NYPD_explore(4).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Manipulation\n",
    "import pandas as pd\n",
    "# Calculations and Numerical Operations\n",
    "import numpy as np\n",
    "# Data Visualization\n",
    "import plotly\n",
    "import seaborn as sns\n",
    "# Geographic Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import folium\n",
    "from folium.plugins import HeatMap\n",
    "from folium.plugins import MarkerCluster\n",
    "from IPython.display import display\n",
    "import gmaps\n",
    "from IPython.display import IFrame\n",
    "from geopy.geocoders import Nominatim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-09-07T05:36:26.539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'DATE', 'TIME', 'BOROUGH', 'ZIP CODE', 'LATITUDE',\n",
       "       'LONGITUDE', 'LOCATION', 'ON STREET NAME', 'CROSS STREET NAME',\n",
       "       'OFF STREET NAME', 'NUMBER OF PERSONS INJURED',\n",
       "       'NUMBER OF PERSONS KILLED', 'NUMBER OF PEDESTRIANS INJURED',\n",
       "       'NUMBER OF PEDESTRIANS KILLED', 'NUMBER OF CYCLIST INJURED',\n",
       "       'NUMBER OF CYCLIST KILLED', 'NUMBER OF MOTORIST INJURED',\n",
       "       'NUMBER OF MOTORIST KILLED', 'CONTRIBUTING FACTOR VEHICLE 1',\n",
       "       'CONTRIBUTING FACTOR VEHICLE 2', 'CONTRIBUTING FACTOR VEHICLE 3',\n",
       "       'CONTRIBUTING FACTOR VEHICLE 4', 'CONTRIBUTING FACTOR VEHICLE 5',\n",
       "       'UNIQUE KEY', 'VEHICLE TYPE CODE 1', 'VEHICLE TYPE CODE 2',\n",
       "       'VEHICLE TYPE CODE 3', 'VEHICLE TYPE CODE 4', 'VEHICLE TYPE CODE 5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('NYPD.csv')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set edge borders for the heatmap and cluster visualization\n",
    "west, south, east, north = -84.03, 30.63, -63.77, 50.85\n",
    "df = df[(df.LATITUDE> south) & (df.LATITUDE < north)]\n",
    "df = df[(df.LONGITUDE> west) & (df.LONGITUDE < east)]\n",
    "df.rename(columns={'LATITUDE':'lat', 'LONGITUDE':'lon'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1100\"\n",
       "            height=\"900\"\n",
       "            src=\"./heatmap.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x221e22d78d0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hm = folium.Map(location=[40.731950, -73.890480],\n",
    "                        zoom_start=11,\n",
    "                        tiles=\"CartoDB dark_matter\")\n",
    "heat_map = HeatMap(list(zip(df['lat'], df['lon'])), min_opacity=0.2, radius=9, blur=15, max_zoom=1)\n",
    "hm.add_child(heat_map)\n",
    "hm.save('heatmap.html')\n",
    "IFrame(src='./heatmap.html', width=1100, height=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1100\"\n",
       "            height=\"900\"\n",
       "            src=\"./cluster.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x221e1871a90>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = folium.Map(location=[40.731950, -73.890480],\n",
    "                        zoom_start=12,\n",
    "                        tiles=\"CartoDB dark_matter\")\n",
    "mc = MarkerCluster()\n",
    "for ind,row in mf.iterrows():\n",
    "    mc.add_child(folium.CircleMarker(location=[row['lat'],row['lon']],\n",
    "                        radius=1,color='#3185cc'))\n",
    "cluster.add_child(mc)\n",
    "cluster.save('cluster.html')\n",
    "IFrame(src='./cluster.html', width=1100, height=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BROOKLYN         31634\n",
       "QUEENS           27250\n",
       "MANHATTAN        24451\n",
       "BRONX            14057\n",
       "STATEN ISLAND     4554\n",
       "Name: BOROUGH, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BOROUGH'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                            0\n",
       "DATE                                  0\n",
       "TIME                                  0\n",
       "BOROUGH                           29202\n",
       "ZIP CODE                          29219\n",
       "LATITUDE                              0\n",
       "LONGITUDE                             0\n",
       "LOCATION                              0\n",
       "ON STREET NAME                    23735\n",
       "CROSS STREET NAME                 42089\n",
       "OFF STREET NAME                  113344\n",
       "NUMBER OF PERSONS INJURED             1\n",
       "NUMBER OF PERSONS KILLED              1\n",
       "NUMBER OF PEDESTRIANS INJURED         0\n",
       "NUMBER OF PEDESTRIANS KILLED          0\n",
