notebooks/HandsOn-SparkSQL_with_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## This notebook is part of the Apache Spark training delivered by CERN-IT\n",
    "### Spark SQL Hands-On Lab with Solutions\n",
    "Contact: Luca.Canali@cern.ch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this notebook from Jupyter with Python kernel\n",
    "- When using on CERN SWAN, do not attach the notebook to a Spark cluster, but rather run locally on the SWAN container\n",
    "- If running this outside CERN SWAN, plese make sure to tha PySpark installed: `pip install pyspark`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples datasets\n",
    "The following examples use sample data provided in the repository.  \n",
    "We will use the movielens dataset from Kaggle, credits: https://www.kaggle.com/grouplens/movielens-20m-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Spark Session, you need this to work with Spark\n",
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder \\\n",
    "        .appName(\"My spark example app\")  \\\n",
    "        .master(\"local[*]\") \\\n",
    "        .config(\"spark.driver.memory\",\"8g\") \\\n",
    "        .config(\"spark.ui.showConsoleProgress\", \"false\") \\\n",
    "        .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - in-memory</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://jupyter-canali:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.3.1</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>My spark example app</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
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      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7f4d0c1b3100>"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sets the path to the directory with datafiles\n",
    "PATH = \"../data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ratings = spark.read.option(\"header\",\"true\").csv(PATH + \"ratings1.csv.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-------+------+----------+\n",
      "|userId|movieId|rating| timestamp|\n",
      "+------+-------+------+----------+\n",
      "|     1|    110|   1.0|1425941529|\n",
      "|     1|    147|   4.5|1425942435|\n",
      "|     1|    858|   5.0|1425941523|\n",
      "|     1|   1221|   5.0|1425941546|\n",
      "|     1|   1246|   5.0|1425941556|\n",
      "+------+-------+------+----------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ratings.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- userId: string (nullable = true)\n",
      " |-- movieId: string (nullable = true)\n",
      " |-- rating: string (nullable = true)\n",
      " |-- timestamp: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ratings.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.62 ms, sys: 6.18 ms, total: 13.8 ms\n",
      "Wall time: 26.9 s\n"
     ]
    }
   ],
   "source": [
    "# infer schema needs to go through the data to estimate the schema, this takes time\n",
    "\n",
    "%time ratings = spark.read.option(\"header\",\"true\").option(\"inferSchema\", \"true\").csv(PATH + \"ratings1.csv.gz\")\n",
    "\n",
    "# note ratings*.csv.gz will read rating1.csv.gz and ratings2.csv.gz, more data, however slower to run\n",
    "# spark.read.option(\"header\",\"true\").option(\"inferSchema\", \"true\").csv(PATH + \"ratings*.csv.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- userId: integer (nullable = true)\n",
      " |-- movieId: integer (nullable = true)\n",
      " |-- rating: double (nullable = true)\n",
      " |-- timestamp: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ratings.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# movielens dataset\n",
    "movies = spark.read.option(\"header\",\"true\").option(\"inferSchema\", \"true\").csv(PATH + \"movies.csv.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------------------------------+-------------------------------------------+\n",
      "|movieId|title                             |genres                                     |\n",
      "+-------+----------------------------------+-------------------------------------------+\n",
      "|1      |Toy Story (1995)                  |Adventure|Animation|Children|Comedy|Fantasy|\n",
      "|2      |Jumanji (1995)                    |Adventure|Children|Fantasy                 |\n",
      "|3      |Grumpier Old Men (1995)           |Comedy|Romance                             |\n",
      "|4      |Waiting to Exhale (1995)          |Comedy|Drama|Romance                       |\n",
      "|5      |Father of the Bride Part II (1995)|Comedy                                     |\n",
      "+-------+----------------------------------+-------------------------------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "movies.show(5, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- movieId: integer (nullable = true)\n",
      " |-- title: string (nullable = true)\n",
      " |-- genres: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "movies.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = spark.read.option(\"header\",\"true\").option(\"inferSchema\", \"true\").csv(PATH + \"tags.csv.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-------+----------+----------+\n",
      "|userId|movieId|       tag| timestamp|\n",
