Implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "start_time = time()\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime\n",
    "import sys\n",
    "from prettytable import PrettyTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_days = 7\n",
    "num_months = 12\n",
    "num_time_range = 6\n",
    "num_n_d = 78"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_details(df,f):\n",
    "    if(f==0):\n",
    "        print(df.index)\n",
    "        print(len(df.columns))\n",
    "        print(df.columns)\n",
    "        print(df.head(5))\n",
    "    else:\n",
    "        print(df)\n",
    "        print(len(df))\n",
    "    h = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_a(f):\n",
    "    if(f==1):\n",
    "        plt.ylabel('Percentage')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "def graph_b(array):\n",
    "    plt.stem(array)\n",
    "    graph_a(0)\n",
    "#     print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formatting date time string to segregate individual values\n",
    "def convert(x):\n",
    "    y = \"\"\n",
    "    f = 0\n",
    "    for j in range(len(x)):\n",
    "        if(x[j]==' ' or x[j]==':'):\n",
    "            if(x[j+1]=='A' or x[j+1]=='P'):\n",
    "                if(x[j+1]=='A'):\n",
    "                    f=1\n",
    "                break\n",
    "            else:\n",
    "                y= y + '/'      \n",
    "        else:\n",
    "            y = y + x[j]\n",
    "    month, day, year , hour , minutes , seconds = (int(t) for t in y.split('/')) \n",
    "    return month, day, year , hour , minutes , seconds , f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping hour range to its corresponding type for Denver\n",
    "def conversion(hour,f):\n",
    "    if(f==1):\n",
    "        if(hour>=1 and hour<=4):\n",
    "            return 1\n",
    "        elif(hour>=5 and hour<=8):\n",
    "            return 2 \n",
    "        elif(hour>=9 and hour<=11):\n",
    "            return 3\n",
    "        else:\n",
    "            return 6\n",
    "    else:\n",
    "        if(hour==12):\n",
    "            return 3\n",
    "        elif(hour>=1 and hour<=4):\n",
    "            return 4\n",
    "        elif(hour>=5 and hour<=8):\n",
    "            return 5\n",
    "        else:\n",
    "            return 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping hour range to its corresponding type for Los Angeles\n",
    "def conv(x):\n",
    "    if(x>=1 and x<=4):\n",
    "        return 1\n",
    "    elif(x>=5 and x<=8):\n",
    "        return 2\n",
    "    elif(x>=9 and x<=12):\n",
    "        return 3\n",
    "    elif(x>=13 and x<=16):\n",
    "        return 4\n",
    "    elif(x>=17 and x<=20):\n",
    "        return 5\n",
    "    return 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding percentage of what total value is existing\n",
    "def percentage(a):\n",
    "    total = 0\n",
    "    for i in range(len(a)):\n",
    "        total = total + a[i]\n",
    "    a = [round((x*100/total),2) for x in a]\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding top three values and bottom three values in an array\n",
    "def find_three(a,f):\n",
    "    b = a\n",
    "    ans = []\n",
    "    ind = []\n",
    "    for i in range(3):\n",
    "        maxi = b[0]\n",
    "        index =  0\n",
    "        for j in range(1,len(b)):\n",
    "            if(f==0):\n",
    "                if(b[j] > maxi):\n",
    "                    maxi = b[j]\n",
    "                    index = j\n",
    "            else:\n",
    "                if(b[j] < maxi and b[j]!=-1):\n",
    "                    maxi = b[j]\n",
    "                    index = j\n",
    "        b[index] = -1\n",
    "        ans.append(maxi)\n",
    "        ind.append(index)\n",
    "    for i in range(len(ind)):\n",
    "        b[ind[i]] = ans[i]\n",
    "    return ans,ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def type_graph(day_type,b,c):\n",
    "    day_type = np.transpose(find_type_prob(day_type,b) * 100)\n",
    "    bar_width = 0.75 \n",
    "    plt.bar((np.arange(len(day_type[0]))*7),day_type[0],bar_width,color='blue',label='Assault')\n",
    "    plt.bar(((np.arange(len(day_type[1]))*7)+1),day_type[1],bar_width,color='red',label='Drug Alcohol')\n",
    "    plt.bar(((np.arange(len(day_type[2]))*7)+2),day_type[2],bar_width,color='green',label='Other Crime')\n",
    "    plt.bar(((np.arange(len(day_type[3]))*7)+3),day_type[3],bar_width,color='black',label='Public Disorder')\n",
    "    plt.bar(((np.arange(len(day_type[4]))*7)+4),day_type[4],bar_width,color='yellow',label='Theft')\n",
    "    plt.bar(((np.arange(len(day_type[5]))*7)+5),day_type[5],bar_width,color='brown',label='White Collar Crime')\n",
    "    plt.xticks(np.arange(len(day_type[0]))*7,c, fontsize=10, rotation=30)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    graph_a(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count(a,b):\n",
    "    t = np.zeros(b)\n",
    "    for i in range(len(a)):\n",
    "        t[a[i]-1] = t[a[i]-1] + 1\n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=490038, step=1)\n",
      "19\n",
      "Index(['INCIDENT_ID', 'OFFENSE_ID', 'OFFENSE_CODE', 'OFFENSE_CODE_EXTENSION',\n",
      "       'OFFENSE_TYPE_ID', 'OFFENSE_CATEGORY_ID', 'FIRST_OCCURRENCE_DATE',\n",
      "       'LAST_OCCURRENCE_DATE', 'REPORTED_DATE', 'INCIDENT_ADDRESS', 'GEO_X',\n",
      "       'GEO_Y', 'GEO_LON', 'GEO_LAT', 'DISTRICT_ID', 'PRECINCT_ID',\n",
      "       'NEIGHBORHOOD_ID', 'IS_CRIME', 'IS_TRAFFIC'],\n",
      "      dtype='object')\n",
      "   INCIDENT_ID         OFFENSE_ID  OFFENSE_CODE  OFFENSE_CODE_EXTENSION  \\\n",
      "0   2016376978   2016376978521300          5213                       0   \n",
      "1  20186000994  20186000994239900          2399                       0   \n",
      "2  20166003953  20166003953230500          2305                       0   \n",
      "3    201872333    201872333239900          2399                       0   \n",
      "4   2017411405   2017411405230300          2303                       0   \n",
      "\n",
      "                OFFENSE_TYPE_ID       OFFENSE_CATEGORY_ID  \\\n",
      "0  weapon-unlawful-discharge-of          all-other-crimes   \n",
      "1                   theft-other                   larceny   \n",
      "2      theft-items-from-vehicle  theft-from-motor-vehicle   \n",
      "3                   theft-other                   larceny   \n",
      "4                theft-shoplift                   larceny   \n",
      "\n",
      "    FIRST_OCCURRENCE_DATE   LAST_OCCURRENCE_DATE          REPORTED_DATE  \\\n",
      "0   6/15/2016 11:31:00 PM                    NaN  6/15/2016 11:31:00 PM   \n",
      "1  10/11/2017 12:30:00 PM  10/11/2017 4:55:00 PM   1/29/2018 5:53:00 PM   \n",
      "2     3/4/2016 8:00:00 PM   4/25/2016 8:00:00 AM   4/26/2016 9:02:00 PM   \n",
      "3    1/30/2018 7:20:00 PM                    NaN  1/30/2018 10:29:00 PM   \n",
      "4    6/22/2017 8:53:00 PM                    NaN   6/23/2017 4:09:00 PM   \n",
      "\n",
      "      INCIDENT_ADDRESS      GEO_X      GEO_Y     GEO_LON    GEO_LAT  \\\n",
      "0                  NaN  3193983.0  1707251.0 -104.809881  39.773188   \n",
      "1                  NaN  3201943.0  1711852.0 -104.781434  39.785649   \n",
      "2  2932 S JOSEPHINE ST  3152762.0  1667011.0 -104.957381  39.663490   \n",
      "3  705 S COLORADO BLVD  3157162.0  1681320.0 -104.941440  39.702698   \n",
      "4       2810 E 1ST AVE  3153211.0  1686545.0 -104.955370  39.717107   \n",
      "\n",
      "   DISTRICT_ID  PRECINCT_ID             NEIGHBORHOOD_ID  IS_CRIME  IS_TRAFFIC  \n",
