A_09_Challenge Functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60c9ac92",
   "metadata": {},
   "source": [
    "-- Notepad to myself --"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9accdc53",
   "metadata": {},
   "source": [
    "# Challenge: Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11235d5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.getOrCreate() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7a240c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- ID: integer (nullable = true)\n",
      " |-- Case Number: string (nullable = true)\n",
      " |-- Date: timestamp (nullable = true)\n",
      " |-- Block: string (nullable = true)\n",
      " |-- IUCR: string (nullable = true)\n",
      " |-- Primary Type: string (nullable = true)\n",
      " |-- Description: string (nullable = true)\n",
      " |-- Location Description: string (nullable = true)\n",
      " |-- Arrest: boolean (nullable = true)\n",
      " |-- Domestic: boolean (nullable = true)\n",
      " |-- Beat: integer (nullable = true)\n",
      " |-- District: integer (nullable = true)\n",
      " |-- Ward: integer (nullable = true)\n",
      " |-- Community Area: integer (nullable = true)\n",
      " |-- FBI Code: string (nullable = true)\n",
      " |-- X Coordinate: integer (nullable = true)\n",
      " |-- Y Coordinate: integer (nullable = true)\n",
      " |-- Year: integer (nullable = true)\n",
      " |-- Updated On: string (nullable = true)\n",
      " |-- Latitude: double (nullable = true)\n",
      " |-- Longitude: double (nullable = true)\n",
      " |-- Location: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import to_timestamp, col\n",
    "df = spark.read.csv('data/Crimes-2021.csv', header=True, inferSchema=True) \\\n",
    "    .withColumn('Date', to_timestamp(col('Date'), 'MM/dd/yyyy hh:mm:ss a'))\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181454a7",
   "metadata": {},
   "source": [
    "### Challenge: What is the most frequently reported 'non-criminal' activity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1eb9de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select(col('Primary Type')).distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82d5b312",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------------------+\n",
      "|Primary Type                     |\n",
      "+---------------------------------+\n",
      "|ARSON                            |\n",
      "|ASSAULT                          |\n",
      "|BATTERY                          |\n",
      "|BURGLARY                         |\n",
      "|CONCEALED CARRY LICENSE VIOLATION|\n",
      "|CRIMINAL DAMAGE                  |\n",
      "|CRIMINAL SEXUAL ASSAULT          |\n",
      "|CRIMINAL TRESPASS                |\n",
      "|DECEPTIVE PRACTICE               |\n",
      "|GAMBLING                         |\n",
      "|HOMICIDE                         |\n",
      "|HUMAN TRAFFICKING                |\n",
      "|INTERFERENCE WITH PUBLIC OFFICER |\n",
      "|INTIMIDATION                     |\n",
      "|KIDNAPPING                       |\n",
      "|LIQUOR LAW VIOLATION             |\n",
      "|MOTOR VEHICLE THEFT              |\n",
      "|NARCOTICS                        |\n",
      "|NON-CRIMINAL                     |\n",
      "|OBSCENITY                        |\n",
      "|OFFENSE INVOLVING CHILDREN       |\n",
      "|OTHER NARCOTIC VIOLATION         |\n",
      "|OTHER OFFENSE                    |\n",
      "|PROSTITUTION                     |\n",
      "|PUBLIC INDECENCY                 |\n",
      "|PUBLIC PEACE VIOLATION           |\n",
      "|ROBBERY                          |\n",
      "|SEX OFFENSE                      |\n",
      "|STALKING                         |\n",
      "|THEFT                            |\n",
      "|WEAPONS VIOLATION                |\n",
      "+---------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.select(col('Primary Type')).distinct().orderBy(col('Primary Type')).show(31, truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8aba1a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc = df.filter(col('Primary Type') == 'NON-CRIMINAL')\n",
    "nc.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f223f0ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------------------+-----+\n",
      "|Description                       |count|\n",
      "+----------------------------------+-----+\n",
      "|CONCEALED CARRY LICENSE REVOCATION|4    |\n",
      "+----------------------------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nc.groupBy(col('Description')).count().orderBy('count', ascending=False).show(truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e5b95a",
   "metadata": {},
   "source": [
    "### Challenge: Which day of the week has the most number of reported crime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd9727c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8299d753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function dayofweek in module pyspark.sql.functions:\n",
      "\n",
      "dayofweek(col)\n",
      "    Extract the day of the week of a given date as integer.\n",
      "    \n",
      "    .. versionadded:: 2.3.0\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])\n",
      "    >>> df.select(dayofweek('dt').alias('day')).collect()\n",
      "    [Row(day=4)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(dayofweek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f1380d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+---------------+\n",
      "|               Date|dayofweek(Date)|\n",
      "+-------------------+---------------+\n",
      "|2021-04-16 20:45:00|              6|\n",
      "|2021-10-21 11:00:00|              5|\n",
      "|2021-12-11 20:00:00|              7|\n",
      "+-------------------+---------------+\n",
      "only showing top 3 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.select(col('Date'), dayofweek(col('Date'))).show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c47c6240",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import date_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4850b1d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function date_format in module pyspark.sql.functions:\n",
