06_DataFrame_Operations.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "pd.options.display.max_rows = 6\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes = pd.read_csv('Crimes_-_2001_to_present.csv', parse_dates=['Date'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Group By\n",
      "Given a *key* column, one can partition a table into *groups*:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes50_by_community = crimes.head(50).groupby('Community Area')\n",
      "crimes50_by_community.groups"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "{1: [26],\n",
        " 6: [3, 13],\n",
        " 8: [28, 38, 45, 49],\n",
        " 13: [14],\n",
        " 14: [1, 43],\n",
        " 19: [30],\n",
        " 22: [0, 27],\n",
        " 23: [32],\n",
        " 24: [31],\n",
        " 25: [5, 24, 42],\n",
        " 28: [10, 11, 22, 36],\n",
        " 29: [33, 40],\n",
        " 30: [16, 25],\n",
        " 32: [4],\n",
        " 33: [37],\n",
        " 37: [48],\n",
        " 41: [2],\n",
        " 43: [7, 8, 20],\n",
        " 49: [29],\n",
        " 56: [23],\n",
        " 58: [21, 46],\n",
        " 61: [47],\n",
        " 63: [9],\n",
        " 67: [15, 19],\n",
        " 69: [17],\n",
        " 70: [41, 44],\n",
        " 71: [6, 34],\n",
        " 75: [12],\n",
        " 76: [18],\n",
        " 77: [35, 39]}"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes_by_community = crimes.groupby('Community Area')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Aggregate\n",
      "We can aggregate a column over the groups using an aggregate function.\n",
      "- Built-in functions include 'sum' 'mean' 'median' 'mode' 'count' 'std' 'nunique'.\n",
      "- Can also pass an arbitrary function whose argument is a vector and returns a scalar."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_crime_count = crimes_by_community['ID'].agg('count')\n",
      "community_crime_count"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "Community Area\n",
        "1     259\n",
        "2     263\n",
        "3     263\n",
        "     ... \n",
        "75    118\n",
        "76    112\n",
        "77    166\n",
        "Name: ID, dtype: int64"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_crime_count.plot(kind='bar', figsize=(12,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f199dd22e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1btVBckQ8GdiWmZcC7XM5hr3bundllyRJ0lQa5Yl7hwPHRsQxwN2AnwVeC+weETuV0eR9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\nOp8i8Ad7+IcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f19a0ae6fd0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Daily Timeseries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def to_day(timestamp):\n",
      "    return timestamp.replace(minute=0,hour=0, second=0)\n",
      "\n",
      "crimes['Day'] = crimes['Date'].apply(to_day)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes_by_day = crimes.groupby('Day')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes_by_day['ID'].agg('nunique').plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f199cc1ffd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ERHKRaR1dCV1EpEhlOrlICV1EpEhlOrmowYRuZj3NbIqZvW9mM83swmh/JzOb\nbGZzzGySmXWodsxoM5tnZtPNbJ9sfhARkeYuHyWX9cAf3P17wA+B881sV2AY8IK77wJMAS4HMLPB\nwE7u3hc4F7gjo59ARESAPCR0d1/i7tOj7ZXAbKAncDwwNmo2NnpN9P3+qP3rQAcz65Z+SCIiAnm+\nKWpmfYB9gNeAbu5eBiHpA1VJuwewsNphi6J9IiKSgUwnF7VKt6GZbQmMB37n7ivNzGs0qfm6QSUl\nJd9tp1IpUqlUpqcQEUmszp3h2WdLKSkpTau9uTech82sFfAM8Jy73xztmw2k3L3MzLoDL7n7bmZ2\nR7T9aNTuQ+DQqt58tXN6OtcWEWmunnkGbr8dnn12wz4zw92ttvbpllzuBT6oSuaRp4BfRtu/BCZU\n239GdOEBQHnNZC4iIg3LtIbeYMnFzA4CfgHMNLN3CaWVK4ARwN/M7GzgU+AkAHefaGZHm9lHwCrg\nrEx/CBERyXxiUVoll3xQyUVEpH5lZbDnnrB06YZ99ZVclNBFRIrU2rXQrl34blEKj6OGLiIijax1\na2jTBlasSK+9ErqISBHL5MaoErqISBHLZHKRErqISBFTD11EJCGU0EVEEkIJXUQkIVRDFxFJCPXQ\nRUQSQgldRCQhlNBFRBIikwW6lNBFRIpYp07qoYuIJIJKLiIiCaGELiKSEO3awbp1sGZNw22V0EVE\niphZ+pOLlNBFRIpcumWXBhO6mY0xszIze6/avk5mNtnM5pjZJDPrUO290WY2z8ymm9k+2f4AIiIS\nxJbQgfuAH9fYNwx4wd13AaYAlwOY2WBgJ3fvC5wL3JFBzCIiUovYErq7/wuoWb05HhgbbY+NXlft\nvz867nWgg5l1Sy9kERGpTbqTi7KtoW/j7mUA7r4EqEraPYCF1dotivaJiEiW0p1cFNdNUY/pPCIi\nUkO6JZdWWZ6/zMy6uXuZmXUHlkb7FwG9qrXrGe2rVUlJyXfbqVSKVCqVZTgiIslUWlrKa6+VsmwZ\ntGxZf1tzb7hzbWZ9gKfdfc/o9QjgS3cfYWbDgI7uPszMjgbOd/djzGwAMMrdB9RxTk/n2iIizd1D\nD8HTT8PDD4OZ4e5WW7sGe+hm9hCQArY2s38DVwPXA4+Z2dnAp8BJAO4+0cyONrOPgFXAWfH8OCIi\nzVe6E4saTOjuflodbx1RR/sLGr6siIikK85x6CIiUkBK6CIiCZFuQk/rpmg+6KaoiEh6Kipg881h\n7Vpo2bJrWNeFAAAIbElEQVTum6LqoYuIFLmWLWHLLWH58vrbKaGLiDQB6ZRdlNBFRJoAJXQRkYRQ\nQhcRSYh0JhcpoYuINAHqoYuIJIQSuohIQiihi4gkhGroIiIJoR66iEhCKKGLiCSEErqISEIooYuI\nJESnTkroIiKJsMUW0KKBjK2ELiLSRHTuXP/7eUnoZjbIzD40s7lm9sd8XENEpLlp9IRuZi2AW4Ef\nA98DTjWzXeO+johIc9OpU/3v56OHfgAwz90/dfd1wCPA8Xm4johIs1KIkksPYGG1159F+7JWWlqa\nl7b5PHdTa1sscTS1tsUSRzG0LZY4iqFtvs69++71v98kboo2tV96U2xbLHE0tbbFEkcxtC2WOIqh\nbb7OPXx4/e+bu6d90XSY2QCgxN0HRa+HAe7uI2q0i/fCIiLNhLtbbfvzkdBbAnOAw4HFwBvAqe4+\nO9YLiYjIRlrFfUJ3rzCzC4DJhJLOGCVzEZH8i72HLiIihdEkboomSY1JV5dF+6aa2Ttm9q6ZLTKz\nvxc6zmJjZmPMrMzM3qvlvYvNrNLMGhjU1fyYWU8zm2Jm75vZTDO7MNr/czObZWYVZrZvoeMsNvX8\n3vY2s1ej/1ffMLP9Ch1rdeqhN6Jo0tVcwv2Fz4E3gVPc/cNqbcYDT7r7uMJEWZzM7GBgJXC/u+9V\nbX9P4B5gF+D77t7A8kXNi5l1B7q7+3Qz2xJ4mzAvxIFK4E7gEnd/p4BhFp1afm9vAT8FRgEj3X2y\nmQ0GLnP3gYWMtTr10BtXvZOuzKw9cBjwZIHiK1ru/i+gtgdw3QRc2sjhNBnuvsTdp0fbK4HZQA93\nn+Pu84BaR0s0d7X83j4EtiP8EewQNesILCpMhLWL/aao1Ku2SVcHVHt9PPBC9A9IGmBmxwEL3X2m\nmfJSQ8ysD7AP8HphI2laavzefg9MMrORhD+GBxYusk2ph15cTgUeLnQQTYGZbQFcAVxdfXeBwil6\nUdlgPPA7dRjSV8vv7bxouzchud9byPhqUkJvXIuA3tVe94z2YWZbA/sDzxYgrqZoJ6APMMPMPiH8\nLt82s20KGlURMrNWhKT0gLtPKHQ8TUUdv7cz3f1JAHcfz8afsAtOCb1xvQnsbGbbm1lr4BTgqei9\nE4Fn3H1twaIrfhZ94e6z3L27u+/o7jsQylf93X1pQSMsTvcCH7j7zXW8r082tavt97bIzA4FMLPD\nCYMcioZGuTQyMxsE3MyGSVfXR/unANe7++RCxleszOwhIAVsDZQBV7v7fdXenw/sp1EuGzOzg4Cp\nwEzCyBYnlKraALcAXYByYLq7Dy5UnMWmnt/b18BooCXwLfAbd3+3UHHWpIQuIpIQKrmIiCSEErqI\nSEIooYuIJIQSuohIQiihi4gkhBK6iEhCKKGLiCSEErqISEIooYuIJIQSuohIQiihi4gkhBK6iEhC\nKKGLiCSEErqISEIooYuIJEReHhJtZhXADKA1sA54ALjJtfi6iEje5CWhA6vcfV8AM+tCePBxe6Ak\nT9cTEWn28l5ycfcvgF8DFwCYWQszu8HMXjez6WZ2TlVbM/ujmb1nZu+a2fB8xyYikiT56qFvxN0/\niRJ5V+AEoNzdfxA9KHmamU0GdgN+Auzv7mvMrGNjxCYikhSNktBrOArY08xOjF63B/oCRwD3ufsa\nAHcvL0BsIiJNVqMkdDPbEahw92VmZsBv3f0fNdoMaoxYRESSKl81dPtuI5RZbgduiXZNAn5jZq2i\n9/uaWVvgH8BZZrZFtL9TnmITEUmkfPXQ25jZO2wYtni/u98UvXcP0Ad4J+qtLwVOcPdJZrY38JaZ\nrQEmAlflKT4RkcQxDQ0XEUkGzRQVEUkIJXQRkYSILaGbWU8zm2Jm75vZTDO7MNrfycwmm9kcM5tk\nZh2i/buY2Stm9q2Z/aHGuRaY2YxogtEbccUoIpJksdXQzaw70N3dp5vZlsDbwPHAWcB/3P0GM/sj\n0Mndh0WjX3oTJhp95e43VjvXfOD77v5VLMGJiDQDsfXQ3X2Ju0+PtlcCs4GehKQ+Nmo2lpDAcfdl\n7v42sL6W01mcsYmINAd5SZpm1gfYB3gN6ObuZRCSPrBNGqdwYJKZvVl9rRcREalb7OPQo3LLeOB3\n7r7SzGrWdNKp8Rzk7oujssw/zGy2u/8r7lhFRJIk1h56NPtzPPCAu0+IdpeZWbfo/e6EiUT1cvfF\n0fdlwBPAAXHGKSKSRHGXXO4FPnD3m6vtewr4ZbR9JjCh5kFsvFRA26iXj5m1IyzmNSvmOEVEEifO\nUS4HAVOBmYSyigNXAG8AfwN6AZ8CJ7l7edRrfwvYCqgEVgK7A10JvXInlIQedPfrYwlSRCTBNPVf\nRCQhNDRQRCQhlNBFRBJCCV1EJCGU0EVEEkIJXUQkIZTQRUQSolEeEi1STMysApjBhkckPgDc5BrD\nK02cEro0R6vcfV8AM+sCPAy0B0oKGZRIrlRykWbN3b8Afg1cAGBm25vZVDN7K/oaEO0fa2bHVR1n\nZuPM7CeFiVqkdpopKs2OmX3t7u1r7PsS2AVYAVS6+1oz2xl42N33N7NDgN+7+0/NrD3wLtDX3Ssb\n/QcQqYNKLiJB1QJxrYFbzWwfoALoC+DuU83sNjPbGvg58LiSuRQbJXRp9sxsR2C9uy8zs6uBJe6+\nl5m1BFZXa3o/cDpwChtWEBUpGkro0hxVX665K3A7cEu0qwOwMNo+A2hZ7bixhNVDF7v7h40Qp0hG\nlNClOWpjZu+wYdji/e5+U/Te/wMeN7MzgOeBVVUHuftSM5tNWN5ZpOjopqhImsysLWH8+r7uvqLQ\n8YjUpGGLImkws8OBD4DRSuZSrNRDFxFJCPXQRUQSQgldRCQhlNBFRBJCCV1EJCGU0EVEEkIJXUQk\nIf4/9ax2UxQjkYEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f199caf1e10>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "What about a time series for a single community area?