AltairDataServer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zLWJIthRefRt"
   },
   "source": [
    "# Altair Data Server\n",
    "\n",
    "This notebook shows an example of using the [Altair data server](https://github.com/altair-viz/altair_data_server), a lightweight plugin for [Altair](http://altair-viz.github.io) that lets you efficiently and transparently work with larger datasets.\n",
    "\n",
    "Altair data server can be installed with pip:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "id": "A1uXsLU5fJnq",
    "outputId": "a447c3f8-48f0-4be8-daf1-c4d66ea00208"
   },
   "outputs": [],
   "source": [
    "!pip install altair_data_server"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JZQsUiLgfNwM"
   },
   "source": [
    "## Motivation\n",
    "\n",
    "Altair charts are built on [vega-lite](http://vega.github.io/vega-lite), a visualization grammar that encodes charts in JSON before rendering them in your browser with Javascript.\n",
    "\n",
    "For example, consider the following chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "colab_type": "code",
    "id": "z4hH7BTsjuDX",
    "outputId": "701f7198-378f-4983-fef3-4e1658b72853"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-ce24079b8164f6736574dbd5cd2f7d05"
       },
       "datasets": {
        "data-ce24079b8164f6736574dbd5cd2f7d05": [
         {
          "category": "A",
          "value": 2
         },
         {
          "category": "B",
          "value": 6
         },
         {
          "category": "C",
          "value": 4
         },
         {
          "category": "D",
          "value": 7
         },
         {
          "category": "E",
          "value": 6
         }
        ]
       },
       "encoding": {
        "x": {
         "field": "value",
         "type": "quantitative"
        },
        "y": {
         "field": "category",
         "type": "nominal"
        }
       },
       "mark": "bar"
      },
      "image/png": "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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import altair as alt\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    'value': [2, 6, 4, 7, 6],\n",
    "    'category': list('ABCDE'),\n",
    "})\n",
    "\n",
    "chart = alt.Chart(data).mark_bar().encode(\n",
    "    x='value',\n",
    "    y='category'\n",
    ")\n",
    "\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jJ5TXmMpkIuu"
   },
   "source": [
    "The chart itself, including the data, is encoded to a JSON specification that you can inspect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 816
    },
    "colab_type": "code",
    "id": "VcslPA_AkX8Y",
    "outputId": "fe9da48b-49fa-421f-8f5a-6966560b8afa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\",\n",
      "  \"config\": {\n",
      "    \"view\": {\n",
      "      \"height\": 300,\n",
      "      \"width\": 400\n",
      "    }\n",
      "  },\n",
      "  \"data\": {\n",
      "    \"name\": \"data-ce24079b8164f6736574dbd5cd2f7d05\"\n",
      "  },\n",
      "  \"datasets\": {\n",
      "    \"data-ce24079b8164f6736574dbd5cd2f7d05\": [\n",
      "      {\n",
      "        \"category\": \"A\",\n",
      "        \"value\": 2\n",
      "      },\n",
      "      {\n",
      "        \"category\": \"B\",\n",
      "        \"value\": 6\n",
      "      },\n",
      "      {\n",
      "        \"category\": \"C\",\n",
      "        \"value\": 4\n",
      "      },\n",
      "      {\n",
      "        \"category\": \"D\",\n",
      "        \"value\": 7\n",
      "      },\n",
      "      {\n",
      "        \"category\": \"E\",\n",
      "        \"value\": 6\n",
      "      }\n",
      "    ]\n",
      "  },\n",
      "  \"encoding\": {\n",
      "    \"x\": {\n",
      "      \"field\": \"value\",\n",
      "      \"type\": \"quantitative\"\n",
      "    },\n",
      "    \"y\": {\n",
      "      \"field\": \"category\",\n",
      "      \"type\": \"nominal\"\n",
      "    }\n",
      "  },\n",
      "  \"mark\": \"bar\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(chart.to_json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1Rd9e1jHkZBm"
   },
   "source": [
    "Notice that the data is encoded in the chart specification itself: this is very convenient because it results in a single, well-defined specification that contains **everything** required to recreate the chart.\n",
    "\n",
    "However, this leads to issues for larger datasets. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QmYK5_8xlDNW",
    "outputId": "b5f64ac9-8b81-4a08-fcfc-84cf0ad7c193"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of chart spec: 500.7 KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'timepoint': np.arange(5000),\n",
    "    'value': np.random.randn(5000),\n",
    "    'label': np.random.choice(list('ABCDE'), 5000)\n",
    "})\n",
    "\n",
    "chart = alt.Chart(df).mark_line().encode(\n",
    "    x='timepoint',\n",
    "    y='value',\n",
    "    color='label'\n",
    ")\n",
    "\n",
    "\n",
    "def print_size_of(chart):\n",
    "    spec = chart.to_json()\n",
    "    print(f\"Size of chart spec: {len(spec) / 1024:.1f} KB\")\n",
    "    \n",
    "print_size_of(chart)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "anh5j_S0mZoo"
   },
   "source": [
    "If we had rendered this chart, it would have resulted in about half a megabyte of JSON text being embedded into the notebook. If your notebook contains many charts, this can quickly lead to large and unwieldy notebooks, and in the worst cases to crashing the browser.\n",
    "\n",
