FlightDelayAnalysis_on_WholeData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.types import *\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Spark Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = SparkSession.builder.appName(\"FlightDelayAnalysis\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# increase the maximum number of fields that can be displayed when a plan is printed to 100\n",
    "\n",
    "spark.conf.set(\"spark.sql.debug.maxToStringFields\", \"100\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file= \"\"\"/home/alaa-haggag/Big Data Assesment/Flight Delay/Flight_Delay.parquet\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_whole_df = spark.read.format(\"parquet\").load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_whole_df = flights_whole_df.select([\"FlightDate\", \"Marketing_Airline_Network\", \"CarrierDelay\", \"OriginCityName\", \"DestCityName\", \n",
    "                                      \"DepDelay\", \"ArrDelay\", \"ArrDelayMinutes\", \"DepTime\",\"Distance\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_whole_df.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding The data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_whole_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_rows = flights_whole_df.count()\n",
    "\n",
    "print(\"Number of rows in the DataFrame: \", number_of_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show  the nulls in each column\n",
    "\n",
    "flights_whole_df.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in flights_whole_df.columns]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show the number of duplicates in the DataFrame\n",
    "\n",
    "number_of_duplicates =  flights_whole_df.count() - flights_whole_df.dropDuplicates().count()\n",
    "\n",
    "print(\"\\nNumber of duplicates in the DataFrame: \", number_of_duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_whole_df.describe().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert FlightDate to date type\n",
    "flights_df1 = flights_whole_df.withColumn(\"FlightDate\", flights_whole_df[\"FlightDate\"].cast(\"date\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_df1.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The total delay for each flight\n",
    "flights_df2 = flights_df1.withColumn(\"TotalDelay\", flights_df1[\"DepDelay\"] + flights_df1[\"ArrDelay\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_df2.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping Marketing_Airline_Network to Carrier Name in a new column\n",
    "\n",
    "flights_df3 = flights_df2.withColumn('Carrier_Name',when(col('Marketing_Airline_Network')=='AA','American Airlines')\\\n",
    ".when(col('Marketing_Airline_Network')=='AS','Alaska Airlines')\\\n",
    ".when(col('Marketing_Airline_Network')=='B6','JetBlue Airways')\\\n",
    ".when(col('Marketing_Airline_Network')=='DL','Delta Air Lines')\\\n",
    ".when(col('Marketing_Airline_Network')=='F9','Frontier Airlines')\\\n",
    ".when(col('Marketing_Airline_Network')=='G4','Allegiant Air')\\\n",
    ".when(col('Marketing_Airline_Network')=='HA','Hawaiian Airlines')\\\n",
    ".when(col('Marketing_Airline_Network')=='NK','Spirit Airlines')\\\n",
    ".when(col('Marketing_Airline_Network')=='UA','United Airlines')\\\n",
    ".when(col('Marketing_Airline_Network')=='VX','Virgin America')\\\n",
    ".when(col('Marketing_Airline_Network')=='WN','Southwest Airlines')\\\n",
    ".otherwise('Others'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_df3.select(\"Carrier_Name\").distinct().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map OriginCityName to Origin_City in a new column and DestCityName to Dest_City in a new column\n",
    "\n",
    "flights_df4 = flights_df3.withColumn('Origin_City',when(col('OriginCityName')=='Atlanta, GA','Atlanta')\\\n",
    ".when(col('OriginCityName')=='Boston, MA','Boston')\\\n",
    ".when(col('OriginCityName')=='Charlotte, NC','Charlotte')\\\n",
    ".when(col('OriginCityName')=='Chicago, IL','Chicago')\\\n",
    ".when(col('OriginCityName')=='Dallas/Fort Worth, TX','Dallas')\\\n",
    ".when(col('OriginCityName')=='Denver, CO','Denver')\\\n",
    ".when(col('OriginCityName')=='Detroit, MI','Detroit')\\\n",
    ".when(col('OriginCityName')=='Houston, TX','Houston')\\\n",
    ".when(col('OriginCityName')=='Las Vegas, NV','Las Vegas')\\\n",
