2. Visualization.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Visualizations with Cosmic notebooks\n",
        "In this notebook, we'll run some queries of our data and visualize it using bokeh."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Setup\n",
        "\n",
        "- Create a database **RetailDemo** and container **WebsiteData** to hold our data, if they do not already exist.\n",
        "- Import sample data to visualize\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": false
      },
      "outputs": [],
      "source": [
        "import azure.cosmos\n",
        "from azure.cosmos.partition_key import PartitionKey\n",
        "\n",
        "database = cosmos_client.create_database_if_not_exists('RetailDemo')\n",
        "print('Database RetailDemo created')\n",
        "\n",
        "container = database.create_container_if_not_exists(id='WebsiteData', partition_key=PartitionKey(path='/CartID'))\n",
        "print('Container WebsiteData created')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here's a sample document we will import:\n",
        "\n",
        "```\n",
        "{\"CartID\":5399,\n",
        "\"Action\":\"Viewed\",\n",
        "\"Item\":\"Cosmos T-shirt\",\n",
        "\"Price\":350,\n",
        "\"UserName\":\"Chadrick.Larkin87\",\n",
        "\"Country\":\"Iceland\",\n",
        "\"EventDate\":\"2015-06-25T00:00:00\",\n",
        "\"Year\":2015,\"Latitude\":-66.8673,\n",
        "\"Longitude\":-29.8214,\n",
        "\"Address\":\"852 Modesto Loop, Port Ola, Iceland\",\n",
        "\"id\":\"00ffd39c-7e98-4451-9b91-b2bcf2f9a32d\"}\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We'll update the throughput of the container to allow for a quicker upload."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false,
        "inputHidden": false,
        "outputHidden": false,
        "trusted": false
      },
      "outputs": [],
      "source": [
        "old_throughput = container.read_offer().offer_throughput\n",
        "new_throughput = container.replace_throughput(1000).offer_throughput\n",
        "print(\"Container WebsiteData's throughput updated from {} RU/s to {} RU/s\".format(old_throughput, new_throughput))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We'll use the ``%%upload`` magic function to insert items into the container. This will take a few seconds."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false,
        "inputHidden": false,
        "outputHidden": false,
        "trusted": false
      },
      "outputs": [],
      "source": [
        "%%upload --databaseName RetailDemo --containerName WebsiteData --url https://cosmosnotebooksdata.blob.core.windows.net/notebookdata/websiteData.json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can now lower the throughput of the container."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false,
        "inputHidden": false,
        "outputHidden": false,
        "trusted": false
      },
      "outputs": [],
      "source": [
        "lowered_throughput = container.replace_throughput(400).offer_throughput\n",
        "print(\"Container WebsiteData's throughput lowered from {} RU/s to {} RU/s\".format(new_throughput, lowered_throughput))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Getting our data into a DataFrame\n",
        "\n",
        "We'll use the syntax:   \n",
        "\n",
        "``%%sql --database {database_id} --container {container_id} --output outputDataframeVar\n",
        "{Query text}``\n",
        "\n",
        "We'll run the query ```SELECT c.Action, c.Price as ItemRevenue, c.Country, c.Item FROM c```. The results will be saved into a Pandas dataframe named ```df_cosmos```.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "trusted": false
      },
      "outputs": [],
      "source": [
        "%%sql --database RetailDemo --container WebsiteData --output df_cosmos\n",
        "SELECT c.Action, c.Price as ItemRevenue, c.Country, c.Item FROM c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "trusted": false
      },
      "outputs": [
        {
          "data": {
            "application/vnd.dataresource+json": {
              "data": [
                {
                  "Action": "Viewed",
                  "Country": "Tunisia",
                  "Item": "Black Tee",
                  "ItemRevenue": 9,
                  "index": 0
                },
                {
                  "Action": "Viewed",
                  "Country": "Antigua and Barbuda",
                  "Item": "Flannel Shirt",
                  "ItemRevenue": 19.99,
                  "index": 1
                },
                {
                  "Action": "Added",
                  "Country": "Guinea-Bissau",
                  "Item": "Socks",
                  "ItemRevenue": 3.75,
                  "index": 2
                },
                {
                  "Action": "Viewed",
                  "Country": "Guinea-Bissau",
                  "Item": "Socks",
                  "ItemRevenue": 3.75,
                  "index": 3
                },
                {
                  "Action": "Viewed",
                  "Country": "Czech Republic",
                  "Item": "Rainjacket",
                  "ItemRevenue": 55,
                  "index": 4
                },
                {
                  "Action": "Viewed",
                  "Country": "Iceland",
                  "Item": "Cosmos T-shirt",
                  "ItemRevenue": 350,
                  "index": 5
