{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bright-storm",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Analyzing the European Parliament"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "parallel-hypothetical",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "import matplotlib.patheffects as PathEffects\n",
    "from matplotlib.ticker import FuncFormatter, FixedLocator\n",
    "\n",
    "import bokeh.models as bmo\n",
    "import bokeh.plotting as bpl\n",
    "from bokeh.palettes import d3\n",
    "\n",
    "import prince\n",
    "import palettable\n",
    "\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "atmospheric-faith",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. 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(\"1001\");\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",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\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",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\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.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.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(\"1001\")).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));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. 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(\"1001\");\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    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\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      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\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.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.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(\"1001\")).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));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "bpl.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unexpected-forty",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Download data\n",
    "\n",
    "All data is readily available on [Parltrack](https://parltrack.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cognitive-softball",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %%bash\n",
    "\n",
    "# wget --no-clobber https://parltrack.org/dumps/ep_votes.json.lz\n",
    "# lzip -d ep_votes.json.lz\n",
    "\n",
    "# wget --no-clobber https://parltrack.org/dumps/ep_meps.json.lz\n",
    "# lzip -d ep_meps.json.lz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "median-adobe",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Transform JSON to dataframes\n",
    "\n",
    "To easily work with the data, we transform it from JSON to pandas dataframes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "normal-correspondence",
   "metadata": {
    "tags": []
   },
   "source": [
    "### MEPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "forty-preview",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "17a1a142b189440bb429c1d37321a504",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = 'ep_meps.json'\n",
    "\n",
    "tmp = []\n",
    "with open(fname) as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        line = line.lstrip('[,]')\n",
    "        if len(line) == 0:\n",
    "            continue\n",
    "\n",
    "        data = json.loads(line)\n",
    "\n",
    "        #         if not data['active']:\n",
    "        #             continue\n",
    "\n",
    "        tmp.append(\n",
    "            {\n",
    "                'UserID': data['UserID'],\n",
    "                'name': data['Name']['full'],\n",
    "                'birthday': data['Birth']['date'] if 'Birth' in data else np.nan,\n",
    "                'active': data['active'],\n",
    "                'group': data.get('Groups', [{'groupid': np.nan}])[-1][\n",
    "                    'groupid'\n",
    "                ],  # assumption: last group is latest one. Is this true?\n",
    "            }\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "painful-sender",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>birthday</th>\n",
       "      <th>active</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <td>Hubert PIRKER</td>\n",
       "      <td>1948-10-03</td>\n",
       "      <td>False</td>\n",
       "      <td>PPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111496</th>\n",
       "      <td>María Auxiliadora CORREA ZAMORA</td>\n",
       "      <td>1972-05-24</td>\n",
       "      <td>False</td>\n",
       "      <td>PPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110987</th>\n",
       "      <td>Gino TREMATERRA</td>\n",
       "      <td>1940-09-03</td>\n",
       "      <td>False</td>\n",
       "      <td>PPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>Jan MULDER</td>\n",
       "      <td>1943-10-03</td>\n",
       "      <td>False</td>\n",
       "      <td>ALDE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39321</th>\n",
