CrimeDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d94c19d-ec6f-40d8-a1a3-2438e66c69c4",
   "metadata": {},
   "source": [
    "## Crime Calculus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "556331a0-a425-475a-af21-06f0f336bf77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyspark in c:\\users\\enduser\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.5.0)\n",
      "Requirement already satisfied: py4j==0.10.9.7 in c:\\users\\enduser\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pyspark) (0.10.9.7)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pyspark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7cf567-fc08-4bd8-a7be-c4d32ed65e59",
   "metadata": {},
   "source": [
    "## Create Spark Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1f9c6b9-4d0b-4649-9f23-2e1f0ddcee93",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import col, when"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43163a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Record the start time\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1dbe2c4-3664-4f78-8e34-badccfccab90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(INCIDENT_KEY=228798151, OCCUR_DATE='05/27/2021', OCCUR_TIME=datetime.datetime(2023, 12, 8, 21, 30), BORO='QUEENS', LOC_OF_OCCUR_DESC=None, PRECINCT=105, JURISDICTION_CODE=0, LOC_CLASSFCTN_DESC=None, LOCATION_DESC=None, STATISTICAL_MURDER_FLAG=False, PERP_AGE_GROUP=None, PERP_SEX=None, PERP_RACE=None, VIC_AGE_GROUP='18-24', VIC_SEX='M', VIC_RACE='BLACK', X_COORD_CD=1058925.0, Y_COORD_CD=180924.0, Latitude=40.662964620000025, Longitude=-73.73083868899994, Lon_Lat='POINT (-73.73083868899994 40.662964620000025)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a SparkSession\n",
    "#spark = spark = SparkSession.builder.appName(\"DataCleaning\").getOrCreate()\n",
    "# Create a SparkSession with appropriate configurations\n",
    "# Create a SparkSession\n",
    "spark = SparkSession.builder.appName(\"ReadFromHadoop\").config(\"spark.hadoop.fs.defaultFS\", \"hdfs://localhost:9000\").config(\"spark.hadoop.rpc.message.max\", \"52428800\").getOrCreate()\n",
    "\n",
    "\n",
    "#HDFS paths for the files\n",
    "hdfs_file_path_crime_data = \"hdfs://192.168.1.239:9001/input/NYPD_Complaint_Data_Historic_20231117.csv\"\n",
    "hdfs_file_path_shooting_data = \"hdfs://192.168.1.239:9001/input/NYPD_Shooting_Incident_Data__Historic__till_2022.csv\"\n",
    "\n",
    "# Read the CSV files from HDFS into PySpark DataFrames\n",
    "df1 = spark.read.csv(hdfs_file_path_crime_data, header=True, inferSchema=True)\n",
    "df2 = spark.read.csv(hdfs_file_path_shooting_data, header=True, inferSchema=True)\n",
    "\n",
    "df1.head()\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "552c9600",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test time taken to read data from HDFS for comparison across different partition and block size\n",
    "# Record the end time\n",
    "end_time = time.time()\n",
    "\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "print(f\"Execution time: {elapsed_time} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891bf75b-11f4-4be6-a697-7b84175e3eaf",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6f38506-ebde-4891-bb48-081c8034894c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+----------+-------------------+-------+-----------------+--------+-----------------+------------------+-------------+-----------------------+--------------+--------+---------+-------------+-------+--------------+------------+-------------+------------------+------------------+--------------------+\n",
      "|CMPLNT_NUM|    RPT_DT|       CMPLNT_FR_TM|BORO_NM|LOC_OF_OCCUR_DESC|PRECINCT|JURISDICTION_CODE|LOC_CLASSFCTN_DESC|LOCATION_DESC|STATISTICAL_MURDER_FLAG|PERP_AGE_GROUP|PERP_SEX|PERP_RACE|VIC_AGE_GROUP|VIC_SEX|      VIC_RACE|  X_COORD_CD|   Y_COORD_CD|          Latitude|         Longitude|             Lon_Lat|\n",
      "+----------+----------+-------------------+-------+-----------------+--------+-----------------+------------------+-------------+-----------------------+--------------+--------+---------+-------------+-------+--------------+------------+-------------+------------------+------------------+--------------------+\n",
      "| 228798151|05/27/2021|2023-12-08 21:30:00| QUEENS|             NULL|     105|                0|              NULL|         NULL|                  false|          NULL|    NULL|     NULL|        18-24|      M|         BLACK|   1058925.0|     180924.0|40.662964620000025|-73.73083868899994|POINT (-73.730838...|\n",
      "| 137471050|06/27/2014|2023-12-08 17:40:00|  BRONX|             NULL|      40|                0|              NULL|         NULL|                  false|          NULL|    NULL|     NULL|        18-24|      M|         BLACK|   1005028.0|     234516.0| 40.81035186300005|-73.92494232599995|POINT (-73.924942...|\n",
      "| 147998800|11/21/2015|2023-12-08 03:56:00| QUEENS|             NULL|     108|                0|              NULL|         NULL|                   true|          NULL|    NULL|     NULL|        25-44|      M|         WHITE|1007667.9375| 209836.53125| 40.74260663300004|-73.91549174199997|POINT (-73.915491...|\n",
      "| 146837977|10/09/2015|2023-12-08 18:30:00|  BRONX|             NULL|      44|                0|              NULL|         NULL|                  false|          NULL|    NULL|     NULL|          <18|      M|WHITE HISPANIC| 1006537.375|244511.140625| 40.83778200300003|-73.91945661499994|POINT (-73.919456...|\n",