      "+------+-------+----------+----------+\n",
      "|     1|    318|  narrated|1425942391|\n",
      "|    20|   4306|Dreamworks|1459855607|\n",
      "|    20|  89302|   England|1400778834|\n",
      "|    20|  89302| espionage|1400778836|\n",
      "|    20|  89302|      jazz|1400778841|\n",
      "+------+-------+----------+----------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tags.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Register the dataframes as Spark Temporary Views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings.createOrReplaceTempView(\"ratings\")\n",
    "movies.createOrReplaceTempView(\"movies\")\n",
    "tags.createOrReplaceTempView(\"tags\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Physical Plan ==\n",
      "*(1) Filter (isnotnull(movieId#85) AND (movieId#85 = 1))\n",
      "+- FileScan csv [movieId#85,title#86] Batched: false, DataFilters: [isnotnull(movieId#85), (movieId#85 = 1)], Format: CSV, Location: InMemoryFileIndex(1 paths)[file:/eos/home-c/canali/SWAN_projects/HadoopSparkTraining/Spark/data/m..., PartitionFilters: [], PushedFilters: [IsNotNull(movieId), EqualTo(movieId,1)], ReadSchema: struct<movieId:int,title:string>\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# note what happens when we query a table in a csv file with a filter\n",
    "spark.sql(\"select movieId, title from movies where movieId=1\").explain()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cache the tables, to improve the performance of the rest of the queries in the notebook\n",
    "# note: default caching level is MEMORY_AND_DISK (i.e. caching in memory if enough heap is available)\n",
    "# note: caching is lazily executed, so a count() action is added to make the operation happen\n",
    "# this operation may take a couple of minutes\n",
    "\n",
    "r = ratings.cache().count()\n",
    "m = movies.cache().count()\n",
    "t = tags.cache().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num ratings = 13012144\n",
      "Num tags = 753170\n",
      "Num movies = 45843\n"
     ]
    }
   ],
   "source": [
    "print(f\"Num ratings = {r}\\nNum tags = {t}\\nNum movies = {m}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL Queries on the loaded tables\n",
    "#### 1) How many movies produced per year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------------------------------+-------------------------------------------+----+\n",
      "|movieId|title                             |genres                                     |Year|\n",
      "+-------+----------------------------------+-------------------------------------------+----+\n",
      "|1      |Toy Story (1995)                  |Adventure|Animation|Children|Comedy|Fantasy|1995|\n",
      "|2      |Jumanji (1995)                    |Adventure|Children|Fantasy                 |1995|\n",
      "|3      |Grumpier Old Men (1995)           |Comedy|Romance                             |1995|\n",
      "|4      |Waiting to Exhale (1995)          |Comedy|Drama|Romance                       |1995|\n",
      "|5      |Father of the Bride Part II (1995)|Comedy                                     |1995|\n",
      "+-------+----------------------------------+-------------------------------------------+----+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add the column Year to \"movies\"\n",
    "movies_year = spark.sql(\"select *, regexp_extract(title,'^(.*) \\\\\\\\(([0-9 \\\\\\\\-]*)\\\\\\\\)$',2) as Year from movies\")\n",
    "movies_year.show(5,False)\n",
    "movies_year.createOrReplaceTempView(\"movies_year\")\n",
    "\n",
    "# This is the DataFrame API equivalent, not that \\\\ oddly need to be changed to \\\\\\\\\\ when using SQL, at least in this version of Spark\n",
    "# from pyspark.sql.functions import regexp_extract\n",
    "# movies_year = movies.withColumn(\"Year\",regexp_extract(\"title\",'^(.*) \\\\(([0-9 \\\\-]*)\\\\)$',2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of movies per year\n",
    "m_yr = spark.sql(\"select year, count(1) as count from movies_year group by year order by year\").toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.style.use('seaborn-darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
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       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\" width=\"697.5833333333334\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_yr.plot(x='year',y='count',kind='line', title='Movies per year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) Top movies by number of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>count(1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>45782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>45546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>43755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>41807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>38860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              title  count(1)\n",
       "0               Forrest Gump (1994)     45782\n",
       "1  Shawshank Redemption, The (1994)     45546\n",
       "2               Pulp Fiction (1994)     43755\n",
       "3  Silence of the Lambs, The (1991)     41807\n",
       "4                Matrix, The (1999)     38860"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A query to perform a join operation between movies and ratings\n",