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes[crimes['Community Area'] == 41].groupby('Day')['ID'].agg('count').plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f199d396e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kiPR1B06bc86BG2+Ed70r3f3edBO85S3J9pFUZF56Cf73f+H++7NpTLMKNaVF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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f199d628dd0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Multiple Group By\n",
      "To get a series for each community area we could just loop over them. But there is a better way.\n",
      "\n",
      "Group by can take *multiple* keys, in this case community area and day. The result has a (row) *multi-index*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes_by_community_day = crimes.groupby(['Community Area', 'Day'])\n",
      "crimes_by_community_day_count = crimes_by_community_day['ID'].agg('count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes_by_community_day_count"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Community Area  Day       \n",
        "1               2015-12-01    14\n",
        "                2015-12-02    12\n",
        "                2015-12-03     6\n",
        "                              ..\n",
        "77              2015-12-28     8\n",
        "                2015-12-29     4\n",
        "                2015-12-30     5\n",
        "Name: ID, dtype: int64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "Similarly for arrests, then we can take their quotient:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes_by_community_day_arrests = crimes_by_community_day['Arrest'].agg('sum')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_day_arrest_prop = crimes_by_community_day_arrests / crimes_by_community_day_count\n",
      "community_day_arrest_prop"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "Community Area  Day       \n",
        "1               2015-12-01    0.214286\n",
        "                2015-12-02    0.166667\n",
        "                2015-12-03    0.166667\n",
        "                                ...   \n",
        "77              2015-12-28    0.250000\n",
        "                2015-12-29    0.500000\n",
        "                2015-12-30    0.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## How can we plot multiple of these time series together? Unstacking"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "crimes.groupby(['Community Area', 'Primary Type'])['ID'].agg('count').unstack('Community Area')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Community Area</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>...</th>\n",
        "      <th>68</th>\n",
        "      <th>69</th>\n",
        "      <th>70</th>\n",
        "      <th>71</th>\n",
        "      <th>72</th>\n",
        "      <th>73</th>\n",
        "      <th>74</th>\n",
        "      <th>75</th>\n",
        "      <th>76</th>\n",
        "      <th>77</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Primary Type</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>ARSON</th>\n",
        "      <td>NaN</td>\n",
        "      <td>1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ASSAULT</th>\n",
        "      <td>19</td>\n",
        "      <td>11</td>\n",
        "      <td>12</td>\n",
        "      <td>10</td>\n",
        "      <td>4</td>\n",
        "      <td>11</td>\n",
        "      <td>3</td>\n",
        "      <td>37</td>\n",
        "      <td>2</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>39</td>\n",
        "      <td>46</td>\n",
        "      <td>16</td>\n",
        "      <td>40</td>\n",
        "      <td>4</td>\n",
        "      <td>14</td>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>4</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BATTERY</th>\n",
        "      <td>50</td>\n",
        "      <td>32</td>\n",
        "      <td>47</td>\n",
        "      <td>17</td>\n",
        "      <td>8</td>\n",
        "      <td>45</td>\n",
        "      <td>23</td>\n",
        "      <td>99</td>\n",
        "      <td>4</td>\n",
        "      <td>15</td>\n",
        "      <td>...</td>\n",
        "      <td>122</td>\n",
        "      <td>139</td>\n",
        "      <td>23</td>\n",
        "      <td>129</td>\n",
        "      <td>6</td>\n",
        "      <td>40</td>\n",
        "      <td>4</td>\n",
        "      <td>21</td>\n",
        "      <td>9</td>\n",
        "      <td>32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>STALKING</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>THEFT</th>\n",
        "      <td>60</td>\n",
        "      <td>62</td>\n",
        "      <td>75</td>\n",
        "      <td>42</td>\n",
        "      <td>40</td>\n",
        "      <td>172</td>\n",
        "      <td>152</td>\n",
        "      <td>392</td>\n",
        "      <td>NaN</td>\n",
        "      <td>29</td>\n",
        "      <td>...</td>\n",
        "      <td>65</td>\n",
        "      <td>87</td>\n",
        "      <td>38</td>\n",
        "      <td>89</td>\n",
        "      <td>12</td>\n",
        "      <td>44</td>\n",
        "      <td>16</td>\n",
        "      <td>25</td>\n",
        "      <td>45</td>\n",
        "      <td>39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WEAPONS VIOLATION</th>\n",
        "      <td>NaN</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>7</td>\n",
        "      <td>9</td>\n",
        "      <td>2</td>\n",
        "      <td>8</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>30 rows \u00d7 77 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "Community Area     1   2   3   4   5    6    7    8   9   10 ...   68   69  \\\n",
        "Primary Type                                                 ...             \n",
        "ARSON             NaN   1 NaN NaN NaN    1  NaN  NaN NaN NaN ...    1    1   \n",
