 Manipulating data in PySpark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Spark in Python\n",
    "\n",
    "The first step in using Spark is connecting to a cluster.\n",
    "\n",
    "In practice, the cluster will be hosted on a remote machine that's connected to all other nodes. There will be one computer, called the master that manages splitting up the data and the computations. The master is connected to the rest of the computers in the cluster, which are called worker. The master sends the workers data and calculations to run, and they send their results back to the master.\n",
    "\n",
    "When you're just getting started with Spark it's simpler to just run a cluster locally. Thus, for this course, instead of connecting to another computer, all computations will be run on DataCamp's servers in a simulated cluster.\n",
    "\n",
    "Creating the connection is as simple as creating an instance of the `SparkContext` class. The class constructor takes a few optional arguments that allow you to specify the attributes of the cluster you're connecting to.\n",
    "\n",
    "An object holding all these attributes can be created with the `SparkConf()` constructor. Take a look at the documentation for all the details!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pyspark.context.SparkContext'>\n",
      "<property object at 0x000000E455278F98>\n"
     ]
    }
   ],
   "source": [
    "from pyspark import  SparkContext as sc# And then try to import SparkContext.\n",
    "# Verify SparkContext\n",
    "print(sc)\n",
    "\n",
    "# Print Spark version\n",
    "print(sc.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using DataFrames\n",
    "Spark's core data structure is the Resilient Distributed Dataset (RDD). This is a low level object that lets Spark work its magic by splitting data across multiple nodes in the cluster. However, RDDs are hard to work with directly, so in this course you'll be using the Spark DataFrame abstraction built on top of RDDs.\n",
    "\n",
    "The Spark DataFrame was designed to behave a lot like a SQL table (a table with variables in the columns and observations in the rows). Not only are they easier to understand, DataFrames are also more optimized for complicated operations than RDDs.\n",
    "\n",
    "When you start modifying and combining columns and rows of data, there are many ways to arrive at the same result, but some often take much longer than others. When using RDDs, it's up to the data scientist to figure out the right way to optimize the query, but the DataFrame implementation has much of this optimization built in!\n",
    "\n",
    "To start working with Spark DataFrames, you first have to create a SparkSession object from your SparkContext. You can think of the SparkContext as your connection to the cluster and the SparkSession as your interface with that connection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyspark.sql.session.SparkSession object at 0x000000E4554E2EC8>\n"
     ]
    }
   ],
   "source": [
    "from pyspark import SparkContext\n",
    "from pyspark import SparkConf\n",
    "import pyspark # only run after findspark.init()\n",
    "from pyspark.sql.functions import to_date, col\n",
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.getOrCreate()\n",
    "# Print spark\n",
    "print(spark)\n",
    "spark.conf.set(\"spark.sql.parquet.compression.codec\", \"gzip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "processFile=('..\\\\PySpark')\n",
    "fileName =(os.listdir(processFile))\n",
    "\n",
    "for file in fileName:\n",
    "    if \".csv\" in file:\n",
    "        file_path = \"%s/%s\" % (processFile, file) \n",
    "        df = (spark.read.format('csv')\n",
    "           .option('header', 'True')\n",
    "           .option(\"inferSchema\", \"true\")\n",
    "           .load(file_path))\n",
    "        Name_table= file.replace(\".csv\",\"\")\n",
    "        corresponding_table = \"{0}\".format( Name_table)\n",
    "        df.write.mode(\"overwrite\").saveAsTable(corresponding_table)\n",
    "        #df.write.mode(SaveMode.Overwrite).saveAsTable(corresponding_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='airports', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='flights_small', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='planes', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+----------+-----------+----+---+---+\n",
      "|faa|                name|       lat|        lon| alt| tz|dst|\n",
      "+---+--------------------+----------+-----------+----+---+---+\n",
      "|04G|   Lansdowne Airport|41.1304722|-80.6195833|1044| -5|  A|\n",
      "|06A|Moton Field Munic...|32.4605722|-85.6800278| 264| -5|  A|\n",
      "|06C| Schaumburg Regional|41.9893408|-88.1012428| 801| -6|  A|\n",
      "|06N|     Randall Airport| 41.431912|-74.3915611| 523| -5|  A|\n",
      "|09J|Jekyll Island Air...|31.0744722|-81.4277778|  11| -4|  A|\n",
      "+---+--------------------+----------+-----------+----+---+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"select * from airports\").show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----+--------------------+----------------+--------+-------+-----+-----+---------+\n",
      "|tailnum|year|                type|    manufacturer|   model|engines|seats|speed|   engine|\n",
      "+-------+----+--------------------+----------------+--------+-------+-----+-----+---------+\n",
      "| N102UW|1998|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N103US|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N104UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N105UW|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "| N107US|1999|Fixed wing multi ...|AIRBUS INDUSTRIE|A320-214|      2|  182|   NA|Turbo-fan|\n",
