Lab4_SparkSQL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2017 Edition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will learn how to use the DataFrame API and SparkSQL to perform simple data analytics tasks.\n",
    "\n",
    "# Goals\n",
    "The main goals of this notebook are the following:\n",
    "1. Understand the advantages and disadvantages of using DataFrame over RDD\n",
    "2. Analyze the airline data with the DataFrame API and SparkSQL\n",
    "\n",
    "# Steps\n",
    "\n",
    "- First, in section 1, we will go through a short introduction about the DataFrame API with a small example to see how can we use it and how it compares to the low-level RDD abstraction.\n",
    "- In section 2, we delve into the details of the use case of this notebook including: providing the context, introducing the data\n",
    "- In section 3, we perform data exploration and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. RDD and DataFrame APIs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our previous notebooks, we have worked with RDDs (Resilient Distributed Dataset), the basic abstraction in Spark. The main reason is due to the task at hand: we have been invovled in the design of distributed algorithms.\n",
    "\n",
    "In this laboratory session, we study (or revisit) the DataFrame API - an immutable distributed collection of data.\n",
    "DataFrames allow developers to impose a **structure** on a distributed collection of data, allowing higher-level abstraction; in addition, DataFrames provide a domain specific language (DSL) API to manipulate distributed, structured data. Ultimately, the goal is to make Spark accessible to a wider audience, beyond researchers and specialized data engineers.\n",
    "\n",
    "Let's take a small example to see the difference between RDDs and DataFrames. The two cells below contain short code snippets that perform the same task: count the frequency of words in a document, then sort the result in descending order. The first snippet is the same you have been working on in the very [first notebook](https://github.com/EURECOM-CLOUDS-COURSE/Introduction/blob/master/introduction-lab.ipynb) of the CLOUDS course.\n",
    "The second code snippet uses the DataFrame API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "<strong>Question 1.</strong> \n",
    "\n",
    "Run both approaches (RDD and DataFrame) detailed below, compare their runtime, and discuss. Is the DataFrame API approach faster? Can you explain why?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 293.69857025146484s\n",
      "[(29556020, 'the'), (18043957, 'of'), (14914748, 'and'), (12892052, 'to'), (9779124, 'a')]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "time_start = time.time()\n",
    "words = (\n",
    "            # read the text file\n",
    "            sc.textFile(\"/datasets/gutenberg/gutenberg_small.txt\").repartition(16)\n",
    "            \n",
    "            # construct words from lines\n",
    "            .flatMap(lambda line: line.split())\n",
    "            \n",
    "            # map each word to (word, 1)\n",
    "            .map(lambda x: (x, 1))\n",
    "    \n",
    "            # reduce by key: accumulate sum the freq of the same word\n",
    "            .reduceByKey(lambda freq1, freq2: freq1 + freq2)\n",
    "            \n",
    "            # swap (word, freq) to (freq, word)\n",
    "            .map(lambda x: (x[1], x[0]))\n",
    "    \n",
    "            # sort result by key DESC\n",
    "            .sortByKey(False)\n",
    "         )\n",
    "print(\"Runtime: {}s\".format(time.time() - time_start))\n",
    "print(words.take(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+--------+\n",
      "|word|   count|\n",
      "+----+--------+\n",
      "| the|29556020|\n",
      "|  of|18043957|\n",
      "|    |16611988|\n",
      "| and|14914748|\n",
      "|  to|12892052|\n",
      "+----+--------+\n",
      "only showing top 5 rows\n",
      "\n",
      "Runtime: 145.55229878425598s\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.types import *\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.functions import explode, split, desc\n",
    "sqlContext = SQLContext(sc)\n",
    "time_start = time.time()\n",
    "\n",
    "# read the text file\n",
    "df = sqlContext.read.text(\"/datasets/gutenberg/gutenberg_small.txt\")\n",
    "\n",
    "# show 3 samples\n",
    "# df.show(3)\n",
    "\n",
    "result = (\n",
    "    # split into words\n",
    "    df.select(explode(split(df.value, \"\\s+|\\t+\")).alias('word'))\n",
    "    \n",
    "    # group by each word\n",
    "    .groupBy(['word'])\n",
    "    \n",
    "    # count the freq\n",
    "    .count()\n",
    "    \n",
    "    # sort the result\n",
    "    .orderBy(desc(\"count\"))\n",
    ")\n",
    "\n",
    "result.show(5)\n",
    "print(\"Runtime: {}s\".format(time.time() - time_start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "<strong>Answer 1.</strong> \n",
    "\n",
    "Executing the two snippets we obtained a run time for the DataFrame API wich is about half the time of the common RDD code. This is expected because the DataFrame API forces a structure to the data (schema), allowing faster aggregation. Moreover, the execution is supported by the Catalyst optimizer, that enanched the execution of the query.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Analysis of flight data using the DataFrame API and SparkSQL\n",
    "\n",
    "## Use case\n",
    "\n",
    "In this notebook, we play the role of a data scientist working in the travel industry, specifically on air transportation of passengers. We want to explore the data collected by the Department of Transportation (DoT) to understand passengers' behavior, as well as the properties of all flights, across several airline companies.\n",
    "\n",
    "The dataset provided by the DoT has 29 features, that can be either categorical or numerical. For example, the ```src_airport``` (source airport) is categorical: there exist no comparison operator between airport names. We can not say \"SGN is bigger than NCE\". The ```departure_time``` feature is numerical, for which a comparison operator exists. For instance, \"flight departing before 6PM\" can be express by \"departure_time < 1800\".\n",
    "\n",
    "In this use case, most features are numerical, except carrier, flight_number, cancelled, cancelation_code and diverted. \n",
    "The data contains a header, that is useless when analyzing the data: it serves the purpose of an \"embedded schema\", to help data scientist figure out what information is available. Note that there are some features with missing values in some lines of the dataset. The missing values are marked by \"NA\". These values can cause problems when processing the data and can lead to unexpected results. Therefore, we need to remove the header and replace all \"NA\" values by empty values, such as they can be interpreted as null values.\n",
    "\n",
    "## DataFrame\n",
    "As we have seen already, there are multiple ways to manipulate data:\n",
    "\n",
    "- Using the RDD abstraction\n",
    "\n",
    "- Using the DataFrame abstraction. \n",
    "\n",
    "A DataFrame is a distributed collection of data organized into named columns. It is based on the data frame concept in R language or in Pandas for Python. In some sense, it is similar to a table in a relational database: each item is a list of values (the columns). Also, the value in each row of each column can be accessed by the column's name.\n",
    "\n",
    "To use DataFrames, the data should be clean (no invalid values). That means we cannot create DataFrame directly from the \"RAW\" data. Instead, we will first create an RDD from RAW data, produce a new, clean RDD, then transform it to a DataFrame and work on it. The RDD `cleaned_data` is an RDD[String]. We need to transform it to RDD[(TypeOfColumn1, TypeOfColumn2, ..., TypeOfColumn29)] then call a function to create a DataFrame from the new RDD. Please note that, package com.databricks.spark.csv can really help us to load the data efficiently without concerns about parsing, conversions, etc... However, the CSV package can have some problems when loading not well-formatted data or the data that has many kinds of missing values. The guidelines defined this notebook are a general way to help you work with any data.\n",
    "\n",
    "\n",
    "DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs.\n",
    "\n",
    "During the labs in this course, we will mainly work with CSV data files. So, in the next sections, we only focus on constructing dataframes from structured data files directly and from existing RDD.\n",
    "\n",
    "### Constructing a DataFrame directly from structured data file\n",
    "\n",
    "To construct DataFrame from a structured file directly, the file type must be supported. Currently, Spark supports csv, json, avro and many more. Among these types, the csv type is one of the most popular in data analytics. A DataFrame is constructed from csv files by using the package spark-csv from Databricks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "sqlContext = SQLContext(sc)\n",
    "\n",
    "df = sqlContext.read.load('/datasets/airline/1994.csv', \n",
    "    format='com.databricks.spark.csv', \n",
    "    header='true', \n",
    "    inferSchema='true',\n",
    "    nullValue='NA'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the function load parametrized with `com.databricks.spark.csv`, we ask the SqlContext to use the parser from the DataBricks package. Additionally, we can specify whether the file has a header, or ask the parser to guess the data type of columns automatically. The parsed data types is viewed using the function printSchema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Year: integer (nullable = true)\n",
      " |-- Month: integer (nullable = true)\n",
      " |-- DayofMonth: integer (nullable = true)\n",
      " |-- DayOfWeek: integer (nullable = true)\n",
      " |-- DepTime: integer (nullable = true)\n",
      " |-- CRSDepTime: integer (nullable = true)\n",
      " |-- ArrTime: integer (nullable = true)\n",
      " |-- CRSArrTime: integer (nullable = true)\n",
      " |-- UniqueCarrier: string (nullable = true)\n",
      " |-- FlightNum: integer (nullable = true)\n",
      " |-- TailNum: string (nullable = true)\n",
      " |-- ActualElapsedTime: integer (nullable = true)\n",
      " |-- CRSElapsedTime: integer (nullable = true)\n",
      " |-- AirTime: string (nullable = true)\n",
      " |-- ArrDelay: integer (nullable = true)\n",
      " |-- DepDelay: integer (nullable = true)\n",
      " |-- Origin: string (nullable = true)\n",
      " |-- Dest: string (nullable = true)\n",
      " |-- Distance: integer (nullable = true)\n",
      " |-- TaxiIn: string (nullable = true)\n",
      " |-- TaxiOut: string (nullable = true)\n",
      " |-- Cancelled: integer (nullable = true)\n",
      " |-- CancellationCode: string (nullable = true)\n",
      " |-- Diverted: integer (nullable = true)\n",
      " |-- CarrierDelay: string (nullable = true)\n",
      " |-- WeatherDelay: string (nullable = true)\n",
      " |-- NASDelay: string (nullable = true)\n",
      " |-- SecurityDelay: string (nullable = true)\n",
      " |-- LateAircraftDelay: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print(df.dtypes)\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the automatically inferred data types are **not** as expected. For example, we expect that CRSDepTime to be of interger type. The type and the name of each column can be modified using function ```withColumn``` and ```withColumnRename``` respectively. Additionally, we can also compute and print basic descriptive statistics of numerical columns via the function ```describe``` (similar to Pandas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Year: integer (nullable = true)\n",
      " |-- Month: integer (nullable = true)\n",
      " |-- DayofMonth: integer (nullable = true)\n",
      " |-- DayOfWeek: integer (nullable = true)\n",
      " |-- DepTime: integer (nullable = true)\n",
      " |-- scheduled_departure_time: integer (nullable = true)\n",
      " |-- ArrTime: integer (nullable = true)\n",
      " |-- CRSArrTime: integer (nullable = true)\n",
      " |-- UniqueCarrier: string (nullable = true)\n",
      " |-- FlightNum: integer (nullable = true)\n",
      " |-- TailNum: string (nullable = true)\n",
      " |-- ActualElapsedTime: integer (nullable = true)\n",
      " |-- CRSElapsedTime: integer (nullable = true)\n",
      " |-- AirTime: string (nullable = true)\n",
      " |-- ArrDelay: integer (nullable = true)\n",
      " |-- DepDelay: integer (nullable = true)\n",
      " |-- Origin: string (nullable = true)\n",
      " |-- Dest: string (nullable = true)\n",
      " |-- Distance: integer (nullable = true)\n",
      " |-- TaxiIn: string (nullable = true)\n",
      " |-- TaxiOut: string (nullable = true)\n",
      " |-- Cancelled: integer (nullable = true)\n",
      " |-- CancellationCode: string (nullable = true)\n",
      " |-- Diverted: integer (nullable = true)\n",
      " |-- CarrierDelay: string (nullable = true)\n",
      " |-- WeatherDelay: string (nullable = true)\n",
      " |-- NASDelay: string (nullable = true)\n",
      " |-- SecurityDelay: string (nullable = true)\n",
      " |-- LateAircraftDelay: string (nullable = true)\n",
      "\n",
      "+-------+-------+-----------------+------------------+------------------+------------------+------------------------+------------------+------------------+-------------+-----------------+-------+------------------+------------------+-------+------------------+-----------------+-------+-------+-----------------+------+-------+--------------------+----------------+--------------------+------------+------------+--------+-------------+-----------------+\n",
      "|summary|   Year|            Month|        DayofMonth|         DayOfWeek|           DepTime|scheduled_departure_time|           ArrTime|        CRSArrTime|UniqueCarrier|        FlightNum|TailNum| ActualElapsedTime|    CRSElapsedTime|AirTime|          ArrDelay|         DepDelay| Origin|   Dest|         Distance|TaxiIn|TaxiOut|           Cancelled|CancellationCode|            Diverted|CarrierDelay|WeatherDelay|NASDelay|SecurityDelay|LateAircraftDelay|\n",
      "+-------+-------+-----------------+------------------+------------------+------------------+------------------------+------------------+------------------+-------------+-----------------+-------+------------------+------------------+-------+------------------+-----------------+-------+-------+-----------------+------+-------+--------------------+----------------+--------------------+------------+------------+--------+-------------+-----------------+\n",
      "|  count|5180048|          5180048|           5180048|           5180048|           5113308|                 5180048|           5101202|           5180048|      5180048|          5180048|      0|           5101202|           5180048|      0|           5101202|          5113308|5180048|5180048|          5157099|     0|      0|             5180048|               0|             5180048|           0|           0|       0|            0|                0|\n",
      "|   mean| 1994.0|6.579943853802127|15.723382486031017|3.9516016067804776| 1345.266130849149|      1338.6794068317513|1494.4365151585841| 1495.874874904634|         null|920.2637743897354|   null|114.00258860558746|114.82526995888841|   null| 5.662489742613603| 6.65992152242736|   null|   null|670.7402911985982|  null|   null|0.012884050495284986|            null|0.002337043981059635|        null|        null|    null|         null|             null|\n",
      "| stddev|    0.0|3.436720373974635| 8.788272360339205|1.9902384397190018|470.22426611386214|       463.6175011477233| 483.9849559042786|472.67014948621835|         null|608.1342943927892|   null| 64.88861058427699| 64.20406191593892|   null|23.618156040817194|22.22629770848844|   null|   null| 522.269668889085|  null|   null| 0.11277435077764975|            null| 0.04828646452784185|        null|        null|    null|         null|             null|\n",
      "|    min|   1994|                1|                 1|                 1|                 1|                       1|                 1|                 1|           AA|                1|   null|               -40|                -5|   null|              -115|             -109|    ABE|    ABE|               11|  null|   null|                   0|            null|                   0|        null|        null|    null|         null|             null|\n",