       "      <td>Vicente Miguel GARCÉS RAMÓN</td>\n",
       "      <td>1946-11-10</td>\n",
       "      <td>False</td>\n",
       "      <td>S&amp;D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name   birthday  active group\n",
       "UserID                                                          \n",
       "2307                      Hubert PIRKER 1948-10-03   False   PPE\n",
       "111496  María Auxiliadora CORREA ZAMORA 1972-05-24   False   PPE\n",
       "110987                  Gino TREMATERRA 1940-09-03   False   PPE\n",
       "1965                         Jan MULDER 1943-10-03   False  ALDE\n",
       "39321       Vicente Miguel GARCÉS RAMÓN 1946-11-10   False   S&D"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meps = pd.DataFrame(tmp)\n",
    "df_meps['birthday'] = pd.to_datetime(df_meps['birthday'])\n",
    "\n",
    "df_meps.set_index('UserID', inplace=True)\n",
    "\n",
    "df_meps['group'].replace(\n",
    "    {'Group of the European United Left - Nordic Green Left': 'GUE/NGL'}, inplace=True\n",
    ")  # is there a difference?\n",
    "\n",
    "df_meps.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "straight-costs",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "least-ridge",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1aa04c4e34b434ebda8bf97f2931ae4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/23906 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = 'ep_votes.json'\n",
    "\n",
    "tmp = []\n",
    "tmp_matrix = {}\n",
    "with open(fname) as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        line = line.lstrip('[,]')\n",
    "        if len(line) == 0:\n",
    "            continue\n",
    "\n",
    "        data = json.loads(line)\n",
    "        tmp.append(\n",
    "            {'date': data['ts'], 'voteid': data['voteid'], 'title': data['title']}\n",
    "        )\n",
    "\n",
    "        if 'votes' in data:\n",
    "            tmp_matrix[data['voteid']] = {\n",
    "                **{\n",
    "                    mep['mepid']: '+'\n",
    "                    for mep_list in data['votes']\n",
    "                    .get('+', {'groups': {'foo': []}})['groups']\n",
    "                    .values()\n",
    "                    for mep in mep_list\n",
    "                    if 'mepid' in mep\n",
    "                },\n",
    "                **{\n",
    "                    mep['mepid']: '-'\n",
    "                    for mep_list in data['votes']\n",
    "                    .get('-', {'groups': {'foo': []}})['groups']\n",
    "                    .values()\n",
    "                    for mep in mep_list\n",
    "                    if 'mepid' in mep\n",
    "                },\n",
    "                **{\n",
    "                    mep['mepid']: '0'\n",
    "                    for mep_list in data['votes']\n",
    "                    .get('0', {'groups': {'foo': []}})['groups']\n",
    "                    .values()\n",
    "                    for mep in mep_list\n",
    "                    if 'mepid' in mep\n",
    "                },\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "previous-warrior",
   "metadata": {
    "tags": []
   },
   "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>mepid</th>\n",
       "      <th>1</th>\n",
       "      <th>234</th>\n",
       "      <th>684</th>\n",
       "      <th>729</th>\n",
       "      <th>840</th>\n",
       "      <th>945</th>\n",
       "      <th>966</th>\n",
       "      <th>988</th>\n",
       "      <th>997</th>\n",
       "      <th>1002</th>\n",
       "      <th>...</th>\n",
       "      <th>204416</th>\n",
       "      <th>204418</th>\n",
       "      <th>204419</th>\n",
       "      <th>204420</th>\n",
       "      <th>204421</th>\n",
       "      <th>204443</th>\n",
       "      <th>204449</th>\n",
       "      <th>204733</th>\n",
       "      <th>205452</th>\n",
       "      <th>206158</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voteid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7754</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7818</th>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>-</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7759</th>\n",
       "      <td>+</td>\n",
       "      <td>+</td>\n",
       "      <td>+</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7755</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>+</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7760</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>-</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2329 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "mepid  1      234    684    729    840    945    966    988    997    1002    \\\n",
       "voteid                                                                         \n",