      "+-------+----+--------------------+----------------+--------+-------+-----+-----+---------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"select * from planes\").show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|\n",
      "|2014|    1| 15|    1037|        7|    1352|        2|     WN| N646SW|    48|   PDX| DEN|     121|     991|  10|    37|\n",
      "|2014|    7|  2|     847|       42|    1041|       51|     WN| N422WN|  1520|   PDX| OAK|      90|     543|   8|    47|\n",
      "|2014|    5| 12|    1655|       -5|    1842|      -18|     VX| N361VA|   755|   SEA| SFO|      98|     679|  16|    55|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|\n",
      "|2014|   11|  8|    1653|       -2|    1924|       -1|     AS| N323AS|   448|   SEA| LAX|     130|     954|  16|    53|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20|\n",
      "|2014|   10| 30|     811|       21|    1038|       29|     AS| N433AS|   608|   SEA| LAS|     127|     867|   8|    11|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|\n",
      "|2014|   10| 31|    1314|       89|    1544|      111|     AS| N713AS|   306|   SEA| SFO|     129|     679|  13|    14|\n",
      "|2014|    1| 29|    2009|        3|    2159|        9|     UA| N27205|  1458|   PDX| SFO|      90|     550|  20|     9|\n",
      "|2014|   12| 17|    2015|       50|    2150|       41|     AS| N626AS|   368|   SEA| SMF|      76|     605|  20|    15|\n",
      "|2014|    8| 11|    1017|       -3|    1613|       -7|     WN| N8634A|   827|   SEA| MDW|     216|    1733|  10|    17|\n",
      "|2014|    1| 13|    2156|       -9|     607|      -15|     AS| N597AS|    24|   SEA| BOS|     290|    2496|  21|    56|\n",
      "|2014|    6|  5|    1733|      -12|    1945|      -10|     OO| N215AG|  3488|   PDX| BUR|     111|     817|  17|    33|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"select * from flights_small\").show()\n",
    "       \n",
    "        #df.write.mode(\"overwrite\").saveAsTable(\"df_\"+file)\n",
    "        #df.repartition(2).write.mode(\"overwrite\").save(\"/PySpark/df_final.parquet\")\n",
    "        #df.repartition(2).write.mode(\"overwrite\").save(\"df_View.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pyspark.sql.session.SparkSession object at 0x000000E4554E2EC8>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Table(name='airports', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='flights_small', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='planes', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import SparkSession from pyspark.sql\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "# Create my_spark\n",
    "my_spark = SparkSession.builder.getOrCreate()\n",
    "\n",
    "# Print my_spark\n",
    "print(my_spark)\n",
    "spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|\n",
      "|2014|    1| 15|    1037|        7|    1352|        2|     WN| N646SW|    48|   PDX| DEN|     121|     991|  10|    37|\n",
      "|2014|    7|  2|     847|       42|    1041|       51|     WN| N422WN|  1520|   PDX| OAK|      90|     543|   8|    47|\n",
      "|2014|    5| 12|    1655|       -5|    1842|      -18|     VX| N361VA|   755|   SEA| SFO|      98|     679|  16|    55|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Don't change this query\n",
    "query = \"FROM flights_small SELECT * LIMIT 10\"\n",
    "\n",
    "# Get the first 10 rows of flights\n",
    "flights10 = spark.sql(query)\n",
    "\n",
    "# Show the results\n",
    "flights10.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  origin dest    N\n",
      "0    SEA  RNO    8\n",
      "1    SEA  DTW   98\n",
      "2    SEA  CLE    2\n",
      "3    SEA  LAX  450\n",
      "4    PDX  SEA  144\n"
     ]
    }
   ],
   "source": [
    "# Don't change this query\n",
    "query = \"SELECT origin, dest, COUNT(*) as N FROM flights_small GROUP BY origin, dest\"\n",
    "\n",
    "# Run the query\n",
    "flight_counts = spark.sql(query)\n",
    "\n",
    "# Convert the results to a pandas DataFrame\n",
    "pd_counts = flight_counts.toPandas()\n",
    "\n",
    "# Print the head of pd_counts\n",
    "print(pd_counts.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Put some Spark in your data\n",
    "In the last exercise, you saw how to move data from Spark to pandas. However, maybe you want to go the other direction, and put a pandas DataFrame into a Spark cluster! The SparkSession class has a method for this as well.\n",
    "\n",
    "The .createDataFrame() method takes a pandas DataFrame and returns a Spark DataFrame.\n",
    "\n",
    "The output of this method is stored locally, not in the SparkSession catalog. This means that you can use all the Spark DataFrame methods on it, but you can't access the data in other contexts.\n",
    "\n",
    "For example, a SQL query (using the .sql() method) that references your DataFrame will throw an error. To access the data in this way, you have to save it as a temporary table.\n",
    "\n",
    "You can do this using the .createTempView() Spark DataFrame method, which takes as its only argument the name of the temporary table you'd like to register. This method registers the DataFrame as a table in the catalog, but as this table is temporary, it can only be accessed from the specific SparkSession used to create the Spark DataFrame.\n",
    "\n",
    "There is also the method .createOrReplaceTempView(). This safely creates a new temporary table if nothing was there before, or updates an existing table if one was already defined. You'll use this method to avoid running into problems with duplicate tables."