      "\n",
      "date_format(date, format)\n",
      "    Converts a date/timestamp/string to a value of string in the format specified by the date\n",
      "    format given by the second argument.\n",
      "    \n",
      "    A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All\n",
      "    pattern letters of `datetime pattern`_. can be used.\n",
      "    \n",
      "    .. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html\n",
      "    \n",
      "    .. versionadded:: 1.5.0\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Whenever possible, use specialized functions like `year`.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])\n",
      "    >>> df.select(date_format('dt', 'MM/dd/yyy').alias('date')).collect()\n",
      "    [Row(date='04/08/2015')]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(date_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "baa0d45a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+---------------+--------------------+\n",
      "|Date               |dayofweek(Date)|date_format(Date, E)|\n",
      "+-------------------+---------------+--------------------+\n",
      "|2021-04-16 20:45:00|6              |Fri                 |\n",
      "|2021-10-21 11:00:00|5              |Thu                 |\n",
      "|2021-12-11 20:00:00|7              |Sat                 |\n",
      "+-------------------+---------------+--------------------+\n",
      "only showing top 3 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.select(col('Date'), \n",
    "          dayofweek(col('Date')), \n",
    "          date_format(col('Date'), 'E')).show(3, truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bcbcc212",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-----+\n",
      "|date_format(Date, E)|count|\n",
      "+--------------------+-----+\n",
      "|Fri                 |30924|\n",
      "|Sat                 |30169|\n",
      "|Sun                 |29882|\n",
      "|Mon                 |29754|\n",
      "|Wed                 |29233|\n",
      "|Tue                 |29020|\n",
      "|Thu                 |28897|\n",
      "+--------------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.groupBy(date_format(col('Date'), 'E')).count().orderBy('count', ascending=False).show(7, truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f7400b",
   "metadata": {},
   "source": [
    "### Challenge: Using a bar chart, plot which day of the week has the most number of reported crime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b295d172",
   "metadata": {},
   "source": [
    "To plot this; we'll need to use *pandas* and *matplotlib* as one potential solution. So why don't we work with the row objects instead? I'm going to collect that to allow me to access the row objects. If we're able to extract the day of the week, and the count, then we can plot them against each other. So we can easily do this with a list comprehension, and all we need to do is capture the first element and the second element of this row object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7502b348",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e57ad399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(date_format(Date, E)='Sun', count=29882),\n",
       " Row(date_format(Date, E)='Mon', count=29754),\n",
       " Row(date_format(Date, E)='Thu', count=28897),\n",
       " Row(date_format(Date, E)='Sat', count=30169),\n",
       " Row(date_format(Date, E)='Wed', count=29233),\n",
       " Row(date_format(Date, E)='Fri', count=30924),\n",
       " Row(date_format(Date, E)='Tue', count=29020)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupBy(date_format(col('Date'), 'E')).count().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b67c70fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sun', 'Mon', 'Thu', 'Sat', 'Wed', 'Fri', 'Tue']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow = [x[0] for x in df.groupBy(date_format(col('Date'), 'E')).count().collect()]\n",
    "dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83a5b388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[29882, 29754, 28897, 30169, 29233, 30924, 29020]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt = [x[1] for x in df.groupBy(date_format(col('Date'), 'E')).count().collect()]\n",
    "cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa11618c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day_of_week</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sun</td>\n",
       "      <td>29882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mon</td>\n",
       "      <td>29754</td>\n",
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       "      <th>2</th>\n",
       "      <td>Thu</td>\n",
       "      <td>28897</td>\n",
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       "      <th>3</th>\n",
       "      <td>Sat</td>\n",
       "      <td>30169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wed</td>\n",
       "      <td>29233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fri</td>\n",
       "      <td>30924</td>\n",
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      "text/plain": [
       "  Day_of_week  Count\n",
       "0         Sun  29882\n",
       "1         Mon  29754\n",
       "2         Thu  28897\n",
       "3         Sat  30169\n",
       "4         Wed  29233\n",
       "5         Fri  30924\n",
       "6         Tue  29020"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc = pd.DataFrame({'Day_of_week':dow, 'Count':cnt})\n",
    "dc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ea3a90ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No. of reported crimes per day of the week on 2021')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.sort_values('Count', ascending=False).plot(kind='bar', color='olive', x='Day_of_week', y='Count')\n",
    "plt.xlabel('Day of the week')\n",
    "plt.ylabel('No. of reported crimes')\n",
    "plt.title('No. of reported crimes per day of the week on 2021')"
   ]
  }
 ],
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