        "ASSAULT            19  11  12  10   4   11    3   37   2   4 ...   39   46   \n",
        "BATTERY            50  32  47  17   8   45   23   99   4  15 ...  122  139   \n",
        "...                ..  ..  ..  ..  ..  ...  ...  ...  ..  .. ...  ...  ...   \n",
        "STALKING          NaN NaN NaN NaN NaN  NaN  NaN  NaN NaN NaN ...  NaN  NaN   \n",
        "THEFT              60  62  75  42  40  172  152  392 NaN  29 ...   65   87   \n",
        "WEAPONS VIOLATION NaN   3   2 NaN NaN    1  NaN    2 NaN NaN ...    7    9   \n",
        "\n",
        "Community Area     70   71  72  73  74  75  76  77  \n",
        "Primary Type                                        \n",
        "ARSON             NaN    2 NaN NaN NaN NaN NaN NaN  \n",
        "ASSAULT            16   40   4  14   5   4   4  10  \n",
        "BATTERY            23  129   6  40   4  21   9  32  \n",
        "...                ..  ...  ..  ..  ..  ..  ..  ..  \n",
        "STALKING          NaN  NaN NaN NaN NaN NaN NaN NaN  \n",
        "THEFT              38   89  12  44  16  25  45  39  \n",
        "WEAPONS VIOLATION   2    8 NaN NaN NaN   2 NaN   2  \n",
        "\n",
        "[30 rows x 77 columns]"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_day_arrest_prop.unstack('Community Area')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Community Area</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>...</th>\n",
        "      <th>68</th>\n",
        "      <th>69</th>\n",
        "      <th>70</th>\n",
        "      <th>71</th>\n",
        "      <th>72</th>\n",
        "      <th>73</th>\n",
        "      <th>74</th>\n",
        "      <th>75</th>\n",
        "      <th>76</th>\n",
        "      <th>77</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Day</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2015-12-01</th>\n",
        "      <td>0.214286</td>\n",
        "      <td>0.133333</td>\n",
        "      <td>0.100000</td>\n",
        "      <td>0.375</td>\n",
        "      <td>0.200000</td>\n",
        "      <td>0.076923</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.318182</td>\n",
        "      <td>0.111111</td>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.400000</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-02</th>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.100000</td>\n",
        "      <td>0.250</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.173913</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.173913</td>\n",
        "      <td>0.307692</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.347826</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.500000</td>\n",
        "      <td>0.500000</td>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-03</th>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.181818</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.031250</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.210526</td>\n",
        "      <td>0.142857</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-29</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.083333</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.217391</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.277778</td>\n",
        "      <td>0.200000</td>\n",
        "      <td>0.142857</td>\n",
        "      <td>0.277778</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-30</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.428571</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.272727</td>\n",
        "      <td>0.133333</td>\n",
        "      <td>0.222222</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>...</td>\n",
        "      <td>0.181818</td>\n",
        "      <td>0.260870</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.142857</td>\n",
        "      <td>0.5</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-31</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>31 rows \u00d7 77 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "Community Area        1         2         3      4         5         6   \\\n",
        "Day                                                                       \n",
        "2015-12-01      0.214286  0.133333  0.100000  0.375  0.200000  0.076923   \n",
        "2015-12-02      0.166667  0.000000  0.100000  0.250  0.000000  0.000000   \n",
        "2015-12-03      0.166667  0.181818  0.333333  0.000  0.000000  0.166667   \n",
        "...                  ...       ...       ...    ...       ...       ...   \n",
        "2015-12-29      0.000000  0.285714  0.083333  0.000  0.333333  0.125000   \n",
        "2015-12-30      0.000000  0.250000  0.428571  0.000  0.000000  0.272727   \n",
        "2015-12-31           NaN       NaN       NaN    NaN       NaN  0.000000   \n",
        "\n",
        "Community Area        7         8   9         10 ...         68        69  \\\n",
        "Day                                              ...                        \n",
        "2015-12-01      0.000000  0.000000 NaN  0.000000 ...   0.333333  0.318182   \n",
        "2015-12-02      0.000000  0.173913   0  0.000000 ...   0.173913  0.307692   \n",
        "2015-12-03      0.000000  0.031250   0  0.000000 ...   0.250000  0.210526   \n",
        "...                  ...       ...  ..       ... ...        ...       ...   \n",
        "2015-12-29      0.000000  0.217391 NaN  0.000000 ...   0.277778  0.200000   \n",
        "2015-12-30      0.133333  0.222222 NaN  0.333333 ...   0.181818  0.260870   \n",
        "2015-12-31           NaN       NaN NaN       NaN ...        NaN       NaN   \n",
        "\n",
        "Community Area        70        71   72        73        74        75  \\\n",
        "Day                                                                     \n",
        "2015-12-01      0.111111  0.285714  0.0  0.000000  0.000000  0.000000   \n",
        "2015-12-02      0.000000  0.347826  0.0  0.500000  0.500000  0.166667   \n",
        "2015-12-03      0.142857  0.125000  0.0  0.285714  0.000000  0.000000   \n",
        "...                  ...       ...  ...       ...       ...       ...   \n",
        "2015-12-29      0.142857  0.277778  NaN  0.125000  0.333333  0.000000   \n",
        "2015-12-30      0.000000  0.142857  0.5  0.125000  0.000000  0.000000   \n",
        "2015-12-31           NaN       NaN  NaN       NaN       NaN       NaN   \n",
        "\n",
        "Community Area        76   77  \n",
        "Day                            \n",
        "2015-12-01      0.400000  0.0  \n",
        "2015-12-02      0.166667  0.0  \n",
        "2015-12-03      0.000000  0.0  \n",
        "...                  ...  ...  \n",