      "|  58921844|02/19/2009|2023-12-08 22:58:00|  BRONX|             NULL|      47|                0|              NULL|         NULL|                   true|         25-44|       M|    BLACK|        45-64|      M|         BLACK|1024921.8125| 262189.40625| 40.88623791800006|-73.85290950899997|POINT (-73.852909...|\n",
      "+----------+----------+-------------------+-------+-----------------+--------+-----------------+------------------+-------------+-----------------------+--------------+--------+---------+-------------+-------+--------------+------------+-------------+------------------+------------------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Rename the names of the columns in df2 so that we can merge it with df1\n",
    "df2 = df2.withColumnRenamed(\"OCCUR_DATE\", \"RPT_DT\") \\\n",
    ".withColumnRenamed(\"OCCUR_TIME\", \"CMPLNT_FR_TM\") \\\n",
    ".withColumnRenamed(\"BORO\", \"BORO_NM\") \\\n",
    ".withColumnRenamed(\"INCIDENT_KEY\", \"CMPLNT_NUM\") \\\n",
    "\n",
    "# Show the DataFrame to verify the changes\n",
    "df2.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "618c148a-8ccd-44a2-81f4-9f333449f268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+----------+-------------------+---------+-------------+-------+------------+-------------+------------------+------------------+\n",
      "|CMPLNT_NUM|    RPT_DT|       CMPLNT_FR_TM|  BORO_NM|VIC_AGE_GROUP|VIC_SEX|  X_COORD_CD|   Y_COORD_CD|          Latitude|         Longitude|\n",
      "+----------+----------+-------------------+---------+-------------+-------+------------+-------------+------------------+------------------+\n",
      "| 228798151|05/27/2021|2023-12-08 21:30:00|   QUEENS|        18-24|      M|   1058925.0|     180924.0|40.662964620000025|-73.73083868899994|\n",
      "| 137471050|06/27/2014|2023-12-08 17:40:00|    BRONX|        18-24|      M|   1005028.0|     234516.0| 40.81035186300005|-73.92494232599995|\n",
      "| 147998800|11/21/2015|2023-12-08 03:56:00|   QUEENS|        25-44|      M|1007667.9375| 209836.53125| 40.74260663300004|-73.91549174199997|\n",
      "| 146837977|10/09/2015|2023-12-08 18:30:00|    BRONX|          <18|      M| 1006537.375|244511.140625| 40.83778200300003|-73.91945661499994|\n",
      "|  58921844|02/19/2009|2023-12-08 22:58:00|    BRONX|        45-64|      M|1024921.8125| 262189.40625| 40.88623791800006|-73.85290950899997|\n",
      "| 219559682|10/21/2020|2023-12-08 21:36:00| BROOKLYN|        25-44|      M|1004234.3125|186461.703125|40.678456718000064|-73.92795224099996|\n",
      "|  85295722|06/17/2012|2023-12-08 22:47:00|   QUEENS|        25-44|      M|    998860.0|     214885.0| 40.75648234300007|-73.94726649399996|\n",
      "|  71662474|03/08/2010|2023-12-08 19:41:00| BROOKLYN|        18-24|      M| 1002882.875|192219.703125|40.694264056000065|-73.93280863699994|\n",
      "|  83002139|02/05/2012|2023-12-08 05:45:00|   QUEENS|        25-44|      M| 1054366.375| 196628.40625|40.706106731000034|-73.74710653899996|\n",
      "|  86437261|08/26/2012|2023-12-08 01:10:00|   QUEENS|        25-44|      M| 1053936.625|157130.421875| 40.59769719800005|-73.74906464199995|\n",
      "|  74998200|10/10/2010|2023-12-08 03:21:00|MANHATTAN|        25-44|      M|  999061.375|229911.671875| 40.79772626500005|-73.94650650799997|\n",
      "|  74363835|08/29/2010|2023-12-08 01:27:00| BROOKLYN|        25-44|      M|   1013136.0|     179968.0| 40.66060839100004|-73.89588694299994|\n",
      "| 234756217|10/09/2021|2023-12-08 20:17:00| BROOKLYN|        25-44|      M|    996148.0|     181562.0| 40.66502269100005|-73.95711436799998|\n",
      "|  78961869|05/25/2011|2023-12-08 21:58:00|    BRONX|        18-24|      M|1010353.4375| 261534.28125|40.884494922000044|-73.90559937499995|\n",
      "|  53891791|11/09/2008|2023-12-08 20:13:00| BROOKLYN|        25-44|      M| 988398.4375|   187950.625|40.682565055000055|-73.98504421199993|\n",
      "|  32914993|07/05/2007|2023-12-08 01:27:00|    BRONX|        18-24|      M|   1026013.0|   261421.125| 40.88412413300006|-73.84896796399994|\n",
      "|  73879554|07/27/2010|2023-12-08 02:22:00|MANHATTAN|        25-44|      M| 999967.4375|  240325.5625| 40.82630793100003|-73.94320965699995|\n",
      "|  87694905|11/14/2012|2023-12-08 21:07:00| BROOKLYN|        25-44|      M|  1005555.75|  186370.9375|40.678204504000064|-73.92318836699997|\n",
      "| 230311079|07/01/2021|2023-12-08 02:44:00| BROOKLYN|        25-44|      M|   1009826.0|     183194.0| 40.66947322100003|-73.90780500799997|\n",
      "| 225297069|03/07/2021|2023-12-08 21:17:00| BROOKLYN|        25-44|      M|    995792.0|     178767.0| 40.65735150300002|-73.95840237599998|\n",
      "+----------+----------+-------------------+---------+-------------+-------+------------+-------------+------------------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Drop the columns that are not relevant to the analysis(feature selection)\n",
    "df2 = df2.drop('LOC_OF_OCCUR_DESC','PRECINCT','JURISDICTION_CODE','LOC_CLASSFCTN_DESC','LOCATION_DESC','STATISTICAL_MURDER_FLAG','PERP_AGE_GROUP','PERP_SEX','PERP_RACE','Lon_Lat','VIC_RACE')\n",
    "df2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f439b96a-fc0c-4052-9475-171f55affea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Common columns in both DataFrames:\n",
      "{'Y_COORD_CD', 'CMPLNT_FR_TM', 'X_COORD_CD', 'BORO_NM', 'VIC_SEX', 'RPT_DT', 'CMPLNT_NUM', 'VIC_AGE_GROUP', 'Latitude', 'Longitude'}\n"
     ]
    }
   ],
   "source": [
    "# Find common columns between both dataframes\n",
    "\n",
    "# Get column names for each DataFrame\n",
    "columns_df1 = set(df1.columns)\n",
    "columns_df2 = set(df2.columns)\n",
    "\n",
    "# Find common columns between both DataFrames as all columns need to be the same to merge them\n",
    "common_columns = columns_df1.intersection(columns_df2)\n",
