{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Structured Data - Analysing\n",
    "\n",
    "This notebook demonstrates various ways of analysing data with SparkSQL DataFrames.\n",
    "\n",
    "We will analyse the `Air Temperature` data set from BOM,  which contains historial maximum daily temperatures from multiple weather stations in New South Wales going back to the 19th century. \n",
    "\n",
    "Let's have look at the data first (`data/nsw_temp.csv` file):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product code,Bureau of Meteorology station number,Year,Month,Day,Maximum temperature (Degree C),Days of accumulation of maximum temperature,Quality\r\n",
      "IDCJAC0010,061087,1965,01,01,25.6,,Y\r\n",
      "IDCJAC0010,061087,1965,01,02,32.2,1,Y\r\n",
      "IDCJAC0010,061087,1965,01,03,23.1,1,Y\r\n",
      "IDCJAC0010,061087,1965,01,04,25.6,1,Y\r\n"
     ]
    }
   ],
   "source": [
    "%%sh \n",
    "\n",
    "head -n 5 data/nsw_temp.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#setup display options for local mode notebooks\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 5)\n",
    "\n",
    "# load the \n",
    "badBomDF = spark.read.csv('data/nsw_temp.csv', inferSchema='True', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Product code: string (nullable = true)\n",
      " |-- Bureau of Meteorology station number: integer (nullable = true)\n",
      " |-- Year: integer (nullable = true)\n",
      " |-- Month: integer (nullable = true)\n",
      " |-- Day: integer (nullable = true)\n",
      " |-- Maximum temperature (Degree C): double (nullable = true)\n",
      " |-- Days of accumulation of maximum temperature: integer (nullable = true)\n",
      " |-- Quality: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "badBomDF.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# will fail because the file is corrupted\n",
    "# badBomDF.groupBy().avg('Maximum temperature (Degree C)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the previous section this file is actually corrupted as it has multiple header lines embedde in the data. \n",
    "\n",
    "We can clean it up by filtering out all the header lines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%sh\n",
    "\n",
    "# remove the condioned file if exists\n",
    "rm -rf 'output/nsw_temp_ok.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select only the data lines (starting with IDC) and save the correct file to `output/nsw_temp_ok.csv`\n",
    "sc.textFile('data/nsw_temp.csv').filter(lambda l:l.startswith('IDC')).saveAsTextFile('output/nsw_temp_ok.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- product_id: string (nullable = true)\n",
      " |-- station_id: long (nullable = true)\n",
      " |-- year: long (nullable = true)\n",
      " |-- month: long (nullable = true)\n",
      " |-- day: long (nullable = true)\n",
      " |-- max_temp: double (nullable = true)\n",
      " |-- days_of_acc: long (nullable = true)\n",
      " |-- quality: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create a DataFrame with customised column names from an RDD\n",
    "bomDF = spark.createDataFrame(spark.read.csv('output/nsw_temp_ok.csv', inferSchema='True', header=False).rdd, \n",
    "        schema = ['product_id', 'station_id', 'year', 'month', 'day', 'max_temp', 'days_of_acc', 'quality'])\n",
    "\n",
    "# cache the DataFrame for performance\n",
    "bomDF.cache()\n",
    "bomDF.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic queries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg(max_temp)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.812952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[avg(max_temp): double]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the average `max_temp` (from all data)\n",
    "bomDF.groupBy().avg('max_temp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of different 'year' values (in all data)\n",
    "bomDF.select('year').distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1859</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1860</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2012</td>\n",
       "      <td>7686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2013</td>\n",
       "      <td>4646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[year: bigint, count: bigint]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the number of observations for each year\n",
    "bomDF.groupBy('year').count().sort('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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7N7r7eqAOmGBmA4Fe7l4b1ZuTcYyISFlQULevRw/47Gdh9uzDy91h1So4/ngY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112427410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise the number of observations per year\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (8,6)\n",
    "plt.close()\n",
    "bomDF.groupBy('year').count().sort('year').toPandas().set_index('year').plot()\n",
    "display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66062</td>\n",
       "      <td>56470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[station_id: bigint, count: bigint]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the station with most observations\n",