      "0            5          521                   montbello         1           0  \n",
      "1            5          522  gateway-green-valley-ranch         1           0  \n",
      "2            3          314                   wellshire         1           0  \n",
      "3            3          312                     belcaro         1           0  \n",
      "4            3          311                cherry-creek         1           0  \n"
     ]
    }
   ],
   "source": [
    "# Loading Denver Dataset\n",
    "data = pd.read_csv('denver.csv')\n",
    "show_details(data,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'traffic-accident', 'larceny', 'public-disorder', 'burglary', 'other-crimes-against-persons', 'robbery', 'sexual-assault', 'theft-from-motor-vehicle', 'white-collar-crime', 'all-other-crimes', 'auto-theft', 'arson', 'murder', 'drug-alcohol', 'aggravated-assault'}\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "# To know what unique crime types are present in dataset\n",
    "crime_categories = pd.unique(data[data.columns[5]].values.ravel()).tolist()\n",
    "crime_categories = set(crime_categories)\n",
    "show_details(crime_categories,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
      "                 9,     10,\n",
      "            ...\n",
      "            490020, 490023, 490024, 490027, 490031, 490032, 490033, 490034,\n",
      "            490035, 490037],\n",
      "           dtype='int64', length=362315)\n",
      "5\n",
      "Index(['INCIDENT_ID', 'OFFENSE_CATEGORY_ID', 'FIRST_OCCURRENCE_DATE',\n",
      "       'NEIGHBORHOOD_ID', 'IS_CRIME'],\n",
      "      dtype='object')\n",
      "   INCIDENT_ID       OFFENSE_CATEGORY_ID   FIRST_OCCURRENCE_DATE  \\\n",
      "0   2016376978          all-other-crimes   6/15/2016 11:31:00 PM   \n",
      "1  20186000994                   larceny  10/11/2017 12:30:00 PM   \n",
      "2  20166003953  theft-from-motor-vehicle     3/4/2016 8:00:00 PM   \n",
      "3    201872333                   larceny    1/30/2018 7:20:00 PM   \n",
      "4   2017411405                   larceny    6/22/2017 8:53:00 PM   \n",
      "\n",
      "              NEIGHBORHOOD_ID  IS_CRIME  \n",
      "0                   montbello         1  \n",
      "1  gateway-green-valley-ranch         1  \n",
      "2                   wellshire         1  \n",
      "3                     belcaro         1  \n",
      "4                cherry-creek         1  \n"
     ]
    }
   ],
   "source": [
    "data_d = data[[data.columns[0], data.columns[5], data.columns[6], data.columns[len(data.columns)-1-2], data.columns[len(data.columns)-1-1]] ]\n",
    "# Taking into consideration only those columns that have crime involved and not an accident\n",
    "data_d = data_d[(data_d[data_d.columns[4]] != 0)]\n",
    "# Showing details of dataframe created\n",
    "show_details(data_d,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to classify crimes into respective categories\n",
    "def func(s):\n",
    "    s = s.lower()\n",
    "    if(s=='assault' or s=='murder'):\n",
    "        return 1\n",
    "    if(s=='drug' or s=='alcohol' or s=='drugs' or s=='drunk' or s=='roll'):\n",
    "        return 2\n",
    "    if(s=='other'):\n",
    "        return 3\n",
    "    if(s=='pimping' or s=='initmate' or s=='public' or s=='disorder' or s=='sexual' or s=='indecent' or s=='bigamy' or s=='lewd' or s=='sex' or s=='pandering'):\n",
    "        return 4\n",
    "    if(s=='purse' or s=='prowlwer' or s=='pickpocket' or s=='till' or s=='theft' or s=='shoplifting' or s=='burglary' or s=='larceny' or s=='robbery' or s=='stolen'):\n",
    "        return 5\n",
    "    if(s=='counterfeit' or s=='identity' or s=='bunco' or s=='white' or s=='collar' or s=='embezzlement' or s=='credit' or s=='extortion' or s=='bribery' or s=='employee'):\n",
    "        return 6\n",
    "    return 3\n",
    "\n",
    "# Iterating through crime type name to find crime category\n",
    "def transform(s):\n",
    "    x = \"\"\n",
    "    s = str(s)\n",
    "    s = s.lower()\n",
    "    if(s==\"theft of identity\"):\n",
    "        return 6\n",
    "    elif(s==\"other assault\"):\n",
    "        return 1\n",
    "    for i in range(len(s)):\n",
    "        if(s[i]==' ' or s[i]=='(' or s[i]=='-' or s[i]=='/' or s[i]==','):\n",
    "            t = func(x)\n",
    "            if(t!=3):\n",
    "                return t\n",
    "            else:\n",
    "                x = \"\"\n",
    "        else:\n",
    "            x = x + s[i]\n",
    "    return func(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b542b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
      "                 9,     10,\n",
      "            ...\n",
      "            490020, 490023, 490024, 490027, 490031, 490032, 490033, 490034,\n",
      "            490035, 490037],\n",
      "           dtype='int64', length=362315)\n",
      "5\n",
      "Index(['INCIDENT_ID', 'FIRST_OCCURRENCE_DATE', 'NEIGHBORHOOD_ID', 'IS_CRIME',\n",
      "       'Crime_Type'],\n",
      "      dtype='object')\n",
      "   INCIDENT_ID   FIRST_OCCURRENCE_DATE             NEIGHBORHOOD_ID  IS_CRIME  \\\n",
      "0   2016376978   6/15/2016 11:31:00 PM                   montbello         1   \n",
      "1  20186000994  10/11/2017 12:30:00 PM  gateway-green-valley-ranch         1   \n",
      "2  20166003953     3/4/2016 8:00:00 PM                   wellshire         1   \n",
      "3    201872333    1/30/2018 7:20:00 PM                     belcaro         1   \n",
      "4   2017411405    6/22/2017 8:53:00 PM                cherry-creek         1   \n",
      "\n",
      "  Crime_Type  \n",
      "0          3  \n",
      "1          5  \n",
      "2          5  \n",
      "3          5  \n",
      "4          5  \n"
     ]
    }
   ],
   "source": [
    "all_crimes = data_d[data_d.columns[1]].values\n",
    "c_count_d = np.zeros(6)\n",
    "for i in range(len(all_crimes)):\n",
    "    # Mappping crime type to a numerical representation\n",
    "    all_crimes[i] = transform(all_crimes[i])\n",
    "    # Counting the total number of that crime_type present in data\n",
    "    c_count_d[all_crimes[i]-1] = c_count_d[all_crimes[i]-1] + 1\n",
    "graph_b(c_count_d)\n",
    "data_d['Crime_Type'] = all_crimes\n",
    "data_d = data_d.drop([data_d.columns[1]], axis=1)\n",
    "# Showing graph corresponding to teh count of every crime type in Denver dataset\n",
    "show_details(data_d,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1235f9e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1235f9438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b546a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
      "                 9,     10,\n",
      "            ...\n",
      "            490020, 490023, 490024, 490027, 490031, 490032, 490033, 490034,\n",
      "            490035, 490037],\n",
      "           dtype='int64', length=362315)\n",
      "8\n",
      "Index(['INCIDENT_ID', 'NEIGHBORHOOD_ID', 'IS_CRIME', 'Crime_Type',\n",
      "       'Crime_Month', 'Crime_Day', 'Crime_Time', 'Crime_Year'],\n",
      "      dtype='object')\n",
      "   INCIDENT_ID             NEIGHBORHOOD_ID  IS_CRIME Crime_Type  Crime_Month  \\\n",
      "0   2016376978                   montbello         1          3            6   \n",
      "1  20186000994  gateway-green-valley-ranch         1          5           10   \n",
      "2  20166003953                   wellshire         1          5            3   \n",
      "3    201872333                     belcaro         1          5            1   \n",
      "4   2017411405                cherry-creek         1          5            6   \n",