      "|    max|   1994|               12|                31|                 7|              2400|                    2400|              2400|              2400|           WN|             3599|   null|               740|               578|   null|              1313|             1439|    YAK|    YAK|             4502|  null|   null|                   1|            null|                   1|        null|        null|    null|         null|             null|\n",
      "+-------+-------+-----------------+------------------+------------------+------------------+------------------------+------------------+------------------+-------------+-----------------+-------+------------------+------------------+-------+------------------+-----------------+-------+-------+-----------------+------+-------+--------------------+----------------+--------------------+------------+------------+--------+-------------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = (df\n",
    "          # change type of column CRSDepTime by casting its values to interger type\n",
    "          .withColumn('CRSDepTime', df.CRSDepTime.cast('int'))\n",
    "\n",
    "          # rename the column\n",
    "          .withColumnRenamed('CRSDepTime', 'scheduled_departure_time')\n",
    "    )\n",
    "\n",
    "# print schema of the current data\n",
    "df.printSchema()\n",
    "\n",
    "# run jobs to calculate basic statistic information and show it\n",
    "df.describe().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing a DataFrame from an existing RDD\n",
    "\n",
    "Another way to construct a DataFrame is using data from an existing RDD. The main advantage of this approach is that it does not need a third party library. However, with this method, we have to remove the header ourself and provide a clear schema.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import explode, split, desc, col, udf, round\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pyspark.sql.functions as func\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "\n",
    "sqlContext = SQLContext(sc)\n",
    "\n",
    "data = sc.textFile('/datasets/airline/1994.csv')\n",
    "\n",
    "# extract the header\n",
    "header = data.first()\n",
    "\n",
    "# replace invalid data with NULL and remove header\n",
    "cleaned_data = (\n",
    "        data\n",
    "    \n",
    "        # filter out the header\n",
    "        .filter(lambda line: line != header)\n",
    "    \n",
    "         # replace the 'missing data' by empty value\n",
    "        .map(lambda line: ','.join(list(map(lambda x: x if x != 'NA' else '',line.split(',')))))\n",
    "    )\n",
    "\n",
    "airline_data_schema = StructType([ \\\n",
    "    #StructField( name, dataType, nullable)\n",
    "    StructField(\"year\",                     IntegerType(), True), \\\n",
    "    StructField(\"month\",                    IntegerType(), True), \\\n",
    "    StructField(\"day_of_month\",             IntegerType(), True), \\\n",
    "    StructField(\"day_of_week\",              IntegerType(), True), \\\n",
    "    StructField(\"departure_time\",           IntegerType(), True), \\\n",
    "    StructField(\"scheduled_departure_time\", IntegerType(), True), \\\n",
    "    StructField(\"arrival_time\",             IntegerType(), True), \\\n",
    "    StructField(\"scheduled_arrival_time\",   IntegerType(), True), \\\n",
    "    StructField(\"carrier\",                  StringType(),  True), \\\n",
    "    StructField(\"flight_number\",            StringType(),  True), \\\n",
    "    StructField(\"tail_number\",              StringType(), True), \\\n",
    "    StructField(\"actual_elapsed_time\",      IntegerType(), True), \\\n",
    "    StructField(\"scheduled_elapsed_time\",   IntegerType(), True), \\\n",
    "    StructField(\"air_time\",                 IntegerType(), True), \\\n",
    "    StructField(\"arrival_delay\",            IntegerType(), True), \\\n",
    "    StructField(\"departure_delay\",          IntegerType(), True), \\\n",
    "    StructField(\"src_airport\",              StringType(),  True), \\\n",
    "    StructField(\"dest_airport\",             StringType(),  True), \\\n",
    "    StructField(\"distance\",                 IntegerType(), True), \\\n",
    "    StructField(\"taxi_in_time\",             IntegerType(), True), \\\n",
    "    StructField(\"taxi_out_time\",            IntegerType(), True), \\\n",
    "    StructField(\"cancelled\",                StringType(),  True), \\\n",
    "    StructField(\"cancellation_code\",        StringType(),  True), \\\n",
    "    StructField(\"diverted\",                 StringType(),  True), \\\n",
    "    StructField(\"carrier_delay\",            IntegerType(), True), \\\n",
    "    StructField(\"weather_delay\",            IntegerType(), True), \\\n",
    "    StructField(\"nas_delay\",                IntegerType(), True), \\\n",
    "    StructField(\"security_delay\",           IntegerType(), True), \\\n",
    "    StructField(\"late_aircraft_delay\",      IntegerType(), True)\\\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# convert each line into a tuple of features (columns) with the corresponding data type\n",
    "cleaned_data_to_columns = (\n",
    "    cleaned_data.map(lambda l: l.split(\",\"))\n",
    "    .map(lambda cols: \n",
    "         (\n",
    "            int(cols[0])  if cols[0] else None,\n",
    "            int(cols[1])  if cols[1] else None,\n",
    "            int(cols[2])  if cols[2] else None,\n",
    "            int(cols[3])  if cols[3] else None,\n",
    "            int(cols[4])  if cols[4] else None,\n",
    "            int(cols[5])  if cols[5] else None,\n",
    "            int(cols[6])  if cols[6] else None,\n",
    "            int(cols[7])  if cols[7] else None,\n",
    "            cols[8]       if cols[8] else None,\n",
    "            cols[9]       if cols[9] else None,\n",
    "            cols[10]      if cols[10] else None,\n",
    "            int(cols[11]) if cols[11] else None,\n",
    "            int(cols[12]) if cols[12] else None,\n",
    "            int(cols[13]) if cols[13] else None,\n",
    "            int(cols[14]) if cols[14] else None,\n",
    "            int(cols[15]) if cols[15] else None,\n",
    "            cols[16]      if cols[16] else None,\n",
    "            cols[17]      if cols[17] else None,\n",
    "            int(cols[18]) if cols[18] else None,\n",
    "            int(cols[19]) if cols[19] else None,\n",
    "            int(cols[20]) if cols[20] else None,\n",
    "            cols[21]      if cols[21] else None,\n",
    "            cols[22]      if cols[22] else None,\n",
    "            cols[23]      if cols[23] else None,\n",
    "            int(cols[24]) if cols[24] else None,\n",
    "            int(cols[25]) if cols[25] else None,\n",
    "            int(cols[26]) if cols[26] else None,\n",
    "            int(cols[27]) if cols[27] else None,\n",
    "            int(cols[28]) if cols[28] else None\n",
    "         ))             \n",
    ")\n",
    "    \n",
    "# create dataframe\n",
    "df = sqlContext.createDataFrame(cleaned_data_to_columns, airline_data_schema)\\\n",
    "    .select(['year', 'month', 'day_of_month', 'day_of_week',\n",
    "            'scheduled_departure_time','scheduled_arrival_time',\n",
    "            'arrival_delay', 'distance', \n",
    "            'src_airport', 'dest_airport', 'carrier'])\\\n",
    "    .cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples\n",
    "\n",
    "Using the contructed DataFrame, we can answer some simple questions:\n",
    "\n",
    "<ul>\n",
    "<li>How many night flights do we have in our data?</li>\n",
    "<li>How many night flights per unique carrier?</li>\n",
    "</ul>\n",
    "\n",
    "<strong>NOTE:</strong> We define \"night\" to start at 6pm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1078203 night flights.\n"
     ]
    }
   ],
   "source": [
    "night_flights = df[df.scheduled_departure_time > 1800].count()\n",
    "print(\"There are\", str(night_flights), \"night flights.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------+\n",
      "|carrier| count|\n",
      "+-------+------+\n",
      "|     DL|208224|\n",
      "|     US|170397|\n",
      "|     AA|142832|\n",
      "|     WN|124024|\n",
      "|     UA|113640|\n",
      "|     NW|100453|\n",
      "|     CO| 94501|\n",
      "|     TW| 54771|\n",
      "|     HP| 44351|\n",
      "|     AS| 25010|\n",
      "+-------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "night_flights_per_carrier = df[df.scheduled_departure_time > 1800].groupBy(df.carrier).count().orderBy('count', ascending=0)\n",
    "night_flights_per_carrier.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data exploration\n",
    "Next, we will go over a series of simple queries on our data, to perform exploration and compute statistics using the DataFrame API and SparkSQL. These queries directly map to the questions you need to answer. \n",
    "\n",
    "**NOTE:** finding the right question to ask is difficult! Don't be afraid to complement the questions below, with your own questions that, in your opinion, are valuable ways to inspect data. This can give you extra points!\n",
    "\n",
    "- Basic queries:\n",
    "  - How many unique origin airports?\n",
    "  - How many unique destination airports?\n",
    "  - How many carriers?\n",
    "  - How many flights that have a scheduled departure time later than 18h00?\n",
    "\n",
    "\n",
    "- Statistics on flight volume: this kind of statistics are helpful to reason about delays. Indeed, it is plausible to assume that \"*the more flights in an airport, the higher the probability of delay*\".\n",
    "  - How many flights in each month of the year?\n",
    "  - Is there any relationship between the number of flights and the days of week?\n",
    "  - How many flights in different days of months and in different hours of days?\n",
    "  - Which are the top 20 busiest airports (this depends on inbound and outbound traffic)?\n",
    "  - Which are the top 20 busiest carriers?\n",
    "\n",
    "\n",
    "- Statistics on the fraction of delayed flights\n",
    "  - What is the percentage of delayed flights (over total flights) for different hours of the day?\n",
    "  - Which hours of the day are characterized by the longest flight delay?\n",
    "  - What are the fluctuation of the percentage of delayed flights over different time granularities?\n",
    "  - What is the percentage of delayed flights which depart from one of the top 20 busiest airports?\n",
    "  - What is the percentage of delayed flights which belong to one of the top 20 busiest carriers?\n",
    "  \n",
    "- Other interesting queries\n",
    "  - Is there a difference between the various carriers in terms of distance of flights? (some carriers may only offer national flights while others could take care more of the international ones)\n",
    "  - Is there a relation between the distance of flight and the delay?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Basic queries\n",
    "\n",
    "### Question 2.1\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "How many origin airports? How many destination airports? Discuss the results, indicating any discrepancies or strange facts about the data.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 224 origin airports.\n"
     ]
    }
   ],
   "source": [
    "# Query 2.1.1\n",
    "src_airports = (df.select(df.src_airport)\n",
    "       .distinct()\n",
    "       .count())\n",
    "print(\"There are\", str(src_airports), \"origin airports.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 225 destination airports.\n"
     ]
    }
   ],
   "source": [
    "# Query 2.1.2\n",
    "dst_airports = (df.select(df.dest_airport)\n",
    "       .distinct()\n",
    "       .count())\n",
    "print(\"There are\", str(dst_airports), \"destination airports.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 2.1\n",
    "<div class=\"alert alert-info\">\n",
    "From the result of the queries it can be noticed that there are more destination airports than origin ones. It will be seen in the last queries of the notebook that SPN is a source airport that does not belong to the origin while BFI and GUM are destinations not present in the origins.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.2\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "How many unique carriers are present in the data?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10 unique carriers.\n"
     ]
    }
   ],
   "source": [
    "# Query 2.2\n",
    "car = (df.select(df.carrier)\n",
    "       .distinct()\n",
    "       .count())\n",
    "print(\"There are\", str(car), \"unique carriers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "<ul>\n",
    "<li>How many night flights (that is, flights departing later than 6pm or earlier than 6am)?</li>\n",
    "<li>What is the percentage of night flights over the total volume of flights</li>\n",
    "</ul>\n",
    "<strong>NOTE:</strong> Here we're using a more refined definition of what is a night flight, than in our earlier examples.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1121918 night flights.\n"
     ]
    }
   ],
   "source": [
    "# Query 2.3.1\n",
    "night_flights = df[(df.scheduled_departure_time > 1800) | (df.scheduled_departure_time < 600)].count()\n",
    "print(\"There are\", str(night_flights), \"night flights.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of night flights is: 21.658447952605844 %.\n"
     ]
    }
   ],
   "source": [
    "# Query 2.3.2\n",
    "perc_night_flights = df[(df.scheduled_departure_time > 1800) | (df.scheduled_departure_time < 600)].count()/df.count()\n",
    "print(\"The percentage of night flights is:\", str(perc_night_flights*100), \"%.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: Flight volume statistics\n",
    "\n",
    "### Question 3.1: \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "How many flights in each month of the year? Plot the changes over months by a line chart and comment the figure.\n",
    "\n",
    "<br>\n",
    "From the result, we can learn the dynamics of flight volume over months. For example, if we only consider flights in 1994 (to start, it's always better to focus on smaller amount of data), we can discuss about which months are most likely to have flights experiencing delays.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Number of flights per month\n",
    "flights_per_month = (\n",
    "    df.groupBy(df.month)\n",
    "    .count()\n",
    "    .orderBy('month')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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hhBCtlCQBQgghRCslSYAQQgjRSkkSIIQQQrRSkgQIIYQQrZQkAUIIIUQrJUmAEEII0UpJ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f908c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot number of flights per month\n",
    "plt.plot([x[0] for x in flights_per_month], [x[1] for x in flights_per_month], \"-o\")\n",
    "\n",
    "# Add plot's labels\n",
    "plt.title('Number of flights per month')\n",
    "months = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "plt.xticks([x[0] for x in flights_per_month], months)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 3.1: \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "The plot shows that the values generally oscillate between 420000 and 450000 flights per month. The only month that makes an exception is February, where the number of flights drops to less than 390000.\n",
    "It can also be noticed that the months with higher number of flights are in the second half of the year, therefore we can expect an higher number of delays in the second semester, in particular, in the months of July, August, October and December.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.2: \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Is there any relationship between the number of flights and the days of the week?  \n",
    "\n",
    "Plot a bar chart and interpret the figure.\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "<strong>NOTE:</strong> You should study both global aggregates (irrespectively of the month of the year), and monthly aggregates (day statistics, for each different month of the year).\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "By answering this question, we could learn about the importance of the weekend/weekday feature for our predictive task.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights per day of the week\n",