                },
                {
                  "Action": "Added",
                  "Country": "Syrian Arab Republic",
                  "Item": "Button-Up Shirt",
                  "ItemRevenue": 19.99,
                  "index": 6
                },
                {
                  "Action": "Viewed",
                  "Country": "Syrian Arab Republic",
                  "Item": "Button-Up Shirt",
                  "ItemRevenue": 19.99,
                  "index": 7
                },
                {
                  "Action": "Viewed",
                  "Country": "Tuvalu",
                  "Item": "Red Top",
                  "ItemRevenue": 33,
                  "index": 8
                },
                {
                  "Action": "Viewed",
                  "Country": "Cape Verde",
                  "Item": " Flip Flop Shoes",
                  "ItemRevenue": 14,
                  "index": 9
                }
              ],
              "schema": {
                "fields": [
                  {
                    "name": "index",
                    "type": "integer"
                  },
                  {
                    "name": "Action",
                    "type": "string"
                  },
                  {
                    "name": "ItemRevenue",
                    "type": "number"
                  },
                  {
                    "name": "Country",
                    "type": "string"
                  },
                  {
                    "name": "Item",
                    "type": "string"
                  }
                ],
                "pandas_version": "0.20.0",
                "primaryKey": [
                  "index"
                ]
              }
            },
            "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>Action</th>\n      <th>ItemRevenue</th>\n      <th>Country</th>\n      <th>Item</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Viewed</td>\n      <td>9.00</td>\n      <td>Tunisia</td>\n      <td>Black Tee</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Viewed</td>\n      <td>19.99</td>\n      <td>Antigua and Barbuda</td>\n      <td>Flannel Shirt</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Added</td>\n      <td>3.75</td>\n      <td>Guinea-Bissau</td>\n      <td>Socks</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Viewed</td>\n      <td>3.75</td>\n      <td>Guinea-Bissau</td>\n      <td>Socks</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Viewed</td>\n      <td>55.00</td>\n      <td>Czech Republic</td>\n      <td>Rainjacket</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Viewed</td>\n      <td>350.00</td>\n      <td>Iceland</td>\n      <td>Cosmos T-shirt</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Added</td>\n      <td>19.99</td>\n      <td>Syrian Arab Republic</td>\n      <td>Button-Up Shirt</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Viewed</td>\n      <td>19.99</td>\n      <td>Syrian Arab Republic</td>\n      <td>Button-Up Shirt</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Viewed</td>\n      <td>33.00</td>\n      <td>Tuvalu</td>\n      <td>Red Top</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Viewed</td>\n      <td>14.00</td>\n      <td>Cape Verde</td>\n      <td>Flip Flop Shoes</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   Action  ItemRevenue               Country              Item\n0  Viewed         9.00               Tunisia         Black Tee\n1  Viewed        19.99   Antigua and Barbuda     Flannel Shirt\n2   Added         3.75         Guinea-Bissau             Socks\n3  Viewed         3.75         Guinea-Bissau             Socks\n4  Viewed        55.00        Czech Republic        Rainjacket\n5  Viewed       350.00               Iceland    Cosmos T-shirt\n6   Added        19.99  Syrian Arab Republic   Button-Up Shirt\n7  Viewed        19.99  Syrian Arab Republic   Button-Up Shirt\n8  Viewed        33.00                Tuvalu           Red Top\n9  Viewed        14.00            Cape Verde   Flip Flop Shoes"
          },
          "execution_count": 62,
          "metadata": {
            "application/vnd.dataresource+json": {
              "dx": {
                "areaType": "hexbin",
                "barGrouping": "Clustered",
                "chart": {
                  "dim1": "Action",
                  "dim2": "Country",
                  "dim3": "none",
                  "metric1": "ItemRevenue",
                  "metric2": "none",
                  "metric3": "none",
                  "metric4": "none",
                  "networkLabel": "none",
                  "timeseriesSort": "array-order"
                },
                "colors": [
                  "#DA752E",
                  "#E5C209",
                  "#1441A0",
                  "#B86117",
                  "#4D430C",
                  "#1DB390",
                  "#B3331D",
                  "#088EB2",
                  "#417505",
                  "#E479A8",
                  "#F9F39E",
                  "#5782DC",
                  "#EBA97B",
                  "#A2AB60",
                  "#B291CF",
                  "#8DD2C2",
                  "#E6A19F",
                  "#3DC7E0",
                  "#98CE5B"
                ],
                "hierarchyType": "dendrogram",
                "lineType": "line",
                "marginalGraphics": "none",
                "networkType": "force",
                "pieceType": "bar",