      "\n",
      "   Crime_Day  Crime_Time  Crime_Year  \n",
      "0          3           6        2016  \n",
      "1          3           3        2017  \n",
      "2          5           5        2016  \n",
      "3          2           5        2018  \n",
      "4          4           5        2017  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INCIDENT_ID</th>\n",
       "      <th>NEIGHBORHOOD_ID</th>\n",
       "      <th>IS_CRIME</th>\n",
       "      <th>Crime_Type</th>\n",
       "      <th>Crime_Month</th>\n",
       "      <th>Crime_Day</th>\n",
       "      <th>Crime_Time</th>\n",
       "      <th>Crime_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016376978</td>\n",
       "      <td>montbello</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20186000994</td>\n",
       "      <td>gateway-green-valley-ranch</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20166003953</td>\n",
       "      <td>wellshire</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201872333</td>\n",
       "      <td>belcaro</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017411405</td>\n",
       "      <td>cherry-creek</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INCIDENT_ID             NEIGHBORHOOD_ID  IS_CRIME Crime_Type  Crime_Month  \\\n",
       "0   2016376978                   montbello         1          3            6   \n",
       "1  20186000994  gateway-green-valley-ranch         1          5           10   \n",
       "2  20166003953                   wellshire         1          5            3   \n",
       "3    201872333                     belcaro         1          5            1   \n",
       "4   2017411405                cherry-creek         1          5            6   \n",
       "\n",
       "   Crime_Day  Crime_Time  Crime_Year  \n",
       "0          3           6        2016  \n",
       "1          3           3        2017  \n",
       "2          5           5        2016  \n",
       "3          2           5        2018  \n",
       "4          4           5        2017  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding month, hour, day count in dataset for dataset to analyze what is unsafe time period for staying out\n",
    "months = []\n",
    "days = []\n",
    "hours = []\n",
    "years_d = []\n",
    "d_count_d = np.zeros(num_days)\n",
    "m_count_d = np.zeros(num_months)\n",
    "h_count_d = np.zeros(num_time_range)\n",
    "date = data_d[data_d.columns[1]].values\n",
    "for i in range(len(date)):\n",
    "    x = date[i]\n",
    "    x = str(x)\n",
    "    month, day, year , hour , minutes , seconds , f = convert(x)  \n",
    "    years_d.append(year)\n",
    "    ans = datetime.date(year, month, day)\n",
    "    months.append(month)\n",
    "    t = ans.weekday()\n",
    "    days.append(t+1)\n",
    "    j = conversion(hour,f)\n",
    "    hours.append(j)\n",
    "    if(year==2014):\n",
    "        m_count_d[month-1] = m_count_d[month-1]+1\n",
    "        d_count_d[t] = d_count_d[t]+1\n",
    "        h_count_d[j-1] = h_count_d[j-1] + 1\n",
    "graph_b(m_count_d)\n",
    "graph_b(d_count_d)\n",
    "graph_b(h_count_d)\n",
    "data_d['Crime_Month'] = months\n",
    "data_d['Crime_Day'] = days\n",
    "data_d['Crime_Time'] = hours\n",
    "data_d['Crime_Year'] = years_d\n",
    "data_d = data_d.drop([data_d.columns[1]], axis=1)\n",
    "show_details(data_d,0)\n",
    "data_d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=78, step=1)\n",
      "127\n",
      "Index(['NBHD_ID', 'NBRHD_NAME', 'POPULATION_2010', 'HISPANIC_2010',\n",
      "       'WHITE_2010', 'BLACK_2010', 'NATIVEAM_2010', 'ASIAN_2010',\n",
      "       'HAWPACIS_2010', 'OTHER_2010',\n",
      "       ...\n",
      "       'RENTED_AGE_25_TO_34', 'RENTED_AGE_35_TO_44', 'RENTED_AGE_45_TO_54',\n",
      "       'RENTED_AGE_55_TO_59', 'RENTED_AGE_60_TO_64', 'RENTED_AGE_65_TO_74',\n",
      "       'RENTED_AGE_75_TO_84', 'RENTED_AGE_85_PLUS', 'SHAPE_Length',\n",
      "       'SHAPE_Area'],\n",
      "      dtype='object', length=127)\n",
      "   NBHD_ID       NBRHD_NAME  POPULATION_2010  HISPANIC_2010  WHITE_2010  \\\n",
      "0       32          Hampden          17547.0         2505.0     11750.0   \n",
      "1        3            Baker           4879.0         1664.0      2854.0   \n",
      "2       70  Washington Park           6905.0          295.0      6356.0   \n",
      "3       13     Cherry Creek           5589.0          332.0      4896.0   \n",
      "4       22     Country Club           3001.0           94.0      2740.0   \n",
      "\n",
      "   BLACK_2010  NATIVEAM_2010  ASIAN_2010  HAWPACIS_2010  OTHER_2010  \\\n",
      "0      1963.0           64.0       726.0           20.0        38.0   \n",
      "1       144.0           43.0        52.0           10.0         7.0   \n",
      "2        28.0            9.0       119.0            3.0        13.0   \n",
      "3        79.0           17.0       168.0            4.0         3.0   \n",
      "4        20.0            8.0        77.0            1.0         5.0   \n",
      "\n",
      "      ...      RENTED_AGE_25_TO_34  RENTED_AGE_35_TO_44  RENTED_AGE_45_TO_54  \\\n",
      "0     ...                   1363.0                791.0                700.0   \n",
      "1     ...                    486.0                235.0                231.0   \n",
      "2     ...                    329.0                132.0                 70.0   \n",
      "3     ...                    521.0                261.0                211.0   \n",
      "4     ...                     90.0                 55.0                 34.0   \n",
      "\n",
      "   RENTED_AGE_55_TO_59  RENTED_AGE_60_TO_64  RENTED_AGE_65_TO_74  \\\n",
      "0                304.0                253.0                285.0   \n",
      "1                113.0                 64.0                 82.0   \n",
      "2                 31.0                 36.0                 27.0   \n",
      "3                 79.0                 92.0                161.0   \n",
      "4                 14.0                 17.0                  9.0   \n",
      "\n",
      "   RENTED_AGE_75_TO_84  RENTED_AGE_85_PLUS  SHAPE_Length  SHAPE_Area  \n",
      "0                200.0               229.0      0.200466    0.000768  \n",
      "1                 23.0                 7.0      0.101767    0.000406  \n",
      "2                  6.0                21.0      0.090445    0.000411  \n",
      "3                224.0               207.0      0.063923    0.000228  \n",
      "4                  4.0                 2.0      0.052865    0.000177  \n",
      "\n",
      "[5 rows x 127 columns]\n"
     ]
    }
   ],
   "source": [
    "# Loading Demographics Dataset which includes information about neighbourhood areas of denver\n",
    "demographics = pd.read_csv('demographic.csv')\n",
    "show_details(demographics,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([69, 59,  1, 72, 40, 77, 60, 63,  8, 11, 70, 56,  3, 36,  7, 10,  5,\n",
      "            22, 51, 21, 18,  4, 28, 34, 54, 13, 76, 39, 16, 26, 47,  0, 52, 71,\n",
      "            38, 15, 57, 33, 67, 50, 58, 37, 43, 75, 74, 41,  9, 48, 53, 55, 42,\n",
      "            64, 35, 24,  6, 62, 49, 32, 20, 14, 65, 23, 12, 25, 29, 17, 68, 46,\n",
      "            27,  2, 19, 45, 66, 30, 31, 44, 73, 61],\n",
      "           dtype='int64')\n",
      "2\n",
      "Index(['NBHD_ID', 'NBRHD_NAME'], dtype='object')\n",
      "    NBHD_ID   NBRHD_NAME\n",
      "69        1  athmar-park\n",
      "59        2      auraria\n",
      "1         3        baker\n",
      "72        4       barnum\n",
      "40        5  barnum-west\n"