    "from pyspark.sql.functions import *\n",
    "bomDF.groupBy('station_id').count().sort(desc('count')).limit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>avg_max_temp</th>\n",
       "      <th>sd_max_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1859</td>\n",
       "      <td>21.399452</td>\n",
       "      <td>4.969578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1860</td>\n",
       "      <td>20.276986</td>\n",
       "      <td>4.398397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2012</td>\n",
       "      <td>22.669126</td>\n",
       "      <td>4.107855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2013</td>\n",
       "      <td>23.114414</td>\n",
       "      <td>4.502314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[year: bigint, avg_max_temp: double, sd_max_temp: double]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the yearly average and standard deviation of `max_temp` for station `66062`\n",
    "bomDF.where(col('station_id') == 66062).groupBy('year') \\\n",
    "    .agg(avg('max_temp').alias('avg_max_temp'), stddev('max_temp').alias('sd_max_temp')).sort('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>month</th>\n",
       "      <th>avg_max_temp</th>\n",
       "      <th>sd_max_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61087</td>\n",
       "      <td>1</td>\n",
       "      <td>27.644565</td>\n",
       "      <td>4.273773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61087</td>\n",
       "      <td>2</td>\n",
       "      <td>28.143529</td>\n",
       "      <td>3.948642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>68192</td>\n",
       "      <td>11</td>\n",
       "      <td>25.009268</td>\n",
       "      <td>4.583716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>68192</td>\n",
       "      <td>12</td>\n",
       "      <td>27.904545</td>\n",
       "      <td>4.757572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[station_id: bigint, month: bigint, avg_max_temp: double, sd_max_temp: double]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ind the monthly average and standard deviation of `max_temp` for all station \n",
    "# in the years between 1990 and 2001\n",
    "tempByStationAndMonth = bomDF.where((col('year') > 1990) &  (col('year')< 2001)) \\\n",
    "    .groupBy(col('station_id'), col('month')) \\\n",
    "    .agg(avg('max_temp').alias('avg_max_temp'), stddev('max_temp').alias('sd_max_temp')) \\\n",
    "    .sort(col('station_id'), col('month'))\n",
    "tempByStationAndMonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/6PoMFnnoIZEzZ8qr6IqiKKVy7e/miiYtLU00TZOFCxcWpi1fvlwCAgJk5syZ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\n2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F9eRs/Vmkh3Oo2o1EfI652HkSNHYuGGYnjZRsFbkgoFbR84tN8LRVVFyOXFuHevB/T0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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112427250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise the average monthly `max_temp` per station\n",
    "import pandas as pd\n",
    "tempByStationAndMonthPD = tempByStationAndMonth.toPandas()\n",
    "plt.close()\n",
    "tempByStationAndMonthPD.set_index(['month', 'station_id']).unstack()['avg_max_temp'].plot()\n",
    "display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Defined Functions (UDFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "# define a UDF uncertanity \n",
    "uncertanity = udf(lambda temp, quality: temp * (0.1 if quality == 'Y' else 0.3) if temp is not None else None \n",
    "                  , DoubleType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uncertanity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352694</th>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352695</th>\n",
       "      <td>6.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>352696 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[uncertanity: double]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply the UDF uncertanity\n",
    "bomDF.select(uncertanity(col('max_temp'), col('quality')).alias('uncertanity'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Window functions\n",
    "\n",
    "Window functions allow users of Spark SQL to calculate results such as the rank of a given row or a moving average over a range of input row. For more information on Window functions please see:  [Introduction to Window Functions](https://databricks.com/blog/2015/07/15/introducing-window-functions-in-spark-sql.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# extend the bom DataFrame with the `date` column\n",
    "bomWithDateDF = bomDF.withColumn('date', \n",
    "                                 to_date(format_string(\"%04d-%02d-%02d\", col('year'), col('month'), col('day'))))        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>station_id</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>67113</td>\n",
       "      <td>23.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>68192</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>66137</td>\n",
