BigData_csv_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part B: Analysing CSV Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part of the assignment we have been give a csv file which contains crime data of South Australia. The dataset contains incidents of crime and the suburb based crime stats for crimes against people and crime against property than occured in South Australis since 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from pyspark.sql.functions import col, udf\n",
    "from pyspark.sql.types import DateType\n",
    "import os\n",
    "from pyspark.sql.functions import year, month, dayofweek\n",
    "from pyspark.sql.functions import mean, min, max, stddev, count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step1: Initialising Spark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first task in this part of the assignment is to initialise Spark and configure it. We start a spark session and connect it to the MongoDB database. The MongoDB database is running locally on our machine hence we specify the IP adress of local host followed by the database name and the collection name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.mongodb.spark:mongo-spark-connector_2.11:2.4.0 pyspark-shell'\n",
    "# create entry points to spark\n",
    "from pyspark import SparkContext # Spark\n",
    "from pyspark.sql import SparkSession # Spark SQL\n",
    "\n",
    "# We add this line to avoid an error : \"Cannot run multiple SparkContexts at once\". \n",
    "# If there is an existing spark context, we will reuse it instead of creating a new context.\n",
    "sc = SparkContext.getOrCreate()\n",
    "\n",
    "# local[*]: run Spark locally with as many working processors as logical cores on your machine.\n",
    "# In the field of `master`, we use a local server with as many working processors (or threads) as possible (i.e. `local[*]`). \n",
    "# If we want Spark to run locally with 'k' worker threads, we can specify as `local[k]`.\n",
    "# The `appName` field is a name to be shown on the Sparking cluster UI. \n",
    "\n",
    "# If there is no existing spark context, we now create a new context\n",
    "if (sc is None):\n",
    "    sc = SparkContext(master=\"local[*]\")\n",
    "my_spark = SparkSession(sparkContext=sc)\\\n",
    "    .builder \\\n",
    "    .appName(\"Assignment1-PartB\") \\\n",
    "    .config(\"spark.mongodb.input.uri\", \"mongodb://127.0.0.1/Assignment1PartB.crime_stats\") \\\n",
    "    .config(\"spark.mongodb.output.uri\", \"mongodb://127.0.0.1/Assignment1PartB.crime_stats\") \\\n",
    "    .getOrCreate()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step2: Creating a Dataframe "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step we create a dataframe of the csv file data which is given to us. We can read the data from the csv file as a dataframe using pyspark. As seen we specify header as True which means that the first row of the data would be taken as a heading for all the columns in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime_stats = my_spark.read.csv(\"Crime_Statistics_SA_2010_present.csv\", header=True, inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-----------------+-------------------+---------------------------+---------------------------+---------------------------+-------------+\n",
      "|Reported Date|Suburb - Incident|Postcode - Incident|Offence Level 1 Description|Offence Level 2 Description|Offence Level 3 Description|Offence Count|\n",
      "+-------------+-----------------+-------------------+---------------------------+---------------------------+---------------------------+-------------+\n",
      "|    1/07/2010|         ADELAIDE|               5000|       OFFENCES AGAINST ...|       FRAUD DECEPTION A...|       Obtain benefit by...|            2|\n",
      "|    1/07/2010|         ADELAIDE|               5000|       OFFENCES AGAINST ...|       PROPERTY DAMAGE A...|       Other property da...|            2|\n",
      "|    1/07/2010|         ADELAIDE|               5000|       OFFENCES AGAINST ...|       SERIOUS CRIMINAL ...|        SCT - Non Residence|            1|\n",
      "|    1/07/2010|         ADELAIDE|               5000|       OFFENCES AGAINST ...|       THEFT AND RELATED...|                Other theft|            6|\n",
      "|    1/07/2010|         ADELAIDE|               5000|       OFFENCES AGAINST ...|       THEFT AND RELATED...|            Theft from shop|            3|\n",
      "+-------------+-----------------+-------------------+---------------------------+---------------------------+---------------------------+-------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "crime_stats.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have created the pyspark dataframe our next task would be to find the number of records which is there in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of rows in the dataframe are:  727408\n"
     ]
    }
   ],
   "source": [
    "print(\"the number of rows in the dataframe are: \",crime_stats.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step3: Writing data to mongoDB database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step as specified in the assignment specification we would be using MongoDB as the data source. Now we have to write all the records of the pyspark dataframe which we have created to the MongoDB database. This can be done using standard pyspark methods. As specified in the assignment we use ***overwrite*** mode when we are inserting the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime_stats.write.format(\"com.mongodb.spark.sql.DefaultSource\").mode(\"overwrite\").save()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step4: Reading mongo DB data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now in this step we read the data from the MongoDB database. We read the collection from MongoDB database and create a dataframe of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = my_spark.read.format(\"com.mongodb.spark.sql.DefaultSource\").option(\"uri\",\n",