     ]
    }
   ],
   "source": [
    "current = demographics[[demographics.columns[0], demographics.columns[1]]]\n",
    "current = current.sort_values(by=[current.columns[0]])\n",
    "n_ref = current[current.columns[1]].values\n",
    "for i in range(len(n_ref)):\n",
    "    x = n_ref[i]\n",
    "    x = str(x)\n",
    "    x = x.lower()\n",
    "    y = \"\"\n",
    "    f = 1\n",
    "    for j in range(len(x)):\n",
    "        if(x[j]=='/' and x[j+1]==' '):\n",
    "            f=0  \n",
    "        elif(x[j]==' ' and f==1):\n",
    "            y = y + '-'\n",
    "            f = 0\n",
    "        elif(x[j]==' ' and f==0):\n",
    "            continue\n",
    "        elif(x[j]=='-'):\n",
    "            continue\n",
    "        else:\n",
    "            y = y + x[j]\n",
    "            f = 1\n",
    "    n_ref[i] = y\n",
    "show_details(current,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145adf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
      "                 9,     10,\n",
      "            ...\n",
      "            490020, 490023, 490024, 490027, 490031, 490032, 490033, 490034,\n",
      "            490035, 490037],\n",
      "           dtype='int64', length=362315)\n",
      "8\n",
      "Index(['INCIDENT_ID', 'IS_CRIME', 'Crime_Type', 'Crime_Month', 'Crime_Day',\n",
      "       'Crime_Time', 'Crime_Year', 'Crime_Location'],\n",
      "      dtype='object')\n",
      "   INCIDENT_ID  IS_CRIME Crime_Type  Crime_Month  Crime_Day  Crime_Time  \\\n",
      "0   2016376978         1          3            6          3           6   \n",
      "1  20186000994         1          5           10          3           3   \n",
      "2  20166003953         1          5            3          5           5   \n",
      "3    201872333         1          5            1          2           5   \n",
      "4   2017411405         1          5            6          4           5   \n",
      "\n",
      "   Crime_Year  Crime_Location  \n",
      "0        2016              45  \n",
      "1        2017              28  \n",
      "2        2016              73  \n",
      "3        2018               7  \n",
      "4        2017              13  \n"
     ]
    }
   ],
   "source": [
    "ans = []\n",
    "n_count_d = np.zeros(78)\n",
    "neighbours = data_d[data_d.columns[1]].values\n",
    "for i in range(len(neighbours)):\n",
    "    x = neighbours[i]\n",
    "    x = str(x)\n",
    "    for j in range(len(n_ref)):\n",
    "        if(x==n_ref[j]):\n",
    "            ans.append(j+1)\n",
    "            if(years_d[i]==2014):\n",
    "                n_count_d[j] = n_count_d[j] + 1\n",
    "            break\n",
    "data_d['Crime_Location'] = ans\n",
    "data_d = data_d.drop(data_d.columns[1],axis=1)\n",
    "graph_b(n_count_d)\n",
    "show_details(data_d,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=1584316, step=1)\n",
      "26\n",
      "Index(['DR Number', 'Date Reported', 'Date Occurred', 'Time Occurred',\n",
      "       'Area ID', 'Area Name', 'Reporting District', 'Crime Code',\n",
      "       'Crime Code Description', 'MO Codes', 'Victim Age', 'Victim Sex',\n",
      "       'Victim Descent', 'Premise Code', 'Premise Description',\n",
      "       'Weapon Used Code', 'Weapon Description', 'Status Code',\n",
      "       'Status Description', 'Crime Code 1', 'Crime Code 2', 'Crime Code 3',\n",
      "       'Crime Code 4', 'Address', 'Cross Street', 'Location '],\n",
      "      dtype='object')\n",
      "   DR Number Date Reported Date Occurred  Time Occurred  Area ID    Area Name  \\\n",
      "0    1208575    03/14/2013    03/11/2013           1800       12  77th Street   \n",
      "1  102005556    01/25/2010    01/22/2010           2300       20      Olympic   \n",
      "2        418    03/19/2013    03/18/2013           2030       18    Southeast   \n",
      "3  101822289    11/11/2010    11/10/2010           1800       18    Southeast   \n",
      "4   42104479    01/11/2014    01/04/2014           2300       21      Topanga   \n",
      "\n",
      "   Reporting District  Crime Code                    Crime Code Description  \\\n",
      "0                1241         626         INTIMATE PARTNER - SIMPLE ASSAULT   \n",
      "1                2071         510                          VEHICLE - STOLEN   \n",
      "2                1823         510                          VEHICLE - STOLEN   \n",
      "3                1803         510                          VEHICLE - STOLEN   \n",
      "4                2133         745  VANDALISM - MISDEAMEANOR ($399 OR UNDER)   \n",
      "\n",
      "              MO Codes          ...           \\\n",
      "0  0416 0446 1243 2000          ...            \n",
      "1                  NaN          ...            \n",
      "2                  NaN          ...            \n",
      "3                  NaN          ...            \n",
      "4                 0329          ...            \n",
      "\n",
      "                               Weapon Description Status Code  \\\n",
      "0  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)          AO   \n",
      "1                                             NaN          IC   \n",
      "2                                             NaN          IC   \n",
      "3                                             NaN          IC   \n",
      "4                                             NaN          IC   \n",
      "\n",
      "  Status Description  Crime Code 1 Crime Code 2  Crime Code 3 Crime Code 4  \\\n",
      "0        Adult Other         626.0          NaN           NaN          NaN   \n",
      "1        Invest Cont         510.0          NaN           NaN          NaN   \n",
      "2        Invest Cont         510.0          NaN           NaN          NaN   \n",
      "3        Invest Cont         510.0          NaN           NaN          NaN   \n",
      "4        Invest Cont         745.0          NaN           NaN          NaN   \n",
      "\n",
      "                                   Address Cross Street             Location   \n",
      "0  6300    BRYNHURST                    AV          NaN  (33.9829, -118.3338)  \n",
      "1                                 VAN NESS         15TH  (34.0454, -118.3157)  \n",
      "2   200 E  104TH                        ST          NaN   (33.942, -118.2717)  \n",
      "3                                     88TH         WALL  (33.9572, -118.2717)  \n",
      "4  7200    CIRRUS                       WY          NaN  (34.2009, -118.6369)  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('los_angeles.csv')\n",
    "show_details(data,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=1584316, step=1)\n",
      "5\n",
      "Index(['DR Number', 'Date Occurred', 'Time Occurred', 'Area ID',\n",
      "       'Crime Code Description'],\n",
      "      dtype='object')\n",
      "   DR Number Date Occurred  Time Occurred  Area ID  \\\n",
      "0    1208575    03/11/2013           1800       12   \n",
      "1  102005556    01/22/2010           2300       20   \n",
      "2        418    03/18/2013           2030       18   \n",
      "3  101822289    11/10/2010           1800       18   \n",
      "4   42104479    01/04/2014           2300       21   \n",
      "\n",
      "                     Crime Code Description  \n",
      "0         INTIMATE PARTNER - SIMPLE ASSAULT  \n",
      "1                          VEHICLE - STOLEN  \n",
      "2                          VEHICLE - STOLEN  \n",
      "3                          VEHICLE - STOLEN  \n",
      "4  VANDALISM - MISDEAMEANOR ($399 OR UNDER)  \n"
     ]
    }
   ],
   "source": [
