biketheft.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas\n",
    "import seaborn\n",
    "from matplotlib import pyplot\n",
    "\n",
    "seaborn.set(style='ticks', palette='deep')\n",
    "df = pandas.read_csv(\"https://raw.githubusercontent.com/oregonian/bikethefts/master/bikethefts20072014.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GSHOL', 'DWNTN', 'PRHTS', 'IRVTN', 'OLDTN', 'SULGL', 'NWEST',\n",
       "       'PEARL', 'HAZEL', 'SUNSD', 'ELIOT', 'HMBLT', 'PWHST', 'SLWMR',\n",
       "       'OVRLK', 'RHMND', 'CULLY', 'KENTN', 'ARGAY', 'CENTN', 'GNTPK',\n",
       "       'MTTBR', 'KERNS', 'PROSE', 'MILPK', 'PDMNT', 'STABR', 'ESTMR', nan,\n",
       "       'WDSTK', 'LLOYD', 'MNTVL', 'RUSEL', 'STJHN', 'REED', 'ROSE',\n",
       "       'UNIPK', 'LENTS', 'BUKNW', 'BNTWD', 'HFDAB', 'BUKNE', 'GLENF',\n",
       "       'BRKLN', 'SUNDR', 'PTSMT', 'SMADN', 'CLNSV', 'SABIN', 'WODLN',\n",
       "       'NTABR', 'KING', 'BRIDG', 'GRSRG', 'CTHED', 'HAYDN', 'RCTPK',\n",
       "       'HLYWD', 'MTSCT', 'SPORT', 'SWHLS', 'ARBRL', 'VRNON', 'CNCDA',\n",
       "       'LRHST', 'FTRPW', 'CRSTN', 'BOISE', 'HILLS', 'SUMNR', 'HMSTD',\n",
       "       'BMWSH', 'ALMDA', 'CLMBA', 'BRLGM', 'PLSVL', 'MULTN', 'WPORT',\n",
       "       'WILKS', 'FSTPK', 'GRSWE', 'MSHPK', 'ARDNW', 'HYHST', 'WDPRK',\n",
       "       'BRDGR', 'ARLHT', 'SYLHI', 'FARSW', 'HILSD', 'NWIND', 'MARKM',\n",
       "       'NWHGT', 'ASHCR', 'MPLWD', 'LINTN'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Neighborhood'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9161320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = (\n",
    "    df.groupby(by=['Neighborhood', 'Case Year'])\n",
    "      .size()\n",
    "      .fillna(0)\n",
    "      .reset_index()\n",
    "      .rename(columns={0: 'Thefts', 'Case Year': 'Year'})  \n",
    "      .query(\"Year < 2015\")  \n",
    ")\n",
    "hoods = ['Woodstock', 'IRVTN', 'REED', 'STJHN', 'OVRLK', 'DWNTN']\n",
    "cp = seaborn.color_palette(\"colorblind\")\n",
    "\n",
    "fg = seaborn.FacetGrid(ts, hue='Neighborhood', hue_order=hoods,\n",
    "                       palette=cp, size=5, aspect=2)\n",
    "\n",
    "\n",
    "fg.map(pyplot.plot, 'Year', 'Thefts')\n",
    "\n",
    "formatter = pyplot.ScalarFormatter(useOffset=False)\n",
    "fg.axes[0, 0].xaxis.set_major_formatter(formatter)\n",
    "fg.add_legend()"
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}