    "\"mongodb://127.0.0.1/Assignment1PartB.crime_stats\").load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the code we can see that we read the collection named crime_stats from the Assignment1PartB database and store it in a variable named df. Thus df is the required dataframe which we will be using from this step onwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------------------+---------------------------+---------------------------+-------------------+-------------+-----------------+--------------------+\n",
      "|Offence Count|Offence Level 1 Description|Offence Level 2 Description|Offence Level 3 Description|Postcode - Incident|Reported Date|Suburb - Incident|                 _id|\n",
      "+-------------+---------------------------+---------------------------+---------------------------+-------------------+-------------+-----------------+--------------------+\n",
      "|            1|       OFFENCES AGAINST ...|       THEFT AND RELATED...|            Theft from shop|               5169|   15/05/2012|          SEAFORD|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       ROBBERY AND RELAT...|         Aggravated robbery|               5023|   15/05/2012|           SEATON|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       THEFT AND RELATED...|            Theft from shop|               5019|   15/05/2012|        SEMAPHORE|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       ACTS INTENDED TO ...|             Assault Police|               5019|   15/05/2012|   SEMAPHORE PARK|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       SEXUAL ASSAULT AN...|       Aggravated sexual...|               5019|   15/05/2012|   SEMAPHORE PARK|[5d725e9350f59766...|\n",
      "+-------------+---------------------------+---------------------------+---------------------------+-------------------+-------------+-----------------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step5: Calculate the statistics of numeric and string columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to calculate the statistics of two columns namely \"Offence Count\" and \"Reported Date\". We can find the statistics of any pyspark dataframe column using the describe method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics of Offence Count column:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now calculate the mean, minimum, maximum, standard deviation and count of the Offence Count column. this can be done using two methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Offence Count', 'int'),\n",
       " ('Offence Level 1 Description', 'string'),\n",
       " ('Offence Level 2 Description', 'string'),\n",
       " ('Offence Level 3 Description', 'string'),\n",
       " ('Postcode - Incident', 'string'),\n",
       " ('Reported Date', 'string'),\n",
       " ('Suburb - Incident', 'string'),\n",
       " ('_id', 'struct<oid:string>')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the statistics values for the Offence Count column we have to have it in float or int format. Thus the first step we do is to convert the Offence Count column to float format. We also drop all the rows with null values in the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***In the first method we find the statistics values by using the standard pyspark sql functions.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ofc = df.dropna().withColumn(\"Offence Count\",df[\"Offence Count\"].cast('float'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = df_ofc.select([mean(\"Offence Count\"), \n",
    "                        min(\"Offence Count\"), \n",
    "                        max(\"Offence Count\"), \n",
    "                        stddev(\"Offence Count\"), \n",
    "                        count(\"Offence Count\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statistics values for Offence Count is as follows:\n",
      "+------------------+------------------+------------------+--------------------------+--------------------+\n",
      "|avg(Offence Count)|min(Offence Count)|max(Offence Count)|stddev_samp(Offence Count)|count(Offence Count)|\n",
      "+------------------+------------------+------------------+--------------------------+--------------------+\n",
      "| 1.171599114621809|               1.0|              28.0|         0.578848526499556|              726921|\n",
      "+------------------+------------------+------------------+--------------------------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"The statistics values for Offence Count is as follows:\")\n",
    "result.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***The statistics values can also be found using the describe method which is a standard pyspark library method. Thus the statistics values of Offence Count using the describe method is as follows:***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics values of Offence Count column using describe method: \n",
      "+-------+-----------------+\n",
      "|summary|    Offence Count|\n",
      "+-------+-----------------+\n",
      "|  count|           726921|\n",
      "|   mean|1.171599114621809|\n",
      "| stddev|0.578848526499553|\n",
      "|    min|              1.0|\n",
      "|    max|             28.0|\n",
      "+-------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Statistics values of Offence Count column using describe method: \")\n",
    "df_ofc.describe('Offence Count').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics of Reported Date column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have to find the statistics of the Reported Date column. We can do this using the describe method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also we drop the values which are null values date as they result in faulty values of statistics values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stats value of Reported Date found without changing the Dataframe is: \n",
      "+-------+-------------+\n",
      "|summary|Reported Date|\n",
      "+-------+-------------+\n",
      "|  count|       726921|\n",