    "data_la = data[[data.columns[0], data.columns[2], data.columns[3], data.columns[4], data.columns[8]]]\n",
    "show_details(data_la,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BkZ6/CHy01XZSW+uB9QBdXV309fVNeBvV6jAjIyNNrduManUYIJf2yjy2ent5ja/MY6u3B94v29VeGffLdpxWuhH4QUScAKg/A0j6MvDN9HIIWNiw3oJU4xz1t4mIzcBmgJ6enqhUKhPubG//XqrVKs2s24ze/r0AVCrX5tJWWcdWby+v8ZV5bPX2wPtlu9or437ZjtNKa2k4pSRpfsO8DwJPp+kdwBpJ75C0GFgCPAnsA5ZIWpyOQtakZc3MrCAtHTlIuoDaXUa3N5T/s6Rl1E4rvVCfFxFHJD1I7ULzaeCOiBhJ29kA7AJmAFsi4kgr/TIzs9a0FA4R8RrwzjNqHz7H8ncDd49S3wnsbKUvZmbWPv6EtJmZZTgczMwsw+FgZmYZDgczM8twOJiZWYbDwczMMhwOZmaW4XAwM7MMh4OZmWU4HMzMLMPhYGZmGQ4HMzPLcDiYmVmGw8HMzDIcDmZmluFwMDOzDIeDmZllOBzMzCzD4WBmZhkOBzMzy2g5HCS9IOmwpEOS9qfaRZJ2SzqanuemuiTdK2lA0lOS3tOwnXVp+aOS1rXaLzMza167jhz+eUQsi4ie9HojsCcilgB70muAG4El6bEe6IVamAB3AlcDVwF31gPFzMzyN1mnlVYBW9P0VmB1Q/2BqHkCmCNpPrAC2B0RpyLip8BuYOUk9c3MzMbQjnAI4DuSDkhan2pdEXE8Tb8MdKXpbuClhnUHU+1sdTMzK8DMNmzjn0XEkKR/COyW9MPGmRERkqIN7ZDCZz1AV1cXfX19E95GtTrMyMhIU+s2o1odBsilvTKPrd5eXuMr89jq7YH3y3a1V8b9suVwiIih9HxS0jeoXTM4IWl+RBxPp41OpsWHgIUNqy9ItSGgcka9b5S2NgObAXp6eqJSqZy5yJh6+/dSrVZpZt1m9PbvBaBSuTaXtso6tnp7eY2vzGOrtwfeL9vVXhn3y5ZOK0m6QNKv16eBG4CngR1A/Y6jdcBjaXoHcEu6a+ka4Gfp9NMu4AZJc9OF6BtSzczMCtDqkUMX8A1J9W39j4j4tqR9wIOSbgNeBG5Oy+8EbgIGgF8AtwJExClJdwH70nKfi4hTLfbNzMya1FI4RMQx4N2j1F8Brh+lHsAdZ9nWFmBLK/0xM7P28Cekzcwsw+FgZmYZDgczM8twOJiZWYbDwczMMhwOZmaW4XAwM7MMh4OZmWU4HMzMLMPhYGZmGQ4HMzPLcDiYmVmGw8HMzDIcDmZmluFwMDOzDIeDmZllOBzMzCzD4WBmZhkOBzMzy3A4mJlZRtPhIGmhpO9JekbSEUmfTPXPShqSdCg9bmpYZ5OkAUn9klY01Fem2oCkja0NyczMWjWzhXVPA38YET+Q9OvAAUm707wvRcQXGheWtBRYA1wOXAL8laRL0+z7gPcDg8A+STsi4pkW+mZmZi1oOhwi4jhwPE3/raRnge5zrLIK2B4RrwPPSxoArkrzBiLiGICk7WlZh4OZWUFaOXL4JUmLgOXA94HrgA2SbgH2Uzu6+Cm14HiiYbVB/j5MXjqjfvVZ2lkPrAfo6uqir69vwn2tVocZGRlpat1mVKvDALm0V+ax1dvLa3xlHlu9PfB+2a72yrhfthwOkn4NeBj4VES8KqkXuAuI9PxF4KOttgMQEZuBzQA9PT1RqVQmvI3e/r1Uq1WaWbcZvf17AahUrs2lrbKOrd5eXuMr89jq7YH3y3a1V8b9sqVwkHQ+tWD4WkQ8AhARJxrmfxn4Zno5BCxsWH1BqnGOupmZFaCVu5UEfBV4NiL+tKE+v2GxDwJPp+kdwBpJ75C0GFgCPAnsA5ZIWixpFrWL1jua7ZeZmbWulSOH64APA4clHUq1PwbWSlpG7bTSC8DtABFxRNKD1C40nwbuiIgRAEkbgF3ADGBLRBxpoV9mZtaiVu5W+j+ARpm18xzr3A3cPUp957nWMzOzfPkT0mZmluFwMDOzDIeDmZllOBzMzCzD4WBmZhkOBzMzy3A4mJlZhsPBzMwyHA5mZpbhcDAzswyHg5mZZTgczMwsw+FgZmYZDgczM8twOJiZWYbDwczMMhwOZmaW4XAwM7MMh4OZmWVMmXCQtFJSv6QBSRuL7o+Z2XQ2JcJB0gzgPuBGYCmwVtLSYntlZjZ9TYlwAK4CBiLiWES8AWwHVhXcJzOzaUsRUXQfkPT7wMqI+Dfp9YeBqyNiw9nW6enpif3790+4rb9Y+wfMO/48v7Xwoqb7OxHPHH8VgKXzL8ylrdOnT5dybPX28hpfmcdWbw+8X7arvTz3y5cvXsit2/5r09uQdCAiesZabmbTLRRA0npgPUBXVxd9fX0T3saseINZ5wXVarXNvRvdjHgLIJf2ZsRbnFfSsdXby2t8ZR5bvT3wftmu9vLcL2fFG029903UVAmHIWBhw+sFqfY2EbEZ2Ay1I4dKpTLxlioV+vr6eHcz63aAMo8Nyj0+j61zlXF8U+Wawz5giaTFkmYBa4AdBffJzGzamhJHDhFxWtIGYBcwA9gSEUcK7paZ2bQ1JcIBICJ2AjuL7oeZmU2d00pmZjaFOBzMzCzD4WBmZhkOBzMzy3A4mJlZxpT49xnNkPQT4MUmV58H/E0buzOVlHlsUO7xeWydq5PG948i4uKxFurYcGiFpP3j+d8inajMY4Nyj89j61xlHJ9PK5mZWYbDwczMMqZrOGwuugOTqMxjg3KPz2PrXKUb37S85mBmZuc2XY8czMzsHKZdOEhaKalf0oCkjUX3p10kLZT0PUnPSDoi6ZNF96ndJM2QdFDSN4vuS7tJmiPpIUk/lPSspGuL7lO7SPp3aZ98WtI2Sb9SdJ9aIWmLpJOSnm6oXSRpt6Sj6XlukX1sh2kVDpJmAPcBNwJLgbWSlhbbq7Y5DfxhRCwFrgHuKNHY6j4JPFt0JybJfwG+HRH/BHg3JRmnpG7g3wI9EXEFtX/Jv6bYXrXsfmDlGbWNwJ6IWALsSa872rQKB+AqYCAijkXEG8B2YFXBfWqLiDgeET9I039L7c2lu9hetY+kBcC/AL5SdF/aTdI/AN4LfBUgIt6IiHy+4zIfM4HZkmYCvwr8uOD+tCQiHgdOnVFeBWxN01uB1bl2ahJMt3DoBl5qeD1Iid5A6yQtApYD3y+2J231Z8BngLeK7sgkWAz8BPiLdNrsK5IuKLpT7RARQ8AXgB8Bx4GfRcR3iu3VpOiKiONp+mWgq8jOtMN0C4fSk/RrwMPApyLi1aL70w6Sfg84GREHiu7LJJkJvAfojYjlwGuU4LQEQDr3vopaAF4CXCDpXxfbq8kVtVtAO/420OkWDkPAwobXC1KtFCSdTy0YvhYRjxTdnza6DviApBeonQp8n6T/XmyX2moQGIyI+pHeQ9TCogx+F3g+In4SEW8CjwC/XXCfJsMJSfMB0vPJgvvTsukWDvuAJZIWS5pF7cLYjoL71BaSRO2c9bMR8adF96edImJTRCyIiEXUfmffjYjS/PUZES8DL0m6LJWuB54psEvt9CPgGkm/mvbR6ynJxfYz7ADWpel1wGMF9qUtpsx3SOchIk5L2gDsonbXxJaIOFJwt9rlOuDDwGFJh1Ltj9N3c9vU9wfA19IfLceAWwvuT1tExPclPQT8gNoddQfp8E8TS9oGVIB5kgaBO4F7gAcl3Ubtv0XfXFwP28OfkDYzs4zpdlrJzMzGweFgZmYZDgczM8twOJiZWYbDwczMMhwOZmaW4XAwM7MMh4OZmWX8f5/nxm6HcGqJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e7db550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e7db5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=1584316, step=1)\n",
      "7\n",
      "Index(['DR Number', 'Time Occurred', 'Area ID', 'Crime Code Description',\n",
      "       'Crime_Month', 'Crime_Day', 'Crime_Year'],\n",
      "      dtype='object')\n",
      "   DR Number  Time Occurred  Area ID  \\\n",
      "0    1208575           1800       12   \n",
      "1  102005556           2300       20   \n",
      "2        418           2030       18   \n",
      "3  101822289           1800       18   \n",
      "4   42104479           2300       21   \n",
      "\n",
      "                     Crime Code Description  Crime_Month  Crime_Day  \\\n",
      "0         INTIMATE PARTNER - SIMPLE ASSAULT            3          1   \n",
      "1                          VEHICLE - STOLEN            1          5   \n",
      "2                          VEHICLE - STOLEN            3          1   \n",
      "3                          VEHICLE - STOLEN           11          3   \n",
      "4  VANDALISM - MISDEAMEANOR ($399 OR UNDER)            1          6   \n",
      "\n",
      "   Crime_Year  \n",
      "0        2013  \n",
      "1        2010  \n",
      "2        2013  \n",
      "3        2010  \n",
      "4        2014  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "months = []\n",
    "days = []\n",
    "years = []\n",
    "d_count_la = np.zeros(num_days)\n",
    "m_count_la = np.zeros(num_months)\n",
    "date = data_la[data_la.columns[1]].values\n",
    "for i in range(len(date)):\n",
    "    x = date[i]\n",
    "    x = str(x)\n",
    "    month, day, year = (int(t) for t in x.split('/'))  \n",
    "    years.append(year)\n",
    "    ans = datetime.date(year, month, day)\n",
    "    months.append(month)\n",
    "    t = ans.weekday()\n",
    "    days.append(t+1)\n",
    "    if(year==2014):\n",
    "        m_count_la[month-1] = m_count_la[month-1]+1\n",
    "        d_count_la[t] = d_count_la[t]+1\n",
    "graph_b(m_count_la)\n",
    "graph_b(d_count_la)\n",
    "data_la['Crime_Month'] = months\n",
    "data_la['Crime_Day'] = days\n",
    "data_la['Crime_Year'] = years\n",
    "data_la = data_la.drop([data_la.columns[1]], axis=1)\n",
    "show_details(data_la,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a70358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=1584316, step=1)\n",
      "7\n",
      "Index(['DR Number', 'Area ID', 'Crime Code Description', 'Crime_Month',\n",
      "       'Crime_Day', 'Crime_Year', 'Crime_Time'],\n",
      "      dtype='object')\n",
      "   DR Number  Area ID                    Crime Code Description  Crime_Month  \\\n",
      "0    1208575       12         INTIMATE PARTNER - SIMPLE ASSAULT            3   \n",
      "1  102005556       20                          VEHICLE - STOLEN            1   \n",
      "2        418       18                          VEHICLE - STOLEN            3   \n",
      "3  101822289       18                          VEHICLE - STOLEN           11   \n",
      "4   42104479       21  VANDALISM - MISDEAMEANOR ($399 OR UNDER)            1   \n",
      "\n",
      "   Crime_Day  Crime_Year  Crime_Time  \n",
      "0          1        2013           5  \n",
      "1          5        2010           6  \n",
      "2          1        2013           5  \n",
      "3          3        2010           5  \n",
      "4          6        2014           6  \n"
     ]
    }
   ],
   "source": [
    "hours = []\n",
    "h_count_la = np.zeros(num_time_range)\n",
    "times = data_la[data_la.columns[1]].values\n",
    "for i in range(len(times)):\n",
    "    x = times[i]\n",
    "    x = int(x)\n",
    "    x = int(x/100)\n",
    "    t = conv(x)\n",