       "7754      NaN    NaN      +      +    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7818        -      +      -      -    NaN      -    NaN    NaN      +      -   \n",
       "7759        +      +      +      +    NaN      +      +    NaN      +    NaN   \n",
       "7755      NaN      0      +    NaN    NaN      +      0    NaN      +      +   \n",
       "7760        -      -      -      -    NaN      -      +    NaN      +      -   \n",
       "\n",
       "mepid   ... 204416 204418 204419 204420 204421 204443 204449 204733 205452  \\\n",
       "voteid  ...                                                                  \n",
       "7754    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7818    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7759    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7755    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7760    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "mepid  206158  \n",
       "voteid         \n",
       "7754      NaN  \n",
       "7818      NaN  \n",
       "7759      NaN  \n",
       "7755      NaN  \n",
       "7760      NaN  \n",
       "\n",
       "[5 rows x 2329 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votematrix = pd.DataFrame.from_dict(tmp_matrix, orient='index')\n",
    "\n",
    "df_votematrix.index.name = 'voteid'\n",
    "df_votematrix.columns.name = 'mepid'\n",
    "\n",
    "# df_votematrix.sort_values('voteid', axis=0, inplace=True)\n",
    "df_votematrix.sort_values('mepid', axis=1, inplace=True)\n",
    "\n",
    "df_votematrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "angry-session",
   "metadata": {
    "tags": []
   },
   "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>date</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voteid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116359</th>\n",
       "      <td>2020-07-23 12:49:32</td>\n",
       "      <td>B9-0229/2020 - Am 23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116398</th>\n",
       "      <td>2020-07-23 12:49:32</td>\n",
       "      <td>B9-0229/2020 - § 26/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116399</th>\n",
       "      <td>2020-07-23 12:49:32</td>\n",
       "      <td>B9-0229/2020 - § 26/2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116360</th>\n",
       "      <td>2020-07-23 12:49:32</td>\n",
       "      <td>B9-0229/2020 - Am 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116401</th>\n",
       "      <td>2020-07-23 16:52:06</td>\n",
       "      <td>B9-0229/2020 - Résolution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      date                      title\n",
       "voteid                                               \n",
       "116359 2020-07-23 12:49:32       B9-0229/2020 - Am 23\n",
       "116398 2020-07-23 12:49:32      B9-0229/2020 - § 26/1\n",
       "116399 2020-07-23 12:49:32      B9-0229/2020 - § 26/2\n",
       "116360 2020-07-23 12:49:32        B9-0229/2020 - Am 1\n",
       "116401 2020-07-23 16:52:06  B9-0229/2020 - Résolution"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes = pd.DataFrame(tmp)\n",
    "df_votes['date'] = pd.to_datetime(df_votes['date'])\n",
    "\n",
    "df_votes.set_index('voteid', inplace=True)\n",
    "\n",
    "df_votes.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interstate-luxury",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exploration\n",
    "\n",
    "We can then look at a few simple statistics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capable-breakfast",
   "metadata": {
    "tags": []
   },
   "source": [
    "### MEP party distribution\n",
    "\n",
    "How many members (MEPs) does each party of the European parliament have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "interpreted-southwest",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "705"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meps['active'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bizarre-authorization",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.1107175100739686,\n",
       " 1.1005103586792213,\n",
       " -1.1057638158926402,\n",
       " 1.1094386022707399)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_counts = df_meps.loc[df_meps['active'], 'group'].value_counts()\n",
    "labels = group_counts.to_frame().apply(lambda x: f'{x.name} ({x.iloc[0]})', axis=1)\n",
    "\n",
    "ax = group_counts.plot.pie(figsize=(8, 6), labels=labels, wedgeprops=dict(width=0.5))\n",
    "ax.axis('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-mounting",
   "metadata": {
    "tags": []
   },
   "source": [
    "### MEP age distribution\n",
    "\n",
    "And how old are these members?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "leading-parameter",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_meps['age'] = (pd.Timestamp.today() - df_meps['birthday']) / np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "tired-submission",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1635ff130>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(\n",