    "\n",
    "# Display common columns\n",
    "print(\"Common columns in both DataFrames:\")\n",
    "print(common_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33736bd2-ed4a-4878-b239-1dbcc20b3f01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+----------+-------------------+---------+-------------+-------+------------+-------------+------------------+------------------+---------+\n",
      "|CMPLNT_NUM|    RPT_DT|       CMPLNT_FR_TM|  BORO_NM|VIC_AGE_GROUP|VIC_SEX|  X_COORD_CD|   Y_COORD_CD|          Latitude|         Longitude|OFNS_DESC|\n",
      "+----------+----------+-------------------+---------+-------------+-------+------------+-------------+------------------+------------------+---------+\n",
      "| 228798151|05/27/2021|2023-12-08 21:30:00|   QUEENS|        18-24|      M|   1058925.0|     180924.0|40.662964620000025|-73.73083868899994| SHOOTING|\n",
      "| 137471050|06/27/2014|2023-12-08 17:40:00|    BRONX|        18-24|      M|   1005028.0|     234516.0| 40.81035186300005|-73.92494232599995| SHOOTING|\n",
      "| 147998800|11/21/2015|2023-12-08 03:56:00|   QUEENS|        25-44|      M|1007667.9375| 209836.53125| 40.74260663300004|-73.91549174199997| SHOOTING|\n",
      "| 146837977|10/09/2015|2023-12-08 18:30:00|    BRONX|          <18|      M| 1006537.375|244511.140625| 40.83778200300003|-73.91945661499994| SHOOTING|\n",
      "|  58921844|02/19/2009|2023-12-08 22:58:00|    BRONX|        45-64|      M|1024921.8125| 262189.40625| 40.88623791800006|-73.85290950899997| SHOOTING|\n",
      "| 219559682|10/21/2020|2023-12-08 21:36:00| BROOKLYN|        25-44|      M|1004234.3125|186461.703125|40.678456718000064|-73.92795224099996| SHOOTING|\n",
      "|  85295722|06/17/2012|2023-12-08 22:47:00|   QUEENS|        25-44|      M|    998860.0|     214885.0| 40.75648234300007|-73.94726649399996| SHOOTING|\n",
      "|  71662474|03/08/2010|2023-12-08 19:41:00| BROOKLYN|        18-24|      M| 1002882.875|192219.703125|40.694264056000065|-73.93280863699994| SHOOTING|\n",
      "|  83002139|02/05/2012|2023-12-08 05:45:00|   QUEENS|        25-44|      M| 1054366.375| 196628.40625|40.706106731000034|-73.74710653899996| SHOOTING|\n",
      "|  86437261|08/26/2012|2023-12-08 01:10:00|   QUEENS|        25-44|      M| 1053936.625|157130.421875| 40.59769719800005|-73.74906464199995| SHOOTING|\n",
      "|  74998200|10/10/2010|2023-12-08 03:21:00|MANHATTAN|        25-44|      M|  999061.375|229911.671875| 40.79772626500005|-73.94650650799997| SHOOTING|\n",
      "|  74363835|08/29/2010|2023-12-08 01:27:00| BROOKLYN|        25-44|      M|   1013136.0|     179968.0| 40.66060839100004|-73.89588694299994| SHOOTING|\n",
      "| 234756217|10/09/2021|2023-12-08 20:17:00| BROOKLYN|        25-44|      M|    996148.0|     181562.0| 40.66502269100005|-73.95711436799998| SHOOTING|\n",
      "|  78961869|05/25/2011|2023-12-08 21:58:00|    BRONX|        18-24|      M|1010353.4375| 261534.28125|40.884494922000044|-73.90559937499995| SHOOTING|\n",
      "|  53891791|11/09/2008|2023-12-08 20:13:00| BROOKLYN|        25-44|      M| 988398.4375|   187950.625|40.682565055000055|-73.98504421199993| SHOOTING|\n",
      "|  32914993|07/05/2007|2023-12-08 01:27:00|    BRONX|        18-24|      M|   1026013.0|   261421.125| 40.88412413300006|-73.84896796399994| SHOOTING|\n",
      "|  73879554|07/27/2010|2023-12-08 02:22:00|MANHATTAN|        25-44|      M| 999967.4375|  240325.5625| 40.82630793100003|-73.94320965699995| SHOOTING|\n",
      "|  87694905|11/14/2012|2023-12-08 21:07:00| BROOKLYN|        25-44|      M|  1005555.75|  186370.9375|40.678204504000064|-73.92318836699997| SHOOTING|\n",
      "| 230311079|07/01/2021|2023-12-08 02:44:00| BROOKLYN|        25-44|      M|   1009826.0|     183194.0| 40.66947322100003|-73.90780500799997| SHOOTING|\n",
      "| 225297069|03/07/2021|2023-12-08 21:17:00| BROOKLYN|        25-44|      M|    995792.0|     178767.0| 40.65735150300002|-73.95840237599998| SHOOTING|\n",
      "+----------+----------+-------------------+---------+-------------+-------+------------+-------------+------------------+------------------+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Importing the 'lit' function from the 'pyspark.sql.functions' module.\n",
    "# 'lit' is used to create a literal column expression with a constant value, which can be added to Spark DataFrame.\n",
    "from pyspark.sql.functions import lit\n",
    "\n",
    "# Introduce a static column 'OFNS_DESC' in df2 with a constant value 'SHOOTING'.\n",
    "# This column signifies the type of crime in df1 and is not present in df2. \n",
    "# By adding this column with the value 'SHOOTING', we facilitate the identification of shooting crimes in the merged dataset.\n",
    "df2 = df2.withColumn('OFNS_DESC', lit('SHOOTING'))\n",
    "\n",
    "# Show the resulting DataFrame\n",
    "df2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe2695cd-9d25-4b82-9f1b-10a2be06203c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- CMPLNT_NUM: string (nullable = true)\n",
      " |-- CMPLNT_FR_TM: timestamp (nullable = true)\n",
      " |-- RPT_DT: string (nullable = true)\n",
      " |-- OFNS_DESC: string (nullable = true)\n",
      " |-- BORO_NM: string (nullable = true)\n",
      " |-- X_COORD_CD: integer (nullable = true)\n",
      " |-- Y_COORD_CD: integer (nullable = true)\n",
      " |-- Latitude: double (nullable = true)\n",
      " |-- Longitude: double (nullable = true)\n",
      " |-- VIC_AGE_GROUP: string (nullable = true)\n",
      " |-- VIC_SEX: string (nullable = true)\n",
      "\n",
      "+----------+-------------------+----------+--------------------+-------------+----------+----------+----------------+-----------------+-------------+-------+\n",