    "    hours.append(t)\n",
    "    if(years[i]==2014):\n",
    "        h_count_la[t-1] = h_count_la[t-1] + 1\n",
    "graph_b(h_count_la)\n",
    "data_la['Crime_Time'] = hours\n",
    "data_la = data_la.drop([data_la.columns[1]],axis=1)\n",
    "show_details(data_la,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['criminal homicide', 'violation of restraining order', 'cruelty to animals', 'till tap   grand theft  $950.01 & over', 'till tap   attempt', 'credit cards  fraud use  $950 & under', 'intimate partner   simple assault', 'other miscellaneous crime', 'robbery', 'grand theft   auto repair', 'burglary', 'failure to yield', 'theft from person   attempt', 'pandering', 'peeping tom', 'theft from motor vehicle   grand  $400 and over', 'child abuse  physical    simple assault', 'dishonest employee   petty theft', 'pickpocket', 'bunco  grand theft', 'violation of temporary restraining order', 'extortion', 'intimate partner   aggravated assault', 'defrauding innkeeper theft of services  $400 & under', 'assault with deadly weapon  aggravated assault', 'credit cards  fraud use  $950.01 & over', 'theft  coin machine   petty  $950 & under', 'shoplifting   attempt', 'burglary from vehicle  attempted', 'discharge firearms shots fired', 'purse snatching', 'vandalism   felony  $400 & over  all church vandalisms  0114', 'oral copulation', 'purse snatching   attempt', 'shots fired at moving vehicle  train or aircraft', 'driving without owner consent  dwoc', 'theft of identity', 'document worthless  $200.01 & over', 'vandalism   misdeameanor  $399 or under', 'shoplifting   petty theft  $950 & under', 'battery police  simple', 'stalking', 'arson', 'illegal dumping', 'child neglect  see 300 w.i.c.', 'telephone property   damage', 'bunco  attempt', 'vehicle   stolen', 'bomb scare', 'abortion illegal', 'pimping', 'bribery', 'child annoying  17yrs & under', 'battery   simple assault', 'rape  attempted', 'grand theft   insurance fraud', 'theft  coin machine   attempt', 'counterfeit', 'manslaughter  negligent', 'embezzlement  petty theft  $950 & under', 'reckless driving', 'violation of court order', 'letters  lewd', 'theft grand  $950.01 & over excpt guns fowl livestk prod0036', 'lynching   attempted', 'train wrecking', 'inciting a riot', 'false police report', 'kidnapping', 'kidnapping   grand attempt', 'disrupt school', 'drunk roll   attempt', 'indecent exposure', 'theft plain   attempt', 'sodomy sexual contact b w penis of one pers to anus oth 0007=02', 'replica firearms sale display manufacture or distribute 0132', 'child abandonment', 'bunco  petty theft', 'incest  sexual acts between blood relatives', 'threatening phone calls letters', 'bike   attempted stolen', 'resisting arrest', 'burglary from vehicle', 'dishonest employee attempted theft', 'theft from motor vehicle   attempt', 'failure to disperse', 'pickpocket  attempt', 'bigamy', 'assault with deadly weapon on police officer', 'lewd conduct', 'bike   stolen', 'contributing', 'boat   stolen', 'rape  forcible', 'child stealing', 'defrauding innkeeper theft of services  over $400', 'till tap   petty  $950 & under', 'shoplifting grand theft  $950.01 & over', 'prowler', 'document forgery   stolen felony', 'blocking door induction center', 'beastiality  crime against nature sexual asslt with anim0065', 'false imprisonment', 'burglary  attempted', 'shots fired at inhabited dwelling', 'drugs  to a minor', 'crm agnst chld  13 or under   14 15 & susp 10 yrs older 0060', 'theft  coin machine   grand  $950.01 & over', 'conspiracy', 'drunk roll', 'battery with sexual contact', 'theft  person', 'criminal threats   no weapon displayed', 'sex  unlawful', 'theft from motor vehicle   petty  $950 & under', 'other assault', 'disturbing the peace', 'unauthorized computer access', 'child abuse  physical    aggravated assault', 'attempted robbery', 'petty theft   auto repair', 'battery on a firefighter', 'contempt of court', 'throwing object at moving vehicle', 'document worthless  $200 & under', 'trespassing', 'sexual pentration with a foreign object', 'embezzlement  grand theft  $950.01 & over', 'lynching', 'theft plain   petty  $950 & under', 'weapons possession bombing', 'nan', 'brandish weapon', 'vehicle   attempt stolen', 'dishonest employee   grand theft']\n",
      "135\n"
     ]
    }
   ],
   "source": [
    "crime_categories = pd.unique(data[data.columns[8]].values.ravel())\n",
    "crime_categories\n",
    "final_cc = []\n",
    "for i in range(len(crime_categories)):\n",
    "    x = str(crime_categories[i])\n",
    "    y = \"\"\n",
    "    for j in range(len(x)):\n",
    "        if(x[j]=='-' or x[j]=='/' or x[j]=='(' or x[j]==',' or x[j]==')'):\n",
    "            y = y + ' ' \n",
    "        else:\n",
    "            y = y + x[j]\n",
    "    if(y[len(y)-1]==' '):\n",
    "        y =y[:-1]\n",
    "    final_cc.append(y)\n",
    "final_cc = set(final_cc)\n",
    "final_cc = [x.lower() for x in final_cc]\n",
    "show_details(final_cc,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x137fe0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=1584316, step=1)\n",
      "7\n",
      "Index(['DR Number', 'Area ID', 'Crime_Month', 'Crime_Day', 'Crime_Year',\n",
      "       'Crime_Time', 'Crime_Type'],\n",
      "      dtype='object')\n",
      "   DR Number  Area ID  Crime_Month  Crime_Day  Crime_Year  Crime_Time  \\\n",
      "0    1208575       12            3          1        2013           5   \n",
      "1  102005556       20            1          5        2010           6   \n",
      "2        418       18            3          1        2013           5   \n",
      "3  101822289       18           11          3        2010           5   \n",
      "4   42104479       21            1          6        2014           6   \n",
      "\n",
      "   Crime_Type  \n",
      "0           1  \n",
      "1           5  \n",
      "2           5  \n",
      "3           5  \n",
      "4           3  \n"
     ]
    }
   ],
   "source": [
    "all_crimes = data_la[data_la.columns[2]].values\n",
    "ans = []\n",
    "c_count_la = np.zeros(6)\n",
    "for i in range(len(all_crimes)):\n",
    "    y = transform(all_crimes[i])\n",
    "    ans.append(y)\n",
    "    c_count_la[y-1] = c_count_la[y-1] + 1\n",
    "data_la['Crime_Type'] = ans\n",
    "data_la = data_la.drop(data_la.columns[2],axis=1)\n",
    "plt.title('Crime occurence in Los Angeles category wise in 2014')\n",
    "graph_b(c_count_la)\n",
    "show_details(data_la,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad41470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neigh = data_la[data_la.columns[1]].values\n",
    "n_count_la = np.zeros(21)\n",
    "for i in range(len(neigh)):\n",
    "    n_count_la[neigh[i]-1] = n_count_la[neigh[i]-1] + 1\n",
    "plt.title('Crime occurence in Los Angeles area wise in 2014')\n",
    "graph_b(n_count_la)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_count_d = percentage(n_count_d)\n",
    "n_count_la = percentage(n_count_la)\n",
    "h_count_d = percentage(h_count_d)\n",
    "h_count_la = percentage(h_count_la)\n",
    "d_count_d = percentage(d_count_d)\n",
    "d_count_la = percentage(d_count_la)\n",
    "m_count_d = percentage(m_count_d)\n",
    "m_count_la = percentage(m_count_la)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x142027e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_width = 4\n",
    "months = ['January','February','March','April','May','June','July','August','September','October','November','December']\n",
    "plt.bar((np.arange(len(m_count_d))*10),m_count_d,bar_width,color='blue',label='Denver')\n",
    "plt.xticks(np.arange(len(m_count_d))*10, months, fontsize=10, rotation=30)\n",