   ]
  },
  {
   "attachments": {
    "spark_figure.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![spark_figure.png](attachment:spark_figure.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='airports', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='flights_small', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='planes', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Create pd_temp\n",
    "pd_temp = pd.DataFrame(np.random.random(10))\n",
    "\n",
    "# Create spark_temp from pd_temp\n",
    "spark_temp = spark.createDataFrame(pd_temp)\n",
    "\n",
    "# Examine the tables in the catalog\n",
    "spark.catalog.listTables()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='airports', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='flights_small', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='planes', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    # Add spark_temp to the catalog\n",
    "    spark_temp.createOrReplaceTempView('temp')\n",
    "\n",
    "    # Examine the tables in the catalog again\n",
    "    spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating columns\n",
    "In this chapter, you'll learn how to use the methods defined by Spark's DataFrame class to perform common data operations.\n",
    "\n",
    "Let's look at performing column-wise operations. In Spark you can do this using the .withColumn() method, which takes two arguments. First, a string with the name of your new column, and second the new column itself.\n",
    "\n",
    "The new column must be an object of class Column. Creating one of these is as easy as extracting a column from your DataFrame using df.colName.\n",
    "\n",
    "Updating a `Spark` DataFrame is somewhat different than working in `pandas` because the Spark DataFrame is **immutable**. This means that it can't be changed, and so columns can't be updated in place.\n",
    "\n",
    "Thus, all these methods return a new DataFrame. To overwrite the original DataFrame you must reassign the returned DataFrame using the method like so:\n",
    "\n",
    "`df = df.withColumn(\"newCol\", df.oldCol + 1)`\n",
    "The above code creates a DataFrame with the same columns as df plus a new column, newCol, where every entry is equal to the corresponding entry from oldCol, plus one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|\n",
      "|2014|    1| 15|    1037|        7|    1352|        2|     WN| N646SW|    48|   PDX| DEN|     121|     991|  10|    37|\n",
      "|2014|    7|  2|     847|       42|    1041|       51|     WN| N422WN|  1520|   PDX| OAK|      90|     543|   8|    47|\n",
      "|2014|    5| 12|    1655|       -5|    1842|      -18|     VX| N361VA|   755|   SEA| SFO|      98|     679|  16|    55|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|\n",
      "|2014|   11|  8|    1653|       -2|    1924|       -1|     AS| N323AS|   448|   SEA| LAX|     130|     954|  16|    53|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20|\n",
      "|2014|   10| 30|     811|       21|    1038|       29|     AS| N433AS|   608|   SEA| LAS|     127|     867|   8|    11|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|\n",
      "|2014|   10| 31|    1314|       89|    1544|      111|     AS| N713AS|   306|   SEA| SFO|     129|     679|  13|    14|\n",
      "|2014|    1| 29|    2009|        3|    2159|        9|     UA| N27205|  1458|   PDX| SFO|      90|     550|  20|     9|\n",
      "|2014|   12| 17|    2015|       50|    2150|       41|     AS| N626AS|   368|   SEA| SMF|      76|     605|  20|    15|\n",
      "|2014|    8| 11|    1017|       -3|    1613|       -7|     WN| N8634A|   827|   SEA| MDW|     216|    1733|  10|    17|\n",
      "|2014|    1| 13|    2156|       -9|     607|      -15|     AS| N597AS|    24|   SEA| BOS|     290|    2496|  21|    56|\n",
      "|2014|    6|  5|    1733|      -12|    1945|      -10|     OO| N215AG|  3488|   PDX| BUR|     111|     817|  17|    33|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create the DataFrame flights\n",
    "flights = spark.table(\"flights_small\")\n",
    "\n",
    "# Show the head\n",
    "flights.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|      duration_hrs|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|               2.2|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|               6.0|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|              1.85|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|1.3833333333333333|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|2.1166666666666667|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add duration_hrs\n",