        "2015-12-29      0.000000  0.5  \n",
        "2015-12-30      0.250000  0.0  \n",
        "2015-12-31           NaN  NaN  \n",
        "\n",
        "[31 rows x 77 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "There are missing values after reshaping.<br>\n",
      "That means there were no crimes in that area on that day so `fillna(0)`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_arrest_timeseries = community_day_arrest_prop.unstack('Community Area')\n",
      "#community_arrest_timeseries.fillna(0, inplace=True)\n",
      "community_arrest_timeseries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Community Area</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>...</th>\n",
        "      <th>68</th>\n",
        "      <th>69</th>\n",
        "      <th>70</th>\n",
        "      <th>71</th>\n",
        "      <th>72</th>\n",
        "      <th>73</th>\n",
        "      <th>74</th>\n",
        "      <th>75</th>\n",
        "      <th>76</th>\n",
        "      <th>77</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Day</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2015-12-01</th>\n",
        "      <td>0.214286</td>\n",
        "      <td>0.133333</td>\n",
        "      <td>0.100000</td>\n",
        "      <td>0.375</td>\n",
        "      <td>0.200000</td>\n",
        "      <td>0.076923</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.318182</td>\n",
        "      <td>0.111111</td>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.400000</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-02</th>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.100000</td>\n",
        "      <td>0.250</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.173913</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.173913</td>\n",
        "      <td>0.307692</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.347826</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.500000</td>\n",
        "      <td>0.500000</td>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-03</th>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.181818</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.031250</td>\n",
        "      <td>0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.210526</td>\n",
        "      <td>0.142857</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-29</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.083333</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.217391</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.277778</td>\n",
        "      <td>0.200000</td>\n",
        "      <td>0.142857</td>\n",
        "      <td>0.277778</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-30</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.428571</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.272727</td>\n",
        "      <td>0.133333</td>\n",
        "      <td>0.222222</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>...</td>\n",
        "      <td>0.181818</td>\n",
        "      <td>0.260870</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.142857</td>\n",
        "      <td>0.5</td>\n",
        "      <td>0.125000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-31</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>31 rows \u00d7 77 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "Community Area        1         2         3      4         5         6   \\\n",
        "Day                                                                       \n",
        "2015-12-01      0.214286  0.133333  0.100000  0.375  0.200000  0.076923   \n",
        "2015-12-02      0.166667  0.000000  0.100000  0.250  0.000000  0.000000   \n",
        "2015-12-03      0.166667  0.181818  0.333333  0.000  0.000000  0.166667   \n",
        "...                  ...       ...       ...    ...       ...       ...   \n",
        "2015-12-29      0.000000  0.285714  0.083333  0.000  0.333333  0.125000   \n",
        "2015-12-30      0.000000  0.250000  0.428571  0.000  0.000000  0.272727   \n",
        "2015-12-31           NaN       NaN       NaN    NaN       NaN  0.000000   \n",
        "\n",
        "Community Area        7         8   9         10 ...         68        69  \\\n",
        "Day                                              ...                        \n",
        "2015-12-01      0.000000  0.000000 NaN  0.000000 ...   0.333333  0.318182   \n",
        "2015-12-02      0.000000  0.173913   0  0.000000 ...   0.173913  0.307692   \n",
        "2015-12-03      0.000000  0.031250   0  0.000000 ...   0.250000  0.210526   \n",
        "...                  ...       ...  ..       ... ...        ...       ...   \n",
        "2015-12-29      0.000000  0.217391 NaN  0.000000 ...   0.277778  0.200000   \n",
        "2015-12-30      0.133333  0.222222 NaN  0.333333 ...   0.181818  0.260870   \n",
        "2015-12-31           NaN       NaN NaN       NaN ...        NaN       NaN   \n",
        "\n",
        "Community Area        70        71   72        73        74        75  \\\n",
        "Day                                                                     \n",
        "2015-12-01      0.111111  0.285714  0.0  0.000000  0.000000  0.000000   \n",
        "2015-12-02      0.000000  0.347826  0.0  0.500000  0.500000  0.166667   \n",
        "2015-12-03      0.142857  0.125000  0.0  0.285714  0.000000  0.000000   \n",
        "...                  ...       ...  ...       ...       ...       ...   \n",
        "2015-12-29      0.142857  0.277778  NaN  0.125000  0.333333  0.000000   \n",
        "2015-12-30      0.000000  0.142857  0.5  0.125000  0.000000  0.000000   \n",
        "2015-12-31           NaN       NaN  NaN       NaN       NaN       NaN   \n",
        "\n",
        "Community Area        76   77  \n",
        "Day                            \n",
        "2015-12-01      0.400000  0.0  \n",
        "2015-12-02      0.166667  0.0  \n",
        "2015-12-03      0.000000  0.0  \n",
        "...                  ...  ...  \n",
        "2015-12-29      0.000000  0.5  \n",
        "2015-12-30      0.250000  0.0  \n",
        "2015-12-31           NaN  NaN  \n",
        "\n",
        "[31 rows x 77 columns]"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "Now we can plot multiple community area timeseries:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_arrest_timeseries[[40,41,42]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Community Area</th>\n",
        "      <th>40</th>\n",
        "      <th>41</th>\n",
        "      <th>42</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Day</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2015-12-01</th>\n",