      "|CMPLNT_NUM|       CMPLNT_FR_TM|    RPT_DT|           OFNS_DESC|      BORO_NM|X_COORD_CD|Y_COORD_CD|        Latitude|        Longitude|VIC_AGE_GROUP|VIC_SEX|\n",
      "+----------+-------------------+----------+--------------------+-------------+----------+----------+----------------+-----------------+-------------+-------+\n",
      "|  10361314|1970-01-01 16:35:00|02/11/2006|ASSAULT 3 & RELAT...|     BROOKLYN|    999496|    190300|  40.68900139556|-73.9450265328727|        45-64|      F|\n",
      "|  10363948|1970-01-01 17:15:00|02/11/2006|       HARRASSMENT 2|     BROOKLYN|   1007052|    193757|40.6984738177025| -73.917768981221|        18-24|      F|\n",
      "|  16719307|1970-01-01 00:01:00|06/08/2006|                RAPE|        BRONX|   1017478|    256069|40.8694704770483|-73.8798608037303|          <18|      F|\n",
      "|  16939015|1970-01-01 19:00:00|06/29/2006|                RAPE|        BRONX|   1006490|    244533|40.8378421209284|-73.9196277517392|          <18|      F|\n",
      "|  34631321|1970-01-01 05:00:00|09/19/2007|                RAPE|        BRONX|   1005028|    234516|40.8103518634571| -73.924942325642|          <18|      F|\n",
      "|  45954344|1970-01-01 17:45:00|04/24/2008|       GRAND LARCENY|        BRONX|   1017478|    256069|40.8694704770483|-73.8798608037303|        18-24|      F|\n",
      "|  61229622|1970-01-01 05:00:00|04/27/2009|                RAPE|        BRONX|   1011751|    246839|40.8441566000203|-73.9006054489734|        25-44|      F|\n",
      "|  76569662|1970-01-01 06:30:00|12/21/2010|          SEX CRIMES|        BRONX|   1013037|    236657|40.8162058439227|-73.8960011932583|        25-44|      F|\n",
      "|  71409557|1970-01-01 19:30:00|02/18/2010|ASSAULT 3 & RELAT...|    MANHATTAN|    999433|    236538|  40.81591307653|-73.9451493066481|          65+|      F|\n",
      "|  71601562|1970-01-01 02:40:00|03/02/2010|       HARRASSMENT 2|    MANHATTAN|    985764|    213806|40.7535327012632|-73.9945368920152|        18-24|      F|\n",
      "|  71315200|1970-01-01 21:15:00|02/14/2010|       HARRASSMENT 2|        BRONX|   1026480|    262584|40.8873136344706|-73.8472717577564|        45-64|      M|\n",
      "|  75549435|1970-01-01 16:00:00|11/12/2010|             ROBBERY|        BRONX|   1026480|    262584|40.8873136344706|-73.8472717577564|          <18|      M|\n",
      "|  76841740|1970-01-01 09:00:00|01/14/2011|                RAPE|     BROOKLYN|    997843|    175671|40.6488507469884| -73.951016510623|          <18|      F|\n",
      "|  77796839|1970-01-01 17:00:00|03/15/2011|ASSAULT 3 & RELAT...|        BRONX|   1017478|    256069|40.8694704770483|-73.8798608037303|        25-44|      M|\n",
      "|  78700361|1970-01-01 22:00:00|05/10/2011|                RAPE|        BRONX|   1026480|    262584|40.8873136344706|-73.8472717577564|        25-44|      F|\n",
      "|  79646988|1970-01-01 17:08:00|07/04/2011|               ARSON|STATEN ISLAND|    954827|    148519|40.5742856881925|-74.1059144051212|        25-44|      F|\n",
      "|  80459404|1970-01-01 09:30:00|08/23/2011|                RAPE|        BRONX|   1026480|    262584|40.8873136344706|-73.8472717577564|          <18|      F|\n",
      "|  84212362|1970-01-01 15:50:00|04/13/2012|            BURGLARY|        BRONX|   1026480|    262584|40.8873136344706|-73.8472717577564|        25-44|      F|\n",
      "|  85981430|1970-01-01 15:00:00|07/28/2012|ASSAULT 3 & RELAT...|        BRONX|   1013037|    236657|40.8162058439227|-73.8960011932583|        18-24|      F|\n",
      "|  89343120|1970-01-01 02:00:00|02/25/2013|                RAPE|        BRONX|   1011755|    250279|40.8535983673823|-73.9005768807295|          <18|      F|\n",
      "+----------+-------------------+----------+--------------------+-------------+----------+----------+----------------+-----------------+-------------+-------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Cleaning DF1\n",
    "\n",
    "from pyspark.sql.functions import to_timestamp\n",
    "\n",
    "columns_to_drop_na = ['Y_COORD_CD', 'X_COORD_CD', 'Latitude', 'Longitude', 'CMPLNT_FR_TM','Lat_Lon', 'CMPLNT_FR_DT', 'BORO_NM', 'OFNS_DESC']\n",
    "for column in columns_to_drop_na:\n",
    "    df1 = df1.filter(col(column).isNotNull())\n",
    "\n",
    "# Dropping columns that are not significant for future data exploration\n",
    "columns_to_drop = ['PARKS_NM','STATION_NAME','TRANSIT_DISTRICT','HADEVELOPT','HOUSING_PSA','VIC_RACE', 'LOC_OF_OCCUR_DESC', 'Lat_Lon',\n",
    "                  'PREM_TYP_DESC','SUSP_AGE_GROUP','SUSP_SEX','SUSP_RACE','JURISDICTION_CODE', 'ADDR_PCT_CD',\n",
    "                   'PD_CD','PD_DESC','PATROL_BORO','CMPLNT_TO_DT','CMPLNT_TO_TM','KY_CD','LAW_CAT_CD','CMPLNT_FR_DT','JURIS_DESC','CRM_ATPT_CPTD_CD']\n",
    "\n",
    "df1 = df1.drop(*columns_to_drop)\n",
    "\n",
    "# Replacing NaN values in specific columns with 'UNKNOWN'\n",
    "columns_to_fill = ['VIC_AGE_GROUP', 'VIC_SEX']\n",
    "for column in columns_to_fill:\n",
    "    df1 = df1.withColumn(column, when(col(column).isNull(), 'UNKNOWN').otherwise(col(column)))\n",
    "\n",
    "# Remove rows in the dataset where values are either null or the string \"(null)\".\n",
    "specified_value = '(null)'\n",
    "# Filter the DataFrame to exclude rows with the specified value\n",
    "df1 = df1.filter(df1['BORO_NM'] != specified_value)\n",
    "df1 = df1.filter(df1['CMPLNT_FR_TM'] != specified_value)\n",
    "df1 = df1.filter(df1['VIC_AGE_GROUP'] != specified_value)\n",
    "df1 = df1.filter(df1['OFNS_DESC'] != specified_value)\n",
    "\n",
    "\n",
    "# Convert Complaint_Time Column from String to timestamp format\n",
