NY_crime_data_modified.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## A Practical Learning of Python for Data Analysis and Visualization using Ipython, Pandas and Seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project analysis may help for the NYPD to take necessary measures and security to its borough over in real time.\n",
    "\n",
    "The NYPD data can be found and downloaded at [NYPD 7 Major Felony Incidents](https://data.cityofnewyork.us/Public-Safety/NYPD-7-Major-Felony-Incidents/hyij-8hr7). Lets open it using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Now let's open it with pandas\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#opening the csv data\n",
    "Crime_ny_df = pd.read_csv('NYPD_7_Major_Felony_Incidents.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Identifier</th>\n",
       "      <th>Occurrence Date</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Occurrence Month</th>\n",
       "      <th>Occurrence Day</th>\n",
       "      <th>Occurrence Year</th>\n",
       "      <th>Occurrence Hour</th>\n",
       "      <th>CompStat Month</th>\n",
       "      <th>CompStat Day</th>\n",
       "      <th>CompStat Year</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Offense Classification</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Precinct</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Jurisdiction</th>\n",
       "      <th>XCoordinate</th>\n",
       "      <th>YCoordinate</th>\n",
       "      <th>Location 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>987726459</td>\n",
       "      <td>09/21/1982 11:20:00 PM</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Sep</td>\n",
       "      <td>21</td>\n",
       "      <td>1982</td>\n",
       "      <td>2300</td>\n",
       "      <td>Apr</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>079</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>999634</td>\n",
       "      <td>190253</td>\n",
       "      <td>(40.688872153, -73.9445290319999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>987726401</td>\n",
       "      <td>08/08/1991 10:15:00 PM</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Aug</td>\n",
       "      <td>8</td>\n",
       "      <td>1991</td>\n",
       "      <td>2200</td>\n",
       "      <td>Mar</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>A</td>\n",
       "      <td>030</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>996234</td>\n",
       "      <td>238317</td>\n",
       "      <td>(40.8208008500001, -73.956703266)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>987726875</td>\n",
       "      <td>03/17/2014 12:00:00 PM</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Mar</td>\n",
       "      <td>17</td>\n",
       "      <td>2014</td>\n",
       "      <td>1200</td>\n",
       "      <td>Jun</td>\n",
       "      <td>26</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>067</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>999132</td>\n",
       "      <td>175384</td>\n",
       "      <td>(40.6480609220001, -73.946371991)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>987725414</td>\n",
       "      <td>07/30/2014 09:41:00 PM</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Jul</td>\n",
       "      <td>30</td>\n",
       "      <td>2014</td>\n",
       "      <td>2100</td>\n",
       "      <td>Jan</td>\n",
       "      <td>28</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>H</td>\n",
       "      <td>120</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>959784</td>\n",
       "      <td>171202</td>\n",
       "      <td>(40.6365609370001, -74.0881530469999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>987726422</td>\n",
       "      <td>08/08/2014 11:46:00 PM</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Aug</td>\n",
       "      <td>8</td>\n",
       "      <td>2014</td>\n",
       "      <td>2300</td>\n",
       "      <td>Mar</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>FELONY</td>\n",
       "      <td>D</td>\n",
       "      <td>041</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>1013232</td>\n",
       "      <td>236725</td>\n",
       "      <td>(40.816391847, -73.895296414)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Identifier         Occurrence Date Day of Week Occurrence Month  \\\n",
       "0         1   987726459  09/21/1982 11:20:00 PM     Tuesday              Sep   \n",
       "1         2   987726401  08/08/1991 10:15:00 PM    Thursday              Aug   \n",
       "2         3   987726875  03/17/2014 12:00:00 PM      Monday              Mar   \n",
       "3         4   987725414  07/30/2014 09:41:00 PM   Wednesday              Jul   \n",
       "4         5   987726422  08/08/2014 11:46:00 PM      Friday              Aug   \n",
       "\n",
       "   Occurrence Day  Occurrence Year  Occurrence Hour CompStat Month  \\\n",
       "0              21             1982             2300            Apr   \n",
       "1               8             1991             2200            Mar   \n",
       "2              17             2014             1200            Jun   \n",
       "3              30             2014             2100            Jan   \n",
       "4               8             2014             2300            Mar   \n",
       "\n",
       "   CompStat Day  CompStat Year Offense Offense Classification Sector Precinct  \\\n",
       "0             9           2015  MURDER                 FELONY    NaN      079   \n",
       "1            10           2015  MURDER                 FELONY      A      030   \n",
       "2            26           2015  MURDER                 FELONY    NaN      067   \n",