    "flights = flights.withColumn(\"duration_hrs\", flights.air_time/60)\n",
    "flights.show(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+-----------------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|     duration_hrs|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+-----------------+\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|              6.0|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|             2.25|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|              3.3|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20|2.566666666666667|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|             3.05|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+-----------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+-----------------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|     duration_hrs|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+-----------------+\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|              6.0|\n",
      "|2014|    4| 19|    1236|       -4|    1508|       -7|     AS| N309AS|   490|   SEA| SAN|     135|    1050|  12|    36|             2.25|\n",
      "|2014|   11| 19|    1812|       -3|    2352|       -4|     AS| N564AS|    26|   SEA| ORD|     198|    1721|  18|    12|              3.3|\n",
      "|2014|    8|  3|    1120|        0|    1415|        2|     AS| N305AS|   656|   SEA| PHX|     154|    1107|  11|    20|2.566666666666667|\n",
      "|2014|   11| 12|    2346|       -4|     217|      -28|     AS| N765AS|   121|   SEA| ANC|     183|    1448|  23|    46|             3.05|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+-----------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Filter flights by passing a string\n",
    "long_flights1 = flights.filter(\"distance > 1000\")\n",
    "\n",
    "# Filter flights by passing a column of boolean values\n",
    "long_flights2 = flights.filter(flights.distance > 1000)\n",
    "\n",
    "# Print the data to check they're equal\n",
    "long_flights1.show(5)\n",
    "long_flights2.show(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting\n",
    "The Spark variant of SQL's `SELECT` is the `.select()` method. This method takes multiple arguments - one for each column you want to select. These arguments can either be the column name as a string (one for each column) or a column object (using the `df.colName` syntax). When you pass a column object, you can perform operations like addition or subtraction on the column to change the data contained in it, much like inside `.withColumn()`.\n",
    "\n",
    "The difference between `.select()` and `.withColumn()` methods is that \n",
    "- `.select()` returns only the columns you specify, while `.withColumn()` returns all the columns of the DataFrame in addition to the one you defined. \n",
    "- It's often a good idea to drop columns you don't need at the beginning of an operation so that you're not dragging around extra data as you're wrangling. In this case, you would use `.select()` and not `.withColumn()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the first set of columns\n",
    "selected1 = flights.select(\"tailnum\", \"origin\", \"dest\")\n",
    "\n",
    "# Select the second set of columns\n",
    "temp = flights.select(flights.origin, flights.dest, flights.carrier)\n",
    "\n",
    "# Define first filter\n",
    "filterA = flights.origin == \"SEA\"\n",
    "\n",
    "# Define second filter\n",
    "filterB = flights.dest == \"PDX\"\n",
    "\n",
    "# Filter the data, first by filterA then by filterB\n",
    "selected2 = temp.filter(filterA).filter(filterB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------+----+\n",
      "|tailnum|origin|dest|\n",
      "+-------+------+----+\n",
      "| N846VA|   SEA| LAX|\n",
      "| N559AS|   SEA| HNL|\n",
      "| N847VA|   SEA| SFO|\n",
      "| N360SW|   PDX| SJC|\n",
      "| N612AS|   SEA| BUR|\n",
      "+-------+------+----+\n",
      "only showing top 5 rows\n",
      "\n",
      "+------+----+-------+\n",
      "|origin|dest|carrier|\n",
      "+------+----+-------+\n",
      "|   SEA| LAX|     VX|\n",
      "|   SEA| HNL|     AS|\n",
      "|   SEA| SFO|     VX|\n",
      "|   PDX| SJC|     WN|\n",
      "|   SEA| BUR|     AS|\n",
      "+------+----+-------+\n",
      "only showing top 5 rows\n",
      "\n",
      "+------+----+-------+\n",
      "|origin|dest|carrier|\n",
      "+------+----+-------+\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "|   SEA| PDX|     OO|\n",