    "df1 = df1.withColumn('CMPLNT_FR_TM', to_timestamp('CMPLNT_FR_TM', 'HH:mm:ss'))\n",
    "\n",
    "# Examining the changes\n",
    "df1.printSchema()\n",
    "df1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55a8ca6f-dafc-478e-922d-9f92a4f2029e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------------+----------+---------+---------+------------+-------------+------------------+------------------+-------------+-------+\n",
      "|CMPLNT_NUM|       CMPLNT_FR_TM|    RPT_DT|OFNS_DESC|  BORO_NM|  X_COORD_CD|   Y_COORD_CD|          Latitude|         Longitude|VIC_AGE_GROUP|VIC_SEX|\n",
      "+----------+-------------------+----------+---------+---------+------------+-------------+------------------+------------------+-------------+-------+\n",
      "| 228798151|2023-12-08 21:30:00|05/27/2021| SHOOTING|   QUEENS|   1058925.0|     180924.0|40.662964620000025|-73.73083868899994|        18-24|      M|\n",
      "| 137471050|2023-12-08 17:40:00|06/27/2014| SHOOTING|    BRONX|   1005028.0|     234516.0| 40.81035186300005|-73.92494232599995|        18-24|      M|\n",
      "| 147998800|2023-12-08 03:56:00|11/21/2015| SHOOTING|   QUEENS|1007667.9375| 209836.53125| 40.74260663300004|-73.91549174199997|        25-44|      M|\n",
      "| 146837977|2023-12-08 18:30:00|10/09/2015| SHOOTING|    BRONX| 1006537.375|244511.140625| 40.83778200300003|-73.91945661499994|          <18|      M|\n",
      "|  58921844|2023-12-08 22:58:00|02/19/2009| SHOOTING|    BRONX|1024921.8125| 262189.40625| 40.88623791800006|-73.85290950899997|        45-64|      M|\n",
      "| 219559682|2023-12-08 21:36:00|10/21/2020| SHOOTING| BROOKLYN|1004234.3125|186461.703125|40.678456718000064|-73.92795224099996|        25-44|      M|\n",
      "|  85295722|2023-12-08 22:47:00|06/17/2012| SHOOTING|   QUEENS|    998860.0|     214885.0| 40.75648234300007|-73.94726649399996|        25-44|      M|\n",
      "|  71662474|2023-12-08 19:41:00|03/08/2010| SHOOTING| BROOKLYN| 1002882.875|192219.703125|40.694264056000065|-73.93280863699994|        18-24|      M|\n",
      "|  83002139|2023-12-08 05:45:00|02/05/2012| SHOOTING|   QUEENS| 1054366.375| 196628.40625|40.706106731000034|-73.74710653899996|        25-44|      M|\n",
      "|  86437261|2023-12-08 01:10:00|08/26/2012| SHOOTING|   QUEENS| 1053936.625|157130.421875| 40.59769719800005|-73.74906464199995|        25-44|      M|\n",
      "|  74998200|2023-12-08 03:21:00|10/10/2010| SHOOTING|MANHATTAN|  999061.375|229911.671875| 40.79772626500005|-73.94650650799997|        25-44|      M|\n",
      "|  74363835|2023-12-08 01:27:00|08/29/2010| SHOOTING| BROOKLYN|   1013136.0|     179968.0| 40.66060839100004|-73.89588694299994|        25-44|      M|\n",
      "| 234756217|2023-12-08 20:17:00|10/09/2021| SHOOTING| BROOKLYN|    996148.0|     181562.0| 40.66502269100005|-73.95711436799998|        25-44|      M|\n",
      "|  78961869|2023-12-08 21:58:00|05/25/2011| SHOOTING|    BRONX|1010353.4375| 261534.28125|40.884494922000044|-73.90559937499995|        18-24|      M|\n",
      "|  53891791|2023-12-08 20:13:00|11/09/2008| SHOOTING| BROOKLYN| 988398.4375|   187950.625|40.682565055000055|-73.98504421199993|        25-44|      M|\n",
      "|  32914993|2023-12-08 01:27:00|07/05/2007| SHOOTING|    BRONX|   1026013.0|   261421.125| 40.88412413300006|-73.84896796399994|        18-24|      M|\n",
      "|  73879554|2023-12-08 02:22:00|07/27/2010| SHOOTING|MANHATTAN| 999967.4375|  240325.5625| 40.82630793100003|-73.94320965699995|        25-44|      M|\n",
      "|  87694905|2023-12-08 21:07:00|11/14/2012| SHOOTING| BROOKLYN|  1005555.75|  186370.9375|40.678204504000064|-73.92318836699997|        25-44|      M|\n",
      "| 230311079|2023-12-08 02:44:00|07/01/2021| SHOOTING| BROOKLYN|   1009826.0|     183194.0| 40.66947322100003|-73.90780500799997|        25-44|      M|\n",
      "| 225297069|2023-12-08 21:17:00|03/07/2021| SHOOTING| BROOKLYN|    995792.0|     178767.0| 40.65735150300002|-73.95840237599998|        25-44|      M|\n",
      "+----------+-------------------+----------+---------+---------+------------+-------------+------------------+------------------+-------------+-------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DF2 data cleaning\n",
    "\n",
    "from pyspark.sql.functions import date_format\n",
    "\n",
    "# Remove null values from DF2\n",
    "columns_to_drop_na = ['Y_COORD_CD', 'X_COORD_CD', 'Latitude', 'Longitude', 'CMPLNT_FR_TM','VIC_AGE_GROUP', 'RPT_DT', 'BORO_NM', 'OFNS_DESC', 'VIC_SEX']\n",
    "for column in columns_to_drop_na:\n",
    "    df1 = df1.filter(col(column).isNotNull())\n",
    "\n",
    "# Rearrange the columns in df2 to align with the order in df1. \n",
    "# This step is crucial for a proper merging of these dataframes.\n",
    "columns_df1 = df1.columns\n",
    "df2 = df2.select(columns_df1)\n",
    "df2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3888496-40a7-4ed6-be4b-0815cf1cb29c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Common columns in both DataFrames:\n",
      "{'Y_COORD_CD', 'CMPLNT_FR_TM', 'X_COORD_CD', 'BORO_NM', 'VIC_SEX', 'RPT_DT', 'OFNS_DESC', 'CMPLNT_NUM', 'Longitude', 'Latitude', 'VIC_AGE_GROUP'}\n"
     ]
    }
   ],
   "source": [
    "# Check if the 'OFNS_DESC' column is present in both dataframes, confirming its addition to DF2.\n",
    "# Additionally, ensure that no essential columns were inadvertently dropped during the data cleaning process.\n",
    "\n",
    "# Get column names for each DataFrame\n",
    "columns_df1 = set(df1.columns)\n",
    "columns_df2 = set(df2.columns)\n",
    "\n",
    "# Find common columns between both DataFrames\n",
    "common_columns = columns_df1.intersection(columns_df2)\n",
