integration-tests/examples/test_templates/jupyter/template_preparation_pyspark.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flights data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql import DataFrame\n",
    "from pyspark.sql import Row\n",
    "from pyspark.sql.types import *\n",
    "import pandas as pd\n",
    "import StringIO\n",
    "import matplotlib.pyplot as plt\n",
    "hc = sc._jsc.hadoopConfiguration()\n",
    "hc.set(\"hive.execution.engine\", \"mr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to parse CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "def parseCsv(csvStr):\n",
    "    f = StringIO.StringIO(csvStr)\n",
    "    reader = csv.reader(f, delimiter=',')\n",
    "    row = reader.next()\n",
    "    return row\n",
    "\n",
    "scsv = '\"02Q\",\"Titan Airways\"'\n",
    "row = parseCsv(scsv)\n",
    "print row[0]\n",
    "print row[1]\n",
    "\n",
    "working_storage = 'WORKING_STORAGE'\n",
    "output_directory = 'jupyter/py2'\n",
    "protocol_name = 'PROTOCOL_NAME://'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse and convert Carrier data to parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "carriersHeader = 'Code,Description'\n",
    "carriersText = sc.textFile(protocol_name + working_storage + \"/jupyter_dataset/carriers.csv\").filter(lambda x: x != carriersHeader)\n",
    "carriers = carriersText.map(lambda s: parseCsv(s)) \\\n",
    "    .map(lambda s: Row(code=s[0], description=s[1])).cache().toDF()\n",
    "carriers.write.mode(\"overwrite\").parquet(protocol_name + working_storage + \"/\" + output_directory + \"/carriers\")    \n",
    "sqlContext.registerDataFrameAsTable(carriers, \"carriers\")\n",
    "carriers.limit(20).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse and convert to parquet Airport data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "airportsHeader= '\"iata\",\"airport\",\"city\",\"state\",\"country\",\"lat\",\"long\"'\n",
    "airports = sc.textFile(protocol_name + working_storage + \"/jupyter_dataset/airports.csv\") \\\n",
    "    .filter(lambda x: x != airportsHeader) \\\n",
    "    .map(lambda s: parseCsv(s)) \\\n",
    "    .map(lambda p: Row(iata=p[0], \\\n",
    "                       airport=p[1], \\\n",
    "                       city=p[2], \\\n",
    "                       state=p[3], \\\n",
    "                       country=p[4], \\\n",
    "                       lat=float(p[5]), \\\n",
    "                       longt=float(p[6])) \\\n",
    "        ).cache().toDF()\n",
    "airports.write.mode(\"overwrite\").parquet(protocol_name + working_storage + \"/\" + output_directory + \"/airports\")    \n",
    "sqlContext.registerDataFrameAsTable(airports, \"airports\")\n",
    "airports.limit(20).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse and convert Flights data to parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flightsHeader = 'Year,Month,DayofMonth,DayOfWeek,DepTime,CRSDepTime,ArrTime,CRSArrTime,UniqueCarrier,FlightNum,TailNum,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay,Origin,Dest,Distance,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,CarrierDelay,WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay'\n",
    "flights = sc.textFile(protocol_name + working_storage + \"/jupyter_dataset/2008.csv.bz2\") \\\n",
    "    .filter(lambda x: x!= flightsHeader) \\\n",
    "    .map(lambda s: parseCsv(s)) \\\n",
    "    .map(lambda p: Row(Year=int(p[0]), \\\n",
    "                       Month=int(p[1]), \\\n",
    "                       DayofMonth=int(p[2]), \\\n",
    "                       DayOfWeek=int(p[3]), \\\n",
    "                       DepTime=p[4], \\\n",
    "                       CRSDepTime=p[5], \\\n",
    "                       ArrTime=p[6], \\\n",
    "                       CRSArrTime=p[7], \\\n",
    "                       UniqueCarrier=p[8], \\\n",
    "                       FlightNum=p[9], \\\n",
    "                       TailNum=p[10], \\\n",
    "                       ActualElapsedTime=p[11], \\\n",
    "                       CRSElapsedTime=p[12], \\\n",
    "                       AirTime=p[13], \\\n",
    "                       ArrDelay=int(p[14].replace(\"NA\", \"0\")), \\\n",
    "                       DepDelay=int(p[15].replace(\"NA\", \"0\")), \\\n",
    "                       Origin=p[16], \\\n",
    "                       Dest=p[17], \\\n",
    "                       Distance=long(p[18]), \\\n",
    "                       TaxiIn=p[19], \\\n",
    "                       TaxiOut=p[20], \\\n",
    "                       Cancelled=p[21], \\\n",
    "                       CancellationCode=p[22], \\\n",
    "                       Diverted=p[23], \\\n",
    "                       CarrierDelay=int(p[24].replace(\"NA\", \"0\")), \\\n",
    "                                              CarrierDelayStr=p[24], \\\n",
    "                       WeatherDelay=int(p[25].replace(\"NA\", \"0\")), \\\n",
    "                                              WeatherDelayStr=p[25], \\\n",
    "                       NASDelay=int(p[26].replace(\"NA\", \"0\")), \\\n",
    "                       SecurityDelay=int(p[27].replace(\"NA\", \"0\")), \\\n",
    "                       LateAircraftDelay=int(p[28].replace(\"NA\", \"0\")))) \\\n",
    "         .toDF()\n",
    "\n",
    "flights.write.mode(\"ignore\").parquet(protocol_name + working_storage + \"/\" + output_directory + \"/flights\")\n",
    "sqlContext.registerDataFrameAsTable(flights, \"flights\")\n",
    "flights.limit(10).toPandas()[[\"ArrDelay\",\"CarrierDelay\",\"CarrierDelayStr\",\"WeatherDelay\",\"WeatherDelayStr\",\"Distance\"]]"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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