    "# Find the highest rated movies\n",
    "\n",
    "spark.sql(\"\"\"\n",
    "select title, count(*) \n",
    "from movies m, ratings r \n",
    "where m.movieId = r.movieId\n",
    "group by title \n",
    "order by 2 desc\"\"\").limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What happened in the background? How did the join happen?\n",
    "#### How is the query executed? Can we get more information?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Parsed Logical Plan ==\n",
      "'Sort [2 DESC NULLS LAST], true\n",
      "+- 'Aggregate ['title], ['title, unresolvedalias('count(1), None)]\n",
      "   +- 'Filter ('m.movieId = 'r.movieId)\n",
      "      +- 'Join Inner\n",
      "         :- 'SubqueryAlias m\n",
      "         :  +- 'UnresolvedRelation [movies], [], false\n",
      "         +- 'SubqueryAlias r\n",
      "            +- 'UnresolvedRelation [ratings], [], false\n",
      "\n",
      "== Analyzed Logical Plan ==\n",
      "title: string, count(1): bigint\n",
      "Sort [count(1)#866L DESC NULLS LAST], true\n",
      "+- Aggregate [title#86], [title#86, count(1) AS count(1)#866L]\n",
      "   +- Filter (movieId#85 = movieId#62)\n",
      "      +- Join Inner\n",
      "         :- SubqueryAlias m\n",
      "         :  +- SubqueryAlias movies\n",
      "         :     +- View (`movies`, [movieId#85,title#86,genres#87])\n",
      "         :        +- Relation [movieId#85,title#86,genres#87] csv\n",
      "         +- SubqueryAlias r\n",
      "            +- SubqueryAlias ratings\n",
      "               +- View (`ratings`, [userId#61,movieId#62,rating#63,timestamp#64])\n",
      "                  +- Relation [userId#61,movieId#62,rating#63,timestamp#64] csv\n",
      "\n",
      "== Optimized Logical Plan ==\n",
      "Sort [count(1)#866L DESC NULLS LAST], true\n",
      "+- Aggregate [title#86], [title#86, count(1) AS count(1)#866L]\n",
      "   +- Project [title#86]\n",
      "      +- Join Inner, (movieId#85 = movieId#62)\n",
      "         :- Project [movieId#85, title#86]\n",
      "         :  +- Filter isnotnull(movieId#85)\n",
      "         :     +- InMemoryRelation [movieId#85, title#86, genres#87], StorageLevel(disk, memory, deserialized, 1 replicas)\n",
      "         :           +- FileScan csv [movieId#85,title#86,genres#87] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex(1 paths)[file:/eos/home-c/canali/SWAN_projects/HadoopSparkTraining/Spark/data/m..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<movieId:int,title:string,genres:string>\n",
      "         +- Project [movieId#62]\n",
      "            +- Filter isnotnull(movieId#62)\n",
      "               +- InMemoryRelation [userId#61, movieId#62, rating#63, timestamp#64], StorageLevel(disk, memory, deserialized, 1 replicas)\n",
      "                     +- FileScan csv [userId#61,movieId#62,rating#63,timestamp#64] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex(1 paths)[file:/eos/home-c/canali/SWAN_projects/HadoopSparkTraining/Spark/data/r..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<userId:int,movieId:int,rating:double,timestamp:int>\n",
      "\n",
      "== Physical Plan ==\n",
      "AdaptiveSparkPlan isFinalPlan=false\n",
      "+- Sort [count(1)#866L DESC NULLS LAST], true, 0\n",
      "   +- Exchange rangepartitioning(count(1)#866L DESC NULLS LAST, 200), ENSURE_REQUIREMENTS, [id=#514]\n",
      "      +- HashAggregate(keys=[title#86], functions=[count(1)], output=[title#86, count(1)#866L])\n",
      "         +- Exchange hashpartitioning(title#86, 200), ENSURE_REQUIREMENTS, [id=#511]\n",
      "            +- HashAggregate(keys=[title#86], functions=[partial_count(1)], output=[title#86, count#975L])\n",
      "               +- Project [title#86]\n",
      "                  +- BroadcastHashJoin [movieId#85], [movieId#62], Inner, BuildLeft, false\n",
      "                     :- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#506]\n",
      "                     :  +- Filter isnotnull(movieId#85)\n",
      "                     :     +- InMemoryTableScan [movieId#85, title#86], [isnotnull(movieId#85)]\n",
      "                     :           +- InMemoryRelation [movieId#85, title#86, genres#87], StorageLevel(disk, memory, deserialized, 1 replicas)\n",
      "                     :                 +- FileScan csv [movieId#85,title#86,genres#87] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex(1 paths)[file:/eos/home-c/canali/SWAN_projects/HadoopSparkTraining/Spark/data/m..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<movieId:int,title:string,genres:string>\n",
      "                     +- Filter isnotnull(movieId#62)\n",
      "                        +- InMemoryTableScan [movieId#62], [isnotnull(movieId#62)]\n",
      "                              +- InMemoryRelation [userId#61, movieId#62, rating#63, timestamp#64], StorageLevel(disk, memory, deserialized, 1 replicas)\n",
      "                                    +- FileScan csv [userId#61,movieId#62,rating#63,timestamp#64] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex(1 paths)[file:/eos/home-c/canali/SWAN_projects/HadoopSparkTraining/Spark/data/r..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<userId:int,movieId:int,rating:double,timestamp:int>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"\n",