    "plt.bar((np.arange(len(m_count_la))*10)+bar_width,m_count_la,bar_width,color='red',label='Los Angeles')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.title('Comparison of Crime occurance in Denver and Los Angeles over months')\n",
    "graph_a(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143fe240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_width = 4\n",
    "days = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']\n",
    "plt.bar((np.arange(len(d_count_d))*10),d_count_d,bar_width,color='blue',label='Denver')\n",
    "plt.xticks(np.arange(len(d_count_d))*10,days, fontsize=10, rotation=30)\n",
    "plt.bar((np.arange(len(d_count_la))*10)+bar_width,d_count_la,bar_width,color='red',label='Los Angeles')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.title('Comparison of Crime occurance in Denver and Los Angeles over days')\n",
    "graph_a(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a70198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_width = 4\n",
    "days = ['1:00AM - 4:59AM','5:00AM - 8:59AM','9:00AM - 12:59PM','1:00PM - 4:59PM','5:00PM - 8:59PM','9:00PM - 00:59AM']\n",
    "plt.bar((np.arange(len(h_count_d))*10),h_count_d,bar_width,color='blue',label='Denver')\n",
    "plt.bar(np.arange(len(h_count_la))*10+bar_width, h_count_la, bar_width, color='red' ,label='Los Angeles')\n",
    "plt.xticks(np.arange(len(h_count_d))*10,days, fontsize=10, rotation=30)\n",
    "plt.legend(loc = 'lower right')\n",
    "graph_a(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143f6668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_count_t , index_t = find_three(n_count_d,0)\n",
    "n_count_l , index_l = find_three(n_count_d,1)\n",
    "n_count_l.reverse()\n",
    "index_l.reverse()\n",
    "n_count_t = n_count_t + n_count_l\n",
    "index_t = index_t + index_l\n",
    "bar_width = 0.5\n",
    "current = demographics[[demographics.columns[0], demographics.columns[1]]]\n",
    "current = current.sort_values(by=[current.columns[0]])\n",
    "cities = []\n",
    "for i in range(len(index_t)):\n",
    "    cities.append(current[current.columns[1]][index_t[i]])\n",
    "plt.bar((np.arange(len(n_count_t))*1),n_count_t,bar_width,color='blue')\n",
    "plt.xticks(np.arange(len(n_count_t))*1, cities, fontsize=10, rotation=30)\n",
    "plt.title('Percentage of crime in Denver: City Wise')\n",
    "graph_a(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a83358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_count_t , index_t = find_three(n_count_la,0)\n",
    "n_count_l , index_l = find_three(n_count_la,1)\n",
    "n_count_l.reverse()\n",
    "index_l.reverse()\n",
    "n_count_t = n_count_t + n_count_l\n",
    "index_t = index_t + index_l\n",
    "bar_width = 0.5\n",
    "plt.bar((np.arange(len(n_count_t))*1),n_count_t,bar_width,color='blue')\n",
    "plt.title('Percentage of crime in Los Angeles: City Wise')\n",
    "graph_a(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c514668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def find_type_prob(a,b):\n",
    "    for i in range(b):\n",
    "        s = 0\n",
    "        for j in range(6):\n",
    "            s = s + a[i][j]\n",
    "        for j in range(6):\n",
    "            a[i][j] = a[i][j] / s\n",
    "    return a\n",
    "\n",
    "day_d = data_d[data_d.columns[4]].values\n",
    "type_d = data_d[data_d.columns[2]].values\n",
    "day_type_d = np.zeros((7,6))\n",
    "for i in range(len(day_d)):\n",
    "    b = day_d[i] - 1\n",
    "    a = type_d[i] - 1\n",
    "    day_type_d[b][a] = day_type_d[b][a] + 1\n",
    "days = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']\n",
    "plt.title('Crime type distribution in Denver on every day')\n",
    "type_graph(day_type_d,7,days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c8b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_la = data_la[data_la.columns[3]].values\n",
    "type_la = data_la[data_la.columns[5]].values\n",
    "day_type_la = np.zeros((7,6))\n",
    "for i in range(len(day_la)):\n",
    "    b = day_la[i] - 1\n",
    "    a = type_la[i] - 1\n",
    "    day_type_la[b][a] = day_type_la[b][a] + 1\n",
    "days = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']\n",
    "plt.title('Crime type distribution in Los Angeles on every day')\n",
    "type_graph(day_type_la,7,days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c5145c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_d = data_d[data_d.columns[5]].values\n",
    "time_type_d = np.zeros((6,6))\n",
    "for i in range(len(time_d)):\n",
    "    b = time_d[i] - 1\n",
    "    a = type_d[i] - 1\n",
    "    time_type_d[b][a] = time_type_d[b][a] + 1    \n",
    "a = ['1:00AM - 4:59AM','5:00AM - 8:59AM','9:00AM - 12:59PM','1:00PM - 4:59PM','5:00PM - 8:59PM','9:00PM - 00:59AM']\n",
    "plt.title('Crime type distribution in Denver based on time')\n",
    "type_graph(time_type_d,6,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111311240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_la = data_la[data_la.columns[5]].values\n",
    "time_type_la = np.zeros((6,6))\n",
    "for i in range(len(time_la)):\n",
    "    b = time_la[i] - 1\n",
    "    a = type_la[i] - 1\n",
    "    time_type_la[b][a] = time_type_la[b][a] + 1    \n",
    "a = ['1:00AM - 4:59AM','5:00AM - 8:59AM','9:00AM - 12:59PM','1:00PM - 4:59PM','5:00PM - 8:59PM','9:00PM - 00:59AM']\n",
    "plt.title('Crime type distribution in Los Angeles based on time')\n",
    "type_graph(time_type_la,6,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_d = data_d.iloc[0:343100]\n",
    "test_d = data_d.iloc[343101:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_m_d = train_d[train_d.columns[3]].values\n",
    "train_n_d = train_d[train_d.columns[7]].values\n",
    "train_c_d = train_d[train_d.columns[2]].values\n",
    "train_d_d = train_d[train_d.columns[4]].values\n",
    "train_t_d = train_d[train_d.columns[5]].values\n",
    "train_y_d = train_d[train_d.columns[6]].values\n",
    "test_m_d = test_d[test_d.columns[3]].values\n",
    "test_n_d = test_d[test_d.columns[7]].values\n",
    "test_c_d = test_d[test_d.columns[2]].values\n",
    "test_d_d = test_d[test_d.columns[4]].values\n",
    "test_t_d = test_d[test_d.columns[5]].values\n",
    "test_y_d = test_d[test_d.columns[6]].values\n",
    "\n",
    "for i in range(len(train_y_d)):\n",
    "    t = train_y_d[i] - 2013\n",
    "    train_y_d[i] = t\n",
    "\n",
    "for i in range(len(test_y_d)):\n",
    "    t = test_y_d[i] - 2013\n",
    "    test_y_d[i] = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob(a):\n",
    "    s = 0\n",
    "    for i in range(len(a)):\n",
    "        s = s+a[i]\n",
    "    for i in range(len(a)):\n",
    "        a[i] = a[i]/s\n",
    "    return a\n",
    "\n",
    "prob_m_d = prob(count(train_m_d,num_months))\n",
    "prob_n_d = prob(count(train_n_d,num_n_d))\n",
    "prob_c_d = prob(count(train_c_d,6))\n",
    "prob_d_d = prob(count(train_d_d,num_days))\n",
    "prob_t_d = prob(count(train_t_d,num_time_range))\n",
    "prob_y_d = prob(count(train_y_d,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bayesian_formula(a,b,c):\n",
    "    for i in range(6):\n",
    "        for j in range(c):\n",
    "            a[i][j] = a[i][j] * b[i]\n",
    "    return a\n",
    "\n",
    "def check_valid(a):\n",
    "    if round(np.sum(a),1)==1.0:\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def type_prob(a,b):\n",
    "    for i in range(6):\n",
    "        s = 0\n",
    "        for j in range(b):\n",
    "            s = s + a[i][j]\n",
    "        for j in range(b):\n",
    "            a[i][j] = a[i][j] / s\n",
    "    return a\n",