      "+------+----+-------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "selected1.show(5)\n",
    "temp.show(5)\n",
    "selected2.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----+-------+------------------+\n",
      "|origin|dest|tailnum|         avg_speed|\n",
      "+------+----+-------+------------------+\n",
      "|   SEA| LAX| N846VA| 433.6363636363636|\n",
      "|   SEA| HNL| N559AS| 446.1666666666667|\n",
      "|   SEA| SFO| N847VA|367.02702702702703|\n",
      "|   PDX| SJC| N360SW| 411.3253012048193|\n",
      "|   SEA| BUR| N612AS| 442.6771653543307|\n",
      "+------+----+-------+------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "+------+----+-------+------------------+\n",
      "|origin|dest|tailnum|         avg_speed|\n",
      "+------+----+-------+------------------+\n",
      "|   SEA| LAX| N846VA| 433.6363636363636|\n",
      "|   SEA| HNL| N559AS| 446.1666666666667|\n",
      "|   SEA| SFO| N847VA|367.02702702702703|\n",
      "|   PDX| SJC| N360SW| 411.3253012048193|\n",
      "|   SEA| BUR| N612AS| 442.6771653543307|\n",
      "+------+----+-------+------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define avg_speed\n",
    "avg_speed = (flights.distance/(flights.air_time/60)).alias(\"avg_speed\")\n",
    "\n",
    "# Select the correct columns\n",
    "speed1 = flights.select(\"origin\", \"dest\", \"tailnum\", avg_speed)\n",
    "\n",
    "# Create the same table using a SQL expression\n",
    "speed2 = flights.selectExpr(\"origin\", \"dest\", \"tailnum\", \"distance/(air_time/60) as avg_speed\")\n",
    "\n",
    "speed1.show(5)\n",
    "speed2.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping and Aggregating I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|dest|air_time|distance|hour|minute|      duration_hrs|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA| LAX|     132|     954|   6|    58|               2.2|\n",
      "|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA| HNL|     360|    2677|  10|    40|               6.0|\n",
      "|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA| SFO|     111|     679|  14|    43|              1.85|\n",
      "|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX| SJC|      83|     569|  17|     5|1.3833333333333333|\n",
      "|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA| BUR|     127|     937|   7|    54|2.1166666666666667|\n",
      "+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+----+--------+--------+----+------+------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "flights.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE: DataFrame[year: int, month: int, day: int, dep_time: string, dep_delay: string, arr_time: string, arr_delay: string, carrier: string, tailnum: string, flight: int, origin: string, dest: string, air_time: string, distance: int, hour: string, minute: string, duration_hrs: double]\n",
      "AFTER: DataFrame[year: int, month: int, day: int, dep_time: string, dep_delay: string, arr_time: string, arr_delay: string, carrier: string, tailnum: string, flight: int, origin: string, dest: string, air_time: int, distance: int, hour: string, minute: string, duration_hrs: double]\n"
     ]
    }
   ],
   "source": [
    "print(\"BEFORE:\",flights)\n",
    "\n",
    "flights=flights.withColumn(\"air_time\", flights[\"air_time\"].cast('int'))\n",
    "\n",
    "print(\"AFTER:\",flights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-----+\n",
      "|tailnum|count|\n",
      "+-------+-----+\n",
      "| N442AS|   38|\n",
      "| N102UW|    2|\n",
      "| N36472|    4|\n",
      "| N38451|    4|\n",
      "| N73283|    4|\n",
      "+-------+-----+\n",
      "only showing top 5 rows\n",
      "\n",
      "+------+------------------+\n",
      "|origin|     avg(air_time)|\n",
      "+------+------------------+\n",
      "|   SEA| 160.4361496051259|\n",
      "|   PDX|137.11543248288737|\n",
      "+------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Group by tailnum\n",
    "by_plane = flights.groupBy(\"tailnum\")\n",
    "\n",
    "# Number of flights each plane made\n",
    "by_plane.count().show(5)\n",
    "\n",
    "# Group by origin\n",
    "by_origin = flights.groupBy(\"origin\")\n",
    "# Average duration of flights from PDX and SEA\n",
    "by_origin.avg(\"air_time\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----+------------------+\n",
      "|month|dest|    avg(dep_delay)|\n",
      "+-----+----+------------------+\n",
      "|    1| ABQ|              -3.0|\n",
      "|    1| ANC|               1.5|\n",