    "For this reason, Altair builds in a protection that prevents you from embedding extremely large data. Here's what happens when you use a dataset with a large number of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2360
    },
    "colab_type": "code",
    "id": "dMa55WLmn2m2",
    "outputId": "35d3c60b-ad3d-4abb-c913-68071687ee66"
   },
   "outputs": [
    {
     "ename": "MaxRowsError",
     "evalue": "The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMaxRowsError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/altair/vegalite/v2/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0moriginal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m         \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/altair/vegalite/v2/api.py\u001b[0m in \u001b[0;36m_prepare_data\u001b[0;34m(data, context)\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconsolidate_datasets\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m'values'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_dataset_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m    550\u001b[0m     \"\"\"\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/altair/vegalite/data.py\u001b[0m in \u001b[0;36mdefault_data_transformer\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mcurry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdefault_data_transformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_rows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m    550\u001b[0m     \"\"\"\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/altair/utils/data.py\u001b[0m in \u001b[0;36mlimit_rows\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m     70\u001b[0m                            \u001b[0;34m'than the maximum allowed ({0}). '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m                            \u001b[0;34m'For information on how to plot larger datasets '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m                            'in Altair, see the documentation'.format(max_rows))\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMaxRowsError\u001b[0m: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Chart({\n",
       "  data:            x         y\n",
       "  0          0 -0.187207\n",
       "  1          1  0.236761\n",
       "  2          2  0.204160\n",
       "  3          3  1.510038\n",
       "  4          4  0.855619\n",
       "  5          5 -1.630006\n",
       "  6          6 -1.017096\n",
       "  7          7 -1.100416\n",
       "  8          8 -2.332064\n",
       "  9          9  0.979970\n",
       "  10        10  1.274392\n",
       "  11        11 -0.500785\n",
       "  12        12 -0.567306\n",
       "  13        13 -1.946447\n",
       "  14        14 -0.984827\n",
       "  15        15 -0.045963\n",
       "  16        16  2.503872\n",
       "  17        17  0.481599\n",
       "  18        18  0.872812\n",
       "  19        19 -0.280674\n",
       "  20        20  0.587096\n",
       "  21        21  0.345636\n",
       "  22        22  0.226660\n",
       "  23        23 -0.103801\n",
       "  24        24 -1.070477\n",
       "  25        25 -0.391096\n",
       "  26        26  1.324717\n",
       "  27        27 -2.045824\n",
       "  28        28 -0.858693\n",
       "  29        29 -0.024313\n",
       "  ...      ...       ...\n",
       "  49970  49970  1.778256\n",
       "  49971  49971  0.334451\n",
       "  49972  49972 -0.788223\n",
       "  49973  49973 -1.012857\n",
       "  49974  49974 -0.019542\n",
       "  49975  49975 -1.143520\n",
       "  49976  49976  1.499920\n",
       "  49977  49977 -1.715173\n",
       "  49978  49978 -1.476321\n",
       "  49979  49979  1.200555\n",
       "  49980  49980 -0.738952\n",
       "  49981  49981 -0.228170\n",
       "  49982  49982 -0.456527\n",
       "  49983  49983  1.236492\n",
       "  49984  49984  0.503881\n",
       "  49985  49985 -0.342733\n",
       "  49986  49986 -0.164370\n",
       "  49987  49987  0.217805\n",
       "  49988  49988  1.980753\n",
       "  49989  49989  1.574927\n",
       "  49990  49990  1.191852\n",
       "  49991  49991 -0.462079\n",
       "  49992  49992  1.255304\n",
       "  49993  49993  0.671050\n",
       "  49994  49994  1.563930\n",
       "  49995  49995  1.986160\n",
       "  49996  49996 -0.630699\n",
       "  49997  49997  0.455879\n",
       "  49998  49998 -2.018491\n",
       "  49999  49999  1.606584\n",
       "  \n",
       "  [50000 rows x 2 columns],\n",
       "  encoding: EncodingWithFacet({\n",
       "    x: X({\n",
       "      shorthand: 'x'\n",
       "    }),\n",
       "    y: Y({\n",
       "      shorthand: 'y'\n",
       "    })\n",
       "  }),\n",
       "  mark: 'line'\n",
       "})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'x': np.arange(50000),\n",
    "    'y': np.random.randn(50000),\n",
    "})\n",
    "\n",
    "big_chart = alt.Chart(df).mark_line().encode(\n",
    "    x='x',\n",
    "    y='y'\n",
    ")\n",
    "\n",
    "big_chart.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XSjoiii9w-N2"
   },
   "source": [
    "We can print the size of the chart by temporarily disabling the maximum rows check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dyGpfv-ewWmD",
    "outputId": "6fbca078-5529-4405-e72c-754522b8e811"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of chart spec: 3389.5 KB\n"
     ]
    }
   ],
   "source": [
    "with alt.data_transformers.enable(max_rows=None):\n",
    "    print_size_of(big_chart)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QfiXTsM6n8hp"
   },
   "source": [
    "Had Altair displayed this, it would have added  3MB of JSON text to the notebook, and if we created multiple charts, it would be another 3MB each time. This can quickly add-up in the context of interactive data exploration.\n",