       "      <td>21.8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>66194</td>\n",
       "      <td>21.5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[date: date, station_id: bigint, max_temp: double, rank: int]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import Window\n",
    "\n",
    "# rank the stations each day in year 2000 based on the `max_temp` \n",
    "# (the stations with higher temperatures are ranked higher)\n",
    "\n",
    "bomRankDF = bomWithDateDF.dropna().select(col('date'), col('station_id'), col('max_temp'),  rank() \\\n",
    "            .over(Window.partitionBy(col('date')).orderBy(desc('max_temp')) \\\n",
    "                     .rowsBetween(Window.unboundedPreceding, Window.currentRow)).alias('rank')) \\\n",
    "    .where(col('year')==2000).sort('date', 'rank', 'station_id')\n",
    "\n",
    "display(bomRankDF.limit(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67113</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61087</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>67117</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>66137</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[station_id: bigint, count: bigint]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the number of days in the year 2000 when each station was the `hottest` one\n",
    "hotestStationDF = bomRankDF.where(col('rank') == 1).groupBy('station_id').count().sort(desc('count'))\n",
    "display(hotestStationDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yq40kD17My4xa122lRXtHbRmPEr+3i6OP9i75O3AluSzJnyXZv/W8bJXk0z1N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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a22410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "hotestStationDF.toPandas().set_index('station_id').plot(kind='bar')\n",
    "display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D7N3runSXGvLzpRsHq5X1j8uj6WAkGQTRWeBCrOZ653CwzxdfFzcszLvAciHm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6JbiA2GtkxaGlRUyOtgR5yeLmIYzoAe8UT3Ie7dG+69V5bdeiscd1zL7L8l6NED7rvPeZ+TIxNb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\nkECMEBsMBoPBkECMEBsMBoPBkED+P2FfrbbbX3irAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128d0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use Window functions to calculate 14 days moving average for station `66062` in the year `2000`\n",
    "from pyspark.sql import Window\n",
    "plt.close()\n",
    "bomWithDateDF.where((col('station_id') == 66062) & (col('year') == 2000)) \\\n",
    "             .select(col('date'), col('max_temp'), avg(col('max_temp')) \\\n",
    "             .over(Window.orderBy(col('date')) \\\n",
    "                   .partitionBy(col('station_id')).rowsBetween(-14,Window.currentRow )).alias('run_avg')).sort('date')\\\n",
    "    .toPandas().set_index('date').plot()\n",
    "display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL\n",
    "\n",
    "SparkSQL DataFrames can be also queries with SQL (Structured Query Language), commonly used in to query relational databases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# register the DataFrame as a table named `bom`\n",
    "bomDF.registerTempTable('bom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count(1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>352696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[count(1): bigint]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run a simple query that counts number of rows in table `bom`\n",
    "display(sql('SELECT COUNT(*) FROM bom'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>month</th>\n",
       "      <th>avg_max_temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61087</td>\n",
       "      <td>1</td>\n",
       "      <td>28.214653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61087</td>\n",
       "      <td>2</td>\n",
       "      <td>27.224859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>68257</td>\n",
       "      <td>11</td>\n",
       "      <td>26.891878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>68257</td>\n",
       "      <td>12</td>\n",
       "      <td>27.208019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>281 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DataFrame[station_id: bigint, month: bigint, avg_max_temperature: double]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run a query that computes the average monthly `max_temp` for all stations \n",
    "# in years after 2000.\n",
    "\n",
    "display(sql('''SELECT station_id, month, avg(max_temp) AS avg_max_temperature FROM bom\n",
    "        WHERE year > 2000 GROUP by station_id, month ORDER BY station_id, month\n",
    "    '''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Databricks Platform the SQL queries can be entered direclty in %sql cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%sql\n",
    "#SELECT station_id, month, avg(max_temp) AS avg_max_temperature FROM bom\n",
    "#        WHERE year > 2000 GROUP by station_id, month ORDER BY station_id, month"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PySpark",
   "language": "python",
   "name": "pyspark"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}