    "\n",
    "def find_matrix(a,b,c,d):\n",
    "    t = np.zeros((d,6))\n",
    "    for i in range(len(a)):\n",
    "        t[a[i]-1][b[i]-1] = t[a[i]-1][b[i]-1] + 1\n",
    "    t = np.transpose(t)\n",
    "    t = type_prob(t,d)\n",
    "    t = bayesian_formula(t,c,d)\n",
    "    return t\n",
    "\n",
    "month_type_d = find_matrix(train_m_d,train_c_d,prob_c_d,num_months)\n",
    "day_type_d = find_matrix(train_d_d,train_c_d,prob_c_d,num_days)\n",
    "time_type_d = find_matrix(train_t_d,train_c_d,prob_c_d,num_time_range)\n",
    "n_type_d = find_matrix(train_n_d,train_c_d,prob_c_d,num_n_d)\n",
    "y_type_d = find_matrix(train_y_d,train_c_d,prob_y_d,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_final_catogory_prob(a,b,d):\n",
    "    c = []\n",
    "    for i in range(len(a)):\n",
    "        t = a[i]\n",
    "        ans = -1\n",
    "        maxi = 0\n",
    "        for j in range(b):\n",
    "            if(d[j][t-1]>maxi):\n",
    "                maxi=d[j][t-1]\n",
    "                ans = j+1\n",
    "        c.append(ans)\n",
    "    return c\n",
    "\n",
    "test_m_c_d = find_final_catogory_prob(test_m_d,6,month_type_d)\n",
    "test_m_d_d = find_final_catogory_prob(test_d_d,6,day_type_d)\n",
    "test_m_n_d = find_final_catogory_prob(test_n_d,6,n_type_d)\n",
    "test_m_t_d = find_final_catogory_prob(test_t_d,6,time_type_d)\n",
    "test_m_t_d = find_final_catogory_prob(test_t_d,6,time_type_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[    0.     0.  4164.     0. 15050.     0.]\n",
      "8767\n",
      "19214\n",
      "0.4562818777974394\n"
     ]
    }
   ],
   "source": [
    "count = np.zeros(6)\n",
    "test_results = []\n",
    "for i in range(len(test_m_d)):\n",
    "    maxi = 0\n",
    "    for j in range(6):\n",
    "        k = month_type_d[j][test_m_d[i]-1]*time_type_d[j][test_t_d[i]-1]*day_type_d[j][test_d_d[i]-1]*n_type_d[j][test_n_d[i]-1]*y_type_d[j][test_y_d[i]-1]\n",
    "        if(maxi<k):\n",
    "            maxi = k\n",
    "            ans = j+1\n",
    "    test_results.append(ans)\n",
    "    count[ans-1] = count[ans-1] + 1\n",
    "print(count)\n",
    "\n",
    "correct=0\n",
    "for i in range(len(test_results)):\n",
    "    if(test_c_d[i]==test_results[i]):\n",
    "        correct = correct + 1\n",
    "print(correct)\n",
    "print(len(test_results))\n",
    "print(correct/len(test_results))\n",
    "#     one = month_type_d[test_m_d[i]-1][0]*time_type_d[test_t_d[i]-1][0]*day_type_d[test_d_d[i]-1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0.    0.  220.    0.  450.    0.]\n",
      " [   0.    0.  440.    0. 1127.    0.]\n",
      " [   0.    0. 1823.    0. 4088.    0.]\n",
      " [   0.    0.  591.    0. 2138.    0.]\n",
      " [   0.    0. 1052.    0. 6944.    0.]\n",
      " [   0.    0.   38.    0.  303.    0.]]\n"
     ]
    }
   ],
   "source": [
    "y = np.zeros((6,6))\n",
    "for i in range(len(test_c_d)):\n",
    "    y[test_c_d[i]-1][test_results[i]-1] = y[test_c_d[i]-1][test_results[i]-1] + 1\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate(true_positive,false_positive,false_negative,true_negative):\n",
    "    result = {}\n",
    "    result['precision'] = true_positive / (true_positive + false_positive)\n",
    "    result['recall'] = true_positive / (true_positive + false_negative)\n",
    "    return result\n",
    "\n",
    "def confusion_matrix(true_positive,false_positive,false_negative,true_negative):\n",
    "    matrix = PrettyTable([' ', 'Ham' , 'Spam'])\n",
    "    matrix.add_row(['Ham', true_positive , false_positive])\n",
    "    matrix.add_row(['Spam', false_negative , true_negative])\n",
    "    return matrix , calculate(true_positive,false_positive,false_negative,true_negative)\n",
    "\n",
    "for i in range()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "end_time = time()\n",
    "print(round(end_time - start_time,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# month_type_d = np.zeros((num_months,6))\n",
    "# for i in range(len(train_m_d)):\n",
    "#      month_type_d[train_m_d[i]-1][train_c_d[i]-1] = month_type_d[train_m_d[i]-1][train_c_d[i]-1] + 1\n",
    "# month_type_d = np.transpose(month_type_d)\n",
    "# month_type_d = type_prob(month_type_d,num_months)\n",
    "# month_type_d = bayesian_formula(month_type_d,prob_c_d,num_months)\n",
    "# print(month_type_d)\n",
    "\n",
    "# day_type_d = np.zeros((num_days,6))\n",
    "# for i in range(len(train_d_d)):\n",
    "#      day_type_d[train_d_d[i]-1][train_c_d[i]-1] = day_type_d[train_d_d[i]-1][train_c_d[i]-1] + 1\n",
    "# day_type_d = np.transpose(day_type_d)\n",
    "# day_type_d = type_prob(day_type_d,num_days)\n",
    "# day_type_d = bayesian_formula(day_type_d,prob_c_d,num_days)\n",
    "# print(day_type_d)\n",
    "\n",
    "# day_type_d = np.zeros((num_days,6))\n",
    "# for i in range(len(train_d_d)):\n",
    "#      day_type_d[train_d_d[i]-1][train_c_d[i]-1] = day_type_d[train_d_d[i]-1][train_c_d[i]-1] + 1\n",
    "# day_type_d = np.transpose(day_type_d)\n",
    "# day_type_d = type_prob(day_type_d,num_days)\n",
    "# day_type_d = bayesian_formula(day_type_d,prob_c_d,num_days)\n",
    "# print(day_type_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Given a month what is the probability that crime committed belongs to a particular category\n",
    "# month_type_d = np.zeros((12,6))\n",
    "# for i in range(len(train_m_d)):\n",
    "#      month_type_d[train_m_d[i]-1][train_c_d[i]-1] = month_type_d[train_m_d[i]-1][train_c_d[i]-1] + 1\n",
    "# month_type_d = find_type_prob(month_type_d,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Given a day what is the probability that crime committed belongs to a particular category\n",
    "# day_type_d = np.zeros((7,6))\n",
    "# for i in range(len(train_d_d)):\n",
    "#      day_type_d[train_d_d[i]-1][train_c_d[i]-1] = day_type_d[train_d_d[i]-1][train_c_d[i]-1] + 1\n",
    "# day_type_d = find_type_prob(day_type_d,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Given a time range what is the probability that crime committed belongs to a particular category\n",
    "# time_type_d = np.zeros((6,6))\n",
    "# for i in range(len(train_t_d)):\n",
    "#      time_type_d[train_t_d[i]-1][train_c_d[i]-1] = time_type_d[train_t_d[i]-1][train_c_d[i]-1] + 1\n",
    "# time_type_d = find_type_prob(time_type_d,num_time_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Given an area what is the probability that crime committed belongs to a particular category\n",
    "# n_type_d = np.zeros((78,6))\n",
    "# for i in range(len(train_n_d)):\n",
    "#      n_type_d[train_n_d[i]-1][train_c_d[i]-1] = n_type_d[train_n_d[i]-1][train_c_d[i]-1] + 1\n",
    "# n_type_d = find_type_prob(n_type_d,num_n_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Finding probablity that of month intersection with category\n",
    "# month_type_d = bayesian_formula(month_type_d,prob_m_d,num_months)\n",
    "\n",
    "# # Finding probablity that of day intersection with category\n",
    "# day_type_d = bayesian_formula(day_type_d,prob_d_d,num_days)\n",
    "\n",
    "# # Finding probablity that of time intersection with category\n",
    "# time_type_d = bayesian_formula(time_type_d,prob_t_d,num_time_range)\n",
    "\n",
    "# # Finding probablity that of neighbourhood area intersection with category\n",
    "# n_type_d = bayesian_formula(n_type_d,prob_n_d,num_n_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(check_valid(month_type_d))\n",
    "# print(check_valid(day_type_d))\n",
    "# print(check_valid(time_type_d))\n",
    "# print(check_valid(n_type_d))"
   ]
  }
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