                "selectedDimensions": [
                  "Action"
                ],
                "selectedMetrics": [],
                "summaryType": "violin",
                "trendLine": "none",
                "view": "grid"
              }
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# See a sample of the result\n",
        "df_cosmos.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Built-in visualizations with nteract\n",
        "We'll run some simple group by queries on our data and use the built-in [nteract data explorer](https://blog.nteract.io/designing-the-nteract-data-explorer-f4476d53f897) to create some visualizations. In each cell, we'll set the following options to enable the nteract data explorer:\n",
        "\n",
        "``pd.options.display.html.table_schema = True``\n",
        "\n",
        "``pd.options.display.max_rows = None``"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Sum revenue by country\n",
        "\n",
        "With the built-in table visualization, we can use filters to further explore our data. For example, we can further filter our data to countries with revenue greater than $1500.\n",
        "\n",
        "![Built-in nteract data explorer table with filters](https://cosmosnotebooksdata.blob.core.windows.net/notebookdata/nteract-table-filters.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": false
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "pd.options.display.html.table_schema = True\n",
        "pd.options.display.max_rows = None\n",
        "\n",
        "df_revenue = df_cosmos.groupby(\"Country\").sum().reset_index()\n",
        "display(df_revenue)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Analyze top 10 popular purchased items\n",
        "\n",
        "We can also use other built-in visualizations, including bar charts, scatterplots, and more. Here, we'll analyze the top 10 popular purchased items and graph them using a bar chart. \n",
        "\n",
        "![Built-in nteract data explorer bar chart](https://cosmosnotebooksdata.blob.core.windows.net/notebookdata/nteract-built-in-chart.png)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": false
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "pd.options.display.html.table_schema = True\n",
        "pd.options.display.max_rows = None\n",
        "\n",
        "## What are the top 10 purchased items?\n",
        "pd.DataFrame(df_cosmos[df_cosmos['Action']=='Purchased'].groupby('Item').size().sort_values(ascending=False).head(10), columns=['Count'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Custom visualization #1: Sales revenue by country on a world map\n",
        "\n",
        "In addition to using the built-in nteract data explorer, we can also create custom visualizations.\n",
        "\n",
        "First, we'll take our data on revenue from our Cosmos container and visualize it using bokeh. Credit to https://towardsdatascience.com/a-complete-guide-to-an-interactive-geographical-map-using-python-f4c5197e23e0 for inspiration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": false
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "!{sys.executable} -m pip install bokeh==1.4.0 --user"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Prepare our data to be plotted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "trusted": false
      },
      "outputs": [],
      "source": [
        "import urllib.request, json \n",
        "import geopandas as gpd\n",
        "\n",
        "# Load country information for mapping\n",
        "countries = gpd.read_file(\"https://cosmosnotebooksdata.blob.core.windows.net/notebookdata/countries.json\")\n",
        "\n",
        "# Merge the countries dataframe with our data in Azure Cosmos DB, joining on country code\n",
        "df_merged = countries.merge(df_revenue, left_on = 'admin', right_on = 'Country', how='left')\n",
        "\n",
        "# Convert to GeoJSON so bokeh can plot it\n",
        "merged_json = json.loads(df_merged.to_json())\n",
        "json_data = json.dumps(merged_json)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Plot the sales revenue on a world map\n",
        "This may take a few seconds..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "trusted": false
      },
      "outputs": [
        {
          "data": {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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revenue by country\"},\"id\":\"1227\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null},\"id\":\"1231\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1233\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1293\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1292\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"source\":{\"id\":\"1223\",\"type\":\"GeoJSONDataSource\"}},\"id\":\"1264\",\"type\":\"CDSView\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1291\",\"type\":\"BoxAnnotation\"}],\"root_ids\":[\"1226\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n  var render_items = [{\"docid\":\"789fa617-8e70-4621-ba4a-17b3751e1ea6\",\"roots\":{\"1226\":\"b0b577a6-6797-4d53-a932-b13cc94f1fa6\"}}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    var attempts = 0;\n    var timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "1226"
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from bokeh.io import output_notebook, show\n",
        "from bokeh.plotting import figure\n",
        "from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar\n",