       "3            28           2015  MURDER                 FELONY      H      120   \n",
       "4            13           2015  MURDER                 FELONY      D      041   \n",
       "\n",
       "         Borough Jurisdiction  XCoordinate  YCoordinate  \\\n",
       "0       Brooklyn         NYPD       999634       190253   \n",
       "1      Manhattan         NYPD       996234       238317   \n",
       "2       Brooklyn         NYPD       999132       175384   \n",
       "3  Staten Island         NYPD       959784       171202   \n",
       "4          Bronx         NYPD      1013232       236725   \n",
       "\n",
       "                              Location 1  \n",
       "0      (40.688872153, -73.9445290319999)  \n",
       "1      (40.8208008500001, -73.956703266)  \n",
       "2      (40.6480609220001, -73.946371991)  \n",
       "3  (40.6365609370001, -74.0881530469999)  \n",
       "4          (40.816391847, -73.895296414)  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see a preview of the the first 5 rows of the data\n",
    "Crime_ny_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 76755 entries, 0 to 76754\n",
      "Data columns (total 20 columns):\n",
      "OBJECTID                  76755 non-null int64\n",
      "Identifier                76755 non-null int64\n",
      "Occurrence Date           76755 non-null object\n",
      "Day of Week               76755 non-null object\n",
      "Occurrence Month          76755 non-null object\n",
      "Occurrence Day            76755 non-null int64\n",
      "Occurrence Year           76755 non-null int64\n",
      "Occurrence Hour           76755 non-null int64\n",
      "CompStat Month            76755 non-null object\n",
      "CompStat Day              76755 non-null int64\n",
      "CompStat Year             76755 non-null int64\n",
      "Offense                   76755 non-null object\n",
      "Offense Classification    76755 non-null object\n",
      "Sector                    74735 non-null object\n",
      "Precinct                  76755 non-null object\n",
      "Borough                   76509 non-null object\n",
      "Jurisdiction              76746 non-null object\n",
      "XCoordinate               76755 non-null int64\n",
      "YCoordinate               76755 non-null int64\n",
      "Location 1                76755 non-null object\n",
      "dtypes: int64(9), object(11)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# We could also get overall info for the dataset\n",
    "Crime_ny_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# lets delete some columns, they are repeated or not important for my analysis.\n",
    "Drop = ['Occurrence Date', 'OBJECTID', 'Identifier', 'Offense Classification','XCoordinate','YCoordinate','Location 1','Sector',\n",
    "       'Jurisdiction']\n",
    "Crime_ny_df =Crime_ny_df.drop(Drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Occurrence Month</th>\n",
       "      <th>Occurrence Day</th>\n",
       "      <th>Occurrence Year</th>\n",
       "      <th>Occurrence Hour</th>\n",
       "      <th>CompStat Month</th>\n",
       "      <th>CompStat Day</th>\n",
       "      <th>CompStat Year</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Precinct</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Sep</td>\n",
       "      <td>21</td>\n",
       "      <td>1982</td>\n",
       "      <td>2300</td>\n",
       "      <td>Apr</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>079</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Aug</td>\n",
       "      <td>8</td>\n",
       "      <td>1991</td>\n",
       "      <td>2200</td>\n",
       "      <td>Mar</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>030</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Mar</td>\n",
       "      <td>17</td>\n",
       "      <td>2014</td>\n",
       "      <td>1200</td>\n",
       "      <td>Jun</td>\n",
       "      <td>26</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>067</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Jul</td>\n",
       "      <td>30</td>\n",
       "      <td>2014</td>\n",
       "      <td>2100</td>\n",
       "      <td>Jan</td>\n",
       "      <td>28</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>120</td>\n",
       "      <td>Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Aug</td>\n",
       "      <td>8</td>\n",
       "      <td>2014</td>\n",
       "      <td>2300</td>\n",
       "      <td>Mar</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>MURDER</td>\n",
       "      <td>041</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Day of Week Occurrence Month  Occurrence Day  Occurrence Year  \\\n",
       "0     Tuesday              Sep              21             1982   \n",
       "1    Thursday              Aug               8             1991   \n",
       "2      Monday              Mar              17             2014   \n",
       "3   Wednesday              Jul              30             2014   \n",
       "4      Friday              Aug               8             2014   \n",
       "\n",
       "   Occurrence Hour CompStat Month  CompStat Day  CompStat Year Offense  \\\n",
       "0             2300            Apr             9           2015  MURDER   \n",
       "1             2200            Mar            10           2015  MURDER   \n",
       "2             1200            Jun            26           2015  MURDER   \n",
       "3             2100            Jan            28           2015  MURDER   \n",
       "4             2300            Mar            13           2015  MURDER   \n",
       "\n",
       "  Precinct        Borough  \n",
       "0      079       Brooklyn  \n",
       "1      030      Manhattan  \n",
       "2      067       Brooklyn  \n",
       "3      120  Staten Island  \n",
       "4      041          Bronx  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#description of the data\n",
    "Crime_ny_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's import modules that are very importan for data analysis and visualization\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "# importan for real number devision\n",
    "from __future__ import division "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f905e855610>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YC2yI2+59wB2E81lXA9eZ2SJgXV79tpatnMvRXrzXEboxf0DoNr2IkDD6m9kC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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f905d305f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's first check which days are int highest offense\n",
    "sns.factorplot('Day of Week',data=Crime_ny_df, kind = 'count', size=3, aspect=1.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like on Friday, there is more offenses and may be peope are drinking or partying. Sundy is the least. Neat!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f905d823a10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g3XvvvSxatIinnnrKaXufPn1YvXo1n3zyCT/++CMRERH06NGjVvcgIuJKnv77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\n12ZmZmZmFXFxbWZmZmZWkf8H1ad7REvqBDAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f905d0f97d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# At what time is more pronounced \n",
    "Y = np.array([100,200,300, 400, 500, 600, 700, 800, 900, 1000, 1100,1200,1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300,0])\n",
    "sns.factorplot('Occurrence Hour',data=Crime_ny_df, kind = 'count',aspect = 2, size = 5, order=Y,\n",
    "               hue = 'Offense', legend = False)\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All offenses increase from morining to midnight and then decreases. Grand Larceny is the highest offense all on those time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8a07f9b510>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cSZIk9XL2IuZWul4rT+NTOoYzSZIkKcPstVJnOJS+JEmSJCWA4UySJEmSEsBw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a082ce7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# At what time is more pronounced in each cities\n",
    "Y = np.array([100,200,300, 400, 500, 600, 700, 800, 900, 1000, 1100,1200,1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300,0])\n",
    "sns.factorplot('Occurrence Month',data=Crime_ny_df, hue ='Borough', kind = 'count',aspect = 2.0, size = 6,\n",
    "               order=Y,  legend = False)\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting from morning, offenses increase until midnight and then after midnight it decreases. Brooklyn and Manhattan has more offenses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All offenses increase from morining to midnight and then decreases. Grand Larceny is the highest offense all on those time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f905d0d8c90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z32q23QXcA/RSrT77BLAPsAiYDWyamQtK3Z2A7TNz55YulFTDAF+SJI2oVdee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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9059f61fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see what type of crime is more happening in days\n",
    "DD = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday','Sunday']\n",
    "sns.factorplot('Day of Week',data=Crime_ny_df,hue='Offense', kind = 'count', aspect = 1.5, size = 5,\n",
    "               order = DD, legend = True)\n",
    "#plt.legend(loc='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9059f56110>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B+6oip2fmjyLiMOBYYA1wVmaeHRGTKZM27khZK++ozLy93TFLkqQJr3H5te2A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\nUg2ZBEqSJNXQ/w8r2HRy0E8MygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9059f56090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see what type of crime is more happening in days\n",
    "DD = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday','Sunday']\n",
    "sns.factorplot('Day of Week',data=Crime_ny_df,hue='Borough', kind = 'count', aspect = 1.5, size = 5,\n",
    "               order = DD, legend = True)\n",
    "#plt.legend(loc='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brooklyn is always in the highest. Not surprising, we always hear news of crime in Brooklyn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8a07750650>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HpJNPPpn58+dHP8+ePRuAXr16RROq8jnX5Yuplfeibty4kX/+85/8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\nDPeSJEmSJFXc/wdzDuBpbuZkVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a0854bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see what type of crime is more happening in months, first we have to order the months manually\n",
    "X = ['Jan', 'Feb','Mar', 'Apr', 'May','Jun','Jul','Aug','Sep', 'Oct','Nov', 'Dec']\n",
    "sns.factorplot('Occurrence Month',data=Crime_ny_df, hue = 'Offense', kind = 'count',aspect = 1.5,\n",
    "               size = 7,order = X, legend=False) \n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In Summer,  it looks more offenses occur. It is interesting that Oct, Nov and Dec have the least offense records. This may help policy makers to look at why and devise policy based on that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f90592184d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SEsBwJkmSJEkJYDiTJEmSpAQwnEmSJElSAhjOJEmSJCkBDGeSJEmSlACGM0mS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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9059218510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see what type of crime is more happening in months, first we have to order the months manually\n",