    "select title, count(*) \n",
    "from movies m, ratings r \n",
    "where m.movieId = r.movieId\n",
    "group by title \n",
    "order by 2 desc\"\"\").explain(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) Highly rated movies\n",
    "Find the top 5 highly rated movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------------------+----------+\n",
      "|title                              |avg_rating|\n",
      "+-----------------------------------+----------+\n",
      "|The Hardy Bucks Movie (2013)       |5.0       |\n",
      "|Naked Souls (1996)                 |5.0       |\n",
      "|Paul Goodman Changed My Life (2011)|5.0       |\n",
      "|Jimi Plays Berkeley (2012)         |5.0       |\n",
      "|Punk in London (1977)              |5.0       |\n",
      "+-----------------------------------+----------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"select title, avg(rating) as avg_rating from movies m, ratings r\n",
    "            where m.movieId = r.movieId\n",
    "            group by title\n",
    "            order by 2 desc\"\"\").show(5, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drill down on the top entries:\n",
    "   - How many reviews contributed to this rating?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------------------+----------+-----+\n",
      "|title                              |avg_rating|count|\n",
      "+-----------------------------------+----------+-----+\n",
      "|The Hardy Bucks Movie (2013)       |5.0       |1    |\n",
      "|Naked Souls (1996)                 |5.0       |1    |\n",
      "|Paul Goodman Changed My Life (2011)|5.0       |1    |\n",
      "|Jimi Plays Berkeley (2012)         |5.0       |1    |\n",
      "|Punk in London (1977)              |5.0       |1    |\n",
      "+-----------------------------------+----------+-----+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"select title, avg(rating) as avg_rating, count(*) as count from movies m, ratings r\n",
    "            where m.movieId = r.movieId\n",
    "            group by title\n",
    "            order by 2 desc\"\"\").show(5, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets only take in account movies that have more than 100 reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Planet Earth (2006)</td>\n",
       "      <td>4.467391</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Band of Brothers (2001)</td>\n",
       "      <td>4.431655</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>4.426338</td>\n",
       "      <td>45546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>4.335648</td>\n",
       "      <td>28582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>4.299494</td>\n",
       "      <td>29635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Godfather: Part II, The (1974)</td>\n",
       "      <td>4.266718</td>\n",
       "      <td>18319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Seven Samurai (Shichinin no samurai) (1954)</td>\n",
       "      <td>4.265507</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>4.261945</td>\n",
       "      <td>33780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Blue Planet (2001)</td>\n",
       "      <td>4.234615</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>4.232034</td>\n",
       "      <td>29931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>4.230852</td>\n",
       "      <td>19937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12 Angry Men (1957)</td>\n",
       "      <td>4.229520</td>\n",
       "      <td>8374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rear Window (1954)</td>\n",
       "      <td>4.229511</td>\n",
       "      <td>10542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Paths of Glory (1957)</td>\n",
       "      <td>4.218140</td>\n",
       "      <td>2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Casablanca (1942)</td>\n",
       "      <td>4.215292</td>\n",
       "      <td>14903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</td>\n",
       "      <td>4.214214</td>\n",
       "      <td>3975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>North by Northwest (1959)</td>\n",
       "      <td>4.211488</td>\n",
       "      <td>9445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Third Man, The (1949)</td>\n",
       "      <td>4.210196</td>\n",
       "      <td>3825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Spirited Away (Sen to Chihiro no kamikakushi) ...</td>\n",
       "      <td>4.209656</td>\n",
       "      <td>10398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Dr. Strangelove or: How I Learned to Stop Worr...</td>\n",
       "      <td>4.209441</td>\n",
       "      <td>13992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                title  avg_rating  count\n",
       "0                                 Planet Earth (2006)    4.467391    368\n",
       "1                             Band of Brothers (2001)    4.431655    139\n",
       "2                    Shawshank Redemption, The (1994)    4.426338  45546\n",
       "3                               Godfather, The (1972)    4.335648  28582\n",
       "4                          Usual Suspects, The (1995)    4.299494  29635\n",