    "flights_dayofweek = (\n",
    "    df.groupBy(df.day_of_week)\n",
    "    .count()\n",
    "    .orderBy('day_of_week')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R+evFbS0OVquVsWPHMmjQIBo1akT//v0JDQ0lOzvbGLji7+9vPLrjcDiMQWlQ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f8fc8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the background of the plots to be white\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=flights_dayofweek,\n",
    "                     columns=['day_of_week', 'flights'])\n",
    "\n",
    "# plot the number of flights per day of the week\n",
    "ax = sns.barplot(x=\"day_of_week\",\n",
    "                 y=\"flights\",\n",
    "                 data=pddf,\n",
    "                 palette=sns.color_palette(\"BuGn_r\", 7))\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Day of the week', ylabel='Number of flights')\n",
    "daysofweek = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n",
    "ax.set_xticklabels(daysofweek)\n",
    "ax.set_title(\"Number of flights per day of the week during the year\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights per day of the week per month\n",
    "flights_dayofweek_month = (\n",
    "    df.groupBy(df.month, df.day_of_week)\n",
    "    .count()\n",
    "    .orderBy('month', 'day_of_week')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7+/Ppp5/a/SBUEhcvXjSak3fs2JE+ffrg6enJmDFjjNrVgtfXJjY2lltvvZW6detyww03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\n1lZn6Hz55ZftUQcAAAAAwAHVGToffvhhSdLFixf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PP6/S0lLFx8fr7bffVmJi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\nGEInAAAAAMAw/w8G8qkJpNr+tQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e0cb7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=flights_dayofweek_month,\n",
    "                     columns=['month', 'day_of_week', 'flights'])\n",
    "\n",
    "# plot the number of flights per day of the week per month\n",
    "ax = sns.factorplot(x=\"month\",\n",
    "                   y=\"flights\",\n",
    "                   hue=\"day_of_week\", \n",
    "                   data=pddf, \n",
    "                   size=6, \n",
    "                   aspect=2, \n",
    "                   kind=\"bar\", \n",
    "                   palette=sns.light_palette((210, 90, 60), 7, input=\"husl\"),\n",
    "                   legend_out=True)\n",
    "\n",
    "# remove left spine\n",
    "ax.despine(left=\"True\")\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Month', ylabel='Number of flights')\n",
    "ax.set_xticklabels(months)\n",
    "ax._legend.set_title(\"Week's day\")\n",
    "for l, m in zip(ax._legend.texts, daysofweek):\n",
    "    l.set_text(m)   \n",
    "plt.title(\"Number of flights per day of the week for each month\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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IyDQJgiAIgpA9MTwHiEyTIAiCIAhCjuRJpsnT05Pz58/nxaqzVLqQFSsH15b/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o6O+Pn50aZNG5ydnalQocJbt39zrVN+SYh9xW+zfwJApVJRtUFzynjVBODa2aNUeeOTZl3fdmxe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e09cbc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use a heatmap to better visualize the data\n",
    "pddf = pddf.pivot(\"month\", \"day_of_week\", \"flights\")\n",
    "fig, ax=plt.subplots(figsize=(10,8))\n",
    "ax = sns.heatmap(pddf, \n",
    "                  linewidths=.5,\n",
    "                  annot=True,\n",
    "                  vmin=50000,\n",
    "                  vmax=75000,\n",
    "                  fmt='d',\n",
    "                  cmap='Blues', ax=ax)\n",
    "# set plot's labels\n",
    "ax.set_xticklabels(daysofweek)\n",
    "ax.set_yticklabels(list(reversed(months)), rotation=0)\n",
    "ax.set(xlabel='Day of the week', ylabel='Month')\n",
    "ax.set_title(\"Number of flights per day of the week for each month\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 3.2: \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "The first plot shows that, over the year, the number of flights during the week days is generally stable while it decreases on Saturdays and Sundays.\n",
    "<br>\n",
    "<br>\n",
    "The second bar plot delves into the details of each month. The pattern seen in the first plot doesn't actually apply to the monthly aggregates. In fact it can be noticed that each month has its own peaks, for example, in January there are more flights during Mondays and over the weekends while in March they are in the middle of the week.\n",
    "<br>\n",
    "<br>\n",
    "Moreover, the heat map can offer a better data visualization to look for relevant information coming from the data. The map better shows the difference between week days and weekends, the highest number of flights are always during the week while the minimums are in the weekends, even if some weekends present increasing values in the months containing holidays like Christmas, Summer or the spring break.\n",
    "<br>\n",
    "<br>\n",
    "Finally, a strange pattern comes out from this plot, showing that the days segment with higher number of flights shifts over the week for each month. Unfortunately, further deductions from this pattern would need the support of a bigger and wider data set.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "How many flights in different days of months and in different hours of days?  \n",
    "<br>\n",
    "<strong>NOTE:</strong> Similarly to the previous note, you need to compute both global statistcs and monthly statistics.\n",
    "<br>\n",
    "<br>\n",
    "Plot  bar charts, and interpret your figures.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights per day of the month\n",
    "flights_dayofmonth = (\n",
    "    df.groupBy(df.day_of_month)\n",
    "    .count()\n",
    "    .orderBy('day_of_month')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1cuRIHTx40Er2e++9p+ef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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e0c47780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=flights_dayofmonth,\n",
    "                     columns=['day_of_month', 'flights'])\n",
    "\n",
    "# plot the number of flights per day of the month\n",
    "f, ax = plt.subplots(figsize=(18, 6))\n",
    "sns.barplot(x=\"day_of_month\",\n",
    "            y=\"flights\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 31),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Day of the month', ylabel='Number of flights')\n",
    "ax.set_title(\"Number of flights per day of the month during the year\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights per day of the month per month\n",
    "flights_dayofmonth_month=(\n",
    "    df.groupBy(df.month, df.day_of_month)\n",
    "    .count()\n",
    "    .orderBy('month', 'day_of_month')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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LAQAAAACWR2UYAAAAAKzOgpVhkmEAAAAAsDoL3jNsczgcjvJuRFk1+fN3To+R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\n2BkmIiIiIiIi1WFnmIiIiIiIiFTn/wDxNa2pNskncwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e0c6d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=flights_dayofmonth_month,\n",
    "                     columns=['month', 'day_of_month', 'flights'])\n",
    "\n",
    "# use a heatmap to better visualize the data\n",
    "pddf = pddf.pivot(\"month\", \"day_of_month\", \"flights\")\n",
    "f, ax = plt.subplots(figsize=(18, 6))\n",
    "sns.heatmap(pddf,\n",
    "            square=True,\n",
    "            vmin=11400,\n",
    "            vmax=15000,\n",
    "            cmap='Blues')\n",
    "\n",
    "# set plot's labels\n",
    "ax.set_yticklabels(list(reversed(months)), rotation=0)\n",
    "ax.set(xlabel='Day of the month', ylabel='Month')\n",
    "ax.set_title(\"Number of flights per day of the month for each month\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights per hour\n",
    "flights_hour=(\n",
    "    df.groupBy(((df.scheduled_departure_time/100).cast('int')%24).alias('hour'))\n",
    "    .count()\n",
    "    .orderBy('hour')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/uW+//VanTp1SVFSUQkNDVVBQoNjY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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31ded0d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=flights_hour,\n",
    "                     columns=['hour', 'flights'])\n",
    "\n",
    "# plot the number of flights per hour\n",
    "f, ax = plt.subplots(figsize=(18, 6))\n",
    "sns.barplot(x=\"hour\",\n",
    "            y=\"flights\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 24),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Hour of the day', ylabel='Number of flights')\n",
    "ax.set_title(\"Number of flights per hour during the year\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights per hour per month\n",
    "flights_hour_month=(\n",
    "    df.groupBy(df.month, ((df.scheduled_departure_time/100)%24).cast('int').alias('hour'))\n",
    "    .count()\n",
    "    .orderBy('month', 'hour')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZlR+zw+FwOPWoTZCVW2RaLDP7HkqSu5u58Vz9Br35BeZdK08t32daLElateYH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\nIrJqLHqIiIiIiMiqseghIiIiIiKr9n8FV5JSHFHvpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31ded08b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=flights_hour_month,\n",
    "                     columns=['month', 'hour', 'flights'])\n",
    "\n",
    "# use a heatmap to better visualize the data\n",
    "pddf = pddf.pivot(\"month\", \"hour\", \"flights\")\n",
    "f, ax = plt.subplots(figsize=(18, 6))\n",
    "sns.heatmap(pddf,\n",
    "            square=True,\n",
    "            cmap='Blues')\n",
    "\n",
    "# set plot's labels\n",
    "ax.set_yticklabels(list(reversed(months)), rotation=0)\n",
    "ax.set(xlabel='Hour of the day', ylabel='Month')\n",
    "ax.set_title(\"Number of flights per hour for each month\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 3.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "The first plot shows a stable number of flights during the days of the month. This result is expected when we look at the previous plot because by plotting the number of flights per day during the year we loose information about the day of the week and, as the plots show, this information is more relevant than the actual day of the month. Furthermore, the number of flights decreases in the last days of the months, this is due to the fact that not all the months have the same number of days (as can be noticed from the white boxes in the next plot).\n",
    "<br>\n",
    "<br>\n",
    "The heat map shows the number of flights per day of the month for each month. The same patterns previously seen can be observed in this map too, again, under the form of lighter diagonals crossing the map. A relevant information comes out of the map: the number of flights increases in the second semester of the year. This is further confirmation of what was already seen in the first line plot about the number of flights per month.\n",
    "Finally, an anomaly can be seen in the 24th and 25th of Novemeber, these days present a very low number of flights compared to the others, further information should be needed to justify this result.\n",
    "<br>\n",
    "<br>\n",
    "Finally, the second bar plot and heat map, clearly show how, regardless of the month, the number of flights drops down at night and it reaches is maximim values between 7 and 8 AM, with other two small peaks between 12 and 13, and 17 and 18.\n",
    "<br>\n",
    "<br>\n",
    "The last plots show also that February has a lower number of flights with respect to the other months, this information was already suggested by the previous plots showing the values per day of the week.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.4\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Which are the **top 20** busiest airports? Compute this in terms of the inbound, outbound and total number of flights.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of outbound flights per airport\n",
    "df_out_airport = (\n",
    "    df.groupBy(df.src_airport.alias('airport'))\n",
    "    .count()\n",
    "    .orderBy('count', ascending=0)\n",
    ")\n",
    "\n",
    "# number of inbound flights per airport\n",
    "df_in_airport = (\n",
    "    df.groupBy(df.dest_airport.alias('airport'))\n",
    "    .count()\n",
    "    .orderBy('count', ascending=0)\n",
    ")\n",
    "\n",
    "# number of flights per airport\n",
    "df_airport = (\n",
    "    df_in_airport.union(df_out_airport)\n",
    "    .groupBy('airport')\n",
    "    .sum()\n",
    "    .orderBy('sum(count)', ascending=0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 busiest airports (outbound)\n",
      "+-------+------+\n",
      "|airport| count|\n",
      "+-------+------+\n",
      "|    ORD|277741|\n",
      "|    DFW|256274|\n",
      "|    ATL|221486|\n",
      "|    LAX|153494|\n",
      "|    STL|151609|\n",
      "|    DEN|141427|\n",
      "|    PHX|139581|\n",
      "|    DTW|136837|\n",
      "|    PIT|130977|\n",
      "|    CLT|129391|\n",
      "|    MSP|123222|\n",
      "|    SFO|118233|\n",
      "|    EWR|116768|\n",
      "|    IAH|104182|\n",
      "|    LGA|101864|\n",
      "|    BOS|100365|\n",
      "|    LAS| 94972|\n",
      "|    PHL| 93585|\n",
      "|    DCA| 88445|\n",
      "|    MCO| 77120|\n",
      "+-------+------+\n",
      "only showing top 20 rows\n",
      "\n",
      "Top 20 busiest airports (inbound)\n",
      "+-------+------+\n",
      "|airport| count|\n",
      "+-------+------+\n",
      "|    ORD|283720|\n",
      "|    DFW|260249|\n",
      "|    ATL|221588|\n",
      "|    LAX|152959|\n",
      "|    STL|152800|\n",
      "|    DEN|144099|\n",
      "|    PHX|140979|\n",
      "|    DTW|139435|\n",
      "|    PIT|131962|\n",
      "|    CLT|130321|\n",
      "|    MSP|124758|\n",
      "|    SFO|117245|\n",
      "|    EWR|117223|\n",
      "|    IAH|104409|\n",
      "|    LGA|101498|\n",
      "|    BOS| 99331|\n",
      "|    LAS| 94948|\n",
      "|    PHL| 93312|\n",
      "|    DCA| 87670|\n",
      "|    MCO| 76600|\n",
      "+-------+------+\n",
      "only showing top 20 rows\n",
      "\n",
      "Top 20 busiest airports\n",
      "+-------+----------+\n",
      "|airport|sum(count)|\n",
      "+-------+----------+\n",
      "|    ORD|    561461|\n",
      "|    DFW|    516523|\n",
      "|    ATL|    443074|\n",
      "|    LAX|    306453|\n",
      "|    STL|    304409|\n",
      "|    DEN|    285526|\n",
      "|    PHX|    280560|\n",
      "|    DTW|    276272|\n",
      "|    PIT|    262939|\n",
      "|    CLT|    259712|\n",
      "|    MSP|    247980|\n",
      "|    SFO|    235478|\n",
      "|    EWR|    233991|\n",
      "|    IAH|    208591|\n",
      "|    LGA|    203362|\n",
      "|    BOS|    199696|\n",
      "|    LAS|    189920|\n",
      "|    PHL|    186897|\n",
      "|    DCA|    176115|\n",
      "|    MCO|    153720|\n",
      "+-------+----------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 20 busiest airports (outbound)\")\n",
    "df_out_airport.show(20)\n",
    "\n",
    "print(\"Top 20 busiest airports (inbound)\")\n",
    "df_in_airport.show(20)\n",
    "\n",
    "print(\"Top 20 busiest airports\")\n",
    "df_airport.show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3.5\n",
    "<div class=\"alert alert-info\">\n",
    "Which are the **top 20** busiest carriers? Compute this in terms of number of flights.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights per carrier\n",
    "df_carrier = (\n",
    "    df.groupBy(df.carrier)\n",
    "    .count()\n",
    "    .orderBy('count', ascending=0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------+\n",
      "|carrier| count|\n",
      "+-------+------+\n",
      "|     DL|874526|\n",
      "|     US|857906|\n",
      "|     AA|722277|\n",
      "|     UA|638750|\n",
      "|     WN|565426|\n",
      "|     CO|484834|\n",
      "|     NW|482798|\n",
      "|     TW|258205|\n",
      "|     HP|177851|\n",
      "|     AS|117475|\n",
      "+-------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_carrier.show(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4 \n",