       "NUMBER OF CYCLIST INJURED             0\n",
       "NUMBER OF CYCLIST KILLED              0\n",
       "NUMBER OF MOTORIST INJURED            0\n",
       "NUMBER OF MOTORIST KILLED             0\n",
       "CONTRIBUTING FACTOR VEHICLE 1       317\n",
       "CONTRIBUTING FACTOR VEHICLE 2     17731\n",
       "CONTRIBUTING FACTOR VEHICLE 3    122788\n",
       "CONTRIBUTING FACTOR VEHICLE 4    129382\n",
       "CONTRIBUTING FACTOR VEHICLE 5    130713\n",
       "UNIQUE KEY                            0\n",
       "VEHICLE TYPE CODE 1                 410\n",
       "VEHICLE TYPE CODE 2               21887\n",
       "VEHICLE TYPE CODE 3              120163\n",
       "VEHICLE TYPE CODE 4              126893\n",
       "VEHICLE TYPE CODE 5              130232\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            40.816662, -73.9389911\n",
       "1               40.59713, -73.95221\n",
       "4           40.8423355, -73.9161838\n",
       "5    40.631707500000005, -73.946481\n",
       "7           40.7171751, -74.0128675\n",
       "Name: location, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'] = df['lat'].astype(str) + ', ' + df['lon'].astype(str)\n",
    "df['location'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.608757000000004, -74.03808599999999    66\n",
       "40.8047, -73.91243                        55\n",
       "40.658577799999996, -73.89062290000001    50\n",
       "40.696034600000004, -73.9845292           49\n",
       "40.696033, -73.98453                      46\n",
       "40.7606005, -73.9643142                   46\n",
       "40.798256, -73.82744                      45\n",
       "40.604153000000004, -74.05198             44\n",
       "40.7572323, -73.9897922                   43\n",
       "40.861862, -73.91282                      43\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency = df['location'].value_counts()\n",
    "frequency.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2219d8a9c50>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequency.nlargest(10).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verrazzano-Narrows Bridge (Staten Island Bound), Fort Hamilton, BK, Kings County, NYC, New York, 11252, USA\n",
      "\n",
      "East 138th Street, Port Morris, The Bronx, Bronx County, NYC, New York, 10454, USA\n",
      "\n",
      "Pennsylvania Avenue, East New York, BK, Kings County, NYC, New York, 11239, USA\n",
      "\n",
      "Flatbush Avenue Extension, Downtown Brooklyn, BK, Kings County, NYC, New York, 11201-1832, USA\n",
      "\n",
      "Flatbush Avenue Extension, Downtown Brooklyn, BK, Kings County, NYC, New York, 11201-1832, USA\n",
      "\n",
      "The Landmark, 300, East 59th Street, Midtown East, Manhattan Community Board 8, Manhattan, New York County, NYC, New York, 10022, USA\n",
      "\n",
      "Bronx-Whitestone Bridge, Whitestone Bridge (Toll), The Bronx, Queens County, NYC, New York, 11357, USA\n",
      "\n",
      "Verrazzano-Narrows Bridge, Fort Hamilton, BK, Kings County, NYC, New York, 11252, USA\n",
      "\n",
      "11 Times Square, 11, 8th Avenue, Times Square, Theater District, Manhattan Community Board 5, Manhattan, New York County, NYC, New York, 10036, USA\n",
      "\n",
      "Major Deegan Expressway, University Heights, The Bronx, Bronx County, NYC, New York, 10463, USA\n",
      "\n"
     ]
    }
   ],
   "source": [
    "geolocator = Nominatim()\n",
    "for c in frequency.nlargest(10).index:\n",
    "    location = geolocator.reverse(c)\n",
    "    print(location.address + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATE'] = pd.to_datetime(df['DATE'])\n",
    "df['Year'] = df['DATE'].dt.year\n",
    "df['Month'] = df['DATE'].dt.month\n",
    "df['Month'] = df['DATE'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x221b1751278>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boroughs = pd.DataFrame(df['BOROUGH'].value_counts())\n",
    "plt.figure(figsize = (12,12))\n",
    "plt.subplot2grid((2,2),(0,0))\n",
    "sns.barplot(x = boroughs.index, y = 'BOROUGH', data = boroughs)\n",
    "years = pd.DataFrame(df['Year'].value_counts())\n",
    "plt.subplot2grid((2,2),(0,1))\n",
    "sns.barplot(x = years.index, y = 'Year', data = years)\n",
    "df['Hour'] = df['TIME'].dt.hour \n",
    "hours = pd.DataFrame(df['Hour'].value_counts())\n",
    "plt.figure(figsize = (24,12))\n",
    "plt.subplot2grid((2,2),(0,0))\n",
    "sns.barplot(x = hours.index, y = 'Hour', data = hours)"
   ]
  }
 ],
 "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}