    "\n",
    "# Display common columns\n",
    "print(\"Common columns in both DataFrames:\")\n",
    "print(common_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2a839b-4703-4ce0-b8f0-1d1b7f618dfc",
   "metadata": {},
   "source": [
    "## Dataset Merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a914805-febf-4afa-b3ff-12c3ab87dd04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------------+----------+--------------------+-------------+----------+----------+----------------+-----------------+-------------+-------+\n",
      "|CMPLNT_NUM|       CMPLNT_FR_TM|    RPT_DT|           OFNS_DESC|      BORO_NM|X_COORD_CD|Y_COORD_CD|        Latitude|        Longitude|VIC_AGE_GROUP|VIC_SEX|\n",
      "+----------+-------------------+----------+--------------------+-------------+----------+----------+----------------+-----------------+-------------+-------+\n",
      "|  10361314|1970-01-01 16:35:00|02/11/2006|ASSAULT 3 & RELAT...|     BROOKLYN|  999496.0|  190300.0|  40.68900139556|-73.9450265328727|        45-64|      F|\n",
      "|  10363948|1970-01-01 17:15:00|02/11/2006|       HARRASSMENT 2|     BROOKLYN| 1007052.0|  193757.0|40.6984738177025| -73.917768981221|        18-24|      F|\n",
      "|  16719307|1970-01-01 00:01:00|06/08/2006|                RAPE|        BRONX| 1017478.0|  256069.0|40.8694704770483|-73.8798608037303|          <18|      F|\n",
      "|  16939015|1970-01-01 19:00:00|06/29/2006|                RAPE|        BRONX| 1006490.0|  244533.0|40.8378421209284|-73.9196277517392|          <18|      F|\n",
      "|  34631321|1970-01-01 05:00:00|09/19/2007|                RAPE|        BRONX| 1005028.0|  234516.0|40.8103518634571| -73.924942325642|          <18|      F|\n",
      "|  45954344|1970-01-01 17:45:00|04/24/2008|       GRAND LARCENY|        BRONX| 1017478.0|  256069.0|40.8694704770483|-73.8798608037303|        18-24|      F|\n",
      "|  61229622|1970-01-01 05:00:00|04/27/2009|                RAPE|        BRONX| 1011751.0|  246839.0|40.8441566000203|-73.9006054489734|        25-44|      F|\n",
      "|  76569662|1970-01-01 06:30:00|12/21/2010|          SEX CRIMES|        BRONX| 1013037.0|  236657.0|40.8162058439227|-73.8960011932583|        25-44|      F|\n",
      "|  71409557|1970-01-01 19:30:00|02/18/2010|ASSAULT 3 & RELAT...|    MANHATTAN|  999433.0|  236538.0|  40.81591307653|-73.9451493066481|          65+|      F|\n",
      "|  71601562|1970-01-01 02:40:00|03/02/2010|       HARRASSMENT 2|    MANHATTAN|  985764.0|  213806.0|40.7535327012632|-73.9945368920152|        18-24|      F|\n",
      "|  71315200|1970-01-01 21:15:00|02/14/2010|       HARRASSMENT 2|        BRONX| 1026480.0|  262584.0|40.8873136344706|-73.8472717577564|        45-64|      M|\n",
      "|  75549435|1970-01-01 16:00:00|11/12/2010|             ROBBERY|        BRONX| 1026480.0|  262584.0|40.8873136344706|-73.8472717577564|          <18|      M|\n",
      "|  76841740|1970-01-01 09:00:00|01/14/2011|                RAPE|     BROOKLYN|  997843.0|  175671.0|40.6488507469884| -73.951016510623|          <18|      F|\n",
      "|  77796839|1970-01-01 17:00:00|03/15/2011|ASSAULT 3 & RELAT...|        BRONX| 1017478.0|  256069.0|40.8694704770483|-73.8798608037303|        25-44|      M|\n",
      "|  78700361|1970-01-01 22:00:00|05/10/2011|                RAPE|        BRONX| 1026480.0|  262584.0|40.8873136344706|-73.8472717577564|        25-44|      F|\n",
      "|  79646988|1970-01-01 17:08:00|07/04/2011|               ARSON|STATEN ISLAND|  954827.0|  148519.0|40.5742856881925|-74.1059144051212|        25-44|      F|\n",
      "|  80459404|1970-01-01 09:30:00|08/23/2011|                RAPE|        BRONX| 1026480.0|  262584.0|40.8873136344706|-73.8472717577564|          <18|      F|\n",
      "|  84212362|1970-01-01 15:50:00|04/13/2012|            BURGLARY|        BRONX| 1026480.0|  262584.0|40.8873136344706|-73.8472717577564|        25-44|      F|\n",
      "|  85981430|1970-01-01 15:00:00|07/28/2012|ASSAULT 3 & RELAT...|        BRONX| 1013037.0|  236657.0|40.8162058439227|-73.8960011932583|        18-24|      F|\n",
      "|  89343120|1970-01-01 02:00:00|02/25/2013|                RAPE|        BRONX| 1011755.0|  250279.0|40.8535983673823|-73.9005768807295|          <18|      F|\n",
      "+----------+-------------------+----------+--------------------+-------------+----------+----------+----------------+-----------------+-------------+-------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Merging both dataframes\n",
    "df = df1.union(df2);\n",
    "\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc73ffb-eb7b-44bc-ab66-3c33ce7b0245",
   "metadata": {},
   "source": [
    "## Cleaning combined dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a51e276-a17a-48e7-a97a-b577c034ce07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows: 3756\n"
     ]
    }
   ],
   "source": [
    "# Check number of duplicate rows\n",
    "\n",
    "# Total number of rows in the DataFrame\n",
    "total_rows = df.count()\n",
    "\n",
    "# Count the number of unique rows\n",
    "unique_rows = df.distinct().count()\n",
    "\n",
    "duplicate_rows = total_rows - unique_rows\n",
    "\n",
    "print(f\"Number of duplicate rows: {duplicate_rows}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "148d53ab-f5d2-4017-a3ad-d13b14713fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove duplicate rows\n",
    "df = df.distinct()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef0c6d2d-10a5-4c18-814a-a6dccf150ce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows: 0\n",
      "Total number of rows: 6740457\n"
     ]
    }
   ],
   "source": [
    "# Confirm removal of duplicate rows\n",