    "In this series of questions we focus on the computation of statistics about the percentage of delayed flights.\n",
    "\n",
    "### Question 4.1\n",
    "<div class=\"alert alert-info\">\n",
    "What is the percentage of delayed flights for different hours of the day?  \n",
    "\n",
    "Plot a bar chart and interpret the figure.  \n",
    "\n",
    "\n",
    "<div class=\"label label-success\">IMPORTANT!</div> A flight is considered as delayed if it's actual arrival time is more than 15 minutes later than the scheduled arrival time.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of delayed flights per hour\n",
    "df_flights_hour_delay = df[df.arrival_delay > 15].groupBy((df.scheduled_departure_time/100).cast('int').alias('hour')).count()\n",
    "\n",
    "# number of flights per hour\n",
    "df_flights_hour = df.groupBy((df.scheduled_departure_time/100).cast('int').alias('hour')).count()\n",
    "\n",
    "# percentage of flight in delay per hour\n",
    "percentage_hour_delay = (\n",
    "    df_flights_hour.join(df_flights_hour_delay, df_flights_hour.hour == df_flights_hour_delay.hour, 'inner')\n",
    "    .select(df_flights_hour.hour, df_flights_hour_delay['count']*100/df_flights_hour['count'])\n",
    "    .orderBy('hour')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Nko+Pj5o1a6ZatWqlGy8xMVG7d+827tuqVStVr15dAwcONG6T0WOwSzvz9uabb6pYsWJ6\n9NFH1aVLF0mpb8n//vvvpo/rVmn779evn4oUKaJatWqpWbNmt93W/pb0hQsX1KVLFz355JPq3r27\nkpOTTfuXpAIFCmjEiBFq2LChqlevrldffdW4zr49X3zxRVksFiUkJGjdunW6du2a8dzbQ1pW97uU\nlBTt379fklSnTh01adJEvr6+Dn+pZuRu15Xu27fPGKN3794qVaqUqlatqk6dOmV6v06dOikgIEDl\nypUzQqF9vzXry/6hwIiICE2bNk0bNmyQt7e3BgwYoMKFC2f7Mfz111/G2/ctWrRQUFCQ/Pz89Pbb\nbxu3yWhmuHfv3goMDFShQoVUqVKlDMc+dOiQ8Qda586dVblyZZUuXTrL7wZkxtE2su/HkjRp0iTV\nqlVLDRs2NI5VMTExxh9ddvny5dP7778vf39/FStWTMWKFbuj18Nzzz2nhg0bqlixYmrcuLGk1He1\n7H9k383+Zna8sz9HFSpU0Msvv6zChQvr5ZdfNvaXrVu33nFt6fbjun3cc+fOaerUqVq+fLni4+PV\ntWtXBQQEOBxn27Zt6V4z5cqVU7ly5dSnTx9JqX9Q2idZsru9snJ7i8WiwYMHq0iRIunWSNu3453s\nP3bZPSb5+Pjo448/VnBwsJ588kl16tTJeL3YlyW2bdtW3t7estlsxnIa+x9iLVu2dPiHMpyD5Rl5\nnP2tpwIFCqhixYpq166d+vbtK0nGrMWtt03r8uXLt93GPmNgl3acypUrZ9hH2ttkVOfSpUu3XZZ2\nuUbaZQ/2X2qZ+emnn/Tuu+8a9ew17Qdd+zrOv//+27hPqVKlMvy3lLod0p6qLyvbKi37HywWi0Wl\nS5e+rY7NZjNukx2O+rcvH8ioB3sft8roObDbt2+f3nrrLaWkpKS7/63bs0KFCmrYsKE2b96spUuX\nys/PT4mJifLw8DB+gWVlv7t06ZLi4uJ048aN2x6bfXbeFWJiYox/Z7R/OHo7OaN91/6hNjPDhg3T\nmTNndPjwYWM2X5KefPJJzZo1S76+vll/AEr/vJcpU8b4d9rHExcXd9v9bn2dZyQ7+19OMTuWSLfv\ny/agnNadvB7u5phkJqOxbTabMba937THDyl1u58+ffq219Wt7K/djGR0XO/WrZt2796tX3/9VYsW\nLTIuL1mypL766iuHwTkr+9udHOuyw/5HXkbPUVb2H0fH8osXL2b5mHTq1Cm99tprxjtu9t9Dtx4z\nCxcurLZt2+q7777T8uXLFRwcrFOnTslisTh8hxHOQ2jO4+xnz8hI2l8i7777bqbnx0zr1jWVacc5\nduyYHnnkkQxrnThxQkWLFtWOHTuyVMfDw8P4d3bXv9nf3ipUqJDmz5+vgIAARUVFqU2bNulul/bA\nFx0dbfx89uzZdLfz8/MzDnjNmjW77a1QM2m30blz51S+fHmjpl1m64kdSXuwTjtu2rCXdvzz58/r\n8ccfz3QpSUbCw8ONX7rTp09Xw4YNlZSUdNuMvJT6duPmzZsVERFh/IJ46qmnjG2b1f0uJSVF+fPn\n140bN9LN0qbdZs6Wdv9Iu03t+4ejma+064Ozu+8+9NBDWrVqlU6dOqWjR49q//79+vrrr7V//34t\nWLAg22eRSLu90+7Xabdp0aJFb+svK2unb93/7DLa/3KK/fHY108/+uijpvfJ6LHcyevhbo5Jdzu2\nvd9bj03214P9+JE2KF67ds34t9n5wG/dRoUKFdLXX3+tuLg4HTx4UFFRUZo+fbpiYmI0adIkh8u5\nsrK/3cmxTsr69rV/Ziej29/J/mP3wAMPZPmYtGXLFiMwjxkzRp06dVKBAgVUt27d2/4ge+mll/Td\nd9/pzJkzxoelK1asqJo1a2a5N+QMlmfkYWYHmMDAQOPt3pkzZ+q3335TcnKyoqOjtXz58iz/lduk\nSRNJqQHivffe09GjR5WQkKDdu3cbZ4awr/mKi4vTJ598oosXL+ratWvat2+fxo4dm25GLauKFCli\n/PvYsWPGDIAk498Wi0Xe3t6Ki4vLMOgGBgYavX/11Ve6cuWK1q9fbyyVsPPy8jL++NiwYYNWrlyp\nhIQEXb58WRs3blT//v2N5RsZsa9RtNe5cOGCDh8+rMWLF0tKnW2w95Id9tBqs9k0ffp0xcXF6bff\nfjPe3kurUaNGkqT//e9/mjlzpuLj43X16lXt3LlTw4YNu+0T5mml3bbe3t5KTEzUpEmTMrztM888\nY8ww2ZePtGvXzrg+q/udh4eHatSoISn1QzobN27U5cuXXfqFKzVr1jR+Ac+bN08xMTE6dOiQli5d\nelfj2vddm82mw4cPp7vum2++0X//+195eHioQYMGev75540gdCczdJUrVzbWoP7000/as2ePLl++\nbKzNlJTldeK3evTRR+Xt7S1JWrJkiY4dO6azZ89q7ty5dzReVtj3Y5vNpo8//lgnTpxQcnKyjh07\nphkzZmjIkCHZGudOXg+O+Pn5Gf8+cuTIXX+RVNpjuP05OnXqlBYuXKgrV65owYIFRhi2X592Jtq+\nZGP79u3GazGr1q1bp++++06XLl1SYGCgWrZsafwRmdl+2KBBA+M1M3v2bJ0+fVqnT582ll95eHio\nfv362erFLu0x/9bXTVbdzf6TnWNS2neWvLy8dP36dX3zzTcZvoPx6KOPGsd/+/OU1dPvIWcx05yH\nma3/Kly4sIYNG6YxY8YoOjpa3bp1S3d9RqE7ozFbt26tFStWaNu2bdq7d69atWplXDdgwAAFBwer\nV69eWrt2rY4dO6Z58+bd9uGNrK5TTVvfx8dHlSpVktVq1dq1a7V27VpJqR/Ka9KkidavX6/ExESj\nn4oVK942XkhIiGbMmKETJ07o+++/Nz6pXrx48ds+DGg/DVZiYqLeeeeddJ+gtlgsxpq9jNStW1fN\nmzfX+vXrtW3bNjVo0CDdfQcPHmyEj4weqyP169dXUFCQ9uzZo82bN6tevXqSUmcOExIS0o0xcOBA\nbd68WefPn9fEiRM1ceLEdGOl7elWTZo0MX7p2WeGM9qeUuosT5cuXYwPSXp5ealFixbG9dnZ7wYM\nGKA+ffooJSVF/fr1Mx5bdt3pWtMHH3xQbdq00cqVK7V//35jHWvas5Pcyezi448/bsxYjR07VmPH\njlWtWrW0cOFCbdq0yTjlV1oWi+WOw+27776rgQMH6urVq+revXu66wIDA2/7BZ3V7eXr66tevXrp\nq6++0pkzZ4zTWt7Jc5RVderUUatWrbRmzRrt2rVLzZs3T3d9Vr/k6G5eD2ml3VaVKlVSkSJFdOnS\nJX377bf69ttvVapUqXSn3syOtGO/9dZb2rhxoy5evKgPP/zQ+GCzlDp7+tZbb0lKXRJRvXp1/fHH\nH/rvf/+rwMBAJSYmqlChQrp27VqGz21Glx05csT4opZb2YNnRipUqKBXXnlFs2fP1qFDh/Tcc88Z\n11ksFr3yyiu3fUdAVve3J5980ri9fZ99/fXX063PN3O3+09Wj0kNGzY0XuPDhw/X8OHDVaRIEfn5\n+WW4/OOll17S3r17JaVup7QTDXAdZprzoKycpszuxRdf1DfffKP69evLz89PBQsWVIUKFdS8efN0\nv0QyGzNfvnyaPn26hgwZoqpVq6pQoULy8vLS448/bhzkfHx89P3336t3796qWLGiPD09VbRoUQUE\nBKh///63zWo7On1TRqfnevLJJ+Xl5ZXuG6U6duyoAQMGqGTJkvL29lbz5s2NIJJ2jPz58+vbb79V\ngwYNVLBgQZUvX15jx441+k47c/T4449ryZIlatWqlYoXL64CBQqoZMmSxnl4H3vssUy39T//+U/9\n4x//0COPPKKCBQvK29tbtWvX1pQpU9KdkzY7z5+U+mnu5s2by8vLS8WLF9ebb75pBNG0Y5QpU0ZL\nly5Vly5dVLZsWRUoUEDFixc3zsN7ayBLe9969erp/fffV7ly5eTl5aX69evfdkqptDp37my83dys\nWTN5eXmluz6r+139+vU1YcIElS9fXgULFlRQUJBxtoasbB+zbZmVt9g/+OADdenSRb6+vipatKhe\neeUV9ezZ07itfR9xVCujGqVLl9aHH36oihUrKn/+/OnW3Xfo0EFPP/20SpUqZbxOAgMDNWnSpEzD\nSmb1nnvuOc2dO1cNGzaUn5+fChQooAcffFD9+vXTnDlzjOcqu/uelBoiXn31VRUtWlSFCxdW69at\njUCX1SUMmV2W0XUTJ07UqFGj9Pjjj6tQoUIqXLiwHn74Yb344osKCwszHVu6u9eDo/4KFSqkTz/9\nVI888og8PT1NT9eXnbHLlSunJUuWKCQkRCVKlFD+/PlVokQJhYSEaMmSJenOaPH555+rfv368vb2\nlr+/v0aOHGmcRi3tmJlt4wYNGqhly5aqUKGCvLy8VLhwYT3yyCMaNGiQaUh955139NFHH+mJJ56Q\nl5eXChUqpCeeeEJjx45Nd27m7O5vLVu2VM+ePVWiRIl0x3uzMW69Ljv7z62yekyqXLmyJk+erIce\nekiFChVSjRo1NGvWLPn4+GTY6/PPP2/MpNeqVctYagfXstic9LVEZ8+e1bBhw3ThwgVZLBZ16dJF\nPXv21JQpU7R48WJj3dDgwYON2RnAHe3Zs0fVqlUzvols9+7d6tu3r5KTk9W8efN0b2Mjaw4dOqR2\n7drJYrFozpw5qlu3bm63dMcOHz4sX19fY8nJmTNn1LdvX/3111/y9/fXli1b7motKwD8/fffat68\nuRITE/XJJ5+oQ4cOud1SnuS05Rn58+c3ZteuXr1qzI5YLBb17t3bOOcq4O6mTp2qnTt3yt/fX9ev\nXzfWnBUpUiRbb/sh9byy//jHP4xTnNWoUeOeDsxS6ocgv/zySxUpUkQFChRQbGysbDab8uXLp5Ej\nRxKYAdyx6Oho9ezZUzExMUpMTFTZsmVv+8A6XMdpoblEiRLGaYXsb23YP0HqpMltwCmee+45xcXF\n6dSpU0pKSlLZsmXVoEED9e/f3zhXKbLm2rVrOn78uPHBybRfm32vCgwMVJ06dRQVFaW4uDg98MAD\nqlmzpvr06aOgoKDcbg/APez69es6fvy4PD09VbNmTX3wwQecmzkXOW15RlqnTp1Sjx49tHr1as2e\nPVvLli2Tj4+PAgICNHz48HTrQgEAAAB34/TQfPXqVfXo0UNvvPGGmjZtqtjYWGM98+TJk/X333/r\nk08+cWYLAAAAwF1x6innrl+/roEDB6pt27bG1376+/sb13fu3Fn9+/fPdAz799sDAAAAzla7du0M\nL3daaLbZbHr33Xf18MMPq1evXsblMTExxrdEhYeHZ+mrWB01DwAAAOSUzCZrnRaaIyIitGrVKj36\n6KPGOXbDwsK0Zs0aRUZGymKxqHz58ulOwA4AAAC4I6eF5qCgIB08ePC2y+1fqQwAAADcK/hGQAAA\nAMAEoRkAAAAwQWgGAAAATBCaAQAAABOEZgAAAMAEoRkAAAAwQWgGAAAATBCaAQAAABOEZgAAAMAE\noRkAAAAwQWgGAAAATBCaAQAAABOEZgAAAMAEoRkAAAAwQWgGAAAATBCaAQAAABOEZgAAAMAEoRkA\nAAAwQWgGAAAATBCaAQAAABOEZgAAAMAEoRkAAAAwQWgGAAAATBCaAQAAABOEZgAAAMAEoRkAAAAw\nQWgGAAAATBCaAQAAABOEZgAAAMAEoRkAAAAwQWgGAAAATBCaAQAAABOEZgAAAMAEoRkAAAAwQWgG\nAAAATOTP7QYAAACcLTk5WVar1WnjV6pUSZ6enk4bH7mP0AwAAO57VqtVk1YtVbEyZXJ87Atnz2pw\n246qWrVqjo8N90FoBgAAeUKxMmVUskL53G4D9yjWNAMAAAAmmGkGAACSnLvulzW/uNcRmgEAgKTU\ndb8fLl6qB0qVztFxL0af03udWfOLexuhGQAAGB4oVVr+5Vn3C9yKNc0AAACACUIzAAAAYILQDAAA\nAJggNAMAAAAm+CAgAABwOb7WGvcaQjMAAHA5q9Wq8cuW6oEyOXt6O0m6ePachnfgFHfIWYRmAACQ\nKx4oU1olOL0d7hGsaQYAAABMEJoBAAAAE4RmAAAAwAShGQAAADBBaAYAAABMEJoBAAAAE4RmAAAA\nwAShGQCpx3oCAAAgAElEQVQAADBBaAYAAABMOO0bAc+ePathw4bpwoULslgs6tKli3r27Km4uDiF\nhYXpzJkzKleunCZPniw/Pz9ntQEAAOByycnJslqtThu/UqVK8vT0dNr4uJ3TQnP+/Pk1cuRIPfbY\nY7p69ao6dOigp59+WkuXLlWDBg302muvacaMGZoxY4aGDh3qrDYAAABczmq1auraZSpetkyOj33+\nzFkNeKGDqlatmuNjwzGnheYSJUqoRIkSkqTChQvr4YcfVnR0tDZs2KB///vfkqT27durR48ehGYA\nAHDfKV62jEpVLJ/bbSCHuGRN86lTpxQZGanq1asrNjZWxYsXlyQVL15csbGxrmgBAAAAuGNOD81X\nr17VwIED9e6778rHxyfddRaLRRaLxdktAAAAAHfFacszJOn69esaOHCg2rZtq6ZNm0qS/P399fff\nf6tEiRKKiYlRsWLFTMeJiIhwZpsAAEDS8ePHnTb2gQMHFB8f75JaebEenM9podlms+ndd9/Vww8/\nrF69ehmXBwcHa/ny5QoNDdWKFSuMMJ2Z2rVrO6tNAADw//n6+uqXTVudMnZAQEC6D675+vpq63bn\n1HJUb8/ubS6td+D37S6rh5yR2USt05ZnREREaNWqVdq5c6dCQkIUEhKiTZs2KTQ0VNu2bVOLFi20\nY8cOhYaGOqsFAAAAIEc4baY5KChIBw8ezPC6OXPmOKssAAAAkOP4RkAAAADABKEZAAAAMOHUs2cA\nAIA758yvYuZrmIHsITQDAOCmrFarRn23VEVLlc7RceOiz+mjrh05+wKQDYRmAADcWNFSpVWsHF/F\nDOQ21jQDAAAAJgjNAAAAgAlCMwAAAGCCNc0AAAD3OM604nyEZgAAgHuc1WrVN+uXqUTZMjk67t9n\nzuq15h0404oIzQAAAPeFEmXLqPSDnGnFWVjTDAAAAJhgphkAgCxi3SiQdxGaAQDIIqvVqnf+vVR+\nOfwNfZejz2lCd76hD3BnhGYAALLBr1RpPVCWdaNAXsOaZgAAAMCEaWjetWuXrly5IklavHix3nvv\nPZ08edLpjQEAAADuwnR5xtixY7Vq1SodOXJEs2fPVtu2bfXuu+9q3rx5rugPAAAAbiYvfijWNDR7\neHjIYrFo06ZN6tq1q3r27Kl169a5ojcAAAC4IavVqjkblqtkuZz9UGzM6XPqFdzeLT8UaxqaU1JS\ntG/fPq1fv14fffSRcRkAAADyrpLlSqtspQq53YbLmK5pHjRokN577z3VrFlTjzzyiI4dO6YHH3zQ\nFb0BAAAAbsF0prly5cpauXKl8fNDDz2ksLAwpzYFAAAAuBPTmeYhQ4bcdtnQoUOd0gwAAADgjhzO\nNF+4cEGxsbFKSkpSVFSUcfnly5eVkJDgkuYAAAAAd+AwNK9atUrz5s1TTEyMQkNDjct9fHz06quv\nuqQ5AAAAwB04DM29evVSr1699PXXX6t///6u7AkAAABwK6YfBOzfv78SExN17ty5dKeaq1KlilMb\nAwAAANyFaWhesGCBJk6cqCJFiihfvv/73OCGDRuc2hgAAADgLkxD87fffqvVq1erXLlyrugHAAAA\ncDump5wrWbIkgRkAAAB5msOZ5qioKNlsNjVo0ECffvqpWrVqpYIFCxrXs6YZAAAAeYXD0Jz2NHOS\ntG7dunQ/s6YZAAAAeYXD0EwoBgAAAFKZfhAw7bcB2vn6+qpUqVJOaQgAAABwN6ahOTQ0VGfOnJGf\nn5+k1K/R9vf3V8GCBfX5558rMDDQ6U0CAAAAuck0ND/33HOqW7eumjZtKkkKDw/Xjh071LRpU338\n8cdasmSJ05sEAAAAcpPpKed27txpBGZJatq0qXbv3q169eopKSnJqc0BAAAA7sA0NNtsNkVERBg/\n//bbb7p582bqnfOZ3h0AAAC455kuzxgzZozCwsLk5eUlSUpMTNTnn3+uq1evqlevXs7uDwAAAMh1\npqE5KChI4eHhOnbsmCTpoYcekqenpySpffv2zu0OAAAAcAMOQ3NycrI8PT2VmJgoSXrwwQclSSkp\nKUpMTDRmngEAAID7ncPQ3KVLF61YsSLDU8pZLBZFRkY6tTEAAADAXTgMzStWrJAkHTx40GXNAAAA\nAO6I018AAAAAJhzONNetW1cWiyXD6ywWi7Zv3+60pgAAAAB34jA0z58/X97e3rJYLLLZbK7sCQAA\nAHArDkPz8OHDtWzZMv3jH//QZ5995sqeAADIkuTkZFmtVqeMXalSJeMUqwDgMDQnJibqwIEDOnDg\ngKKiom67vkqVKk5tDAAAM1arVWFzl8q3ZJkcHTc+5qz++UpHVa1aNUfHBXDvchiae/TooWHDhunk\nyZMKDQ297foNGzY4tTEAALLCt2QZFS1bPrfbAHCfcxiau3Xrpm7duuntt9/W5MmTXdkTAAAA4FZM\nv0bbXQKzM9etSaxdAwAAcFfu8PkF09DsLqxWq9q076iChQrl+NhJ167ph+VLWbsGAADghqxWqxZu\nWqFS5Urn6LjRp8+pm0KylAHvmdAsSQULFZKXl3dutwEAAAAXK1WutMpVrpBr9e+p0AwAcG/u8BYq\nADiDw9C8YMGCTO/48ssv53gzAIB7m9Vq1ZuzlsqnRM6eAu7K32c1rQ+ngAOQexyG5v3798tiseji\nxYvatWuX6tevL5vNph07dqhu3bqEZgBAhnxKlFGRMpwCDsD9xWFoHj9+vCQpNDRUK1euVIUKqWtI\nTp48qY8++sg13QEAAABuIJ/ZDU6fPm0EZkmqUKGCTp065dSmAAAAAHdiGpqLFy+uadOmKSYmRtHR\n0fr6669VokSJLA0+YsQINWjQQG3atDEumzJliho3bqyQkBCFhIRo06ZNd949AAAA4AKmofnTTz/V\nwYMH1aZNG7Vt21aRkZGaMGFClgbv2LGjZs6cme4yi8Wi3r17a8WKFVqxYoUaN258Z50DAAAALmJ6\nyrlSpUppypQpdzR4UFBQhks5bDbbHY0HAAAA5AbTmeaEhAT985//1JAhQyRJR48eVXh4+F0V/fe/\n/622bdtq5MiRunz58l2NBQAAADibaWh+//33dePGDUVGRkq6u5lnSXrppZf0888/a+XKlSpRooRx\nlg4AAADAXZkuzzh06JA+/fRTbd26VZLk4+NzV8sr/P39jX937txZ/fv3N71PRESEjh8/fsc1s+LA\ngQOKj493ag0AuN8581id0XGaevduPVf/XqdeztUy6hVwcT0v19XLiGlovvUrS5OSku4qNMfExKhk\nyZKSpPDw8Cx9u1Pt2rXl6+t7xzWzIiAggG+aAoC75OvrK/21zSljZ3Sc9vX11X9OurbeyrNbXVpv\n3S+urffLJtfU8/X11dbtzqnlqN6e3c7ZVxzVO/D7dpfWizrgnHqO9pUTh1xbL/roDqfXi4iIcHg7\n09AcFBSkr7/+WklJSdq5c6dmz56t4ODgLDUxePBg7dq1S3FxcWrSpIneeust7dq1S5GRkbJYLCpf\nvrw+/PDDLD4kAAAAIHeYhuawsDDNnDlThQsX1meffabg4GCFhoZmafBJkybddlmnTp2y3yUA4I4k\nJyfLarU6ZexKlSrd9m4kANyvTEOzzWbTG2+8oTfeeMMV/QAAcpDValXoN0tVuESZHB336t9nNeO1\njixrA5BnmIbm4OBgtWnTRt26dVPFihVd0RMAIAcVLlFGfqXL53YbAHBPMz3l3MqVK+Xr66tXXnlF\nffv21S+//OKKvgAAAAC3YRqaixcvrjfffFP//e9/1aVLF33wwQcKDg7WrFmzlJSU5IoeAQAAgFxl\nGpolKTExUYsXL9a0adNUsWJFvf322zp69KheffVVZ/cHAAAA5DrTNc0ffvih1q9fr+DgYE2cONH4\n0Efbtm31/PPPO71BAAAAILeZhuayZctqzZo1KlKkyG3XzZ071ylNAQAAAO7ENDRntgSjVKlSOdoM\nAAAA4I5MQ/PBgwc1ZswYRUZGKjk5WZJksVgUGRnp9OYAAAAAd2D6QcD3339fgwYNUqVKlbRx40b1\n69dPYWFhrugNAAAAcAumoTkpKUkNGjSQzWZTqVKlFBYWpnXr1rmiNwAAAMAtmIZmDw8PSZKfn58i\nIyN14cIFxcXFOb0xAAAAwF2Yrml+4YUXdOHCBfXr10/dunVTSkqKBg4c6IreAOC+k5ycLKvV6pSx\nK1WqJE9PT6eMDQB5nWlo7tOnjySpcePG2rlzp5KTk+Xj4+P0xnIbv9iA3OHM156U+68/q9WqPl99\nL+/ipXN03ITz5zTrjReNc+kDAHKWw9AcFRWV6R2rVKmS4824E6vVqnZdu6lgIe8cHTfpWoJWfreQ\nX2yAA1arVT0nzJB3sRI5PnbChb81753QXH/9eRcvLd9S5XK1BwBA9jgMzaGhoZneccOGDTnejLsp\nWMhbXoUL53YbQJ7jXayEfErk7EwsAAB3w2FozguhGAAAAMgK07NnSNK2bdv073//W5J0/vx5/fXX\nX05tCgAAAHAnpqF5+vTpmjp1qubNmydJun79ukaMGOH0xgAAAAB3YRqaV69erTlz5sjbO/UDcWXK\nlNHVq1ed3hgAAADgLkxDc6FChTg9GgAAAPI00/M0lylTRnv27JEkpaSkaPr06bl+uiYAAADAlUxn\nmkeNGqVp06bpyJEjqlGjhnbt2sWaZgAAAOQppjPNJUuW1OzZs5WQkKCbN2/miW8DBAAAANJyGJp/\n/fVXWSwW2Ww2WSyW265v0qSJUxsDAAAA3IXD0Pztt99KkpKTk7V//35jHfPhw4dVvXp1QjMAAADy\nDIehef78+ZKkwYMHa+TIkapRo4Yk6Y8//tDcuXNd0x0AAADgBkw/CHj48GEjMEtS9erVdfjwYac2\nBQAAALgT0w8Cenl5acWKFQoJCZEkrVq1Sl5eXk5vDABcITk5WVar1WnjV6pUiXPdA8B9wDQ0jxs3\nTsOGDdPo0aMlSVWrVtWnn37q9MYAwBWsVqt6/XOuCvuXzPGxr8bGaE7YK5zbHgDuA6ahuUqVKlq2\nbJmuXLkiSZxyDshj8sJMbGH/kvIpWTZXewAAuDfT0GxHWAbyJqvVqu5jv5RXUf8cHzsxLlb/Hj2Q\nmVgAgNvLcmgGkHd5FfWXT/FSud0GAAC5xvTsGQAAAEBe5zA0d+/eXZL40B8AAADyPIehOTY2Vhcv\nXtSWLVuUmJh4238AAABAXuFwTXPz5s31zDPPKCkpSYGBgemus1gsioyMdHpzAAAAgDtwGJrDwsIU\nFhambt26aeHCha7sCQAAAHArph8EtAfmhIQEJSQkOL0hAAAAwN2YhuYTJ06oS5cuqlu3rurWrauu\nXbvq5MmTrugNAAAAcAumofm9995Tly5dtG/fPu3bt0+dO3fWe++954reAAAAALdgGpovXLigTp06\nKV++fMqXL586duyo2NhYV/QGAAAAuAXT0Ozh4aGjR48aPx87dkz58/NFggAAAMg7TNNvWFiYunfv\nrmrVqkmSDh48yBeeAAAAIE8xDc2NGzfW6tWrtW/fPlksFtWoUUPFihVzRW8AAACAW8jSOgt/f38F\nBwc7uxcAAADALZmuaQYAAADyOkIzAAAAYILQDAAAAJgwDc3nz5/X0KFD1a1bN0mpZ89YtGiR0xsD\nAAAA3IVpaB41apRq1aql+Ph4SdJDDz2khQsXOr0xAAAAwF2YhuaYmBh169ZNHh4ekiRPT09ZLBan\nNwYAAAC4iyx9I6DNZjN+vnz5slMbAgAAANyN6XmamzVrpjFjxujKlStatmyZFixYoA4dOriiNwAA\nAMAtmIbm0NBQrVy5UpcuXdLGjRvVs2dPtWvXzhW9AQAAAG4hS98I2K5dO4IyAAAA8izT0Dxw4EBZ\nLBZjXbPFYpGPj48CAwPVoUMH5cvHqZ4BAABwfzNNvMWLF9e5c+cUFBSk2rVrKzo6WpL0448/6pNP\nPnF6gwAAAEBuM51pPnjwoObPny9PT09JUteuXfXKK69o3rx5pks2RowYoY0bN8rf318//PCDJCku\nLk5hYWE6c+aMypUrp8mTJ8vPzy8HHgoAAADgHKYzzbGxscqf//+ydf78+XXx4kV5enqqYMGCmd63\nY8eOmjlzZrrLZsyYoQYNGuinn35SvXr1NGPGjDtsHQAAAHAN09D81FNPqV+/flq9erV++OEH9e/f\nX0FBQbp69aox++xIUFDQbbPIGzZsUPv27SVJ7du3V3h4+F20DwAAADif6fKM0aNH6/vvv9dPP/0k\nSWrUqJG6du2qAgUK6D//+U+2C8bGxqp48eKSUtdLx8bGZnsMAAAAwJVMQ7Onp6d69OihHj165Hhx\ni8XCV3IDAADA7ZmG5uvXr2vp0qU6ePCgkpKSjMvHjRt3RwX9/f31999/q0SJEoqJiVGxYsVM7xMR\nEaHjx4/fUb2sOnDggOLj442fnVnv1lqAO7ufXnv3e72Mji3Uo5671rufXnv3ez2H+0oBF9fzcl29\njJiG5vfee083b97Ujh071K1bN/3www+qU6fOHTcWHBys5cuXKzQ0VCtWrFDTpk1N71O7dm35+vre\ncc2sCAgIUNWqVY2fnVnv1lqAO/P19ZV+2ee08TN87W0/4tp6v510bb3/bXRJLaNe1DbX1vvLtfX+\nc9K19Vae3erSeut+cW29Xza5pp6vr6+2bndOLUf19ux2zr7iqN6B37e7tF7UAefUc7SvnDjk2nrR\nR3c4vV5ERITD25l+EHD//v0aP368/Pz81K9fPy1atEhHjmTtF9rgwYPVtWtX/fXXX2rSpImWLl2q\n0NBQbdu2TS1atNCOHTsUGhqaxYcEAAAA5A7TmeaCBQvKYrHIw8NDCQkJ8vX1zfKH9yZNmpTh5XPm\nzMlWkwDSS05OltVqdcrYlSpVMj0zDgAAeY1paC5atKji4uLUqFEjvfbaa3rggQdUunRpV/QGwAGr\n1apuoybIu6j5ZwKyIyHughZ+9A7LhwAAuIVpaJ4xY4Y8PDwUFhamVatW6cqVKwoJCXFFbwAy4V20\nmAoXK5HbbQAAkCeYrmmeNWtW6g3z5VNISIi6d++uRYsWOb0xAAAAwF2YhuY1a9Zk6TIAAADgfuVw\necbWrVu1ZcsWxcTE6NNPP5XNZpMkXblyxWXNAQAAAO7AYWguUKCAvL29lS9fPnl7exuhuVSpUurX\nr5/LGgQAAABym8PQ/NRTT+mpp55S8+bN9eijj7qyJwAAAMCtmJ4948EHH9TixYt14sQJ3bhxQ5Jk\nsVg0bNgwpzcHAAAAuAPT0Dxo0CDduHFD1atXV8GCBWWz2WSxWFzRGwAAAOAWTEPziRMn9OOPP7qi\nFwAAAMAtmZ5yrkKFCpwxAwAAAHma6Uyzj4+POnbsqEaNGqlAgQKSWNMMAACAvMU0NFeuXFmVK1eW\nlBqWWdMMAACAvMY0NL/11luu6AMAAABwW6Zrms+fP6+hQ4eqW7dukqSDBw9q0aJFTm8MAAAAcBem\noXnUqFGqVauW4uPjJUkPPfSQFi5c6PTGAAAAAHdhGppjYmLUrVs3eXh4SJI8PT1Z0wwAAIA8xTQ0\ne3h4yGazGT9fvnzZqQ0BAAAA7sb0g4DNmjXTmDFjdOXKFS1btkwLFixQhw4dXNEbAAAA4BZMQ3No\naKhWrlypS5cuaePGjerZs6fatWvnit4AAAAAt2AamiWpXbt2BGUAAADkWaZrmgcMGKC4uDjj54sX\nL2rQoEFObQoAAABwJ6ah+eTJkypatKjx8wMPPKDjx487tSkAAADAnZiG5ps3b+rGjRvGz9evX9f1\n69ed2hQAAADgTkzXNDds2FCDBw9Wz549JUlz585Vo0aNnN4YAAAA4C5MQ/PgwYM1ffp0jR8/XpL0\nzDPPKDQ01OmNAQAAAO4i09CckpKiDz74QB999JEGDBjgqp4AAAAAt5LpmmYPDw8dOnTIVb0AAAAA\nbsl0eUa9evX04YcfKiQkRN7e3sblVapUcWpjAAAAgLswDc1r1qyRJP3666/pLt+wYYNTGgIAAADc\njWloJhwD5pKTk2W1Wp0ydqVKleTp6emUsQEAQNZk6Wu0t23bpmPHjql79+46f/684uPjVblyZWf3\nBtwzrFarur7zobz8HsjRcRMvX9R3E95T1apVc3RcAACQPaahefr06dq4caPOnz+v7t276/r16xox\nYoS+++47V/QH3DO8/B5Q4Qf8c7sNAADgBKbfCLh69WrNmTPH+BBgmTJldPXqVac3BgAAALgL09Bc\nqFAh1lMCAAAgTzNdnlGmTBnt2bNHUuqXnUyfPp31lQAAAMhTTGeaR40apWnTpunIkSOqUaOGdu3a\npREjRriiNwAAAMAtZDrTHBcXp5iYGH355Zfy8PDQzZs35ePj46reAAAAALfgcKZ57dq1atKkiUJD\nQ/Xss89q3759BGYAAADkSQ5nmr/++mt99913euyxx7Rjxw5NnTpV9evXd2VvAAAAgFtwONOcL18+\nPfbYY5KkevXq6cqVKy5rCgAAAHAnDmeak5OTFRUVJUmy2WxKSkoyfpakKlWqOL87AAAAwA04DM1J\nSUkKDQ1Nd1nanzds2OC8rgAAAAA34jA0E4oBAACAVKbnaQYAAADyOkIzAAAAYILQDAAAAJggNAMA\nAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAm\nCM0AAACACUIzAAAAYCJ/bhUODg5W4cKF5eHhofz582vJkiW51QruQ8nJybJarU4Zu1KlSvL09HTK\n2AAAwD3lWmiWpPnz56to0aK52QLuU1arVS++PVJevkVydNzE+Ev6fvInqlq1ao6OCwAA3Fuuhmab\nzZab5d0KM6M5z8u3iLyLFsvtNgAAwH0g10KzxWJR7969lS9fPnXt2lVdunTJrVbcgtVqVfsefVTI\nu3COjnst4aqWz5/FzCgAAMBdyLXQvGjRIpUsWVIXLlxQ79699dBDDykoKCi32nELhbwLy8vHN7fb\nAAAAwC1yLTSXLFlSklSsWDE1a9ZMf/zxh8PQHBERoePHjzu1nwMHDig+Pt742Zn1bq2VG/Xud/f7\n8+fKevfTa+9+r5fb+wr1qJedevfTa+9+r+dwXyng4nperquXkVwJzYmJiUpJSZGPj48SEhK0ZcsW\nDRgwwOHta9euLV9f587ABgQEpFvC4Mx6t9bKjXr3O19fX+n7NU4Z2+Hzt/IX19Zbt90l9Xx9faVf\n9jmllsN624+4tt5vJ11b738bXVLLqBe1zbX1/nJtvf+cdG29lWe3urTeul9cW++XTa6p5+vrq63b\nnVPLUb09u52zrziqd+B35xynHdWLOuCa3wv2eicOubZe9NEdTq8XERHh8Ha5EprPnz9vhOSUlBS1\nadNGDRs2zI1W4CJ80BEAANzLciU0V6hQQStXrsyN0sglVqtVnd8YrEK+fjk67rX4y1r81aQ8N5MO\nAABcK1dPOYe8pZCvn7z9OC83AAC49/A12gAAAIAJQjMAAABggtAMAAAAmCA0AwAAACYIzQAAAIAJ\nQjMAAABggtAMAAAAmCA0AwAAACYIzQAAAIAJQjMAAABggtAMAAAAmCA0AwAAACYIzQAAAIAJQjMA\nAABggtAMAAAAmCA0AwAAACYIzQAAAIAJQjMAAABggtAMAAAAmCA0AwAAACYIzQAAAIAJQjMAAABg\ngtAMAAAAmCA0AwAAACYIzQAAAIAJQjMAAABggtAMAAAAmCA0AwAAACYIzQAAAIAJQjMAAABggtAM\nAAAAmCA0AwAAACYIzQAAAIAJQjMAAABggtAMAAAAmCA0AwAAACYIzQAAAIAJQjMAAABggtAMAAAA\nmCA0AwAAACYIzQAAAIAJQjMAAABggtAMAAAAmCA0AwAAACYIzQAAAIAJQjMAAABggtAMAAAAmMif\n2w0gdyQnJ8tqtTpl7EqVKsnT09MpYwMAAOQGQnMeZbVa1fHVN1SwsG+Ojpt0NV5LZ36lqlWr5ui4\nAAAAuYnQnIcVLOwrb1+/3G4DAADA7bGmGQAAADBBaAYAAABMEJoBAAAAE4RmAAAAwAShGQAAADBB\naAYAAABMEJoBAAAAE7kSmjdt2qTnn39ezZs314wZM3KjBQAAACDLXB6aU1JSNHbsWM2cOVNr1qzR\nmjVrdPToUVe3AQAAAGSZy0PzH3/8oYoVK6p8+fIqUKCAWrVqpZ9//tnVbQAAAABZ5vLQHB0drTJl\nyhg/lypVStHR0a5uAwAAAMiy/K4uaLFY7vi+Sdeu5WAn5uMmXUtwQi3HY15LuJrj9TIbM+lqfI7X\ny2zMa/GXc7xeZmMmxl/K8XqZjZl4+WLO18tkzIS4Czlez9GYiXGxOV4rs3ETLvztlHqOxr0aG+OU\neo7GTTh/LsdrZTbm1b/P5ni9zMa84oR6mY0ZH5Pz9TIb83J0zj9/mY0Z54R6mY150Qn1HI158WzO\n18ps3Atnc35fyWzc82ecU+/8mbNSzdsv/9sJ9f4+c1YKyPi6mNM5//zFnD4nPZrxddFOqBd9+pz0\ncNZua7HZbLYc7yATv//+u6ZMmaJvv/1WkjR9+nRZLBaFhoZmePuIiAhXtgcAAIA8rHbt2hle7vKZ\n5oCAAB0/flynTp1SyZIltXbtWk2aNMnh7R01DgAAALiKy0Nz/vz5NXr0aPXt21c3b95Up06d9PDD\nWZwXBwAAAHKBy5dnAAAAAPcavhEQAAAAMEFoBgAAAEwQmgEAAAAT92Vo3rRpk55//nk1b95cM2bM\ncGqtESNGqEGDBmrTpo1T69idPXtWPXr0UKtWrdS6dWvNmzfPqfWSkpLUuXNntWvXTq1bt9aUKVOc\nWk9K/ar1kJAQvf76606vJUnBwcFq06aNQkJC1KlTJ6fWunz5sgYOHKiWLVvqhRde0O+//+60WseO\nHVNISIjxX+3atZ2+v8yZM0etW7dWmzZtNGTIECUnJzu13ty5c9WmTRu1bt1ac+fOzfHxM3p9x8XF\nqRAAY98AAA/hSURBVHfv3mrRooX69Omjy5dz7vzjGdX78ccf1apVKz322GP6888/c6yWo3oTJkxQ\ny5Yt1bZtWw0YMEDx8Tl3PveM6k2ePFlt27ZVSEiI+vbtq5iYnDtndmbH51mzZqlatWqKi4tzar0p\nU6aocePGxutw06ZNTqslSfPnz1fLli3VunVrffbZZzlSy1G9sLAw43EFBwcrJCTEqfUiIyPVpUsX\nhYSEqGPHjvrjjz+cWu/gwYN68cUX1aZNG73++uu6cuVKjtVz9LvcWccXR/WcdXxxVM9ZxxdH9XL8\n+GK7z9y4ccPWtGlT28mTJ23Jycm2tm3b2qKiopxWb/fu3bY///zT1rp1a6fVSCsmJsb2v//9z2az\n2WxXrlyxNW/e3KmPz2az2RISEmw2m812/fp1W+fOnW2///67U+vNmjXLNnjwYFu/fv2cWsfu2Wef\ntV28eNEltYYNG2ZbvHixzWZL3Z6XL192Sd2UlBTb008/bTtz5ozTapw7d84WHBxsS0pKstlsNtug\nQYNsy5Ytc1q9Q4cO2Vq3bm27du2a7caNG7ZevXrZjh8/nqM1Mnp9T5gwwTZjxgybzWazTZ8+3fbZ\nZ585tV5UVJTt2LFjtu7du9sOHDiQY7Uc1duyZYstJSXFZrPZbJ999pnTH198fLzx73nz5tnee+89\np9az2Wy2M2fO2Pr06ZPjr/2M6k2ZMsU2a9asHKuRWa3t27fbevXqZUtOTrbZbDZbbGysU+ulNX78\neNu0adOcWq937962TZs22Ww2m+3XX3+1de/e3an1OnToYNu9e/f/a+/eY5o6/z+Av4uAk9tEwDpB\nIhQtoAH/gMjQDQRROgXkIkKcGSzRZRcYF3EUjE4d6HQXp9vYnCzLgrKQcXFzjGzcCgaQdTPqAiwy\nGQ6d6EQcswVLeX5/EE5Ai9XsPNUvv88rMaG0Pe/TBj/n0+c5pw9jjLGvv/6aHTx4ULS8yY7lvOrL\nZHm86stkebzqy2R5YteXKTfSfO7cObi6usLFxQUWFhZYs2YNampquOX5+fnBzs6O2/bv5uTkBC8v\nLwCAtbU1ZDKZqCMzhsyYMQMAoNPpMDw8/J9WdTTm6tWrUKlUWL9+PbcMQ5gJvkRmYGAAarVaGM02\nNzeHra0t91wAaGpqwrx58yYsYc+DXq+HVqvF8PAwBgcHIZVKuWVdvHgRPj4+mD59OqZNmwZ/f3/8\n8MMPomYY+v9dW1uL6OhoAEB0dDSqq6u55slkMri5uYmWYSxv2bJlMDMbPTT4+vri6lXxVuAylGdj\nYyP8rNFohGxeeQCwd+9eZGVliZZjLI9HfTGUVVxcjC1btsDCwgIAMGvWLK55Yxhj+P7777F27Vqu\neRKJRBiZHBgYELW+GMrr7u6Gn58fACAwMFDU+mLoWN7b28utvkzWO/CqL5Pl8aovk+WJXV+mXNPc\n29s7oTGQSqXo7e19hHvET09PD9rb2+Hj48M1Z2RkBFFRUQgMDMSyZcu45uXn52Pbtm2iHjiNkUgk\nSE5ORkxMDEpKSrjl9PT0YNasWVAqlYiOjsb27duh1Wq55Y333XffiXpAM0QqlSI5ORnBwcF45pln\nYGtri8DAQG55CxYsgFqtRn9/P7RaLVQqlagN3mRu3LgBR0dHAICjoyNu3OCzxPjjoLS0FEFBQdxz\n3n//fQQHB+PkyZNITU3lmlVdXY05c+bA09OTa854RUVFiIyMRE5Ojqin89ytu7sbarUa8fHx2LRp\nE86fP88tazy1Wg0HBwe4urpyzcnJycGBAwcQHByM/fv3IzMzk2ueh4eH0LRWVVXhL05LcI8/lpui\nvpiqdzCWx6u+3J0nZn2Zck0zz1HQx8nt27eRmpqK3NxcWFtbc80yMzPDiRMn0NDQgLNnz+LChQtc\ncurq6uDg4ABvb2+TjPyOKS4uRkVFBY4ePYpjx45BrVZzyRkeHkZbWxsSExNRXl6OGTNmcD/nHgDu\n3LmDuro6KBQKrjm3bt1CbW0tamtr0djYCI1Gg2+++YZbnkwmw+bNm/Hiiy9i8+bN8PLyMumHLWC0\n3kzVmlNQUAALCwuTXK+Rnp6O+vp6REREoKioiFuOVqvFp59+OuHAybvWJCYmoqamBidOnICTkxP2\n7dvHLUuv1+PWrVsoKSlBVlYW0tLSuGWNd/LkSZP8nRQXFyMnJwf19fVQKpXIycnhmpefn4/i4mLE\nxMTg9u3bwgi+mMYfy8ePigJ86ospe4f75fGqL4byxKwvU65plkqlEz4NXr16lesU8aOg0+mQmpqK\nyMhIrFy50mS5tra2WLp0KRobG7ls/8yZM6itrUVISAgyMzPR0tKCbdu2cckab/bs2QBGpzLDwsJE\nvbhkvDlz5kAqlQqfflevXo22tjYuWeM1NDRg0aJFok7VGtLU1AQXFxfY29vD3NwcYWFhOHPmDNfM\nuLg4lJWVoaioCHZ2dtxOYxjPwcEB169fBwBcu3aN+/v6KJSVlUGlUuGdd94xae7atWtFP8VmvEuX\nLuHy5cuIjIxESEgIent7ERsby3W2wMHBQWh+1q9fz3X0VyqVYtWqVQAAHx8fmJmZ4ebNm9zygNHB\ngOrqau4fygGgoqICYWFhAIDw8HButXqMu7s7CgsLUVZWhjVr1og+km7oWM6zvpi6d5gsj1d9Mfb6\nxKgvU65pXrx4Mbq7u9HT04M7d+6gsrISoaGhj3q3RMMYQ25uLmQyGZKSkrjn9fX1CdOJg4ODaGpq\n4rbseUZGBlQqFWpra/Hee+8hICAA+/fv55I1RqvVCldEazQanDp1CgsXLuSS5eTkhKeeegpdXV0A\ngObmZnh4eHDJGs8Up2YAgLOzM86ePYvBwUEwxkzy+saanStXruDHH380yWhXSEgIysvLAYwexE35\nwdUUMzANDQ0oLCzExx9/jOnTp3PP++OPP4Sfa2pq4O7uzi1LLpejqalJmBGRSqUoKyuDg4MDt8zx\n15xUV1dzqy8AsHLlSrS0tAAAurq6oNPpYG9vzy0PGP2w7O7ubpLBqdmzZ6O1tRUA0NLSgvnz53PN\n6+vrAzB6imJBQQESExNF2/Zkx3Je9eVBegcx68tkebzqy2R5YteXKbmMtkqlQn5+PkZGRhAXF4eX\nXnqJW1ZGRgZaW1vR398PBwcHpKamIjY2llueWq3G888/D7lcLkzbZGRk4Nlnn+WS99tvvyE7Oxt6\nvR6MMSgUCrzyyitcssZrbW3F559/jk8++YRrzp9//onXXnsNwOjUZkREBNe/l46ODuTm5kKn08HV\n1RV79+7lejGgRqPBihUrUFNTc8/UHw+HDx9GZWUlzM3N4e3tjbfeeovLlOaYjRs3or+/H+bm5lAq\nlQgICBB1+4b+f4eGhiItLQ1//fUXnJ2dcfDgQdEuBr47LyUlBTNnzsSePXtw8+ZN2NrawsvLC0eP\nHuWWd+TIEeh0Ojz55JMAgCVLluDNN9/kltfQ0ICuri5IJBI4Oztj165dwuyP2Hl31+fQ0FCUlpZi\n5syZXPJSUlLQ2tqK9vZ2SCQSuLi4YPfu3cI5q2JmjY2w5eTkoKOjAxYWFnjjjTewdOlSEV7Z5O+l\nUqnEkiVLsGHDBlFy7pc3f/585OXlQa/X44knnsDOnTvh7e3NJS8lJQUajQbHjh0DMDozmJGRIUoW\nMPmx3MfHh0t9MZSXnp4OnU7Hpb5MlpeXl8elvkyWV1paKmp9mZJNMyGEEEIIIWKacqdnEEIIIYQQ\nIjZqmgkhhBBCCDGCmmZCCCGEEEKMoKaZEEIIIYQQI6hpJoQQQgghxAhqmgkhhBBCCDGCmmZCCHkI\nISEh6OzsnPC7mJgYYdGFR+H48eNQKBTCcr/jVVdXT1g57fTp01y+S97Q+0IIIVMJNc2EEPIfjS2T\nLBa9Xv9Qjy8qKsKBAwdQVlYGa2vrCfdVV1dzXbqZEEL+v6CmmRBCHtL91oT6+++/8eqrryIyMhIR\nERGoqKgQ7vP09IRWqzV429PTEx9++CHi4uLw0Ucf3bPdc+fOYcOGDYiMjERCQoLQCKelpeHSpUvI\nysrC1q1bJzynsbERdXV1OHLkCNatW4eKigpIJBLo9Xrs2LEDkZGRiIqKwu+//y48p7y8HPHx8YiJ\nicELL7wgLPt+N7VajYiICERERGDPnj0T7nv77bcRFxeHqKgoJCUl4cqVKwCA3bt3o7CwUHhcW1sb\nwsPDJ30vCSHkscIIIYQ8sBUrVrDw8HAWFRUl/PP19WWtra2MMcZef/119sEHHzDGGLt27Rpbvnw5\nu3DhAmOMMblczjQajbCt8bflcjn77LPPDGYODQ2xoKAg1tzczBhjrKmpiQUHBzOdTifs01jG3bKz\ns1lRUZFwu6WlhS1atIi1t7czxhgrKChgmZmZjDHGfvrpJ7ZlyxY2NDTEGGOsvr6eJSQkGNyf5cuX\nC6+5srKSyeVyYR/6+vqEx5aUlLD09HTGGGOdnZ0sLCxMuE+pVLIvv/zS4H4TQsjjxvxRN+2EEPK/\n5vDhw/Dw8BBujz9HuLm5GUqlEgDg5OSEoKAgtLS0THj8ZKKjow3+vqurC5aWlggICAAAPP3007Cw\nsEBXVxcWLFjw0Pvv5uYGT09PAICvry/q6uoAALW1tejo6EB8fDyA0RH1gYGBe55/8eJFWFlZwd/f\nHwCgUCiwY8cO4X6VSoXi4mJoNBoMDw8Lv5fJZJg3bx4aGhqE3JycnIfef0IIeRSoaSaEEI4YY8L5\nztOmTcPIyAgAYGho6J7HWllZmWSfLC0thZ/NzMwmNLaxsbFITU196G2OvcbLly9j3759KC0thbOz\nM3755ZcJp41s2rQJx48fR2dnJ1atWgUbG5v/8EoIIcR06JxmQggRUWBgIEpKSgAA169fR0NDgzBC\n7OrqKnyTxbfffvvA23Rzc4NOp8Pp06cBjI5mDw8Pw83Nzehzra2tDY4WGxISEoKKigr09vYCGL0g\n8ddff73nce7u7hgcHIRarQYAVFVV4Z9//gEA/Pvvv7CwsICjoyNGRkbw1VdfTXhuUFAQurq68MUX\nX2Djxo0PtF+EEPI4oJFmQggR0fbt24WL7Bhj2Lp1K2QyGQAgOzsbO3fuhI2NDRQKxYRv3Ljft29Y\nWlri0KFDyMvLg0ajgZWVFQ4dOgRzc+MlPCoqCkqlElVVVUhKSsLcuXPvyR277efnh/T0dLz88svQ\n6/XQ6XRQKBRYvHjxPfvz7rvvYteuXZBIJPD398fcuXMBAHK5HOHh4Xjuuedgb2+PoKAg/PzzzxPy\n1q1bh1OnTmHhwoUP8I4SQsjjQcLYfS4DJ4QQQkSWnJyMhIQErF69+lHvCiGEPDA6PYMQQohJnD9/\nHmFhYbCzs6OGmRDyP4dGmgkhhBBCCDGCRpoJIYQQQggxgppmQgghhBBCjKCmmRBCCCGEECOoaSaE\nEEIIIcQIapoJIYQQQggxgppmQgghhBBCjPg/TlRBaxOkyoUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31ded10ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=percentage_hour_delay,\n",
    "                     columns=['hour', 'flights'])\n",
    "\n",
    "# plot the percentage of flights in delay per hour\n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x=\"hour\",\n",
    "            y=\"flights\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 24),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Hour of the day', ylabel='Percentage of delayed flights')\n",
    "ax.set_title(\"Percentage of delayed flights for different hours of the day\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 4.1\n",
    "<div class=\"alert alert-info\">\n",
    "This plot shows that, even if the previous plot showed a very low number of flights at night, there a not-indifferent percentage of delayed flights in the late hours (up to 16%). In particular, it can easily be noticed that the percentage starts to increase at 3 AM and reaches its maximum values between 17 and 19 (more than one flight out of five), then it decreases again.\n",
    "To get a better understanding of the delays it would be useful to have statistics about the actual value of the delay (next plots).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.2\n",
    "<div class=\"alert alert-info\">\n",
    "You will realize that saying *\"at 4 A.M. there is a very low chance of a flight being delayed\"* is not giving you a full picture of the situation. Indeed, it might be true that there is very little probability for an early flight to be delayed, but if it does, the delay might be huge, like 6 hours!  \n",
    "\n",
    "<ul></ul>\n",
    "\n",
    "Then, the question is: **which hours of the day are characterized by the largest mean delay?** (you can consider the median too)  \n",
    "\n",
    "Plot a Bar chart and explain it.  \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# average delay per hour\n",
    "hour_avg_delay = (\n",
    "    df.groupBy(((df.scheduled_departure_time/100)%24).cast('int').alias('hour'))\n",
    "    .avg('arrival_delay')\n",
    "    .orderBy('hour')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAXxyIAAAAP6SzMxMWa1WU8YOCQmRl5eXKWP/VQRnAAAA/CVWq1Vz1i5RlerB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\novADCotrOu2pVJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31de8d7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=hour_avg_delay,\n",
    "                     columns=['hour', 'avg'])\n",
    "\n",
    "# plot the avg number of flights in delay per hour\n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x=\"hour\",\n",
    "            y=\"avg\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 24),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Hour of the day', ylabel='Average Delay')\n",
    "ax.set_title(\"Average delay of the flights during the day\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 4.2\n",
    "<div class=\"alert alert-info\">\n",
    "From the average number of delayed flights per hour it can be noticed that night hours have the smallest average delays and this value increases over time until it reaches it maximum at 19. This information is coherent with what we've already seen, infact we know that night hours have less flights and the shape of this curve to the one of the previous one so we can say that there could be a correlation between the percentage of delayed flights and the average value of the delay. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With data of year 1994, the flight from 3AM to 4AM often depart earlier than in their schedule. The flights in the morning have less delay then in the afternoon and evening.\n",
    "\n",
    "So, an attentive student should notice here that we have somehow a problem with the definition of delay! Next, we will improve how to represent and visualize data to overcome this problem.\n",
    "\n",
    "**NOTE**: the following piece of code assumes you correctly answered the previous questions. The naming convention of the DataFrames we use next should be clear enough for you to produce them by answering the questions above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oqBzrqFixIp9++ikrVqzA1dWV5cuX8+mnnxpP3y5dupQRI0YU+DPeW1vGa0tLS5YuXcqp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\ngBGFEQAAAEYURgAAABhRGAEAAGD0N/cFfv4R9G7uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31dece4400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pdf2 = pd.DataFrame(data=mean_delay_per_hour.collect())\n",
    "pdf_delay_ratio_per_hour = pd.DataFrame(data=percentage_hour_delay)\n",
    "pdf_mean_delay_per_hour = pd.DataFrame(data=hour_avg_delay)\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Ratio of delay\")\n",
    "plt.title('Figure 6: The radio of delay over hours in day')\n",
    "plt.grid(True,which=\"both\",ls=\"-\")\n",
    "bars = plt.bar(pdf_delay_ratio_per_hour[0], pdf_delay_ratio_per_hour[1], align='center', edgecolor = \"black\")\n",
    "for i in range(0, len(bars)):\n",
    "    color = 'red'\n",
    "    if pdf_mean_delay_per_hour[1][i] < 0:\n",
    "        color = 'lightgreen'\n",
    "    elif pdf_mean_delay_per_hour[1][i] < 2:\n",
    "        color = 'green'\n",
    "    elif pdf_mean_delay_per_hour[1][i] < 4:\n",
    "        color = 'yellow'\n",
    "    elif pdf_mean_delay_per_hour[1][i] < 8:\n",
    "        color = 'orange'\n",
    "\n",
    "    bars[i].set_color(color)\n",
    "        \n",
    "patch1 = mpatches.Patch(color='lightgreen', label='Depart earlier')\n",
    "patch2 = mpatches.Patch(color='green', label='delay < 2 minutes')\n",
    "patch3 = mpatches.Patch(color='yellow', label='delay < 4 minutes')\n",
    "patch4 = mpatches.Patch(color='orange', label='delay < 8 minutes')\n",
    "patch5 = mpatches.Patch(color='red', label='delay >= 8 minutes')\n",
    "\n",
    "plt.legend(handles=[patch1, patch2, patch3, patch4, patch5], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.margins(0.05, 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the new figure (Figure 6), we have more information in a single plot. The flights in 3AM to 4AM have very low probability of being delayed, and actually depart earlier than their schedule. In contrast, the flights in the 4PM to 8PM range have higher chances of being delayed: in more than 50% of the cases, the delay is 8 minutes or more.\n",
    "\n",
    "This example shows us that the way representing results are also important.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Plot a bar chart to show the percentage of delayed flights over days in a month. Discuss your findings from the figure.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of delayed flights per day of the month\n",
    "df_daymonth_delayed = (\n",
    "    df[df.arrival_delay > 15]\n",
    "    .groupBy(df.day_of_month)\n",
    "    .count()\n",
    ")\n",
    "\n",
    "# number of flights per day of the month\n",
    "df_daymonth = df.groupBy(df.day_of_month).count()\n",
    "\n",
    "# percentage of delayed flights per day of the month\n",
    "percentage_daymonth_delay = (\n",
    "    df_daymonth.join(df_daymonth_delayed, df_daymonth.day_of_month == df_daymonth_delayed.day_of_month, 'inner')\n",
    "    .select(df_daymonth.day_of_month, df_daymonth_delayed['count']*100/df_daymonth['count'])\n",
    "    .orderBy('day_of_month')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4+uuvNX36dP3++++SMrd+EpiBosFrodlx3kIpc/xh3bp1jSOwuS49bjYPPfSQ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e0cad438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=percentage_daymonth_delay,\n",
    "                     columns=['day_of_month', 'percentage'])\n",
    "\n",
    "# plot the number of delayed flights per day of the month\n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x=\"day_of_month\",\n",
    "            y=\"percentage\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 31),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Day of the month', ylabel='Percentage of delayed flight')\n",
    "ax.set_title(\"Percentage of delayed flights over the days of the month\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 4.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "The percentage of delayed flights oscillates around 15% over the whole month. It can be noticed that the value has small periodical drops, probably related to the day of the week, and it generally increases, reaching its peak, at the end of the month.\n",