      "|   mean|         null|\n",
      "| stddev|         null|\n",
      "|    min|    1/01/2011|\n",
      "|    max|    9/12/2018|\n",
      "+-------+-------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Stats value of Reported Date found without changing the Dataframe is: \")\n",
    "df.dropna().describe(\"Reported Date\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***The above min and max values of Date is wrong***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***The Reported date in the df column is of type string. Also it is in format \"dd/mm/yyyy\" thus it is highly likely that there could be an error in finding the minimum and maximum date.*** Consider a case where we have a date value as \"1/12/2019\" and another date as \"23/2/1995\" here the second date would be considered as greater than the first date however this is not true. Hence one way which we can do to obtain the correct dates is swapping the year and day. ***Thus the date would be in \"yyyy/mm/dd\" format and produce correct results.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"1/12/2019\" < \"23/2/1995\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Thus the above statistics values for Reported Date column is wrong.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***The reformatting of date can be done by using a user defined function for the spark dataframe.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "func =  udf(lambda a: str(a.split(\"/\")[2] + \"/\" + a.split(\"/\")[1] + \"/\" + a.split(\"/\")[0]))\n",
    "\n",
    "df_reformat_date = df.dropna().withColumn('Reported Date', func(col('Reported Date')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the above code we have reformatted the date and we store this in a new variable df_reformat_date. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+\n",
      "|Reported Date|\n",
      "+-------------+\n",
      "|   2012/05/15|\n",
      "|   2012/05/15|\n",
      "|   2012/05/15|\n",
      "|   2012/05/15|\n",
      "|   2012/05/15|\n",
      "+-------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_reformat_date.select(\"Reported Date\").show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the above output we have the Reported Date column in the 'yyyy/mm/dd' format. Now the computation for min, max values would be correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the date in the right format we now can find the statistics values for the Reported Date column. This can be done using the standard describe method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statistics values for Reported date are: \n",
      "+-------+-------------+\n",
      "|summary|Reported Date|\n",
      "+-------+-------------+\n",
      "|  count|       726921|\n",
      "|   mean|         null|\n",
      "| stddev|         null|\n",
      "|    min|    2010/07/1|\n",
      "|    max|    2019/03/9|\n",
      "+-------+-------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"The statistics values for Reported date are: \")\n",
    "df_reformat_date.describe('Reported Date').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the above statistics values we can see that they differ from the previous values for the same column. thus the above obtained values are the correct maximum and minimum date of the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Alternative method for finding statistics values of Reported Date column***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we know the mean and standard deviation of a date cannot be found thus the values are null. We can find the min, max and the count of the Reported Date column by another method. We convert the Reported Date to date format and find the minimum, maximum and the count of the column. Code for this is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'As seen we use dropna to drop all null values from the dataframe as the null values cause error while computing\\n   for some values'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Converting Reported Date column to Datetime type\n",
    "\n",
    "func =  udf (lambda x: datetime.strptime(x, '%d/%m/%Y'), DateType())\n",
    "\n",
    "df_data_changed = df.dropna().withColumn('Reported Date', func(col('Reported Date')))\n",
    "\n",
    "'''As seen we use dropna to drop all null values from the dataframe as the null values cause error while computing\n",
    "   for some values'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Code to fint d the min, max and count of the Reported Date column\n",
    "result_date = df_data_changed.select([min(\"Reported Date\"), \n",
    "                        max(\"Reported Date\"), \n",
    "                        count(\"Reported Date\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statistics values for date are: \n",
      "+------------------+------------------+--------------------+\n",
      "|min(Reported Date)|max(Reported Date)|count(Reported Date)|\n",
      "+------------------+------------------+--------------------+\n",
      "|        2010-07-01|        2019-03-31|              726921|\n",
      "+------------------+------------------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"The statistics values for date are: \")\n",
    "result_date.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above we have achieved the values for min, max and count of date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step6: Changing the data type of a column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have the \"Reported Date\" column in string format, we now would have to write a user defined function for converting this to date format. A user defined function or UDF is a feature of Spark SQL to define column based functions. Thus these are functions which are written by the programmer which can be used to perform operations on a column of a pyspark dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Offence Count', 'int'),\n",
       " ('Offence Level 1 Description', 'string'),\n",
       " ('Offence Level 2 Description', 'string'),\n",