    "    data=df_meps[df_meps['active']],\n",
    "    x='age',\n",
    "    col='group',\n",
    "    col_wrap=3,\n",
    "    height=3,\n",
    "    aspect=4 / 3,\n",
    ")\n",
    "\n",
    "g.set_xlabels('MEP age [years]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suited-windows",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Voting patterns\n",
    "\n",
    "Let's now take a look at how these MEPs vote."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "functioning-isaac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th>mepid</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7755</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 2329 columns</p>\n",
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      ],
      "text/plain": [
       "mepid  1      234    684    729    840    945    966    988    997    1002    \\\n",
       "voteid                                                                         \n",
       "7754      NaN    NaN      +      +    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7818        -      +      -      -    NaN      -    NaN    NaN      +      -   \n",
       "7759        +      +      +      +    NaN      +      +    NaN      +    NaN   \n",
       "7755      NaN      0      +    NaN    NaN      +      0    NaN      +      +   \n",
       "7760        -      -      -      -    NaN      -      +    NaN      +      -   \n",
       "\n",
       "mepid   ... 204416 204418 204419 204420 204421 204443 204449 204733 205452  \\\n",
       "voteid  ...                                                                  \n",
       "7754    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7818    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7759    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7755    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "7760    ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "mepid  206158  \n",
       "voteid         \n",
       "7754      NaN  \n",
       "7818      NaN  \n",
       "7759      NaN  \n",
       "7755      NaN  \n",
       "7760      NaN  \n",
       "\n",
       "[5 rows x 2329 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votematrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "descending-swimming",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Who is the most active MEP?\n",
    "\n",
    "Here we equate \"active\" with \"has voted most often\". This is most likely quite misleading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "filled-result",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_hasvoted = ~df_votematrix[df_meps[df_meps['active']].index].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "increased-patrick",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>vote_count</th>\n",
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       "      <th>birthday</th>\n",
       "      <th>active</th>\n",
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       "      <th>28266</th>\n",
       "      <td>22954</td>\n",
       "      <td>Sophia in 't VELD</td>\n",
       "      <td>1963-09-13</td>\n",
       "      <td>True</td>\n",
       "      <td>RE</td>\n",
       "      <td>57.711345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>22703</td>\n",
       "      <td>Evelyne GEBHARDT</td>\n",
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       "      <td>True</td>\n",
       "      <td>S&amp;D</td>\n",
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       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>22394</td>\n",
       "      <td>Rainer WIELAND</td>\n",
       "      <td>1957-02-19</td>\n",
       "      <td>True</td>\n",
       "      <td>PPE</td>\n",
       "      <td>64.274108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4246</th>\n",
       "      <td>22324</td>\n",
       "      <td>Othmar KARAS</td>\n",
       "      <td>1957-12-24</td>\n",
       "      <td>True</td>\n",
       "      <td>PPE</td>\n",
       "      <td>63.430833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28219</th>\n",
       "      <td>22324</td>\n",
       "      <td>Daniel CASPARY</td>\n",
       "      <td>1976-04-04</td>\n",
       "      <td>True</td>\n",
       "      <td>PPE</td>\n",
       "      <td>45.152566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>22269</td>\n",
       "      <td>Michael GAHLER</td>\n",
       "      <td>1960-04-22</td>\n",
       "      <td>True</td>\n",
       "      <td>PPE</td>\n",
       "      <td>61.103612</td>\n",
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       "    <tr>\n",
       "      <th>28224</th>\n",
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       "      <td>Markus PIEPER</td>\n",
       "      <td>1963-05-15</td>\n",
       "      <td>True</td>\n",
       "      <td>PPE</td>\n",