    "# Show count of total number of rows\n",
    "\n",
    "# Total number of rows in the DataFrame\n",
    "total_rows = df.count()\n",
    "\n",
    "# Count the number of unique rows\n",
    "unique_rows = df.distinct().count()\n",
    "\n",
    "duplicate_rows = total_rows - unique_rows\n",
    "\n",
    "print(f\"Number of duplicate rows: {duplicate_rows}\")\n",
    "print(f\"Total number of rows: {total_rows}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "635190e1-921f-49d6-ba24-4ab5c98a8d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename crime for better viewing during visualiation\n",
    "\n",
    "df = df.withColumn(\"OFNS_DESC\", when(df[\"OFNS_DESC\"] == \"HARRASSMENT 2\", \"HARRASSMENT\").otherwise(df[\"OFNS_DESC\"]))\n",
    "df = df.withColumn(\"OFNS_DESC\", when(df[\"OFNS_DESC\"] == \"ASSAULT 3 & RELATED OFFENSES\", \"ASSAULT\").otherwise(df[\"OFNS_DESC\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f8f9f1fc-0ad3-4c4b-961c-9f919ca17f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set 'UNKNOWN' for victim_sex if it's not 'F' or 'M' due to incomplete data.\n",
    "df = df.withColumn('VIC_SEX', when((col('VIC_SEX') != 'M') & (col('VIC_SEX') != 'F'), 'UNKNOWN').otherwise(col('VIC_SEX')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5ecc38-54be-4cf9-a2fd-7e8c2545b61f",
   "metadata": {},
   "source": [
    "## Data Analysis & Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "40342188-1693-437b-aba1-ddb16c23a9da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-------+\n",
      "|           OFNS_DESC|  count|\n",
      "+--------------------+-------+\n",
      "|       PETIT LARCENY|1129048|\n",
      "|         HARRASSMENT|1065198|\n",
      "|             ASSAULT| 847359|\n",
      "|       GRAND LARCENY| 661511|\n",
      "|CRIMINAL MISCHIEF...| 654121|\n",
      "|OFF. AGNST PUB OR...| 396573|\n",
      "|      FELONY ASSAULT| 314684|\n",
      "|             ROBBERY| 275542|\n",
      "|            BURGLARY| 227149|\n",
      "|MISCELLANEOUS PEN...| 171694|\n",
      "+--------------------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show the top 10 types of crime\n",
    "\n",
    "top_10_crime_types = df.groupBy(\"OFNS_DESC\").count().orderBy(col(\"count\").desc()).limit(10)\n",
    "\n",
    "# Show the top 10 crime types\n",
    "top_10_crime_types.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "13f1eb6d-af76-4311-a77b-fb1d5ccbc256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the top 10 types of crimes\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from pyspark.sql import functions as F\n",
    "from pyspark.sql.window import Window\n",
    "\n",
    "# Get the top 10 types of crimes\n",
    "top_10_crime_types = df.groupBy(\"OFNS_DESC\").count().orderBy(\"count\", ascending=False).limit(10)\n",
    "\n",
    "# Collect the data to the local machine (driver) for visualization\n",
    "crime_types_list = [row.OFNS_DESC for row in top_10_crime_types.collect()]\n",
    "crime_counts_list = [row['count'] for row in top_10_crime_types.collect()]\n",
    "\n",
    "# Plotting using Matplotlib\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.barh(crime_types_list, crime_counts_list, color='skyblue')\n",
    "plt.xlabel('Number of Occurrences(million)')\n",
    "plt.title('Top 10 Types of Crimes')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the highest count at the top\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "104ab6d5-af5e-4437-af49-0c177e2ec5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Victim Sex\n",
    "\n",
    "# Group by 'VIC_SEX' and count occurrences of each sex\n",
    "sex_distribution = df.groupBy('VIC_SEX').count()\n",
    "\n",
    "# Convert PySpark DataFrame to Pandas DataFrame for plotting\n",
    "sex_distribution_pandas = sex_distribution.toPandas()\n",
    "\n",
    "# Plotting the pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(sex_distribution_pandas['count'], labels=sex_distribution_pandas['VIC_SEX'], autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Distribution of Victim Sex')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "382440d4-3d32-4878-88aa-5f0c88b6b61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+\n",
      "|VIC_SEX|        percentage|\n",
      "+-------+------------------+\n",
      "|      F|46.889179769264906|\n",
      "|      M|  39.8956480250523|\n",
      "|UNKNOWN| 13.21517220568279|\n",
      "+-------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Count of Crime Victims By Gender\n",
    "\n",
    "# Group by 'VIC_SEX' and count occurrences of each sex\n",
    "sex_distribution = df.groupBy('VIC_SEX').count()\n",
    "\n",
    "# Order by count in descending order and select the top 3\n",
    "top_3_sex = sex_distribution.orderBy(F.desc('count')).limit(3)\n",
    "\n",
    "# Convert PySpark DataFrame to Pandas DataFrame for plotting\n",
    "top_3_sex_pandas = top_3_sex.toPandas()\n",
    "\n",
    "# Plotting the bar graph with explicit tick labels\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(top_3_sex_pandas.index, top_3_sex_pandas['count'], color='#F1C40F')\n",
    "plt.xlabel('Victim Gender')\n",
    "plt.ylabel('Number of Crime Victims (million)')\n",
    "plt.title('Crime Victims by Gender')\n",
    "plt.xticks(top_3_sex_pandas.index, top_3_sex_pandas['VIC_SEX'], rotation=0)\n",
    "plt.show()\n",
    "\n",
    "# Calculate victims by gender percentage\n",
    "total_records = df.count()\n",
    "vic_sex_per = (sex_distribution\n",
    "               .withColumn('percentage', F.col('count') / total_records * 100)\n",
    "               .select('VIC_SEX', 'percentage'))\n",