    ".when(col('OriginCityName')=='Los Angeles, CA','Los Angeles')\\\n",
    ".when(col('OriginCityName')=='Miami, FL','Miami')\\\n",
    ".when(col('OriginCityName')=='Minneapolis, MN','Minneapolis')\\\n",
    ".when(col('OriginCityName')=='New York, NY','New York')\\\n",
    ".when(col('OriginCityName')=='Orlando, FL','Orlando')\\\n",
    ".when(col('OriginCityName')=='Philadelphia, PA','Philadelphia')\\\n",
    ".when(col('OriginCityName')=='Phoenix, AZ','Phoenix')\\\n",
    ".when(col('OriginCityName')=='San Francisco, CA','San Francisco')\\\n",
    ".when(col('OriginCityName')=='Seattle, WA','Seattle')\\\n",
    ".when(col('OriginCityName')=='Washington, DC','Washington')\\\n",
    ".otherwise('Others'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_df4.select(\"Origin_City\").distinct().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_final_df = flights_df4.withColumn('Dest_City',when(col('DestCityName')=='Atlanta, GA','Atlanta')\\\n",
    ".when(col('DestCityName')=='Boston, MA','Boston')\\\n",
    ".when(col('DestCityName')=='Charlotte, NC','Charlotte')\\\n",
    ".when(col('DestCityName')=='Chicago, IL','Chicago')\\\n",
    ".when(col('DestCityName')=='Dallas/Fort Worth, TX','Dallas')\\\n",
    ".when(col('DestCityName')=='Denver, CO','Denver')\\\n",
    ".when(col('DestCityName')=='Detroit, MI','Detroit')\\\n",
    ".when(col('DestCityName')=='Houston, TX','Houston')\\\n",
    ".when(col('DestCityName')=='Las Vegas, NV','Las Vegas')\\\n",
    ".when(col('DestCityName')=='Los Angeles, CA','Los Angeles')\\\n",
    ".when(col('DestCityName')=='Miami, FL','Miami')\\\n",
    ".when(col('DestCityName')=='Minneapolis, MN','Minneapolis')\\\n",
    ".when(col('DestCityName')=='New York, NY','New York')\\\n",
    ".when(col('DestCityName')=='Orlando, FL','Orlando')\\\n",
    ".when(col('DestCityName')=='Philadelphia, PA','Philadelphia')\\\n",
    ".when(col('DestCityName')=='Phoenix, AZ','Phoenix')\\\n",
    ".when(col('DestCityName')=='San Francisco, CA','San Francisco')\\\n",
    ".when(col('DestCityName')=='Seattle, WA','Seattle')\\\n",
    ".when(col('DestCityName')=='Washington, DC','Washington')\\\n",
    ".otherwise('Others'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_final_df.select(\"Dest_City\").distinct().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_final_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cache the final DataFrame in memory for faster access\n",
    "flights_final_df.cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 62:==================================>                      (6 + 4) / 10]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+-------------------+\n",
      "|      Carrier_Name|    avg(TotalDelay)|\n",
      "+------------------+-------------------+\n",
      "|     Allegiant Air| 22.144827586206898|\n",
      "|   United Airlines| 17.662726008344922|\n",
      "| Hawaiian Airlines|-0.8032258064516129|\n",
      "| Frontier Airlines| 23.686851211072664|\n",
      "| American Airlines| 13.076003086419753|\n",
      "|   Delta Air Lines|  8.743724635502781|\n",
      "|   JetBlue Airways|  31.61482820976492|\n",
      "|   Alaska Airlines| 1.2229567307692308|\n",
      "|   Spirit Airlines| 17.734430082256168|\n",
      "|Southwest Airlines|  11.77767265846736|\n",
      "|    Virgin America| 24.428571428571427|\n",
      "+------------------+-------------------+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# What is the average delay time for each airline? \n",
    "\n",
    "flights_final_df.groupBy(\"Carrier_Name\").agg(avg(\"TotalDelay\")).show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+----------------+\n",
      "|      Carrier_Name|Total_Delay_Time|\n",
      "+------------------+----------------+\n",
      "| American Airlines|        101679.0|\n",
      "|   United Airlines|        101596.0|\n",
      "|Southwest Airlines|         62245.0|\n",
      "|   Delta Air Lines|         58172.0|\n",
      "|   JetBlue Airways|         34966.0|\n",
      "|   Spirit Airlines|         15092.0|\n",
      "| Frontier Airlines|         13691.0|\n",
      "|     Allegiant Air|          9633.0|\n",
      "|   Alaska Airlines|          2035.0|\n",
      "|    Virgin America|           171.0|\n",
      "+------------------+----------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# what the top 10 airlines with the most delay time?\n",