       "      <td>58.042632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28298</th>\n",
       "      <td>21992</td>\n",
       "      <td>Iratxe GARCÍA PÉREZ</td>\n",
       "      <td>1974-10-07</td>\n",
       "      <td>True</td>\n",
       "      <td>S&amp;D</td>\n",
       "      <td>46.644725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23821</th>\n",
       "      <td>21912</td>\n",
       "      <td>József SZÁJER</td>\n",
       "      <td>1961-09-07</td>\n",
       "      <td>True</td>\n",
       "      <td>PPE</td>\n",
       "      <td>59.726445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28223</th>\n",
       "      <td>21910</td>\n",
       "      <td>Andreas SCHWAB</td>\n",
       "      <td>1973-04-09</td>\n",
       "      <td>True</td>\n",
       "      <td>PPE</td>\n",
       "      <td>48.139622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       vote_count                 name   birthday  active group        age\n",
       "mepid                                                                     \n",
       "28266       22954    Sophia in 't VELD 1963-09-13    True    RE  57.711345\n",
       "1913        22703     Evelyne GEBHARDT 1954-01-19    True   S&D  67.359729\n",
       "2323        22394       Rainer WIELAND 1957-02-19    True   PPE  64.274108\n",
       "4246        22324         Othmar KARAS 1957-12-24    True   PPE  63.430833\n",
       "28219       22324       Daniel CASPARY 1976-04-04    True   PPE  45.152566\n",
       "2341        22269       Michael GAHLER 1960-04-22    True   PPE  61.103612\n",
       "28224       22164        Markus PIEPER 1963-05-15    True   PPE  58.042632\n",
       "28298       21992  Iratxe GARCÍA PÉREZ 1974-10-07    True   S&D  46.644725\n",
       "23821       21912        József SZÁJER 1961-09-07    True   PPE  59.726445\n",
       "28223       21910       Andreas SCHWAB 1973-04-09    True   PPE  48.139622"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hasvoted.sum(axis=0).sort_values(ascending=False).to_frame('vote_count').merge(\n",
    "    df_meps, how='left', left_index=True, right_index=True\n",
    ").head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "threaded-maldives",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Select subset of data\n",
    "\n",
    "For the subsequent vote clustering, we restrict ourselves to recent votes of active MEPs starting with 2020 (and remove MEPs with no votes at all)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "combined-activity",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1808, 703)\n"
     ]
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111781</th>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 703 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "UserID 96750  4746   23788  96810  96808  4560   38595  1992   125106 4391    \\\n",
       "voteid                                                                         \n",
       "111241      0    NaN    NaN    NaN      +    NaN    NaN    NaN      +    NaN   \n",
       "111141      +    NaN    NaN      +      +      +    NaN    NaN      +    NaN   \n",
       "111142      +    NaN    NaN      +      +      +    NaN      +      +    NaN   \n",
       "111143      +    NaN    NaN      +      +      +    NaN      +      +    NaN   \n",
       "111781      -    NaN    NaN      +      -      -    NaN      -      +    NaN   \n",
       "\n",
       "UserID  ... 204413 204334 204331 204346 204449 204400 197780 204733 205452  \\\n",
       "voteid  ...                                                                  \n",
       "111241  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "111141  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "111142  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "111143  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "111781  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "UserID 206158  \n",
       "voteid         \n",
       "111241    NaN  \n",
       "111141    NaN  \n",
       "111142    NaN  \n",
       "111143    NaN  \n",
       "111781    NaN  \n",
       "\n",
       "[5 rows x 703 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset = df_votematrix.loc[\n",
    "    df_votematrix.index.intersection(df_votes[df_votes['date'] > '2020'].index),\n",
    "    df_meps[df_meps['active']].index,\n",
    "].dropna(how='all', axis=1)\n",
    "\n",
    "print(df_subset.shape)\n",
    "df_subset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incredible-stanley",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Clustered data overview\n",
    "\n",
    "Here, we cluster both MEPs and votes as well as highlight each MEP column with their respective party association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "reliable-russell",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x155e363a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# process data to work with clustermap\n",