    "As already discussed, the values from the 29th to the 31st have to be taken in consideration carefully because not all the months have records for these days.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.4\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Plot a bar chart to show the percentage of delayed flights over days in a week. Discuss your findings from the figure.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of delayed flights per day of the week\n",
    "df_dayweek_delayed = df[df.arrival_delay > 15].groupBy(df.day_of_week).count()\n",
    "\n",
    "# number of flights per day of the week\n",
    "df_dayweek = df.groupBy(df.day_of_week).count()\n",
    "\n",
    "# percentage of delayed flights per hour\n",
    "percentage_dayweek_delay = (\n",
    "    df_dayweek.join(df_dayweek_delayed, df_dayweek.day_of_week == df_dayweek_delayed.day_of_week, 'inner')\n",
    "    .select(df_dayweek.day_of_week, df_dayweek_delayed['count']*100/df_dayweek['count'])\n",
    "    .orderBy('day_of_week')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eXlq9erWxbwGQO+x2IqDtsN3DDz+suLg4o5dj3rx5aty4sV588UVNnjxZkydP\nNg5RAPnJ+PHjtWnTJpUqVUo3btwwzqIuVqyYXnvtNQdXl7fs3r1bb7zxhs6cOSMp5Yz4vByYpZST\nIL/55hsVK1ZMhQoV0sWLF2W1WuXi4qJhw4YRmGGYNWuWpk2bptOnT0tK6RkmMAO5z27DM8qUKWMc\nvvDy8lLVqlUVHR2tyMhI4+5dHTt2NG7/CeQ3jz/+uGrUqKHr168rPj5e9913n7p06aL58+cbY+CQ\nNdevX9fx48fl5uamxo0ba+zYsY4uKdsCAwNVv359ubi4KDY2ViVKlFBISIhmzJiR7nVhUXDFxsbq\n9OnTKlasmLp27apBgwY5uiSgQLLb8IzUTp06pbCwMC1ZskSPPfaYcdap1WpVcHBwplcHAAAAABzN\n7icCxsXFadCgQXrnnXfuOJyU3kkaAAAAgLOx681Nbty4oUGDBik0NFQtW7aUJJUqVUr//POPypQp\no/Pnz6e5cHl6Ut9yGAAAALAn241ubme30Gy1WvXOO++oatWq6tWrlzE9JCRECxYsUHh4uBYuXGiE\n6cxkVDwAAACQUzLrrLXb8Ixt27Zp8eLF2rRpkzp06KAOHTrozz//VHh4uNavX682bdpo48aNCg8P\nt1cJAAAAQI6wW09zUFBQundKk2TcRAAAAADIC7gjIAAAAGCC0AwAAACYIDQDAAAAJgjNAAAAgAlC\nMwAAAGCC0AwAAACYIDQDAAAAJgjNAAAAgAlCMwAAAGCC0AwAAACYIDQDAAAAJgjNAAAAgAlCMwAA\nAGCC0AwAAACYIDQDAAAAJgjNAAAAgAlCMwAAAGCC0AwAAACYIDQDAAAAJtwcXQAA5FVJSUk6duyY\no8vIc6pUqSJ3d3dHlwEAd4XQDAD36NixY+o7aZ68ypR3dCl5Rtw/ZzWlX2dVr17d0aUAwF0hNANA\nNniVKS+fchUdXQYAwM4Y0wwAAACYIDQDAAAAJhieAQDIkzgR8+5xEiZw7wjNAIA86dixY3p95jwV\n9S3n6FLyhCvR5/R5GCdhAveK0AwAyLOK+pZTifs4EROA/TGmGQAAADBBaAYAAABMEJoBAAAAE4Rm\nAAAAwAShGQAAADBBaAYAAABMEJoBAAAAE4RmAAAAwAShGQAAADBBaAYAAABMEJoBAAAAE4RmAAAA\nwAShGQAAADBBaAYAAABMEJoBAAAAE4RmAAAAwAShGQAAADDh5ugCANy7pKQkHTt2zNFl5DlVqlSR\nu7u7o8u+K/jhAAAgAElEQVQAAOQhhGYgDzt27JjCPp0gzxKlHV1KnhF/6YJmDuuv6tWrO7oUAEAe\nQmgG8jjPEqXlXdrX0WUAAJCvMaYZAAAAMEFoBgAAAEwQmgEAAAAThGYAAADABKEZAAAAMEFoBgAA\nAEwQmgEAAAAThGYAAADABKEZAAAAMEFoBgAAAEwQmgEAAAAThGYAAADABKEZAAAAMEFoBgAAAEwQ\nmgEAAAAThGYAAADABKEZAAAAMEFoBgAAAEwQmgEAAAAThGYAAADABKEZAAAAMEFoBgAAAEwQmgEA\nAAAThGYAAADABKEZAAAAMEFoBgAAAEy4ObqAnJCUlKRjx445uow8p0qVKnJ3d3d0GQAAAE4vX4Tm\nY8eOqWPPF1TY08vRpeQZ1+PjtOC7b1W9enVHlwIAAOD07Bqahw4dqtWrV6tUqVJasmSJJGncuHGa\nM2eOSpYsKUmKiIhQs2bNsv1ehT29VMTbJ9vLAQAAAG5n19DcuXNnhYWF6a233jKmWSwW9e7dW717\n97bnWwMAAAA5xq4nAgYFBalo0aJ3TLdarfZ8WwAAACBHOeTqGbNmzVJoaKiGDRumK1euOKIEAAAA\nIMtyPTQ/++yz+v3337Vo0SKVKVNGo0aNyu0SAAAAgLuS61fPKFWqlPH/rl276uWXXzZ9zbZt2zJ9\n/vjx49muqyDau3evrl696ugykA1s+/cmp7Z92v/e0P6Ow34fuHe5HprPnz+vsmXLSpJWrlyZpUue\n1atXL9PnfXy4asa98Pf355JzeZyPj4+05i9Hl5Hn5NS27+PjIx1YnwMVFSw52f7zz6zLgYoKDvb7\nQOYy66i1a2iOiIjQ5s2bFRsbq+bNm2vgwIHavHmzoqKiZLFYVLFiRX344Yf2LAEAAADINruG5i+/\n/PKOaV26dLHnWwIAAAA5ziFXzwAAAADyEkIzAAAAYMJ0eMbhw4dVtWpV02kAAACwv6SkJB07dszR\nZeQpVapUkbu7e7aWYRqahwwZooULF5pOAwAAgP0dO3ZMk3+drzL3lXd0KXnCP2fOKrx1p2xfOSbD\n0BwTE6OLFy8qMTFRhw4dMqZfuXJFCQkJ2XpTAAAA3Lsy95VXucoVHV1GgZJhaF68eLFmzJih8+fP\nKzw83Jju7e2tF154IVeKAwAAAJxBhqG5V69e6tWrlyZOnJilu/ah4GJs1d3LibFVAAAg95iOaX75\n5ZeVkJCgc+fOKTk52ZherVo1uxaGvOPYsWN6OmK4ihQt7uhS8oSEK7H68cuPuSsXAAB5iGlonj17\ntj7//HMVK1ZMLi7/u0JdZGSkXQtD3lKkaHF5Fi/p6DIAAADswjQ0T5kyRUuXLlWFChVyox4AAADA\n6Zje3KRs2bIEZgAAABRoGfY0Hzp0SFarVY0bN9Znn32mtm3bysPDw3ieMc0AAAAoKDIMzakvMydJ\nK1asSPOYMc0AAAAoKDIMzYRiAAAAIIXpiYCp7wZo4+PjI19fX7sUBAAAADgb09AcHh6uM2fOqGjR\nopJSbqNdqlQpeXh46IsvvlBgYKDdiwQAAAAcyTQ0P/7442rQoIFatmwpSVq5cqU2btyoli1b6pNP\nPtHcuXPtXiQAAADgSKaXnNu0aZMRmCWpZcuW2rJlixo2bKjExES7FgcAAAA4A9PQbLVatW3bNuPx\n9u3bdevWrZQXu5i+HAAAAMjzTIdnvPfeexo8eLCKFCkiSUpISNAXX3yhuLg49erVy971AQAAAA5n\nGpqDgoK0cuVKHTlyRJL04IMPyt3dXZLUsWNH+1YHAAAAOIEMQ3NSUpLc3d2VkJAgSapcubIkKTk5\nWQkJCUbPMwAAAJDfZRiau3XrpoULF6Z7STmLxaKoqCi7FgYAAAA4iwxD88KFCyVJ+/bty7ViAAAA\nAGfE5S8AAAAAExn2NDdo0EAWiyXd5ywWizZs2GC3ogAAgPNKSkrSsWPHHF1GnlOlShXjYgrIezIM\nzTNnzpSnp6csFousVmtu1gQAAJzYsWPHNHLePJUoX87RpeQZl86e09DOnVW9enVHl4J7lGFofvvt\ntzV//ny98cYbGjNmTG7WBAAAnFyJ8uVUumJFR5cB5JoMQ3NCQoL27t2rvXv36tChQ3c8X61aNbsW\nBgAAADiLDENzWFiY3nzzTZ08eVLh4eF3PB8ZGWnXwgAAAABnkWFo7t69u7p3767XXntNX331VW7W\nBAAAADgV00vOEZgBAABQ0HGdZgAAAMAEoRkAAAAwQWgGAAAATGR4IuDs2bMzfeFzzz2X48UAAAAA\nzijD0Lxnzx5ZLBZdunRJmzdvVqNGjWS1WrVx40Y1aNCA0AwAAIACI8PQPGrUKElSeHi4Fi1apEqV\nKkmSTp48qY8//jh3qgMAAACcgOmY5tOnTxuBWZIqVaqkU6dO2bUoAAAAwJmYhubSpUtrwoQJOn/+\nvKKjozVx4kSVKVMmN2oDAAAAnIJpaP7ss8+0b98+tWvXTqGhoYqKitLo0aNzozYAAADAKWQ4ptnG\n19dX48aNy41aAAAAAKdk2tMcHx+vsWPHasiQIZKkw4cPa+XKlXYvDAAAAHAWpqH5/fff182bNxUV\nFSWJnmcAAAAUPKahef/+/XrjjTfk7u4uSfL29pbVarV7YQAAAICzMA3NtrBsk5iYSGgGAABAgWJ6\nImBQUJAmTpyoxMREbdq0SdOmTVNISEhu1AYAAAA4BdOe5sGDB8tqtcrLy0tjxoxRQECABg4cmBu1\nAQAAAE7BtKfZarXqlVde0SuvvJIb9QAAAABOx7SnOSQkRKNGjdKJEydyox4AAADA6ZiG5kWLFsnH\nx0c9e/ZU37599ccff+RGXQAAAIDTMA3NpUuXVv/+/fXbb7+pW7du+uCDDxQSEqKpU6cqMTExN2oE\nAAAAHMo0NEtSQkKC5syZowkTJuj+++/Xa6+9psOHD+uFF16wd30AAACAw5meCPjhhx/q119/VUhI\niD7//HNVr15dkhQaGqonnnjC7gUCAAAAjmYamu+77z4tW7ZMxYoVu+O57777zi5FAQAAAM7ENDRn\nNgTD19c3R4sBAAAAnJFpaN63b5/ee+89RUVFKSkpSZJksVgUFRVl9+IAAAAAZ2B6IuD777+vV199\nVVWqVNHq1avVr18/DR48ODdqAwAAAJyCaWhOTExU48aNZbVa5evrq8GDB2vFihW5URsAAADgFExD\ns6urqySpaNGiioqKUkxMjGJjY+1eGAAAAOAsTMc0P/nkk4qJiVG/fv3UvXt3JScna9CgQblRGwAA\nAOAUTENznz59JEnNmjXTpk2blJSUJG9vb7sXBgAAADiLDEPzoUOHMn1htWrVcrwYAAAAwBllGJrD\nw8MzfWFkZGSOFwMAAAA4owxDM6EYAAAASGF69QxJWr9+vWbNmiVJunDhgo4ePWrXogAAAABnYhqa\nJ02apPHjx2vGjBmSpBs3bmjo0KF2LwwAAABwFqaheenSpZo+fbo8PT0lSeXLl1dcXJzdCwMAAACc\nhWloLly4sNzd3XOjFgAAAMApmV6nuXz58tq6daskKTk5WZMmTVL16tXtXhgAAADgLEx7mocPH64J\nEybo4MGDql27tjZv3syYZgAAABQopj3NZcuW1bRp0xQfH69bt25xN0AAAAAUOBmG5lWrVslischq\ntcpisdzxfPPmze1aGAAAAOAsMgzNU6ZMkSQlJSVpz549xjjmAwcOKCAggNAMAACAAiPD0Dxz5kxJ\nUkREhIYNG6batWtLknbv3q3vvvsud6oDAAAAnIDpiYAHDhwwArMkBQQE6MCBA1la+NChQ9W4cWO1\na9fOmBYbG6vevXurTZs26tOnj65cuXIPZQMAAAC5xzQ0FylSRAsXLjQeL168WEWKFMnSwjt37qxv\nv/02zbTJkyercePG+uWXX9SwYUNNnjz5LksGAAAAcpdpaB45cqRmzJihWrVqqVatWvruu+80cuTI\nLC08KChIRYsWTTMtMjJSHTt2lCR17NhRK1euvIeyAQAAgNxjesm5atWqaf78+bp27ZokZfuScxcv\nXlTp0qUlSaVLl9bFixeztTwAAADA3kxDs409rs9ssVjSvZzd7bZt25bp88ePH8+pkgqUvXv36urV\nq9leDu1/92h7x6L9HYv2dxza3rFof8fJibbPcmjOKaVKldI///yjMmXK6Pz58ypZsqTpa+rVq5fp\n8z4+PjlVXoHi7++fI7dE9/HxkeauyIGKCo4cbfs1f+VARQVLjrb/gfU5UFHBkpPtP//MuhyoqODI\nybZfu562v1s52f4H927IgYoKjqy2fWYdtaZjmnNaSEiIFixYIElauHChWrZsmdslAAAAAHclw9Dc\no0cPSdJnn312zwuPiIjQM888o6NHj6p58+aaN2+ewsPDtX79erVp00YbN25UeHj4PS8fAAAAyA0Z\nDs+4ePGiLl26pLVr12rgwIF3PJ+Vy859+eWX6U6fPn161isEAAAAHCzD0Ny6dWu1aNFCiYmJCgwM\nTPOcxWJRVFSU3YsDAAAAnEGGoXnw4MEaPHiwunfvru+//z43awIAAACciumJgLbAHB8fr/j4eLsX\nBAAAADgb09B84sQJdevWTQ0aNFCDBg30zDPP6OTJk7lRGwAAAOAUTEPziBEj1K1bN+3atUu7du1S\n165dNWLEiNyoDQAAAHAKpqE5JiZGXbp0kYuLi1xcXNS5c2dufQ0AAIACxTQ0u7q66vDhw8bjI0eO\nyM0t128kCAAAADiMafodPHiwevToIT8/P0nSvn37snXDEwAAACCvMQ3NzZo109KlS7Vr1y5ZLBbV\nrl1bJUuWzI3aAAAAAKeQpXEWpUqVUkhIiL1rAQAAAJyS6ZhmAAAAoKAjNAMAAAAmCM0AAACACdPQ\nfOHCBb3++uvq3r27pJSrZ/zwww92LwwAAABwFqahefjw4apbt66uXr0qSXrwwQf1/fff270wAAAA\nwFmYhubz58+re/fucnV1lSS5u7vLYrHYvTAAAADAWWTpjoBWq9V4fOXKFbsWBAAAADgb0+s0t2rV\nSu+9956uXbum+fPna/bs2erUqVNu1AYAAAA4BdPQHB4erkWLFuny5ctavXq1nn/+ebVv3z43agMA\nAACcQpbuCNi+fXuCMgAAAAos09A8aNAgWSwWY1yzxWKRt7e3AgMD1alTJ7m4cKlnAAAA5G+mibd0\n6dI6d+6cgoKCVK9ePUVHR0uSfv75Z3366ad2LxAAAABwNNOe5n379mnmzJlyd3eXJD3zzDPq2bOn\nZsyYwZANAAAAFAimPc0XL16Um9v/srWbm5suXbokd3d3eXh42LU4AAAAwBmY9jQHBwerX79+at++\nvaxWq5YsWaKgoCDFxcUZvc8AAABAfmYamt999139+OOP+uWXXyRJjz76qJ555hkVKlRIP/30k90L\nBAAAABzNNDS7u7srLCxMYWFhuVEPAAAA4HRMQ/ONGzc0b9487du3T4mJicb0kSNH2rUwAAAAwFmY\nngg4YsQI7dixQ3/88YeqVKmiPXv2qHDhwrlRGwAAAOAUTEPznj17NGrUKBUtWlT9+vXTDz/8oIMH\nD+ZGbQAAAIBTMA3NHh4eslgscnV1VXx8vHx8fHTx4sXcqA0AAABwCqZjmosXL67Y2Fg9+uijevHF\nF1WiRAmVK1cuN2oDAAAAnIJpaJ48ebJcXV01ePBgLV68WNeuXVOHDh1yozYAAADAKZgOz5g6dWrK\njC4u6tChg3r06KEffvjB7oUBAAAAzsI0NC9btixL0wAAAID8KsPhGevWrdPatWt1/vx5ffbZZ7Ja\nrZKka9eu5VpxAAAAgDPIMDQXKlRInp6ecnFxkaenpxGafX191a9fv1wrEAAAAHC0DENzcHCwgoOD\n1bp1a9WoUSM3awIAAACciunVMypXrqw5c+boxIkTunnzpiTJYrHozTfftHtxAAAAgDMwDc2vvvqq\nbt68qYCAAHl4eMhqtcpiseRGbQAAAIBTMA3NJ06c0M8//5wbtQAAAABOyfSSc5UqVeKKGQAAACjQ\nTHuavb291blzZz366KMqVKiQJMY0AwAAoGAxDc0PPPCAHnjgAUkpYZkxzQAAAChoTEPzwIEDc6MO\nAAAAwGmZjmm+cOGCXn/9dXXv3l2StG/fPv3www92LwwAAABwFqahefjw4apbt66uXr0qSXrwwQf1\n/fff270wAAAAwFmYhubz58+re/fucnV1lSS5u7szphkAAAAFimlodnV1ldVqNR5fuXLFrgUBAAAA\nzsb0RMBWrVrpvffe07Vr1zR//nzNnj1bnTp1yo3aAAAAAKdgGprDw8O1aNEiXb58WatXr9bzzz+v\n9u3b50ZtAAAAgFMwDc2S1L59e4IyAAAACizTMc0DBgxQbGys8fjSpUt69dVX7VoUAAAA4ExMQ/PJ\nkydVvHhx43GJEiV0/PhxuxYFAAAAOBPT0Hzr1i3dvHnTeHzjxg3duHHDrkUBAAAAzsR0THPTpk0V\nERGh559/XpL03Xff6dFHH7V7YQAAAICzMA3NERERmjRpkkaNGiVJatGihcLDw+1eGAAAAOAsMg3N\nycnJ+uCDD/Txxx9rwIABuVUTAAAA4FQyHdPs6uqq/fv351YtAAAAgFMyHZ7RsGFDffjhh+rQoYM8\nPT2N6dWqVbNrYQAAAICzMA3Ny5YtkyStWrUqzfTIyEi7FAQAAAA4G9PQTDgGAABAQWd6nWZJWr9+\nvWbNmiVJunDhgo4ePWrXogAAAABnYhqaJ02apPHjx2vGjBmSUm5uMnToULsXBgAAADgL09C8dOlS\nTZ8+3TgJsHz58oqLi7N7YQAAAICzMA3NhQsXlru7e27UAgAAADgl0xMBy5cvr61bt0pKudnJpEmT\nVL16dbsXBgAAADgL057m4cOHa8KECTp48KBq166tzZs3M6YZAAAABUqmPc2xsbE6f/68vvnmG7m6\nuurWrVvy9vbOrdoAAAAAp5BhT/Py5cvVvHlzhYeH67HHHtOuXbsIzAAAACiQMuxpnjhxov773//q\n4Ycf1saNGzV+/Hg1atQoN2sDAAAAnEKGPc0uLi56+OGHJUkNGzbUtWvXcq0oAAAAwJlk2NOclJSk\nQ4cOSZKsVqsSExONx5JUrVo1+1cHAAAAOIEMQ3NiYqLCw8PTTEv9ODIy0n5VAQAAAE4kw9Bs71Ac\nEhIiLy8vubq6ys3NTXPnzrXr+wEAAAD3yvTmJvY0c+ZMFS9e3JElAAAAAKZMb25iT1ar1ZFvDwAA\nAGSJw0KzxWJR79691alTJ/3000+OKgMAAAAw5bDhGT/88IPKli2rmJgY9e7dWw8++KCCgoIcVQ4A\nAACQIYeF5rJly0qSSpYsqVatWmn37t0ZhuZt27Zluqzjx4/neH0Fwd69e3X16tVsL4f2v3u0vWPR\n/o5F+zsObe9YtL/j5ETbOyQ0JyQkKDk5Wd7e3oqPj9fatWs1YMCADOevV69epsvz8fHJ6RILBH9/\nf1WvXj3by/Hx8ZHmrsiBigqOHG37NX/lQEUFS462/4H1OVBRwZKT7T//zLocqKjgyMm2X7uetr9b\nOdn+B/duyIGKCo6stn1mHbUOCc0XLlwwQnJycrLatWunpk2bOqIUAAAAwJRDQnOlSpW0aNEiR7w1\nAAAAcNccesk5AAAAIC8gNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIz\nAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAA\nYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQ\nDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAA\nAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJgg\nNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMA\nAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAm\nCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0A\nAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACACUIzAAAAYILQDAAAAJggNAMAAAAmCM0AAACA\nCYeE5j///FNPPPGEWrdurcmTJzuiBAAAACDLcj00Jycn66OPPtK3336rZcuWadmyZTp8+HBulwEA\nAABkWa6H5t27d+v+++9XxYoVVahQIbVt21a///57bpcBAAAAZFmuh+bo6GiVL1/eeOzr66vo6Ojc\nLgMAAADIMrfcfkOLxWKX5V6Pj7PLcvOrnG6vhCuxObq8/Cyn2yr+0oUcXV5+l9PtFffP2RxdXn6X\n0+11Jfpcji4vP8vptrp0lra/GzndXv+cYd+TVf+cOSv5Z385FqvVas3+YrJu586dGjdunKZMmSJJ\nmjRpkiwWi8LDw9Odf9u2bblZHgAAAAqwevXqpTs913ua/f39dfz4cZ06dUply5bV8uXL9eWXX2Y4\nf0aFAwAAALkl10Ozm5ub3n33XfXt21e3bt1Sly5dVLVq1dwuAwAAAMiyXB+eAQAAAOQ13BEQAAAA\nMEFoBgAAAEwQmgEAAAATuX4iYH7i5+endu3aacyYMZKkmzdvqmnTpqpTp47+7//+z8HVFQyXLl1S\n7969JUkXLlyQi4uLSpYsKYvFop9++kmFChVycIX5z6effqoKFSqoZ8+ekqS+ffuqfPny+vjjjyVJ\no0aNUrly5dSrV69Ml3Pq1Cm9/PLLWrJkib1Lzncy2u5Pnz6tsmXLatmyZQ6usGB5+OGHVaNGDePx\nv//9b913331p5gkPD9eXX34pb2/v3C4v35o4caKWLVsmFxcXubi46MMPP1RAQEC68y5YsEBNmjRR\n2bJlc7nK/Odu2j2/ITRnQ5EiRXTo0CElJibKw8ND69atU7ly5RxdVoFSokQJLVy4UJI0fvx4eXl5\nGWEC9lGvXj39/PPP6tmzp27duqVLly4pLu5/N8vZuXOnhg0b5sAK87+MtvvTp0/rpZdecnB1BU/h\nwoWN9XE727n2kydPzs2S8r0dO3Zo9erVWrBggQoVKqTY2FglJSVlOP/8+fP10EMPEZqz6W7bPb9h\neEY2NWvWTKtWrZIkLVu2TG3btjWei42N1SuvvKLQ0FA9/fTT2r9/vyRp3LhxGjp0qMLCwtSyZUvN\nnDnTEaXnS1arVUOHDtUvv/xiTAsMDDT+/+2336pLly4KDQ3VuHHjHFFinlenTh3t3LlTknTw4EFV\nr15dXl5eunLlipKSknT48GFJUlhYmDp16qS+ffvqn3/+kSTt3btXoaGhat++vb7//nuHfYb8xhbM\nrFarkpOT9e677+qpp55S3759lZiYKCllfezdu1eSFBMTo5CQEIfVm9+dOnVKbdq00VtvvaV27drp\n7NmzCgkJUWwsd07NKRcuXFDx4sWNo4nFixdX2bJlNWHCBHXp0kXt2rXTiBEjJEkrVqzQ3r179frr\nr6tjx47G3wTuXkbtnnr73rNnj8LCwiTlv7xDaM6mJ598UsuWLVNSUpIOHDig2rVrG8+NGzdOjzzy\niBYvXqzBgwfrrbfeMp47duyYpk6dqjlz5mj8+PFKTk52RPkFytq1a3XixAnNnTtXCxcu1F9//aWt\nW7c6uqw8x9fXV66urjp79qx27typOnXqKCAgQDt27NCePXtUtWpVffrpp/r66681f/58de7cWWPH\njpUkDR06VCNGjNCiRYsc/Cnyr+PHj+u5557T0qVL5ePjk+YHpMVicWBl+VdiYqI6dOigDh06aODA\ngbJYLDpx4oSxHm4fqoHsa9Kkic6dO6c2bdrogw8+0JYtWyRJPXr00Ny5c7VkyRJdv35df/zxh554\n4gn5+/vriy++0IIFC+Th4eHg6vOujNo9M/kp7zA8I5tq1Kih06dPa+nSpWrevHma57Zv3270ZjZs\n2FCxsbG6du2aLBaLWrRooUKFCqlEiRIqVaqULly4IF9fX0d8hAJj3bp1Wrt2rTp06CBJSkhI0PHj\nxxUUFOTgyvKewMBA7dixQzt27FCvXr0UHR2tHTt2yMfHR2XLltW6devUp08fSVJycrLKli2rq1ev\n6tq1a0Z7t2/fXmvWrHHkx8iXKlasKD8/P0nSI488otOnTzu4ovzPw8MjzfCMU6dO6b777isw4zwd\nwdPTU/Pnz9fWrVu1adMmDR48WEOGDJGnp6emTJmihIQEXb58WQ899JAee+wxSf87IoN7l167R0RE\nZDh/fss7hOYcEBISotGjR2vWrFmKiYlJ81xGf6SpT1BzdXXN07+8nI2rq6tu3bolSbp165Zu3Lhh\nPNevXz89/fTTjiot36hbt662b9+uAwcOqEaNGipfvrymTp0qHx8fBQcH6/z58/rvf/+b5jVXrlxJ\n85gvMPtwd3c3/u/q6mocinZzczP+LgrSGERH8fT0dHQJ+Z6Li4uCg4MVHBys6tWr67///a8OHDig\n+fPny9fXV+PHj08zFIMjLTnj9nZfsGBBmv3L7cNf8lPeYXhGDujSpYsGDhyohx56KM30evXqGVcG\n2LRpk0qWLClvb2/Cgp1VqFBBf/31lyQpMjJSN2/elCQ1bdpU8+bNU3x8vCQpOjr6jh85yJq6devq\njz/+UPHixWWxWFSsWDFdvXpVO3fu1FNPPaWYmBhj3PONGzd06NAhFS1aVD4+Ptq2bZskcdWMXFah\nQgVjTPOKFSscXA2QPUePHtWxY8eMx3///bcefPBBWSwWFS9eXHFxcWm2cy8vL127ds0BleYv6bV7\nhZFVxWsAAAdQSURBVAoV0uxffv31V+P5/JZ36GnOBtuvVl9fX/Xo0SPNNEkaOHCghg0bptDQUHl6\nemrUqFHGPPzitQ+LxaJu3brplVdeUfv27fXoo48aPT5NmjTR4cOHjZ5mLy8vjRkzRiVLlnRkyXnS\nQw89pNjYWIWGhhrTatSooYSEBJUsWVLffPONPv74Y129elXJycnq2bOnqlWrppEjR2rYsGGyWCxq\n0qSJAz9B/pKV/UmfPn302muv/X979xfSZBfHAfxrllmswBgJdWMlqRflxSptUVqYln+mW5pT7I+Z\nRYEG2U1IfyxY2aQ/DMOwQTeFRX80wqC8KIaMJhUahKWbaAPByrDJzKb+3ouX96G9ahOpfHn5fq6e\n5znPOef3HMb47XD2HNy5cwcJCQn8DvqFpjKWHO9fy+v14uzZs/B4PAgODkZERAQqKiqwYMECpKen\nQ61W+y2PMRgMOHXqFObNm4e6ujqua56micb9zJkz6OzsRHl5Oa5cuYJ169Ypn/f/W74TJP+3nwFE\nRERERL8Yl2cQEREREQXApJmIiIiIKAAmzUREREREATBpJiIiIiIKgEkzEREREVEATJqJiIiIiAJg\n0kxENAVbtmzB9u3bkZmZieTkZBw+fBivX7/+Y/03NTUhNTUVBoMBXV1dfmUOhwPNzc3KudvtRnx8\n/B+LbTqio6MxNDQ002EQEU0ZNzchIpoii8WCyMhIAMDTp09x4MABWK1Wv00Ufpe6ujocOXIEKSkp\n48pevHgBr9fLDWOIiH4jzjQTEU3D1q1bYTQaYbVaAQB2ux1GoxF6vR4ZGRlobGwEALS1tSEjI8Ov\nrk6nU7YZ/1F3dzf27NkDnU4Hg8EAm80GADCZTHj58iXMZjN2797tV+fdu3e4ffs2GhoakJWVhdra\nWmUHrkuXLkGv12Pbtm3K9uUA8Pz5c+Tl5cFgMMBoNKK1tXVcLC6XC+np6QCAkZERaDQa5VkbGxtR\nVlYGAOjr60NpaSlycnKQkZGBa9eu+bVRXFyM7OxsZGZm4v79++P6GRsbg8lkQllZGb5///6zISci\nmllCREQBbd68WTo6OvyuPXnyRFJTU0VEZGBgQEZHR0VE5OPHj7Jp0yb5+vWriIjs3LlTHA6HiIi0\ntLSIXq+fsI/s7Gy5e/euiIh0dnZKXFyc9Pf3i4hIQUGBPHv2bMJ6FotFKisrlfMPHz5IVFSUcv/D\nhw/FaDSKiEh3d7fk5uaKx+MREZH3799LYmLihO0mJCRIX1+fvHr1SnJzc6WoqEhERE6cOKHEuXfv\nXmlpaRERkeHhYcnLy5Pm5mbx+Xyi1+vF6XSKiIjH45Hk5GRxuVwiIhIVFSX9/f1SUlIiFy5cmLB/\nIqL/Ei7PICKaJhFRjj9//ozjx4+jp6cHwcHBGBgYQFdXF1avXo1du3bh1q1bWLt2LW7evIn8/Pxx\nbQ0ODqK9vR07duwAAKxYsQIxMTFobW1FYmLiuP5+FgsAzJ8/HwkJCQCA2NhYVFZWAgBsNht6enpQ\nUFCg3Ds6Oor+/n4sWrTIr434+HjY7Xa43W4YjUZcv34dPp8PdrsdBw8ehNfrhcPhwJcvX5Q6Xq8X\nLpcLixcvhsvlwtGjR5WykZEROJ1OLFu2DACwf/9+pKWlYd++fZMPMhHRfwSTZiKiaXrz5g1WrlwJ\nADh9+jSSkpJQXV0NAEhJScHw8LByfPHiRbx9+xYOhwPnz5+ftE0RUZZX/Ntk1ycSEhKiHM+aNQsj\nIyPK+caNG5Uk+md+TJqrqqrQ0tKCR48eQUSwdOlSDA4OIigoCPfu3UNwcLBf3Y6ODoSFhaG+vn7S\n9uPi4mCz2ZCfn4/Q0NApPxsR0UzgmmYioin6cTa3qakJdXV1KCwsBAB4PB4sWbIEANDc3Iyenh7l\n3jlz5sBgMODQoUPQ6XSYO3fuuLZVKhViYmLw4MEDAIDT6UR7eztiY2MDxqVSqTA4ODilZ9iwYQNs\nNhs6OzuVa21tbRPeu379ethsNng8HoSHh0Or1cJisUCr1Sr9rlmzxm8dc29vLz59+oTly5cjNDQU\nDQ0NSpnT6fSLs6SkBFqtFkVFRVOOn4hopnCmmYhoikpLSxESEoKhoSFERkaitrZWeXPGsWPHUFFR\nAYvFglWrViE6Otqvbk5ODqqrq5GXlzdp+1VVVTh58iRu3LiB2bNnw2w2IywsLGBcSUlJqK+vR1ZW\nFtLS0pCamjpuVvqf84iICJjNZpSXl+Pbt2/w+XzQaDQTvgEkPDwcKpUKGo0GwN8zw729vX6vs6uq\nqsK5c+eUPzuqVCqYTCao1WrU1NTAZDLBarVibGwMarUaly9f9ounuLgYoaGhKCwshNVqxcKFCwM+\nLxHRTAiSny2SIyKiX6KhoQGPHz9GTU3NTIdCRETTwJlmIqLfrKioCG63G1evXp3pUIiIaJo400xE\nREREFAD/CEhEREREFACTZiIiIiKiAJg0ExEREREFwKSZiIiIiCgAJs1ERERERAEwaSYiIiIiCuAv\n3RGFsiVvRPQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31de939ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=percentage_dayweek_delay,\n",
    "                     columns=['day_of_week', 'percentage'])\n",
    "\n",
    "# plot the number of delayed flights per day of the month\n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x=\"day_of_week\",\n",
    "            y=\"percentage\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 7),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Day of the week', ylabel='Percentage of delayed flight')\n",
    "ax.set_xticklabels(daysofweek)\n",