        "      <td>0.500000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-02</th>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.187500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-03</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-29</th>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.2</td>\n",
        "      <td>0.125000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-30</th>\n",
        "      <td>0.285714</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-31</th>\n",
        "      <td>NaN</td>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>31 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "Community Area        40   41        42\n",
        "Day                                    \n",
        "2015-12-01      0.500000  1.0  0.333333\n",
        "2015-12-02      0.250000  0.0  0.187500\n",
        "2015-12-03      0.000000  0.0  0.333333\n",
        "...                  ...  ...       ...\n",
        "2015-12-29      0.285714  0.2  0.125000\n",
        "2015-12-30      0.285714  0.0  0.500000\n",
        "2015-12-31           NaN  0.0       NaN\n",
        "\n",
        "[31 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_arrest_timeseries[[1, 10]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Community Area</th>\n",
        "      <th>1</th>\n",
        "      <th>10</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Day</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2015-12-01</th>\n",
        "      <td>0.214286</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-02</th>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-03</th>\n",
        "      <td>0.166667</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-29</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-30</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-12-31</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>31 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "Community Area        1         10\n",
        "Day                               \n",
        "2015-12-01      0.214286  0.000000\n",
        "2015-12-02      0.166667  0.000000\n",
        "2015-12-03      0.166667  0.000000\n",
        "...                  ...       ...\n",
        "2015-12-29      0.000000  0.000000\n",
        "2015-12-30      0.000000  0.333333\n",
        "2015-12-31           NaN       NaN\n",
        "\n",
        "[31 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_arrest_timeseries[[40,41,42]].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f199c9d67d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8qucNBloizpiqpfLVV8J2GzAgtLnaUz+vxN5kIOWgr4NsI5QAtqCVCL6IL1um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\nWEI8IqLGEuIRETWWEI+IqLGEeEREjT0AUZMn5nLesiUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f199d06ef10>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Another dataset\n",
      "Let's look at Chicago's affordable housing dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing = pd.read_csv('Affordable_Rental_Housing_Developments.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing['Property Type'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "Multifamily                          135\n",
        "Senior                                76\n",
        "Supportive Housing                    22\n",
        "                                    ... \n",
        "Supportive/Youth/Kinship Families      1\n",
        "Supportive/HIV/AIDS                    1\n",
        "Supportive/Teenage Moms                1\n",
        "Name: Property Type, dtype: int64"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Community Area Name</th>\n",
        "      <th>Community Area Number</th>\n",
        "      <th>Property Type</th>\n",
        "      <th>Property Name</th>\n",
        "      <th>Address</th>\n",
        "      <th>Zip Code</th>\n",
        "      <th>Phone Number</th>\n",
        "      <th>Management Company</th>\n",
        "      <th>Units</th>\n",
        "      <th>X Coordinate</th>\n",
        "      <th>Y Coordinate</th>\n",
        "      <th>Latitude</th>\n",
        "      <th>Longitude</th>\n",
        "      <th>Location</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Albany Park</td>\n",
        "      <td>14</td>\n",
        "      <td>Senior</td>\n",
        "      <td>Mayfair Commons</td>\n",
        "      <td>4444 W. Lawrence Ave.</td>\n",
        "      <td>60630</td>\n",
        "      <td>773-205-7862</td>\n",
        "      <td>Metroplex, Inc.</td>\n",
        "      <td>97</td>\n",
        "      <td>1145674.753818</td>\n",
        "      <td>1931569.979045</td>\n",
        "      <td>41.968224</td>\n",
        "      <td>-87.739747</td>\n",
        "      <td>4444 W. Lawrence Ave.\\n(41.9682242321, -87.739...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Albany Park</td>\n",
        "      <td>14</td>\n",
        "      <td>Senior</td>\n",
        "      <td>Senior Suites of Ravenswood Manor</td>\n",
        "      <td>2800 W. Montrose Ave.</td>\n",
        "      <td>60618</td>\n",
        "      <td>773-509-9333</td>\n",
        "      <td>Senior Lifestyle Corp.</td>\n",
        "      <td>80</td>\n",
        "      <td>1156898.513945</td>\n",
        "      <td>1929148.460973</td>\n",
        "      <td>41.961359</td>\n",
        "      <td>-87.698544</td>\n",
        "      <td>2800 W. Montrose Ave.\\n(41.9613586525, -87.698...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Albany Park</td>\n",
        "      <td>14</td>\n",
        "      <td>Senior</td>\n",
        "      <td>Darul Amaan Senior Living</td>\n",
        "      <td>4814-58 N. Kedzie Ave.</td>\n",
        "      <td>60625</td>\n",
        "      <td>773-463-3004</td>\n",
        "      <td>East Lake Management &amp; Development Corp.</td>\n",
        "      <td>56</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>261</th>\n",
        "      <td>Woodlawn</td>\n",
        "      <td>42</td>\n",
        "      <td>Multifamily</td>\n",
        "      <td>The Grant at Woodlawn Pk Phase I</td>\n",
        "      <td>6227-29 S. Cottage Grove Ave.</td>\n",
        "      <td>60637</td>\n",
        "      <td>312-283-0030</td>\n",
        "      <td>Preservation Housing Management LLC</td>\n",
        "      <td>67</td>\n",
        "      <td>1186722.404223</td>\n",
        "      <td>1863775.146697</td>\n",
        "      <td>41.781408</td>\n",
        "      <td>-87.605807</td>\n",
        "      <td>6227-29 S. Cottage Grove Ave.\\n(41.7814080037,...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>262</th>\n",
        "      <td>Woodlawn</td>\n",
        "      <td>42</td>\n",
        "      <td>Multifamily</td>\n",
        "      <td>The Grant at Woodlawn Pk Phase II</td>\n",