    "\n",
    "The way to get around this is to put the data somewhere that is not in the notebook itself, but is visible to the renderer running in the notebook. Altair has some [existing approaches](https://altair-viz.github.io/user_guide/faq.html#maxrowserror-how-can-i-plot-large-datasets) that work by saving the data to disk, but this is not always desirable, and doesn't always work in cloud-based Jupyter frontends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lXB0KYBGoek9"
   },
   "source": [
    "## A Solution: Altair Data Server\n",
    "\n",
    "The [Altair data server](https://github.com/altair-viz/altair_data_server) plugin provides a nice solution to this. Rather than embedding the data in the notebook or saving the data to disk, when enabled it starts a background server, serves the data, and inserts the appropriate URL into the altair chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "colab_type": "code",
    "id": "1t8btXFXoiBu",
    "outputId": "f72a561f-f2a1-4371-a517-7a6546a2a915"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of chart spec: 0.4 KB\n"
     ]
    },
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "format": {
         "type": "json"
        },
        "url": "http://localhost:21953/ce767407-e3f3-4d15-a806-7b05a362ddb5.json"
       },
       "encoding": {
        "x": {
         "field": "x",
         "type": "quantitative"
        },
        "y": {
         "field": "y",
         "type": "quantitative"
        }
       },
       "mark": "line"
      },
      "image/png": "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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.data_transformers.enable('data_server')\n",
    "print_size_of(big_chart)\n",
    "big_chart.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: If you are running on a cloud-based hosted notebook like MyBinder, you will have to modify the above slightly, and instead run*\n",
    "```python\n",
    "alt.data_transformers.enable('data_server_proxied')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MnmkhYaHpzsn"
   },
   "source": [
    "The resulting spec is only 0.4KB, which is small enough that embedding it in the notebook doesn't cause problems. It's instructive to look at the spec directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "colab_type": "code",
    "id": "OQSCXfEzo_fk",
    "outputId": "4124afd2-8a44-4541-ce6a-894a2300d717"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\",\n",
      "  \"config\": {\n",
      "    \"view\": {\n",
      "      \"height\": 300,\n",
      "      \"width\": 400\n",
      "    }\n",
      "  },\n",
      "  \"data\": {\n",
      "    \"url\": \"http://localhost:21953/ce767407-e3f3-4d15-a806-7b05a362ddb5.json\"\n",
      "  },\n",
      "  \"encoding\": {\n",
      "    \"x\": {\n",
      "      \"field\": \"x\",\n",
      "      \"type\": \"quantitative\"\n",
      "    },\n",
      "    \"y\": {\n",
      "      \"field\": \"y\",\n",
      "      \"type\": \"quantitative\"\n",
      "    }\n",
      "  },\n",
      "  \"mark\": \"line\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(big_chart.to_json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cc2Z_xEmuw6L"
   },
   "source": [
    "What the data transformer has done is to replace the embedded data with a URL reference, and to make that data available at that URL. We can see this by accessing the URL directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QgAYaF0Ju4Y-",
    "outputId": "b9b01893-fef8-4896-cda2-42475046660e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://localhost:21953/ce767407-e3f3-4d15-a806-7b05a362ddb5.json\n"
     ]
    }
   ],
   "source": [
    "url = big_chart.to_dict()['data']['url']\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the data from the backend using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "glrsH04Gpmwa",
    "outputId": "e85cdf09-99db-4129-e360-762f44d3646e"
   },
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.187207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.236761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.204160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.510038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.855619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x         y\n",
       "0  0 -0.187207\n",
       "1  1  0.236761\n",
       "2  2  0.204160\n",
       "3  3  1.510038\n",
       "4  4  0.855619"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not url.startswith('http://localhost'):\n",
    "    # Using proxied URL; reconstruct the host url\n",
    "    *proxy, port, filename = url.split('/')\n",
    "    url = f\"http://localhost:{port}/{filename}\"\n",
    "    \n",
    "served_data = pd.read_json(url)\n",
    "served_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c1AY-0hxpyQ3"
   },
   "source": [
    "## When to use the data server\n",
    "\n",
    "There is one distinct disadvantage of using the data server approach: your charts will only render as long as your Python session is active.\n",
    "\n",
    "So the data server is a good option when you'll be **working interactively, generating multiple charts as part of an exploration of data**.\n",
    "\n",
    "But once you are finished with exploration and want to generate charts that will be fully embedded in the notebook, you can restore the default data transformer:\n",
    "```python\n",
    "alt.data_transformers.enable('default')\n",
    "```\n",
    "and carry on from there."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Altair Data Server.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}