       " ('Offence Level 3 Description', 'string'),\n",
       " ('Postcode - Incident', 'string'),\n",
       " ('Reported Date', 'string'),\n",
       " ('Suburb - Incident', 'string'),\n",
       " ('_id', 'struct<oid:string>')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the above schema we can see that the Reported Date is of datatype string. Now we convert the Reported Date column to date type and store this in a new dataframe namely df_date_changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "func =  udf (lambda x: datetime.strptime(x, '%d/%m/%Y'), DateType())\n",
    "\n",
    "df_data_changed = df.dropna().withColumn('Reported Date', func(col('Reported Date')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------------------+---------------------------+---------------------------+-------------------+-------------+-----------------+--------------------+\n",
      "|Offence Count|Offence Level 1 Description|Offence Level 2 Description|Offence Level 3 Description|Postcode - Incident|Reported Date|Suburb - Incident|                 _id|\n",
      "+-------------+---------------------------+---------------------------+---------------------------+-------------------+-------------+-----------------+--------------------+\n",
      "|            1|       OFFENCES AGAINST ...|       THEFT AND RELATED...|            Theft from shop|               5169|   2012-05-15|          SEAFORD|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       ROBBERY AND RELAT...|         Aggravated robbery|               5023|   2012-05-15|           SEATON|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       THEFT AND RELATED...|            Theft from shop|               5019|   2012-05-15|        SEMAPHORE|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       ACTS INTENDED TO ...|             Assault Police|               5019|   2012-05-15|   SEMAPHORE PARK|[5d725e9350f59766...|\n",
      "|            1|       OFFENCES AGAINST ...|       SEXUAL ASSAULT AN...|       Aggravated sexual...|               5019|   2012-05-15|   SEMAPHORE PARK|[5d725e9350f59766...|\n",
      "+-------------+---------------------------+---------------------------+---------------------------+-------------------+-------------+-----------------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_data_changed.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Offence Count: integer (nullable = true)\n",
      " |-- Offence Level 1 Description: string (nullable = true)\n",
      " |-- Offence Level 2 Description: string (nullable = true)\n",
      " |-- Offence Level 3 Description: string (nullable = true)\n",
      " |-- Postcode - Incident: string (nullable = true)\n",
      " |-- Reported Date: date (nullable = true)\n",
      " |-- Suburb - Incident: string (nullable = true)\n",
      " |-- _id: struct (nullable = true)\n",
      " |    |-- oid: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_data_changed.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen when we print the schema of df_data_changed we can see that the Reported Date column is of type date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step7: Preliminary data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step of the Assignment we have to perform some analytical queries on the given dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task1: How many level 2 offences are there? Display the list of level 2 offences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first task of this step we have to find out the number of level 2 offences and display the unique ones. For doing this we take the column and put it in a list and then this list is used for findding the unique elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Code for conversion of column of the dataframe to a list\n",
    "df_offence_2 =  df_data_changed.select('Offence Level 2 Description').collect()\n",
    "df_offence_2_list = []\n",
    "for i in df_offence_2:\n",
    "    df_offence_2_list.append(i[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the above code we take all the elements from the column and store it in a list. Now the list is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Code for obtaining unique elements from the list\n",
    "df_offence_2_set = set(df_offence_2_list)\n",
    "df_offence_2_unique = list(df_offence_2_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of level 2 offences which are there are:  9\n",
      "\n",
      "the level 2 offences are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['ACTS INTENDED TO CAUSE INJURY',\n",
       " 'HOMICIDE AND RELATED OFFENCES',\n",
       " 'OTHER OFFENCES AGAINST THE PERSON',\n",
       " 'SERIOUS CRIMINAL TRESPASS',\n",
       " 'FRAUD DECEPTION AND RELATED OFFENCES',\n",
       " 'SEXUAL ASSAULT AND RELATED OFFENCES',\n",
       " 'ROBBERY AND RELATED OFFENCES',\n",
       " 'PROPERTY DAMAGE AND ENVIRONMENTAL',\n",
       " 'THEFT AND RELATED OFFENCES']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Printing the number of level 2 offences and all it's types\n",
    "print(\"the number of level 2 offences which are there are: \", len(df_offence_2_unique))\n",
    "print(\"\\nthe level 2 offences are:\")\n",
    "df_offence_2_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task2: What is the number of offences against the person"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this task we have to find the number of Offences which are against the person. If we see the dataset we can observe that the 'Offence Level 1 Description' has the value as 'OFFENCES AGAINST THE PERSON'. Thus we have to find the number of offences against the person. This can be done by obtaining all the rows which has the specified value and then sum up the Offence Count value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "offence_against_person = df_data_changed[df_data_changed['Offence Level 1 Description']=='OFFENCES AGAINST THE PERSON'].\\\n",
    "    select(\"Offence Count\").groupBy().sum().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of Offences against the person is:  175480\n"