        "      <td>6129 S. Cottage Grove Ave.</td>\n",
        "      <td>60637</td>\n",
        "      <td>312-283-0030</td>\n",
        "      <td>Preservation Housing Management LLC</td>\n",
        "      <td>33</td>\n",
        "      <td>1186094.258818</td>\n",
        "      <td>1864209.572438</td>\n",
        "      <td>41.782505</td>\n",
        "      <td>-87.590962</td>\n",
        "      <td>6129 S. Cottage Grove Ave.\\n(41.7825052194, -8...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>263</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1864312.842731</td>\n",
        "      <td>41.782803</td>\n",
        "      <td>-87.593261</td>\n",
        "      <td>(41.7828034603, -87.5932613147)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>264 rows \u00d7 14 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "    Community Area Name  Community Area Number Property Type  \\\n",
        "0           Albany Park                     14        Senior   \n",
        "1           Albany Park                     14        Senior   \n",
        "2           Albany Park                     14        Senior   \n",
        "..                  ...                    ...           ...   \n",
        "261           Woodlawn                      42   Multifamily   \n",
        "262            Woodlawn                     42   Multifamily   \n",
        "263                 NaN                    NaN           NaN   \n",
        "\n",
        "                         Property Name                        Address  \\\n",
        "0                      Mayfair Commons          4444 W. Lawrence Ave.   \n",
        "1    Senior Suites of Ravenswood Manor          2800 W. Montrose Ave.   \n",
        "2            Darul Amaan Senior Living         4814-58 N. Kedzie Ave.   \n",
        "..                                 ...                            ...   \n",
        "261   The Grant at Woodlawn Pk Phase I  6227-29 S. Cottage Grove Ave.   \n",
        "262  The Grant at Woodlawn Pk Phase II     6129 S. Cottage Grove Ave.   \n",
        "263                                NaN                            NaN   \n",
        "\n",
        "     Zip Code  Phone Number                        Management Company  Units  \\\n",
        "0       60630  773-205-7862                           Metroplex, Inc.     97   \n",
        "1       60618  773-509-9333                    Senior Lifestyle Corp.     80   \n",
        "2       60625  773-463-3004  East Lake Management & Development Corp.     56   \n",
        "..        ...           ...                                       ...    ...   \n",
        "261     60637  312-283-0030       Preservation Housing Management LLC     67   \n",
        "262     60637  312-283-0030       Preservation Housing Management LLC     33   \n",
        "263       NaN           NaN                                       NaN    NaN   \n",
        "\n",
        "       X Coordinate    Y Coordinate   Latitude  Longitude  \\\n",
        "0    1145674.753818  1931569.979045  41.968224 -87.739747   \n",
        "1    1156898.513945  1929148.460973  41.961359 -87.698544   \n",
        "2               NaN             NaN        NaN        NaN   \n",
        "..              ...             ...        ...        ...   \n",
        "261  1186722.404223  1863775.146697  41.781408 -87.605807   \n",
        "262  1186094.258818  1864209.572438  41.782505 -87.590962   \n",
        "263             NaN  1864312.842731  41.782803 -87.593261   \n",
        "\n",
        "                                              Location  \n",
        "0    4444 W. Lawrence Ave.\\n(41.9682242321, -87.739...  \n",
        "1    2800 W. Montrose Ave.\\n(41.9613586525, -87.698...  \n",
        "2                                                  NaN  \n",
        "..                                                 ...  \n",
        "261  6227-29 S. Cottage Grove Ave.\\n(41.7814080037,...  \n",
        "262  6129 S. Cottage Grove Ave.\\n(41.7825052194, -8...  \n",
        "263                    (41.7828034603, -87.5932613147)  \n",
        "\n",
        "[264 rows x 14 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Join\n",
      "What if we want to know the crime rate in the community for each housing development? <br>\n",
      "We can find this using what's called a *join* or *merge*. <br>\n",
      "First let's turn the community_crime_count Series into a DataFrame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_area_crime = pd.DataFrame({'Crime Count': community_crime_count})\n",
      "community_area_crime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Crime Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Community Area</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75</th>\n",
        "      <td>118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>76</th>\n",
        "      <td>112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77</th>\n",
        "      <td>166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>77 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "                Crime Count\n",
        "Community Area             \n",
        "1                       259\n",
        "2                       263\n",
        "3                       263\n",
        "...                     ...\n",
        "75                      118\n",
        "76                      112\n",
        "77                      166\n",
        "\n",
        "[77 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing_crime = housing.merge(community_area_crime, \n",
      "                              left_on='Community Area Number', right_index=True)\n",
      "housing_crime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Community Area Name</th>\n",
        "      <th>Community Area Number</th>\n",
        "      <th>Property Type</th>\n",
        "      <th>Property Name</th>\n",
        "      <th>Address</th>\n",
        "      <th>Zip Code</th>\n",
        "      <th>Phone Number</th>\n",
        "      <th>Management Company</th>\n",
        "      <th>Units</th>\n",
        "      <th>X Coordinate</th>\n",
        "      <th>Y Coordinate</th>\n",
        "      <th>Latitude</th>\n",
        "      <th>Longitude</th>\n",
        "      <th>Location</th>\n",
        "      <th>Crime Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Albany Park</td>\n",
        "      <td>14</td>\n",
        "      <td>Senior</td>\n",
        "      <td>Mayfair Commons</td>\n",
        "      <td>4444 W. Lawrence Ave.</td>\n",
        "      <td>60630</td>\n",
        "      <td>773-205-7862</td>\n",
        "      <td>Metroplex, Inc.</td>\n",
        "      <td>97</td>\n",
        "      <td>1145674.753818</td>\n",