    "vic_sex_per.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e203fb01-667f-4c88-a31a-d9c717a09692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crime Events By Year\n",
    "\n",
    "# Extract year from 'RPT_DT' column as a string\n",
    "df = df.withColumn('year', F.substring(df['RPT_DT'], -4, 4))\n",
    "\n",
    "# Group by 'year' and count occurrences of each year\n",
    "crime_events_by_year = df.groupBy('year').count().orderBy('year')\n",
    "\n",
    "# Convert PySpark DataFrame to Pandas DataFrame for plotting\n",
    "crime_events_pandas = crime_events_by_year.toPandas()\n",
    "\n",
    "# Create a fancy line plot using Matplotlib\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(crime_events_pandas['year'], crime_events_pandas['count'], marker='o', color='skyblue', linewidth=2.5)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Crime Events')\n",
    "plt.title('Total Crime Events by Year')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Customize grid and background\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c88b553-2288-4fe1-af56-883ba968aadb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+------------------+\n",
      "|VIC_AGE_GROUP|        percentage|\n",
      "+-------------+------------------+\n",
      "|        25-44| 41.21876306013079|\n",
      "|        45-64|21.315913742940577|\n",
      "|        18-24|12.802351531951023|\n",
      "|          <18| 5.891366119537592|\n",
      "|          65+| 4.445054689911975|\n",
      "+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Crime Victims By Age Group\n",
    "\n",
    "# Group by 'VIC_AGE_GROUP' and count occurrences of each age group\n",
    "age_group_distribution = df.groupBy('VIC_AGE_GROUP').count()\n",
    "\n",
    "# Select specific age groups based on iloc[[0, 1, 2, 4, 5]]\n",
    "selected_age_groups = age_group_distribution.filter(F.col('VIC_AGE_GROUP').isin(['<18', '18-24', '25-44', '45-64', '65+']))\n",
    "\n",
    "# Order by count in descending order\n",
    "selected_age_groups = selected_age_groups.orderBy(F.desc('count'))\n",
    "\n",
    "# Convert PySpark DataFrame to Pandas DataFrame for plotting\n",
    "selected_age_groups_pandas = selected_age_groups.toPandas()\n",
    "\n",
    "# Plotting the bar graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(selected_age_groups_pandas['VIC_AGE_GROUP'], selected_age_groups_pandas['count'], color='#2ECC71')\n",
    "plt.xlabel('Victim Age Group')\n",
    "plt.ylabel('Number of Crime Victims (million)')\n",
    "plt.title('Crime Victims by Age Group')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()\n",
    "\n",
    "# Calculate victims by age group percentage\n",
    "total_records = df.count()\n",
    "vic_age_per = (selected_age_groups\n",
    "               .withColumn('percentage', F.col('count') / total_records * 100)\n",
    "               .select('VIC_AGE_GROUP', 'percentage'))\n",
    "vic_age_per.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3d62a3cb-4794-412b-8b6f-348950c0df19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crime Events by Month\n",
    "\n",
    "# Extract year from 'RPT_DT' column as a string\n",
    "df_month = df.withColumn('Month', F.substring(df['RPT_DT'], 1, 2))\n",
    "\n",
    "# Group by 'year' and count occurrences of each year\n",
    "crime_events_by_year = df_month.groupBy('Month').count().orderBy('Month')\n",
    "\n",
    "# Convert PySpark DataFrame to Pandas DataFrame for plotting\n",
    "crime_events_pandas = crime_events_by_year.toPandas()\n",
    "\n",
    "# Create a fancy line plot using Matplotlib\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(crime_events_pandas['Month'], crime_events_pandas['count'], marker='o', color='skyblue', linewidth=2.5)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Total Crime Events')\n",
    "plt.title('Total Crime Events by Month')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Customize grid and background\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d72d8105-bcf2-423d-9a2e-39c11b6eb83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crime Reports based on hour of the day\n",
    "\n",
    "# Extract hour from the timestamp column\n",
    "crime_data = df.withColumn('hour', F.hour('CMPLNT_FR_TM'))\n",
    "\n",
    "# Group by 'hour' and count occurrences of each hour\n",
    "crime_reports_by_hour = crime_data.groupBy('hour').count().orderBy('hour')\n",
    "\n",
    "# Convert PySpark DataFrame to Pandas DataFrame for plotting\n",
    "crime_reports_pandas = crime_reports_by_hour.toPandas()\n",
    "\n",
    "# Plotting the bar graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(crime_reports_pandas['hour'], crime_reports_pandas['count'], color='#3498DB')\n",
    "plt.xlabel('Hour of the Day')\n",
    "plt.ylabel('Number of Crime Reports')\n",
    "plt.title('Hourly Crime Reports')\n",
    "plt.xticks(range(24)) \n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c34e57a0-d5b2-4c12-a93a-e74732c7bac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crime events in each borough\n",
    "\n",
    "# Count the occurrences of each value in 'BORO_NM' and sort\n",
    "borough_counts = df.groupBy(\"BORO_NM\").count().orderBy(\"count\")\n",
    "\n",
    "# Convert to Pandas DataFrame for plotting\n",
    "borough_counts_pd = borough_counts.toPandas()\n",
    "\n",
    "# Plot using Matplotlib\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.barh(borough_counts_pd['BORO_NM'], borough_counts_pd['count'], color='#1ABC9C')\n",
    "plt.title('Total of Crime Events by Borough')\n",
    "plt.xlabel('Count in Million')\n",
    "plt.ylabel('Borough')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}