       "5                      Godfather: Part II, The (1974)    4.266718  18319\n",
       "6         Seven Samurai (Shichinin no samurai) (1954)    4.265507   6900\n",
       "7                             Schindler's List (1993)    4.261945  33780\n",
       "8                              The Blue Planet (2001)    4.234615    130\n",
       "9                                   Fight Club (1999)    4.232034  29931\n",
       "10             One Flew Over the Cuckoo's Nest (1975)    4.230852  19937\n",
       "11                                12 Angry Men (1957)    4.229520   8374\n",
       "12                                 Rear Window (1954)    4.229511  10542\n",
       "13                              Paths of Glory (1957)    4.218140   2150\n",
       "14                                  Casablanca (1942)    4.215292  14903\n",
       "15      Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)    4.214214   3975\n",
       "16                          North by Northwest (1959)    4.211488   9445\n",
       "17                              Third Man, The (1949)    4.210196   3825\n",
       "18  Spirited Away (Sen to Chihiro no kamikakushi) ...    4.209656  10398\n",
       "19  Dr. Strangelove or: How I Learned to Stop Worr...    4.209441  13992"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.sql(\"\"\"select title, avg(rating) as avg_rating, count(*) as count from movies m, ratings r\n",
    "            where m.movieId = r.movieId\n",
    "            group by title\n",
    "            having count(*) > 100\n",
    "            order by 2 desc\"\"\").limit(20).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 4) Find the top rated movie of every year since 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_ratings = spark.sql(\"\"\"select year, title, round(avg(rating),2) as avg_rating, count(*) as count\n",
    "                           from movies_year m, ratings r where m.movieId = r.movieId \n",
    "                           group by year, title\n",
    "                           having count(*) > 100\"\"\")\n",
    "\n",
    "avg_ratings.createOrReplaceTempView(\"avg_ratings\")\n",
    "\n",
    "# note this is just the definition of a helper view\n",
    "# because of lazy execution no query is run at this step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+--------------------------------------------------------------------------+----------+\n",
      "|year|title                                                                     |avg_rating|\n",
      "+----+--------------------------------------------------------------------------+----------+\n",
      "|2001|Band of Brothers (2001)                                                   |4.43      |\n",
      "|2002|City of God (Cidade de Deus) (2002)                                       |4.19      |\n",
      "|2003|Fog of War: Eleven Lessons from the Life of Robert S. McNamara, The (2003)|4.11      |\n",
      "|2003|Lord of the Rings: The Return of the King, The (2003)                     |4.11      |\n",
      "|2004|Voices from the List (2004)                                               |4.15      |\n",
      "|2005|49 Up (2005)                                                              |4.06      |\n",
      "|2006|Planet Earth (2006)                                                       |4.47      |\n",
      "|2007|Like Stars on Earth (Taare Zameen Par) (2007)                             |4.04      |\n",
      "|2008|Dark Knight, The (2008)                                                   |4.18      |\n",
      "|2009|Secret in Their Eyes, The (El secreto de sus ojos) (2009)                 |4.03      |\n",
      "|2010|Inception (2010)                                                          |4.16      |\n",
      "|2011|Intouchables (2011)                                                       |4.12      |\n",
      "|2012|Hunt, The (Jagten) (2012)                                                 |4.07      |\n",
      "|2013|Doctor Who: The Time of the Doctor (2013)                                 |4.07      |\n",
      "|2014|Wild Tales (2014)                                                         |4.15      |\n",
      "|2015|The Jinx: The Life and Deaths of Robert Durst (2015)                      |4.09      |\n",
      "|2016|Piper (2016)                                                              |4.1       |\n",
      "|2017|Dunkirk (2017)                                                            |4.11      |\n",
      "+----+--------------------------------------------------------------------------+----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# the query for top-rated movies is run here, triggered by the action to show the first 20 rows\n",
    "\n",
    "spark.sql(\"\"\"select a.year, a.title, avg_rating from avg_ratings a,\n",
    "            (select year, max(avg_rating) as max_rating from avg_ratings group by year) m\n",
    "            where a.year = m.year\n",
    "            and a.avg_rating = m.max_rating\n",
    "            and a.year > 2000\n",
    "            order by year\"\"\").show(20, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# End the Spark application\n",
    "spark.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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