    "ax.set_title(\"Percentage of delayed flights over the days of the week\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 4.4\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "As expected from the previous plot, the percentage of delayed flights is higher in the middle of the week, while it decreases at the beginning and at the end. This is probably what creates the bigger oscillations in the days-of-the-month plot.\n",
    "Furthermore it can be noted that the percentage of delayed lights drops to its minimum value in the weekend, where we know from  the previous plots that there are less flights.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.5\n",
    "<div class=\"alert alert-info\">\n",
    "Plot a bar chart to show the percentage of delayed flights over months in a year. Discuss your findings from the figure.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of delayed flights per month\n",
    "df_month_delayed = (\n",
    "    df[df.arrival_delay > 15]\n",
    "    .groupBy(df.month)\n",
    "    .count()\n",
    ")\n",
    "\n",
    "# number of flights per month\n",
    "df_month = df.groupBy(df.month).count()\n",
    "\n",
    "# percentage of delayed flights per hour\n",
    "percentage_month_delay = (\n",
    "    df_month.join(df_month_delayed, df_month.month == df_month_delayed.month, 'inner')\n",
    "    .select(df_month.month, df_month_delayed['count']*100/df_month['count'])\n",
    "    .orderBy('month')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EoRkAAAAwQWgGAAAATBCaAQAAABOeri7gRqSnpys+Pt7VZRRYUFCQvL29XV0G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\niIiIKAAmzUREREREATBpJiIiIiIK4D8MIKSCsfPAGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31ded009e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=percentage_month_delay,\n",
    "                     columns=['month', 'percentage'])\n",
    "\n",
    "# plot the number of delayed flights per month\n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x=\"month\",\n",
    "            y=\"percentage\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 12),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Month', ylabel='Percentage of delayed flight')\n",
    "ax.set_xticklabels(months)\n",
    "ax.set_title(\"Percentage of delayed flights over the months of the year\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready now to draw some observations from our data, even if we have only looked at data coming from a year worth of flights:\n",
    "\n",
    "- The probability for a flight to be delayed is low at the beginning or at the very end of a given months\n",
    "- Flights on two first weekdays and on the weekend, are less likely to be delayed\n",
    "- May and September are very good months for travelling, as the probability of delay is low (remember we're working on US data. Do you think this is also true in France?)\n",
    "\n",
    "Putting things together, we can have a global picture of the whole year!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_with_delay = df.withColumn('is_delay', func.when(df[\"arrival_delay\"] >= 15, 1).otherwise(0))\n",
    "statistic_day = df_with_delay.groupBy(['year','month', 'day_of_month', 'day_of_week'])\\\n",
    "    .agg((func.sum('is_delay')/func.count('*')).alias('delay_ratio'))\n",
    "\n",
    "# assume that we do statistic on year 1994\n",
    "statistic_day = statistic_day\\\n",
    "    .orderBy('year', 'month', 'day_of_month', 'day_of_week')\n",
    "pdf = pd.DataFrame(data=statistic_day.collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZmUlkZGSFxc+GDRsyevRoHnjgAXr37k2dOnXo2bNnuelOnTrFhx9+yCOPPGK0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\noZuRl4iIiIiIiIiIiMgNq7IA2r59e15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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31ded63e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))                                                             \n",
    "ax = fig.add_subplot(1,1,1)     \n",
    "plt.xlabel(\"Weeks/Months in year\")\n",
    "plt.ylabel(\"Day of weeks (1:Monday -> 7 :Sunday)\")\n",
    "plt.title('Figure 10: The change of number flights over days in year')\n",
    "  \n",
    "rec_size = 0.3\n",
    "from matplotlib.patches import Rectangle\n",
    "import datetime\n",
    "num_days = len(pdf[0])\n",
    "ax.patch.set_facecolor('gray')\n",
    "ax.set_aspect('equal', 'box')\n",
    "ax.xaxis.set_major_locator(plt.NullLocator())\n",
    "ax.yaxis.set_major_locator(plt.NullLocator())\n",
    "\n",
    "for i in range(0, num_days):\n",
    "    # extract information from the result\n",
    "    year = pdf[0][i]\n",
    "    month = pdf[1][i]\n",
    "    day_of_month = pdf[2][i]\n",
    "    day_of_week = pdf[3][i]\n",
    "    day_of_year= datetime.date(year=year, month=month, day=day_of_month).timetuple()\n",
    "    week_of_year = datetime.date(year=year, month=month, day=day_of_month).isocalendar()[1]\n",
    "    \n",
    "    # dealing with the week of the previous year\n",
    "    if week_of_year == 52 and month == 1:\n",
    "        week_of_year = 0\n",
    "        \n",
    "    # the coordinate of a day in graph\n",
    "    X = week_of_year*rec_size\n",
    "    Y = day_of_week*rec_size\n",
    "    \n",
    "    # use different colors to show the delay ratio\n",
    "    color = 'white'\n",
    "    if pdf[4][i] <= 0.084:\n",
    "        color = 'lightyellow'\n",
    "    elif pdf[4][i] <= 0.117:\n",
    "        color = 'lightgreen'\n",
    "    elif pdf[4][i] <= 0.152:\n",
    "        color = 'gold'\n",
    "    elif pdf[4][i] <= 0.201:\n",
    "        color = 'orange'\n",
    "    else:\n",
    "        color = 'red'\n",
    "    rect = plt.Rectangle((X - rec_size/2.0, Y - rec_size/2.0), rec_size, rec_size,\n",
    "                      alpha=1, facecolor=color, edgecolor='whitesmoke')\n",
    "\n",
    "    ax.add_patch(rect)\n",
    "    \n",
    "    # drawing boders to separate months\n",
    "    if day_of_month <= 7:\n",
    "        rect2 = plt.Rectangle((X -rec_size/2.0, Y - rec_size/2.0), 0.01, rec_size,\n",
    "                      alpha=1, facecolor='black')\n",
    "        ax.add_patch(rect2)\n",
    "    if day_of_month == 1:\n",
    "        rect2 = plt.Rectangle((X - rec_size/2.0, Y - rec_size/2.0), rec_size, 0.01,\n",
    "                      alpha=1, facecolor='black')\n",
    "        ax.add_patch(rect2)\n",
    "ax.autoscale_view()\n",
    "\n",
    "patch1 = mpatches.Patch(color='lightyellow', label='delay ratio < 8.4%')\n",
    "patch2 = mpatches.Patch(color='lightgreen', label='delay ratio < 11.7%')\n",
    "patch3 = mpatches.Patch(color='gold', label='delay ratio < 15.2%')\n",
    "patch4 = mpatches.Patch(color='orange', label='delay ratio < 20.1%')\n",
    "patch5 = mpatches.Patch(color='red', label='delay ratio >= 20.1%')\n",
    "\n",
    "plt.legend(handles=[patch1, patch2, patch3, patch4, patch5], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 4.5\n",
    "<div class=\"alert alert-info\">\n",
    "From the barplot is possible to notice that the highest percentage of delays is in December, January, February (probably because fo the weather, it will be analysed later) and July.\n",
    "Moreover May and September are the best months to fly because they have the lowest percentage of delayes, this is probably not true in France because of all the different factors that have to be taken into consideration to end up with such a conclusion (weather, holidays...).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.6\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "What is the delay probability for the top 20 busiest airports?  \n",
    "\n",
    "\n",
    "By drawing the flight volume of each airport and the associated delay probability in a single plot, we can observe the relationship between airports, number of flights and the delay.  \n",
    "\n",
    "<div class=\"label label-success\">HINT </div>  Function `<df_colum>.isin(<list>)` helps checking whether a value in column belongs to a list.\n",
    "<ul></ul>\n",
    "<div class=\"label label-success\">SUGGESTION </div>  You can try with different chart type to have a better visualization.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# top 20 busiest airports\n",
    "df_busiest_airports = df_airport.limit(20)\n",
    "\n",
    "# number of delayed flights in the top 20 busiest airport\n",
    "df_delays_busiest_src_airports = (\n",
    "    df[(df.src_airport.isin([x[0] for x in df_busiest_airports.collect()])) & (df.arrival_delay > 15)]\n",
    "    .groupBy(df.src_airport)\n",
    "    .count()\n",
    ")\n",
    "df_delays_busiest_dest_airports = (\n",
    "    df[(df.dest_airport.isin([x[0] for x in df_busiest_airports.collect()])) & (df.arrival_delay > 15)]\n",
    "    .groupBy(df.dest_airport)\n",
    "    .count()\n",
    ")\n",
    "\n",
    "# delay probability per source\n",
    "df_prob_delay_busiest_src = (\n",
    "    df_delays_busiest_src_airports.\n",
    "    join(df_busiest_airports, df_busiest_airports.airport == df_delays_busiest_src_airports.src_airport, \"inner\").\n",
    "    select(df_busiest_airports.airport, (df_delays_busiest_src_airports[\"count\"]/df_busiest_airports[\"sum(count)\"]).alias(\"probability\")).\n",
    "    orderBy('airport')\n",
    ")\n",
    "# delay probability per destination\n",
    "df_prob_delay_busiest_dest = (\n",
    "    df_delays_busiest_dest_airports.\n",
    "    join(df_busiest_airports, df_busiest_airports.airport == df_delays_busiest_dest_airports.dest_airport, \"inner\").\n",
    "    select(df_busiest_airports.airport, (df_delays_busiest_dest_airports[\"count\"]/df_busiest_airports[\"sum(count)\"]).alias(\"probability\")).\n",
    "    orderBy('airport')\n",
    ")\n",
    "\n",
    "# delay propability per source and destination\n",
    "prob_delay_busiest_any = (\n",
    "    df_prob_delay_busiest_src.\n",
    "    join(df_prob_delay_busiest_dest, df_prob_delay_busiest_src.airport == df_prob_delay_busiest_dest.airport, \"inner\").\n",
    "    select(df_prob_delay_busiest_src.airport, ((df_prob_delay_busiest_src[\"probability\"]+df_prob_delay_busiest_dest[\"probability\"])/2).alias(\"probability\")).\n",
    "    orderBy('probability').\n",
    "    collect()\n",
    ")\n",
    "\n",
    "prob_delay_busiest_src = df_prob_delay_busiest_src.collect()\n",
    "prob_delay_busiest_dest = df_prob_delay_busiest_dest.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fNu4/c+aM8e8rj/2SFK1Nunw8ZmdnF/t7upLj772o1157TcOGDdPPP/+sd955x7i/ffv2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\nOteEEEIIIYQYCXWuCSGEEEIIMZL/A8YjF2vehzBmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31deb30b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=prob_delay_busiest_any,\n",
    "                     columns=['airport', 'probability'])\n",
    "\n",
    "# plot \n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x=\"airport\",\n",
    "            y=\"probability\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 20),\n",
    "            ax=ax)\n",
    "ax.set(ylabel=\"All\", xlabel=\"Airport\")\n",
    "ax.set(ylabel=\"Delay probability\", xlabel=\"Airport\")\n",
    "ax.set_title(\"Delay probability of the top 20 busiest airports\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4.7\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "What is the percentage of delayed flights which belong to one of the top 20 busiest carriers?  \n",
    "Comment the figure!\n",
    "\n",
    "<ul></ul>\n",
    "<div class=\"label label-success\">SUGGESTION </div>  You can try with different chart type to have a better visualization.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of delayed fights of the 20 top busiest carriers over the total number of flights in delay is 100.0%\n"
     ]
    }
   ],
   "source": [
    "# number of flights per carrier\n",
    "df_busiest_carriers = df_carrier.limit(20)\n",
    "\n",
    "# number of delayed flights for each carrier\n",
    "df_delayed_per_carrier = df[df.arrival_delay > 15].groupBy(df.carrier).agg(func.count('*').alias('delayed_flights'))\n",
    "\n",
    "# delayed flights for each of the top 20 busiest carrier\n",
    "df_delayed_per_busiest_carrier = df_delayed_per_carrier[df_delayed_per_carrier.carrier.isin([x[0] for x in df_busiest_carriers.collect()])]\n",
    "\n",
    "# number of delayed flights for each of the 20 top busiest carriers\n",
    "top20_delay = (\n",
    "    df_delayed_per_busiest_carrier\n",
    "    .agg(func.sum('delayed_flights').alias('delayed_flights'))\n",
    "    .collect()[0].delayed_flights\n",
    ")\n",
    "\n",
    "# number of delayed flights\n",
    "tot_delay = (\n",
    "    df_delayed_per_carrier\n",
    "    .agg(func.sum('delayed_flights').alias('delayed_flights'))\n",
    "    .collect()[0].delayed_flights\n",
    ")\n",
    "\n",
    "# percentual of delayed flights of the top 20 carriers over the total number of delayed flights\n",
    "perc = top20_delay/tot_delay*100\n",
    "print(\"The percentage of delayed fights of the 20 top busiest carriers over the total number of flights in delay is \" + str(perc) + \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# number of flights of each carrier\n",
    "# percentage of delayed flights over the total number of delayed flights \n",
    "# percentage of delayed flights over the total number of flights of each carrier\n",
    "join_perc_count = (\n",
    "    df_busiest_carriers\n",
    "    .join(df_delayed_per_busiest_carrier, df_delayed_per_busiest_carrier.carrier==df_busiest_carriers.carrier, \"inner\")\n",
    "    .select(df_busiest_carriers.carrier, df_busiest_carriers['count'], \n",
    "            ((df_delayed_per_busiest_carrier.delayed_flights)/df_busiest_carriers['count']*100).alias('perc_rel'),\n",
    "            (df_delayed_per_busiest_carrier.delayed_flights/tot_delay*100).alias('perc_tot')\n",
    "           )\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ld+7cEZanpKQgIyNDxGRERERERBVL9OL8yJEj2L17N549ewZ3d3dhec2aNTFx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\nCco5AAAAYBOUcwAAAMAmKOcAAACATfw/5Smt4D+DCkwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31ded976a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(3,1,figsize=(12, 5*3))\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "\n",
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=join_perc_count,\n",
    "                    columns=['carrier', 'count', 'perc_rel', 'perc_tot'])\n",
    "\n",
    "# plot the percentage of delayed flights over the total number of delayed flights\n",
    "sns.barplot(x=\"carrier\",\n",
    "            y=\"perc_tot\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"Blues\",len(join_perc_count)),\n",
    "            ax=ax[0])\n",
    "\n",
    "# set plot's labels\n",
    "ax[0].set(xlabel='Carrier', ylabel='Percentage of delayed flights')\n",
    "ax[0].set_title('Percetage of total number of delayed fligths',fontweight=\"bold\", size=15)\n",
    "\n",
    "# number of flights of each carrier\n",
    "sns.barplot(x=\"carrier\",\n",
    "            y=\"count\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"Blues\",len(join_perc_count)),\n",
    "            ax=ax[1])\n",
    "\n",
    "# set plot's labels\n",
    "ax[1].set(xlabel='Carrier', ylabel='Number of flights')\n",
    "ax[1].set_title('Number of flights performed',fontweight=\"bold\", size=15)\n",
    "\n",
    "# percentage of delayed flights over the total number of flights of each carrier\n",
    "sns.barplot(x=\"carrier\",\n",
    "            y=\"perc_rel\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"Blues\",len(join_perc_count)),\n",
    "            ax=ax[2])\n",
    "\n",
    "# set plot's labels\n",
    "ax[2].set(xlabel='Carrier', ylabel='Percentage of delayed flights')\n",
    "ax[2].set_title('Percentage of flights performed that experieced a delay',fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 4.7\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "The percentage of delayed fights of the 20 top busiest carriers with respect to the total number of flights in delay is 100%, since there are only 10 carriers in the dataset.\n",
    "<br><br>\n",
    "We decided then to analyse the percentage separately for each carrier\n",
    "<ul>\n",
    "    <li> The first plot show which percentage of the delayed flights was of which carrier, with respect to the total number of delayed fligths. It is, in other words, the conditional probability of a flight to have been performed by a certain carrier, knowing that it has experienced a delay.</li>\n",