     ]
    }
   ],
   "source": [
    "print('The number of Offences against the person is: ', offence_against_person[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task3: How many serious criminal tresspasses with more than 1 offence count?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this task we have to find the number of records which have serious criminal tresspasses and more than one offence count. If we observe the dataset we can see that the 'Offence Level 2 Description' column has the value 'SERIOUS CRIMINAL TRESPASS'. Thus we have to find the number of records which have this value and have more than one offence count. We can do this by obtaining the dataset based on the value parameter conditions and find the count which would give us number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "serious_criminal_tresspasser =  df_data_changed[(\n",
    "                df_data_changed['Offence Level 2 Description']=='SERIOUS CRIMINAL TRESPASS') \n",
    "                & (df_data_changed['Offence Count'] > 1)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of serious criminal tresspasses with more than one offence count is:  8574\n"
     ]
    }
   ],
   "source": [
    "print('The number of serious criminal tresspasses with more than one offence count is: ', serious_criminal_tresspasser)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task4: percentage of crimes are offences against the property"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this task we have to find the percentage of crimes which are offences against the property. For doing this we find the data which has 'Offence Level 1 Description' value as 'OFFENCES AGAINST PROPERTY'. We find the number of records it has and divide by the total number of rows in the dataframe. We then find the percentage value for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_offence_property = df_data_changed[df_data_changed['Offence Level 1 Description']=='OFFENCES AGAINST PROPERTY'].\\\n",
    "groupBy().sum('Offence Count').collect()[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_total_df = df_data_changed.groupBy().sum().collect()[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_property = (number_offence_property / number_total_df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of crimes are offences against the property is:  79.3955334288331\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of crimes are offences against the property is: \", per_property)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step8: Exploratory data Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task1: Find the number of crimes per year. Plot the graph and explain your understanding of the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now in this step of the assignment we have to write code to analyse analytical queries and perform visualisation using the standard python library \"matplotlib\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first EDA step is to find the number of crimes per year and to perform a plot for the same. For this we first consider the Reported Date column of the pyspark dataframe and we obtain only the year from this column. Then we group by year and find the number of offences every year by summing up number of offences based on the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date_plot = df_data_changed.groupBy(year('Reported Date').alias('year')).sum('Offence Count').\\\n",
    "                sort(\"year\", ascending = True).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we make a dictionary for the year and the offence count. We can now use this dictionary for performing a bar plot year vs number of crimes commited."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a python dictionary and in the dictionary we put in all the data which is required for the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data = {}\n",
    "\n",
    "for i in df_date_plot:\n",
    "    plot_data[str(i[0])] = i[1]\n",
    "#plot_data.pop(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data of the number of crimes based on the year we plot this in the form of a bar graph. Matplotlib a standard python package for plotting is used for obtaining the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=[15,5])\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Crimes\")\n",
    "plt.bar(list(plot_data.keys()),list(plot_data.values()), width=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Inference from the above plot is as follows:***\n",
    "\n",
    "As we know that the minimum date is '1/7/2010' and the maximum date is '31/3/2019' we cannot say anything about the years 2010 and 2019 as only a part of the year is in consideration in this dataset.\n",
    "\n",
    "However considering the other years we can say that the number of crimes are high in the initial years that is 2011 to 2016 and then in the years 2017 and 2018 the crimes have reduced. Thus we can tell that the number of crimes have reduced in recent years compared to the past years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task2: Find the number of crimes per month. Plot the graph and explain your understanding of the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now in this task we have to perform a plot of the number of crimes vs month. This is done in a similar manner as we do for year. In the first step we split the date which is retrieved from the Reported Date column and we now take the month only. We then group the values based on the month value and find the total number of offences based on the month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_month_plot = df_data_changed.groupBy(month('Reported Date').alias('month')).sum('Offence Count').\\\n",
    "                sort(\"month\", ascending = True).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the total number of crimes based on the month we now store this in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data_month = {}\n",