    "\n",
    "flights_final_df.groupBy(\"Carrier_Name\").agg(sum(\"TotalDelay\").alias(\"Total_Delay_Time\")).orderBy(desc(\"Total_Delay_Time\")).show(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----+\n",
      "|CarrierDelay|count|\n",
      "+------------+-----+\n",
      "|         0.0|27000|\n",
      "|         3.0|  126|\n",
      "|         1.0|  120|\n",
      "|         6.0|  105|\n",
      "|         7.0|  101|\n",
      "|         5.0|  101|\n",
      "|         4.0|   95|\n",
      "|        15.0|   95|\n",
      "|        16.0|   95|\n",
      "|         2.0|   89|\n",
      "|        10.0|   88|\n",
      "|        17.0|   81|\n",
      "|        18.0|   78|\n",
      "|         9.0|   76|\n",
      "|         8.0|   74|\n",
      "|        11.0|   72|\n",
      "|        19.0|   72|\n",
      "|        14.0|   67|\n",
      "|        12.0|   66|\n",
      "|        20.0|   65|\n",
      "+------------+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# What is the most common cause of delays?\n",
    "\n",
    "flights_final_df.groupBy(\"CarrierDelay\").count().orderBy(desc(\"count\")).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the relationship between distance and delay time?\n",
    "\n",
    "plt.scatter(flights_final_df.select(\"Distance\").collect(), flights_final_df.select(\"DepDelay\").collect())\n",
    "plt.xlabel(\"Distance\")\n",
    "plt.ylabel(\"Delay Time\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-----+\n",
      "|      OriginCityName|count|\n",
      "+--------------------+-----+\n",
      "|         Chicago, IL| 1679|\n",
      "|         Atlanta, GA| 1504|\n",
      "|Dallas/Fort Worth...| 1202|\n",
      "|          Denver, CO| 1197|\n",
      "|        New York, NY| 1086|\n",
      "|       Charlotte, NC|  964|\n",
      "|         Houston, TX|  925|\n",
      "|     Los Angeles, CA|  914|\n",
      "|      Washington, DC|  906|\n",
      "|         Seattle, WA|  758|\n",
      "|         Phoenix, AZ|  734|\n",
      "|         Detroit, MI|  635|\n",
      "|       Las Vegas, NV|  621|\n",
      "|         Orlando, FL|  609|\n",
      "|   San Francisco, CA|  595|\n",
      "|     Minneapolis, MN|  595|\n",
      "|          Newark, NJ|  583|\n",
      "|    Philadelphia, PA|  562|\n",
      "|          Boston, MA|  543|\n",
      "|  Salt Lake City, UT|  489|\n",
      "+--------------------+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# What is the busiest airport in terms of flight volume?\n",
    "flights_final_df.groupBy(\"OriginCityName\").count().orderBy(desc(\"count\")).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+--------------------+\n",
      "|DayOfWeek|avg(ArrDelayMinutes)|\n",
      "+---------+--------------------+\n",
      "|        1|  14.470213733515234|\n",
      "|        2|  14.710874755806381|\n",
      "|        3|  11.588357109986195|\n",
      "|        4|  12.323670394887012|\n",
      "|        5|  15.580208564455292|\n",
      "|        6|  15.030854248215519|\n",
      "|        7|  12.726727509778357|\n",
      "+---------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# what is the average delay time by day of week?\n",
    "flights_final_df.groupBy(dayofweek(\"FlightDate\").alias(\"DayOfWeek\")).agg({\"ArrDelayMinutes\": \"avg\"}).orderBy(\"DayOfWeek\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+------------------+\n",
      "|Month|Average_Delay_Time|\n",
      "+-----+------------------+\n",
      "|    7| 25.02173913043478|\n",
      "|    6| 23.25668934240363|\n",
      "|    8| 20.52224919093851|\n",
      "|   12|19.050978792822185|\n",
      "|    5| 13.80743394536498|\n",
      "|    2|13.756756756756756|\n",
      "|    4|11.982881597717546|\n",
      "|   10| 9.911111111111111|\n",
      "|    3| 7.760740251156642|\n",
      "|    1|7.5597938144329895|\n",
      "|   11|5.8222126560482135|\n",
      "|    9|1.7886075949367088|\n",
      "+-----+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# what is the average delay time by month?\n",
    "\n",
    "flights_final_df.groupBy(month(\"FlightDate\").alias(\"Month\")).agg(avg(\"TotalDelay\").alias(\"Average_Delay_Time\")).orderBy(desc(\"Average_Delay_Time\")).show(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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