    "    <li> The second plot just shows how many flights each carrier has performed</li>\n",
    "    <li> The third and last plot shows the percentage of flight performed by each carrier that experienced a delay. It corresponds to the conditional probability of a flight to be in delay, knowing that it is performed by a certain carrier.\n",
    "    </li>\n",
    "</ul>\n",
    "<br>\n",
    "The first plot lets one think that there are some carriers that are more likely to have delays, but, relating the data with the second plot, is possible to observe that those carriers that have performed more flights, have of course permormed more delays than the others. Actually what happens is that the probability of experiencing a delay is more or less the same with all the carrier, with some exceptions (i.e. NW, WN)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other interesting queries\n",
    "In this section we further explore the dataset with some queries we think may be intresting.\n",
    "\n",
    "### Question 1:\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Is there a difference between the various carriers in terms of distance of flights? (some carriers may only offer national flights while others could take care more of the international ones)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "carriers_distance = (\n",
    "    df[df.distance.isNotNull()].groupBy(df.carrier)\n",
    "    .agg(func.avg('distance').alias('avg_dist'))\n",
    "    .select('carrier', 'avg_dist')\n",
    "    .orderBy('avg_dist')\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Wnock/fLLL2X6pfRni8WiWrVqKSsrq8xqNleqZs2a6tq1q6SS1WlmzJihkydP6tixY8bq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31de7795c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=carriers_distance,\n",
    "                     columns=['carrier', 'avg_dist'])\n",
    "\n",
    "# plot the average distance per carrier\n",
    "f, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.barplot(x=\"carrier\",\n",
    "            y=\"avg_dist\",\n",
    "            data=pddf,\n",
    "            palette=sns.color_palette(\"GnBu_d\", 10),\n",
    "            ax=ax)\n",
    "\n",
    "# set plot's labels\n",
    "ax.set(xlabel='Carrier', ylabel='Average distance')\n",
    "ax.set_title(\"Average flight's distance per carrier\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "Is there a relation between the distance of flight and the delay?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_delay_distance = (df[df.distance.isNotNull()][df.arrival_delay>0]\n",
    "                     .select(df.distance, df.arrival_delay)\n",
    "                     .orderBy(df.distance, df.arrival_delay))\n",
    "delay_distance=df_delay_distance.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# create the pandas dataframe\n",
    "pddf = pd.DataFrame(data=delay_distance,\n",
    "                     columns=['arrival_delay', 'distance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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TjictZyMItY4n3Z0tQQgh2iMh109EGBUGxEYQGWEgJzuBCrOVAbERAAzNHMDo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\n5f8H2fh77YC7kvgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31b8248a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the average distance per carrier\n",
    "ax=sns.jointplot(\"distance\", \"arrival_delay\", stat_func=None, data=pddf, kind=\"reg\")\n",
    "\n",
    "# set plot's labels\n",
    "ax.set_axis_labels('Arrival Delay', 'Distance')\n",
    "ax.fig.get_axes()[0].set_title(\"Average flight's distance per carrier\", fontweight=\"bold\", size=15)\n",
    "ax.fig.get_axes()[0].title.set_position([0.6,1.22])\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "The first plot shows three main different categories based on the average flight's distance. Most of the carriers fall in the central category, having average flight's distance of approximately 650 km, then the other two categories present smaller (400-500 km) and greater (~950 km) distances.\n",
    "<br>\n",
    "Since the previous plots showed almost no correlation between the delay and the carrier of a flight, we decided to use a regression plot to check if there is a correlation between distance and delay. This last plot showed a correlation between the two measures: as the distance of flight increases, the delay increases too.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Addition (bonus) questions\n",
    "\n",
    "### Additional data\n",
    "In the HDFS file system you have used for running the Notebook, you will notice that there are several other years available (in addition to 1994).\n",
    "\n",
    "There are some other datasets that related to this use case:\n",
    "\n",
    "- Airport IATA Codes to City names and Coordinates mapping: http://stat-computing.org/dataexpo/2009/airports.csv\n",
    "- Carrier codes to Full name mapping: http://stat-computing.org/dataexpo/2009/carriers.csv\n",
    "- Information about individual planes: http://stat-computing.org/dataexpo/2009/plane-data.csv\n",
    "- Weather information: http://www.wunderground.com/weather/api/. You can subscribe for free to the developers' API and obtain (at a limited rate) historical weather information in many different formats. Also, to get an idea of the kind of information is available, you can use this link: http://www.wunderground.com/history/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5.1\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Which airports are present in the dataset only as destination airport?\n",
    "\n",
    "Which airport is present only as source airport in the dataset? \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "src_airports = df.select(df.src_airport)\n",
    "dest_airports = df.select(df.dest_airport)\n",
    "\n",
    "# airports present only as destination\n",
    "dest_not_src = dest_airports.subtract(src_airports)\n",
    "\n",
    "# airports present only as origin\n",
    "src_not_dest = src_airports.subtract(dest_airports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+\n",
      "|dest_airport|\n",
      "+------------+\n",
      "|         BFI|\n",
      "|         GUM|\n",
      "+------------+\n",
      "\n",
      "+-----------+\n",
      "|src_airport|\n",
      "+-----------+\n",
      "|        SPN|\n",
      "+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dest_not_src.show()\n",
    "src_not_dest.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5.2\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Which airports have biggest delay probability ?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Calculate the amount of delayed flights in the top 20 busiest airport\n",
    "delays_src_airports=df[df.arrival_delay > 15].groupBy(df.src_airport).count()\n",
    "delays_dest_airports=df[df.arrival_delay > 15].groupBy(df.dest_airport).count()\n",
    "\n",
    "#Calculate the probability per source and per destination\n",
    "p_delay_src=(\n",
    "    delays_src_airports.\n",
    "    join(df_airport, df_airport.airport == delays_src_airports.src_airport, \"inner\").\n",
    "    select(df_airport.airport, (delays_src_airports[\"count\"]/df_airport[\"sum(count)\"]).alias(\"probability\"))\n",
    ")\n",
    "p_delay_dest=(\n",
    "    delays_dest_airports.\n",
    "    join(df_airport, df_airport.airport == delays_dest_airports.dest_airport, \"inner\").\n",
    "    select(df_airport.airport, (delays_dest_airports[\"count\"]/df_airport[\"sum(count)\"]).alias(\"probability\"))\n",
    ")\n",
    "\n",
    "#Calculate the propability of delay regardless if the airport is source or destination and sort the results\n",
    "p_delay_any=(\n",
    "    p_delay_src.\n",
    "    join(p_delay_dest, p_delay_src.airport == p_delay_dest.airport, \"inner\").\n",
    "    select(p_delay_src.airport, ((p_delay_src[\"probability\"]+p_delay_dest[\"probability\"])/2).alias(\"probability\")).\n",
    "    orderBy(\"probability\", ascending=0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------------+\n",
      "|airport|        probability|\n",
      "+-------+-------------------+\n",
      "|    MTJ|0.16666666666666666|\n",
      "|    ACK|0.15476190476190477|\n",
      "|    GUC|0.13365155131264916|\n",
      "|    EWR|0.12512874426794193|\n",
      "|    BFL| 0.1206896551724138|\n",
      "|    PFN|0.11978150604760046|\n",
      "|    JFK| 0.1175976942309473|\n",
      "|    EGE|0.11706881143878463|\n",
      "|    BQN| 0.1154970760233918|\n",
      "|    BOS|0.10369010896562775|\n",
      "+-------+-------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Show top 10 airports in delay probability\n",
    "p_delay_any.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Which cities have largest delay duration in average ?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Create DataFrame for cities\n",
    "df_cities = sqlContext.read.load('airports.csv', \n",
    "    format='com.databricks.spark.csv', \n",
    "    header='true', \n",
    "    inferSchema='true',\n",
    "    nullValue='NA'\n",
    ")\n",
    "\n",
    "#Calculate the average delay duration and sort the results\n",
    "city_delay=df.join(df_cities, df.dest_airport==df_cities.iata, \"inner\").groupBy(df_cities.city).agg(func.avg(df.arrival_delay).alias(\"avg_delay\")).orderBy(\"avg_delay\", ascending=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+------------------+\n",
      "|          city|         avg_delay|\n",
      "+--------------+------------------+\n",
      "|     Nantucket|              20.0|\n",
      "|      Montrose|17.352941176470587|\n",
      "|   Bakersfield|13.368070953436806|\n",
      "|        Newark|12.523816300934863|\n",
      "|     Lafayette| 11.64069264069264|\n",
      "|    St. Mary's|11.181818181818182|\n",
      "|     Aguadilla| 10.39760837070254|\n",
      "|    Petersburg|10.231907894736842|\n",
      "|Grand Junction|10.088669950738916|\n",
      "|      Gunnison|10.014218009478673|\n",
      "+--------------+------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Show top 10 cities in delay duration\n",
    "city_delay.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Is there any relationship between weather condition and the delay of flights in 1994 ?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# dataframe #with weather information\n",
    "weather_schema = StructType([\n",
    "    StructField(\"airport\", StringType(), True),\n",
    "    StructField(\"year\", IntegerType(), True),\n",
    "    StructField(\"month\", IntegerType(), True),\n",
    "    StructField(\"day\", IntegerType(), True),\n",
    "    StructField(\"hour\", IntegerType(), True),\n",
    "    StructField(\"temperature\", DoubleType(), True),\n",
    "    StructField(\"wind speed\", DoubleType(), True),\n",
    "    StructField(\"precipitation\", DoubleType(), True),\n",
    "    StructField(\"rain\", IntegerType(),  True),\n",
    "    StructField(\"snow\", IntegerType(),  True),\n",
    "    StructField(\"thunder\", IntegerType(), True)\n",
    "])\n",
    "df_weather = sqlContext.read.load('weather_top20.csv', \n",
    "                                  format='com.databricks.spark.csv', \n",
    "                                  header='true',\n",
    "                                  #inferSchema='true',\n",
    "                                  schema=weather_schema,\n",
    "                                  nullValue='NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- arrival_delay: integer (nullable = true)\n",
      " |-- temperature: double (nullable = true)\n",
      " |-- precipitation: double (nullable = true)\n",
      " |-- snow: integer (nullable = true)\n",
      " |-- thunder: integer (nullable = true)\n",
      " |-- rain: integer (nullable = true)\n",
      " |-- wind speed: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Join information about the wheather\n",
    "df_delay_weather = (\n",
    "    df[df.arrival_delay > 15].join(df_weather,\n",
    "                                   ((df.dest_airport==df_weather.airport) &\n",
    "                                    (df.month==df_weather.month) &\n",
    "                                    (df.day_of_month==df_weather.day) &\n",
    "                                    ((df.scheduled_arrival_time/100).cast('int')==df_weather.hour)),\n",
    "                                   \"inner\")\n",
    "    .select(df['arrival_delay'],\n",
    "            df_weather['temperature'],\n",
    "            df_weather['precipitation'],\n",
    "            df_weather['snow'], \n",
    "            df_weather['thunder'], \n",
    "            df_weather['rain'], \n",
    "            df_weather['wind speed'])\n",
    ")\n",
    "df_delay_weather.printSchema()\n",
    "delay_weather=df_delay_weather.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Calculate percentages of delayed flight under certain weather condition\n",
    "delays=df_delay_weather.count()\n",
    "rain=df_delay_weather[df_delay_weather.rain=='1'].count()/delays*100\n",
    "thunder=df_delay_weather[df_delay_weather.thunder=='1'].count()/delays*100\n",
    "snow=df_delay_weather[df_delay_weather.snow=='1'].count()/delays*100\n",
    "precipitation=df_delay_weather[df_delay_weather.precipitation>0].count()/delays*100\n",
    "wind=df_delay_weather[df_delay_weather['wind speed']>30].count()/delays*100\n",
    "temp_low=df_delay_weather[df_delay_weather['temperature']<0].count()/delays*100\n",
    "temp_high=df_delay_weather[df_delay_weather['temperature']>30].count()/delays*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i0NBQo48rACBnDuvTXL58eWPoIdulH9uNOnk9ritwN3niiSeUmJio2NhYpaSk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31b8207390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/4fbhtOAdGBio+Ph4JSQkyNfXV6tXr9aUKVPybWsYxg3Pm1NISEiJ1A4AcOTp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\nSkqKhg0bpt9++03S719n526X0549ezR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e0978630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "sns.barplot(y=[temp_low, precipitation, snow, thunder, rain, wind],\n",
    "            x=['temperature<0°', 'precipitation>0mm', 'snow', 'thunder', 'rain', 'wind speed>30n'],\n",
    "            ax=ax)\n",
    "\n",
    "ax.set(xlabel='', ylabel=\"Percetage of delayed flights\")\n",
    "ax.set_title(\"Percentage of flights delayed with weather factors\", fontweight=\"bold\", size=15)\n",
    "plt.show()\n",
    "\n",
    "# Calculation and plotting correlation between arrival delay and weather factors\n",
    "pddf = pd.DataFrame(data=delay_weather,\n",
    "                   columns=['arrival_delay', 'temperature', 'precipitation', 'snow', 'thunder', 'rain', 'wind speed'])\n",
    "corr = pddf.corr().drop(['arrival_delay'])\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "sns.barplot(data=corr,\n",
    "            y='arrival_delay',\n",
    "            x='wind speed',\n",
    "            ax=ax)\n",
    "\n",
    "ax.set_xticklabels(['temperature', 'precipitation', 'snow', 'thunder', 'rain', 'wind speed'])\n",
    "ax.set(xlabel='', ylabel=\"Correlation\")\n",
    "ax.set_title(\"Delay correlation with weather factors\", fontweight=\"bold\", size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer 5.3\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "The first plot shows the percentage of delayed flights that occured under each weather condition. \n",
    "The second plot instead shows a slight correlation between delay's duration and weather.\n",
    "The weather data have been extracted from a dataset taking into consideration only the top 20 busiest airports during the year.\n",
    "<br><br>\n",
    "Apart from the temperature, that seems not to affect too much the delay, correlation with precipitation is relatively high: about 20% of delays have been experienced under circumstances of rain, and the duration of such delays seems to be longer when the intensity is extremely high, i.e. thunders, as shown by the correlation index. \n",
    "<br>\n",
    "Snow instead presents an unexpected behaviour, since the correlation seems to suggest a lower influence on the delay duration. Moreover, the analysis in terms of percentages shows a low rate as well, but in this case it is necessary to take into account that snow is an event extremely more rare than rain or strong wind in most part of the worlds.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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