021_1                  464077   \n",
       "021_2                  423302   \n",
       "027_0                  461337   \n",
       "027_1                   64999   \n",
       "028_0                  451970   \n",
       "028_1                  221007   \n",
       "02_0                   428586   \n",
       "02_1                   441063   \n",
       "02_2                   408771   \n",
       "02_3                   264504   \n",
       "02_4                   379604   \n",
       "03_0                   377547   \n",
       "03_1                   481457   \n",
       "03_2                   457569   \n",
       "03_3                    48241   \n",
       "04_0                   449276   \n",
       "04_1                   484241   \n",
       "04_2                   346497   \n",
       "05_0                   480908   \n",
       "05_1                   144833   \n",
       "...                       ...   \n",
       "972_0                  392534   \n",
       "972_1                  449177   \n",
       "973_0                  462185   \n",
       "973_1                  135381   \n",
       "974_0                  394715   \n",
       "974_1                  161028   \n",
       "97_0                   319301   \n",
       "97_1                   355650   \n",
       "97_2                   389914   \n",
       "980_0                  341896   \n",
       "980_1                  226002   \n",
       "980_2                  382521   \n",
       "980_3                  398103   \n",
       "981_0                  428717   \n",
       "981_1                  432100   \n",
       "982_0                  447430   \n",
       "982_1                  430055   \n",
       "983_0                  388948   \n",
       "983_1                  324660   \n",
       "986_0                  419035   \n",
       "986_1                  154295   \n",
       "98_0                   400115   \n",
       "98_1                   475554   \n",
       "98_2                   219761   \n",
       "98_3                   284655   \n",
       "99_0                   257246   \n",
       "99_1                   342856   \n",
       "99_2                   370680   \n",
       "99_3                   320660   \n",
       "99_4                   389270   \n",
       "\n",
       "                                               Prefixes  \n",
       "Chunk                                                    \n",
       "018_0                    [0180, 0181, 0182, 0183, 0184]  \n",
       "018_1                    [0185, 0186, 0187, 0188, 0189]  \n",
       "01_0                                              [010]  \n",
       "01_1                                    [011, 012, 013]  \n",
       "01_2                                              [014]  \n",
       "01_3                                              [015]  \n",
       "01_4                                              [016]  \n",
       "01_5                                              [017]  \n",
       "01_6                                              [019]  \n",
       "021_0                                [0210, 0211, 0212]  \n",
       "021_1                                      [0213, 0214]  \n",
       "021_2                    [0215, 0216, 0217, 0218, 0219]  \n",
       "027_0  [0270, 0271, 0272, 0273, 0274, 0275, 0276, 0277]  \n",
       "027_1                                      [0278, 0279]  \n",
       "028_0  [0280, 0281, 0282, 0283, 0284, 0285, 0286, 0287]  \n",
       "028_1                                      [0288, 0289]  \n",
       "02_0                                         [020, 022]  \n",
       "02_1                                              [023]  \n",
       "02_2                                              [024]  \n",
       "02_3                                         [025, 026]  \n",
       "02_4                                              [029]  \n",
       "03_0                                              [030]  \n",
       "03_1                               [031, 032, 033, 034]  \n",
       "03_2                               [035, 036, 037, 038]  \n",
       "03_3                                              [039]  \n",
       "04_0                                         [040, 041]  \n",
       "04_1                               [042, 043, 044, 045]  \n",
       "04_2                               [046, 047, 048, 049]  \n",
       "05_0                [050, 051, 052, 053, 054, 055, 056]  \n",
       "05_1                                    [057, 058, 059]  \n",
       "...                                                 ...  \n",
       "972_0                                      [9720, 9721]  \n",
       "972_1  [9722, 9723, 9724, 9725, 9726, 9727, 9728, 9729]  \n",
       "973_0              [9730, 9731, 9732, 9733, 9734, 9735]  \n",
       "973_1                          [9736, 9737, 9738, 9739]  \n",
       "974_0        [9740, 9741, 9742, 9743, 9744, 9745, 9746]  \n",
       "974_1                                [9747, 9748, 9749]  \n",
       "97_0                                              [971]  \n",
       "97_1                                         [975, 976]  \n",
       "97_2                                    [977, 978, 979]  \n",
       "980_0                                      [9800, 9801]  \n",
       "980_1                                            [9802]  \n",
       "980_2                                      [9803, 9804]  \n",
       "980_3                    [9805, 9806, 9807, 9808, 9809]  \n",
       "981_0                                      [9810, 9811]  \n",
       "981_1  [9812, 9813, 9814, 9815, 9816, 9817, 9818, 9819]  \n",
       "982_0                    [9820, 9821, 9822, 9823, 9824]  \n",
       "982_1                    [9825, 9826, 9827, 9828, 9829]  \n",
       "983_0        [9830, 9831, 9832, 9833, 9834, 9835, 9836]  \n",
       "983_1                                [9837, 9838, 9839]  \n",
       "986_0  [9860, 9861, 9862, 9863, 9864, 9865, 9866, 9867]  \n",
       "986_1                                      [9868, 9869]  \n",
       "98_0                                              [984]  \n",
       "98_1                                         [985, 987]  \n",
       "98_2                                              [988]  \n",
       "98_3                                              [989]  \n",
       "99_0                                         [990, 991]  \n",
       "99_1                                              [992]  \n",
       "99_2                                         [993, 994]  \n",
       "99_3                                              [995]  \n",
       "99_4                               [996, 997, 998, 999]  \n",
       "\n",
       "[875 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunks_500000 = build_chunks(500000)\n",
    "chunks_500000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7feacf3a7eb8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feacf1ea828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(chunks_500000['2010 Census Population'], bins=50)\n",
    "plt.title(\"Population per Chunk (500,000)\")\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"# of Chunks\")"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}