txthai/crime-analysis.ipynb

## crime-analysis.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# San Francisco and Seattle have Different Days of the Week with the Lowest Vehicle Thefts"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "printmd('# San Francisco and Seattle have Different Days of the Week with the Lowest Vehicle Thefts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtypes = {'RMS CDW ID': 'str', \n",
    "          'General Offense Number': 'str', \n",
    "          'Offense Code': 'str', \n",
    "          'Offense Code Extension': 'str', \n",
    "          'Offense Type': 'str', \n",
    "          'Summary Offense Code': 'str', \n",
    "          'Summarized Offense Description': 'str', \n",
    "          'Date Reported': 'str', \n",
    "          'Occurred Date or Date Range Start': 'str', \n",
    "          'Occurred Date Range End': 'str', \n",
    "          'Hundred Block Location': 'str', \n",
    "          'District/Sector': 'str', \n",
    "          'Zone/Beat': 'str', \n",
    "          'Census Tract 2000': 'str', \n",
    "          'Longitude': np.float64, \n",
    "          'Latitude': np.float64, \n",
    "          'Location': 'str', \n",
    "          'Month': np.int64, \n",
    "          'Year': np.int64}\n",
    "\n",
    "parse_dates = ['Date Reported', \n",
    "               'Occurred Date or Date Range Start', \n",
    "               'Occurred Date Range End']\n",
    "\n",
    "df_seattle = pd.read_csv('seattle_incidents_summer_2014.csv', header=0, dtype=dtypes, parse_dates=parse_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtypes = {'IncidntNum': 'str',\n",
    "          'Category': 'str',\n",
    "          'Descript': 'str',\n",
    "          'DayOfWeek': 'str',\n",
    "          'Date': 'str',\n",
    "          'Time': 'str',\n",
    "          'PdDistrict': 'str',\n",
    "          'Resolution': 'str',\n",
    "          'Address': 'str',\n",
    "          'X': np.float64,\n",
    "          'Y': np.float64,\n",
    "          'Location': 'str',\n",
    "          'PdId': 'str'}\n",
    "\n",
    "parse_dates = [['Date', \n",
    "               'Time']]\n",
    "\n",
    "parse = lambda x: datetime.strptime(x, '%m/%d/%Y %H:%M')\n",
    "\n",
    "df_sf = pd.read_csv('sanfrancisco_incidents_summer_2014.csv', header=0, dtype=dtypes, parse_dates=parse_dates, date_parser=parse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sf_vehicle = df_sf[['Date_Time']][df_sf['Category']=='VEHICLE THEFT'].reset_index(drop=True)\n",
    "df_seattle_vehicle = df_seattle[['Occurred Date or Date Range Start']][df_seattle['Summarized Offense Description']=='VEHICLE THEFT'].reset_index(drop=True)\n",
    "\n",
    "df_sf_vehicle = df_sf_vehicle[df_sf_vehicle.index!=pd.NaT]\n",
    "df_seattle_vehicle = df_seattle_vehicle[df_seattle_vehicle.index!=pd.NaT]\n",
    "df_seattle_vehicle.columns = ['Date_Time']\n",
    "\n",
    "df_sf_vehicle['n'] = 1\n",
    "df_seattle_vehicle['n'] = 1\n",
    "\n",
    "df_sf_vehicle = df_sf_vehicle.groupby(['Date_Time']).agg(['count'])\n",
    "df_seattle_vehicle = df_seattle_vehicle.groupby(['Date_Time']).agg(['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### From the figure, Tuesday is the day of the week that has the lowest level of vehicle theft in San Francisco in the Summer of 2014"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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DXqVSYcyYMfDw8EBubi6mTZuGVq1aAQCCgoIQFBRksP6NGzdw9OhRREZGIiMj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd2e4dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "a1 = df_sf_vehicle.groupby([lambda x: x.dayofweek]).sum().plot(kind='bar', legend=False, title='Vehicle Thefts in San Francisco\\nSummer of 2014')\n",
    "a1.set_xlabel(\"Day of the Week\")\n",
    "a1.set_xticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])\n",
    "\n",
    "printmd('### From the figure, Tuesday is the day of the week that has the lowest level of vehicle theft in San Francisco in the Summer of 2014')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### From the figure, Saturday is the day of the week that has the lowest level of vehicle theft in Seattle in the Summer of 2014"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AgQPYtWsXvvzyS2zcuBFr166VtomKisLHH3+M6OhonDp1CrGxsVi1alW5sfFly5bhkUce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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd2e4da20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a2 = df_seattle_vehicle.groupby([lambda x: x.dayofweek]).sum().plot(kind='bar', legend=False, title='Vehicle Thefts in Seattle\\nSummer of 2014')\n",
    "a2.set_xlabel(\"Day of the Week\")\n",
    "a2.set_xticklabels(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])\n",
    "\n",
    "printmd('### From the figure, Saturday is the day of the week that has the lowest level of vehicle theft in Seattle in the Summer of 2014')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": 0
}