    "X = ['Jan', 'Feb','Mar', 'Apr', 'May','Jun','Jul','Aug','Sep', 'Oct','Nov', 'Dec']\n",
    "sns.factorplot('Occurrence Month',data=Crime_ny_df, hue = 'Borough', kind = 'count',aspect = 2,\n",
    "               size = 6,order = X, legend=False) \n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8a0717e590>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KbgTuKfp6BTAad/hOstYeAjDGbASuB7oArxXVXQe8YoypDlxirf2yxDluBo4brkVERM4V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\nmZmZVcn/A4CLrloQstb2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a074240d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Borough',data=Crime_ny_df, hue = 'Offense', kind = 'count',aspect = 2, size = 5, legend = False)\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brooklyn and Manhaton are more offense in grand larceny offense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Borough\n",
       "Bronx            15106\n",
       "Brooklyn         23824\n",
       "Manhattan        19406\n",
       "Queens           15793\n",
       "Staten Island     2380\n",
       "Name: Offense, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_crime = ((Crime_ny_df.groupby(['Borough'])['Offense']).count())\n",
    "Total_crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8a08dd03d0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GRlsBYLsrzvdL+p7tvUdY958Ut+RdZvuU9lZWH0lbAwfz7P8TX6itqFGk5RHt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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a0a2f9290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Total_crime.plot(kind='bar')\n",
    "plt.ylabel('total offense')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brooklyn has the most offense in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8a04f56a50>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I2opDxEdV9h2+UJ585wsLCxk9ejTTpk2jU6dOACxZsoRZs2Yxf/58APr06cPE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\nTF4lSZIkSbVn8ipJkiRJqr3/D0NLaIFBJKCnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a05612450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = ['Jan', 'Feb','Mar', 'Apr', 'May','Jun','Jul','Aug','Sep', 'Oct','Nov', 'Dec']\n",
    "sns.factorplot('CompStat Month', data = Crime_ny_df, hue='Offense', kind = 'count', order = X,\n",
    "               size=6.5, aspect=2, legend = False)\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('total offense reported')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no police report in Oct, Nov, Dec."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CompStat Month</th>\n",
       "      <th>CompStat Day</th>\n",
       "      <th>Offense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apr</td>\n",
       "      <td>9</td>\n",
       "      <td>MURDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mar</td>\n",
       "      <td>10</td>\n",
       "      <td>MURDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jun</td>\n",
       "      <td>26</td>\n",
       "      <td>MURDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jan</td>\n",
       "      <td>28</td>\n",
       "      <td>MURDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mar</td>\n",
       "      <td>13</td>\n",
       "      <td>MURDER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CompStat Month  CompStat Day Offense\n",
       "0            Apr             9  MURDER\n",
       "1            Mar            10  MURDER\n",
       "2            Jun            26  MURDER\n",
       "3            Jan            28  MURDER\n",
       "4            Mar            13  MURDER"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sns.factorplot('CompStat Day', data = Crime_ny_df, hue='Offense', kind = 'count', size=6, aspect=2)\n",
    "Report = Crime_ny_df[['CompStat Month', 'CompStat Day','Offense']]\n",
    "Report.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# reported year of offense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8a08a637d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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de4o4M9cCHwM+Vt4a4zjgkxFxa2a+ttrqWqOLA+vHKG5rBfB64OkUk292B64E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\nSaqJ/w9Cu1jTh4ZLFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a091d1d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (10, 4)\n",
    "Report.groupby(['CompStat Month'])['Offense'].count().plot(kind = 'bar')\n",
    "plt.ylabel('total offense')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1915,\n",
       " 1950,\n",
       " 1975,\n",
       " 1982,\n",
       " 1991,\n",
       " 1993,\n",
       " 1999,\n",
       " 2000,\n",
       " 2001,\n",
       " 2002,\n",
       " 2003,\n",
       " 2004,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2008,\n",
       " 2009,\n",
       " 2010,\n",
       " 2011,\n",
       " 2012,\n",
       " 2013,\n",
       " 2014,\n",
       " 2015]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(Crime_ny_df['Occurrence Year'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the offenses happened long time ago and I think it reported or found in 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2015], dtype=int64)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Crime_ny_df['CompStat Year'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All reported in 2015."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}