      "|    1| ATL|             14.36|\n",
      "|    1| AUS|3.6666666666666665|\n",
      "|    1| BOS|            11.625|\n",
      "+-----+----+------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "+-----+----+----------------------+\n",
      "|month|dest|stddev_samp(dep_delay)|\n",
      "+-----+----+----------------------+\n",
      "|    1| ABQ|                  null|\n",
      "|    1| ANC|    13.552367566837415|\n",
      "|    1| ATL|    46.362592679875014|\n",
      "|    1| AUS|     10.96965511460289|\n",
      "|    1| BOS|    40.028338176132884|\n",
      "+-----+----+----------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "flights=flights.withColumn(\"dep_delay\", flights[\"dep_delay\"].cast(\"int\"))#.show(5)\n",
    "\n",
    "# Import pyspark.sql.functions as F\n",
    "import pyspark.sql.functions as F\n",
    "\n",
    "# Group by month and dest\n",
    "by_month_dest = flights.groupBy('month','dest')\n",
    "\n",
    "# Average departure delay by month and destination\n",
    "by_month_dest.avg('dep_delay').sort(\"month\", \"dest\").show(5)\n",
    "\n",
    "# Standard deviation of departure delay\n",
    "by_month_dest.agg(F.stddev('dep_delay')).sort(\"month\", \"dest\").show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='airports', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='flights_small', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='planes', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='temp', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+----------+-----------+----+---+---+\n",
      "|faa|                name|       lat|        lon| alt| tz|dst|\n",
      "+---+--------------------+----------+-----------+----+---+---+\n",
      "|04G|   Lansdowne Airport|41.1304722|-80.6195833|1044| -5|  A|\n",
      "|06A|Moton Field Munic...|32.4605722|-85.6800278| 264| -5|  A|\n",
      "|06C| Schaumburg Regional|41.9893408|-88.1012428| 801| -6|  A|\n",
      "|06N|     Randall Airport| 41.431912|-74.3915611| 523| -5|  A|\n",
      "|09J|Jekyll Island Air...|31.0744722|-81.4277778|  11| -4|  A|\n",
      "+---+--------------------+----------+-----------+----+---+---+\n",
      "only showing top 5 rows\n",
      "\n",
      "+----+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+--------+--------+----+------+------------------+--------------------+---------+-----------+---+---+---+\n",
      "|dest|year|month|day|dep_time|dep_delay|arr_time|arr_delay|carrier|tailnum|flight|origin|air_time|distance|hour|minute|      duration_hrs|                name|      lat|        lon|alt| tz|dst|\n",
      "+----+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+--------+--------+----+------+------------------+--------------------+---------+-----------+---+---+---+\n",
      "| LAX|2014|   12|  8|     658|       -7|     935|       -5|     VX| N846VA|  1780|   SEA|     132|     954|   6|    58|               2.2|    Los Angeles Intl|33.942536|-118.408075|126| -8|  A|\n",
      "| HNL|2014|    1| 22|    1040|        5|    1505|        5|     AS| N559AS|   851|   SEA|     360|    2677|  10|    40|               6.0|       Honolulu Intl|21.318681|-157.922428| 13|-10|  N|\n",
      "| SFO|2014|    3|  9|    1443|       -2|    1652|        2|     VX| N847VA|   755|   SEA|     111|     679|  14|    43|              1.85|  San Francisco Intl|37.618972|-122.374889| 13| -8|  A|\n",
      "| SJC|2014|    4|  9|    1705|       45|    1839|       34|     WN| N360SW|   344|   PDX|      83|     569|  17|     5|1.3833333333333333|Norman Y Mineta S...|  37.3626|-121.929022| 62| -8|  A|\n",
      "| BUR|2014|    3|  9|     754|       -1|    1015|        1|     AS| N612AS|   522|   SEA|     127|     937|   7|    54|2.1166666666666667|            Bob Hope|34.200667|-118.358667|778| -8|  A|\n",
      "+----+----+-----+---+--------+---------+--------+---------+-------+-------+------+------+--------+--------+----+------+------------------+--------------------+---------+-----------+---+---+---+\n",
      "only showing top 5 rows\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "airports = spark.table(\"airports\")\n",
    "\n",
    "# Examine the data\n",
    "airports.show(5)\n",
    "\n",
    "# Rename the faa column\n",
    "airports = airports.withColumnRenamed('faa','dest')\n",
    "\n",
    "# Join the DataFrames\n",
    "flights_with_airports = flights.join(airports, on ='dest', how='leftouter')\n",
    "\n",
    "# Examine the new DataFrame\n",
    "print(flights_with_airports.show(5))"
   ]
  }
 ],
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