        "      <td>1931569.979045</td>\n",
        "      <td>41.968224</td>\n",
        "      <td>-87.739747</td>\n",
        "      <td>4444 W. Lawrence Ave.\\n(41.9682242321, -87.739...</td>\n",
        "      <td>195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Albany Park</td>\n",
        "      <td>14</td>\n",
        "      <td>Senior</td>\n",
        "      <td>Senior Suites of Ravenswood Manor</td>\n",
        "      <td>2800 W. Montrose Ave.</td>\n",
        "      <td>60618</td>\n",
        "      <td>773-509-9333</td>\n",
        "      <td>Senior Lifestyle Corp.</td>\n",
        "      <td>80</td>\n",
        "      <td>1156898.513945</td>\n",
        "      <td>1929148.460973</td>\n",
        "      <td>41.961359</td>\n",
        "      <td>-87.698544</td>\n",
        "      <td>2800 W. Montrose Ave.\\n(41.9613586525, -87.698...</td>\n",
        "      <td>195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Albany Park</td>\n",
        "      <td>14</td>\n",
        "      <td>Senior</td>\n",
        "      <td>Darul Amaan Senior Living</td>\n",
        "      <td>4814-58 N. Kedzie Ave.</td>\n",
        "      <td>60625</td>\n",
        "      <td>773-463-3004</td>\n",
        "      <td>East Lake Management &amp; Development Corp.</td>\n",
        "      <td>56</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>260</th>\n",
        "      <td>Woodlawn</td>\n",
        "      <td>42</td>\n",
        "      <td>Senior</td>\n",
        "      <td>Butler Apartments</td>\n",
        "      <td>6146 S. Kenwood Ave.</td>\n",
        "      <td>60637</td>\n",
        "      <td>773-955-9290</td>\n",
        "      <td>Woodlawn Comm. Dev. Corp.</td>\n",
        "      <td>17</td>\n",
        "      <td>1182676.846895</td>\n",
        "      <td>1862140.114893</td>\n",
        "      <td>41.776909</td>\n",
        "      <td>-87.603954</td>\n",
        "      <td>6146 S. Kenwood Ave.\\n(41.7769092523, -87.6039...</td>\n",
        "      <td>265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>261</th>\n",
        "      <td>Woodlawn</td>\n",
        "      <td>42</td>\n",
        "      <td>Multifamily</td>\n",
        "      <td>The Grant at Woodlawn Pk Phase I</td>\n",
        "      <td>6227-29 S. Cottage Grove Ave.</td>\n",
        "      <td>60637</td>\n",
        "      <td>312-283-0030</td>\n",
        "      <td>Preservation Housing Management LLC</td>\n",
        "      <td>67</td>\n",
        "      <td>1186722.404223</td>\n",
        "      <td>1863775.146697</td>\n",
        "      <td>41.781408</td>\n",
        "      <td>-87.605807</td>\n",
        "      <td>6227-29 S. Cottage Grove Ave.\\n(41.7814080037,...</td>\n",
        "      <td>265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>262</th>\n",
        "      <td>Woodlawn</td>\n",
        "      <td>42</td>\n",
        "      <td>Multifamily</td>\n",
        "      <td>The Grant at Woodlawn Pk Phase II</td>\n",
        "      <td>6129 S. Cottage Grove Ave.</td>\n",
        "      <td>60637</td>\n",
        "      <td>312-283-0030</td>\n",
        "      <td>Preservation Housing Management LLC</td>\n",
        "      <td>33</td>\n",
        "      <td>1186094.258818</td>\n",
        "      <td>1864209.572438</td>\n",
        "      <td>41.782505</td>\n",
        "      <td>-87.590962</td>\n",
        "      <td>6129 S. Cottage Grove Ave.\\n(41.7825052194, -8...</td>\n",
        "      <td>265</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>260 rows \u00d7 15 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "    Community Area Name  Community Area Number Property Type  \\\n",
        "0           Albany Park                     14        Senior   \n",
        "1           Albany Park                     14        Senior   \n",
        "2           Albany Park                     14        Senior   \n",
        "..                  ...                    ...           ...   \n",
        "260            Woodlawn                     42        Senior   \n",
        "261           Woodlawn                      42   Multifamily   \n",
        "262            Woodlawn                     42   Multifamily   \n",
        "\n",
        "                         Property Name                        Address  \\\n",
        "0                      Mayfair Commons          4444 W. Lawrence Ave.   \n",
        "1    Senior Suites of Ravenswood Manor          2800 W. Montrose Ave.   \n",
        "2            Darul Amaan Senior Living         4814-58 N. Kedzie Ave.   \n",
        "..                                 ...                            ...   \n",
        "260                  Butler Apartments           6146 S. Kenwood Ave.   \n",
        "261   The Grant at Woodlawn Pk Phase I  6227-29 S. Cottage Grove Ave.   \n",
        "262  The Grant at Woodlawn Pk Phase II     6129 S. Cottage Grove Ave.   \n",
        "\n",
        "     Zip Code  Phone Number                        Management Company  Units  \\\n",
        "0       60630  773-205-7862                           Metroplex, Inc.     97   \n",
        "1       60618  773-509-9333                    Senior Lifestyle Corp.     80   \n",
        "2       60625  773-463-3004  East Lake Management & Development Corp.     56   \n",
        "..        ...           ...                                       ...    ...   \n",
        "260     60637  773-955-9290                 Woodlawn Comm. Dev. Corp.     17   \n",
        "261     60637  312-283-0030       Preservation Housing Management LLC     67   \n",
        "262     60637  312-283-0030       Preservation Housing Management LLC     33   \n",
        "\n",
        "       X Coordinate    Y Coordinate   Latitude  Longitude  \\\n",
        "0    1145674.753818  1931569.979045  41.968224 -87.739747   \n",
        "1    1156898.513945  1929148.460973  41.961359 -87.698544   \n",
        "2               NaN             NaN        NaN        NaN   \n",
        "..              ...             ...        ...        ...   \n",
        "260  1182676.846895  1862140.114893  41.776909 -87.603954   \n",
        "261  1186722.404223  1863775.146697  41.781408 -87.605807   \n",
        "262  1186094.258818  1864209.572438  41.782505 -87.590962   \n",
        "\n",
        "                                              Location  Crime Count  \n",
        "0    4444 W. Lawrence Ave.\\n(41.9682242321, -87.739...          195  \n",
        "1    2800 W. Montrose Ave.\\n(41.9613586525, -87.698...          195  \n",
        "2                                                  NaN          195  \n",
        "..                                                 ...          ...  \n",
        "260  6146 S. Kenwood Ave.\\n(41.7769092523, -87.6039...          265  \n",