    "\n",
    "for i in df_month_plot:\n",
    "    plot_data_month[str(i[0])] = i[1]\n",
    "# plot_data_month.pop(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the data of the number of crimes based on month, we plot this in the form of a bar graph. We use matplotlib as a standard python library to perform this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=[15,5])\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Number of Crimes\")\n",
    "plt.bar(list(plot_data_month.keys()),list(plot_data_month.values()), width=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Inference from the above plot is as follows***\n",
    "\n",
    "We can see that plot for crimes at the start of the year is high and then it reduces to a considerable extent and then again starts to increase.\n",
    "\n",
    "Thus crimes on the first 3 months is high and then it reduces for the next three months and then the values again increase for the consecutive months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task3: Where do most crimes take place? Find the top 20 suburbs (which would also display postcode for e.g. Caulfield-3162 )?. Plot the graph and explain your understanding of the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this task we have to find the number of crimes based on the suburbs. For this we first group the dataset based on the suburbs and the postcode. Then we would find the number of offences and after sorting we would find the top 20 suburbs based on the crime rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_crime_suburb =   df_data_changed.groupBy([\"Suburb - Incident\", \"Postcode - Incident\"]).\\\n",
    "                        sum(\"Offence Count\").\\\n",
    "                        sort('sum(Offence Count)', ascending = False).\\\n",
    "                        take(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the top 20 suburbs based on the crime rates we now put this into a dictionary so that it would help us in plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_suburb_data = {}\n",
    "for i in df_crime_suburb:\n",
    "    plot_suburb_data[str(i[0]) + \" - \"  +  str(i[1])] = i[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data for top 20 suburbs and the number of crimes we plot the data. We use a horizontal bar graph for the following plot. Matplotlib library is used for doing the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=[10,10])\n",
    "plt.xlabel(\"Number of Crimes\")\n",
    "plt.ylabel(\"Suburbs\")\n",
    "plt.barh(list(plot_suburb_data.keys()), list(plot_suburb_data.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Inference from the above plot is as follows***\n",
    "\n",
    "From the above plot we can see that Adelaide has the maximum number of crimes and this is followed by Port Augusta and Mount Gambier.\n",
    "\n",
    "However we can see that the number of crimes in Adelaide is very very high. this can indicate that the something is seriously wrong in that region and the government should take immediate precationary steps to reduce crime rates in Adelaide."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task4: Find the number of serious criminal trespasses by day and month. Plot a graph and explain your understanding of the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now in this task our job would to plot the 'serious criminal trespasses' based on the month and the day. Thus we first filter the data based on the value and take all the rows which have the value 'SERIOUS CRIMINAL TRESPASS'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_serious_crime =  df_data_changed[df_data_changed['Offence Level 2 Description'] == 'SERIOUS CRIMINAL TRESPASS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the required data we would now group the data by month and day values and then find the number of serious criminal trespasses based on the day and month and store it as a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_serious_crime_plot_month = df_serious_crime.groupBy(month('Reported Date').alias('month'), \n",
    "                dayofweek('Reported Date').\n",
    "                alias('day')).sum('Offence Count').\\\n",
    "                sort(\"month\",\"day\", ascending = True).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a list of lists based on the month and date values and this data is to be plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "serious_data_count = []\n",
    "\n",
    "for i in range(12):\n",
    "    serious_data_count.append([])\n",
    "\n",
    "for i in df_serious_crime_plot_month:\n",
    "        serious_data_count[i[0] - 1].append(i[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the required data to be plotted, hence we make line graphs based on the months. The plot is done using the Matplotlib library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Month_list = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October',\n",
    "             'November', 'December']\n",
    "plt.figure(figsize=(20,10))\n",
    "palette = plt.get_cmap('Set3')\n",
    "\n",
    "for i in range(12):\n",
    "    plt.subplot(4,3,i+1)\n",
    "    plt.plot(serious_data_count[i], color=palette(i), linewidth=2.9, label= Month_list[i], marker = 'o')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the above plot we can see that the plot is done for serious criminal trespasses based on the day of the week. We can see that the crime rates are less for the months April, May and June and they are the highest for the months October, November and December.\n",
    "\n",
    "Also we can see that the first day of the week and the last day of the week has the lowest number of serious criminal tresspasses and the second day of the week has the highest number of serious criminal trespasses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References Used:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some codes from Monash University Lecture 2,3,4 has been used for this assignment. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}