        "from bokeh.palettes import brewer\n",
        "\n",
        "#Input GeoJSON source that contains features for plotting.\n",
        "geosource = GeoJSONDataSource(geojson = json_data)\n",
        "\n",
        "#Choose our choropleth color palette: https://bokeh.pydata.org/en/latest/docs/reference/palettes.html\n",
        "palette = brewer['YlGn'][8]\n",
        "\n",
        "#Reverse color order so that dark green is highest revenue\n",
        "palette = palette[::-1]\n",
        "\n",
        "#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors.\n",
        "color_mapper = LinearColorMapper(palette = palette, low = 0, high = 1000)\n",
        "\n",
        "#Define custom tick labels for color bar.\n",
        "tick_labels = {'0': '$0', '250': '$250', '500':'$500', '750':'$750', '1000':'$1000', '1250':'$1250', '1500':'$1500','1750':'$1750', '2000': '>$2000'}\n",
        "\n",
        "#Create color bar. \n",
        "color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,\n",
        "border_line_color=None,location = (0,0), orientation = 'horizontal', major_label_overrides = tick_labels)\n",
        "\n",
        "#Create figure object.\n",
        "p = figure(title = 'Sales revenue by country', plot_height = 600 , plot_width = 1150, toolbar_location = None)\n",
        "p.xgrid.grid_line_color = None\n",
        "p.ygrid.grid_line_color = None\n",
        "\n",
        "#Add patch renderer to figure. \n",
        "p.patches('xs','ys', source = geosource,fill_color = {'field' :'ItemRevenue', 'transform' : color_mapper},\n",
        "          line_color = 'black', line_width = 0.25, fill_alpha = 1)\n",
        "\n",
        "#Specify figure layout.\n",
        "p.add_layout(color_bar, 'below')\n",
        "\n",
        "#Display figure inline in Jupyter Notebook.\n",
        "output_notebook()\n",
        "\n",
        "#Display figure.\n",
        "show(p)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Custom visualization #2: Conversion rate of Viewed -> Added to cart -> Purchased by item\n",
        "\n",
        "In our WebsiteData container, we have a record of users who viewed an item, added to their cart, and purchased the item. We can visualize the conversion rate for each item. Credit to: https://bokeh.pydata.org/en/latest/docs/user_guide/categorical.html for inspiration."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Plot our data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "trusted": false
      },
      "outputs": [
        {
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1396\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1396\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1396' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1396\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
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            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "1356"
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from bokeh.io import show, output_notebook\n",
        "\n",
        "from bokeh.plotting import figure\n",
        "from bokeh.palettes import Spectral3\n",
        "from bokeh.transform import factor_cmap\n",
        "from bokeh.models import ColumnDataSource, FactorRange\n",
        "\n",
        "\n",
        "# Get the top 10 items as an array\n",
        "top_10_items = df_cosmos[df_cosmos['Action']=='Purchased'].groupby('Item').size().sort_values(ascending=False)[:10].index.values.tolist()\n",
        "\n",
        "# Filter our data to only these 10 items\n",
        "df_top10 = df_cosmos[df_cosmos['Item'].isin(top_10_items)]\n",
        "\n",
        "# Group by Item and Action, sorting by event count\n",
        "df_top10_sorted = df_top10.groupby(['Item', 'Action']).count().rename(columns={'Country':'ResultCount'}, inplace=False).reset_index().sort_values(['Item', 'ResultCount'], ascending = False).set_index(['Item', 'Action'])\n",
        "\n",
        "# Get sorted X-axis values - this way, we can display the funnel of view -> add -> purchase\n",
        "x_axis_values = df_top10_sorted.index.values.tolist()\n",
        "\n",
        "group = df_top10_sorted.groupby(['Item', 'Action'])\n",
        "\n",
        "# Specifiy colors for X axis\n",
        "index_cmap = factor_cmap('Item_Action', palette=Spectral3, factors=sorted(df_top10.Action.unique()), start=1, end=2)\n",
        "\n",
        "# Create the plot\n",
        "\n",
        "p = figure(plot_width=1200, plot_height=500, title=\"Conversion rate of items from View -> Add to cart -> Purchase\", x_range=FactorRange(*x_axis_values), toolbar_location=None, tooltips=[(\"Number of events\", \"@ResultCount_max\"), (\"Item, Action\", \"@Item_Action\")])\n",
        "\n",
        "p.vbar(x='Item_Action', top='ItemRevenue_max', width=1, source=group,\n",
        "       line_color=\"white\", fill_color=index_cmap, )\n",
        "\n",
        "#Configure how the plot looks\n",
        "p.y_range.start = 0\n",
        "p.x_range.range_padding = 0.05\n",
        "p.xgrid.grid_line_color = None\n",
        "p.xaxis.major_label_orientation = 1.2\n",
        "p.outline_line_color = \"black\"\n",
        "p.xaxis.axis_label = \"Item\"\n",
        "p.yaxis.axis_label = \"Count\"\n",
        "\n",
        "#Display figure inline in Jupyter Notebook.\n",
        "output_notebook()\n",
        "\n",
        "#Display figure.\n",
        "show(p)\n"
      ]
    }
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