        "261  6227-29 S. Cottage Grove Ave.\\n(41.7814080037,...          265  \n",
        "262  6129 S. Cottage Grove Ave.\\n(41.7825052194, -8...          265  \n",
        "\n",
        "[260 rows x 15 columns]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Another aggregate\n",
      "We can pass other functions to aggregate:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_housing = pd.DataFrame({\n",
      "    'Affordable Housing Units': housing.groupby('Community Area Number')['Units'].agg('sum')\n",
      "})\n",
      "community_housing"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Affordable Housing Units</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Community Area Number</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>71</th>\n",
        "      <td>352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73</th>\n",
        "      <td>142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77</th>\n",
        "      <td>504</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>53 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "                       Affordable Housing Units\n",
        "Community Area Number                          \n",
        "1                                           517\n",
        "2                                           307\n",
        "3                                           940\n",
        "...                                         ...\n",
        "71                                          352\n",
        "73                                          142\n",
        "77                                          504\n",
        "\n",
        "[53 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "community_housing.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "(53, 1)"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing_crime_aggregate = community_housing.merge(community_area_crime,\n",
      "                                                  left_index=True, right_index=True)\n",
      "housing_crime_aggregate.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(53, 2)"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing_crime_aggregate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Affordable Housing Units</th>\n",
        "      <th>Crime Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>517</td>\n",
        "      <td>259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>307</td>\n",
        "      <td>263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>940</td>\n",
        "      <td>263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>71</th>\n",
        "      <td>352</td>\n",
        "      <td>570</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73</th>\n",
        "      <td>142</td>\n",
        "      <td>218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77</th>\n",
        "      <td>504</td>\n",
        "      <td>166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>53 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "    Affordable Housing Units  Crime Count\n",
        "1                        517          259\n",
        "2                        307          263\n",
        "3                        940          263\n",
        "..                       ...          ...\n",
        "71                       352          570\n",
        "73                       142          218\n",
        "77                       504          166\n",
        "\n",
        "[53 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Join types\n",
      "Why does the above join only have entries for 53 community areas? <br>\n",
      "Because `merge` defaults to a `left` join. There are four types of joins:\n",
      "- left\n",
      "- right\n",
      "- inner\n",
      "- outer\n",
      "\n",
      "In this case we want an `outer` join."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing_crime_aggregate = community_housing.merge(\n",
      "        community_area_crime, left_index=True, right_index=True, how='outer')\n",
      "housing_crime_aggregate.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "(77, 2)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing_crime_aggregate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Affordable Housing Units</th>\n",
        "      <th>Crime Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>517</td>\n",
        "      <td>259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>307</td>\n",
        "      <td>263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>940</td>\n",
        "      <td>263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75</th>\n",
        "      <td>NaN</td>\n",
        "      <td>118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>76</th>\n",
        "      <td>NaN</td>\n",
        "      <td>112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77</th>\n",
        "      <td>504</td>\n",
        "      <td>166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>77 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "    Affordable Housing Units  Crime Count\n",
        "1                        517          259\n",
        "2                        307          263\n",
        "3                        940          263\n",
        "..                       ...          ...\n",
        "75                       NaN          118\n",
        "76                       NaN          112\n",
        "77                       504          166\n",
        "\n",
        "[77 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(columns=['column1', 'asdf'], index=range(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.iloc[0]['column1'] = 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>column1</th>\n",
        "      <th>asdf</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>10</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "   column1 asdf\n",
        "0       10  NaN\n",
        "1      NaN  NaN\n",
        "2      NaN  NaN\n",
        "..     ...  ...\n",
        "7      NaN  NaN\n",
        "8      NaN  NaN\n",
        "9      NaN  NaN\n",
        "\n",
        "[10 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "Rows without a corresponding row in the merge are filled with missing values.\n",
      "In our case those should be zeros, so we can `fillna(0)`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "housing_crime_aggregate.fillna(0, inplace=True)\n",
      "housing_crime_aggregate.plot(kind='scatter', x='Affordable Housing Units', y='Crime Count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f199d028ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f199d028910>"
       ]
      }
     ],
     "prompt_number": 26
    }
   ],
   "metadata": {}
  }
 ]
}