    "tmp = df_subset.fillna(0).replace({'+': 1, '0': 0, '-': -1})  # .iloc[:10, :10]\n",
    "tmp.columns.rename('MEP', inplace=True)\n",
    "tmp.index.rename('Vote', inplace=True)\n",
    "\n",
    "# infer party colors\n",
    "party_colors = {\n",
    "    party: sns.color_palette('tab10')[i]\n",
    "    for i, party in enumerate(df_meps.loc[tmp.columns, 'group'].unique())\n",
    "}\n",
    "party_cmap = (\n",
    "    tmp.T.merge(df_meps['group'], how='inner', left_index=True, right_index=True)[\n",
    "        'group'\n",
    "    ]\n",
    "    .map(party_colors)\n",
    "    .rename('Party')\n",
    ")\n",
    "\n",
    "# main plot\n",
    "g = sns.clustermap(\n",
    "    tmp,\n",
    "    col_colors=party_cmap,\n",
    "    cmap=palettable.tableau.TrafficLight_9.hex_colors[3:6],\n",
    "    figsize=(12, 12),\n",
    ")\n",
    "\n",
    "# plot improvements\n",
    "g.ax_heatmap.tick_params(bottom=False, labelbottom=False, right=False, labelright=False)\n",
    "\n",
    "\n",
    "@FuncFormatter\n",
    "def formatter(x, pos):\n",
    "    return {-1: 'no', 0: 'abstain', 1: ' yes'}[x]\n",
    "\n",
    "\n",
    "g.cax.yaxis.set_major_locator(FixedLocator([-1, 0, 1]))\n",
    "g.cax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "# party color legend\n",
    "g.ax_heatmap.legend(\n",
    "    handles=[\n",
    "        Patch(facecolor=color, label=name) for name, color in party_colors.items()\n",
    "    ],\n",
    "    title='Party',\n",
    "    bbox_to_anchor=(1.05, 1),\n",
    "    loc='upper left',\n",
    ")\n",
    "\n",
    "# TODO: improve overall colorbar/legend placement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "representative-commissioner",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Project MEPs based on vote patterns\n",
    "\n",
    "We will now visualize the landscape of MEPs in two dimensions based on the voting patterns using Multiple Correspondence Analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alike-johnson",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Apply Multiple Correspondence Analysis (MCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fallen-manufacturer",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(703, 3)\n"
     ]
    },
    {
     "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>MCA0</th>\n",
       "      <th>MCA1</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mepid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96750</th>\n",
       "      <td>-0.371809</td>\n",
       "      <td>0.631197</td>\n",
       "      <td>Verts/ALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4746</th>\n",
       "      <td>0.593274</td>\n",
       "      <td>-0.471376</td>\n",
       "      <td>ECR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23788</th>\n",
       "      <td>0.398250</td>\n",
       "      <td>-0.753624</td>\n",
       "      <td>ECR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96810</th>\n",
       "      <td>0.604044</td>\n",
       "      <td>-0.488594</td>\n",
       "      <td>ECR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96808</th>\n",
       "      <td>-0.231881</td>\n",
       "      <td>-0.476251</td>\n",
       "      <td>PPE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MCA0      MCA1      group\n",
       "mepid                               \n",
       "96750 -0.371809  0.631197  Verts/ALE\n",
       "4746   0.593274 -0.471376        ECR\n",
       "23788  0.398250 -0.753624        ECR\n",
       "96810  0.604044 -0.488594        ECR\n",
       "96808 -0.231881 -0.476251        PPE"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mca = prince.MCA(n_components=2)\n",
    "df_mca = mca.fit_transform(df_subset.T)\n",
    "\n",
    "df_mca.columns = ('MCA0', 'MCA1')\n",
    "df_mca.index.rename('mepid', inplace=True)\n",
    "df_mca['group'] = df_meps.loc[df_meps['active'], 'group']\n",
    "\n",
    "print(df_mca.shape)\n",
    "df_mca.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vulnerable-saying",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Static visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "anonymous-growth",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# obtained from the information box on each party's Wikipedia entry\n",
    "party_ideology = {\n",
    "    'Verts/ALE': 'green',\n",
    "    'ECR': 'conservative',\n",
    "    'PPE': 'liberal\\nconservative',\n",
    "    'S&D': 'socialist',\n",
    "    'RE': 'liberal',\n",
    "    'ID': 'right-wing',\n",
    "    'GUE/NGL': 'left-wing',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "third-nursing",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MCA of MEP Voting Patterns')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.scatterplot(data=df_mca, x='MCA0', y='MCA1', hue='group', ax=ax)\n",
    "\n",
    "for party, row in df_mca.groupby('group').mean().iterrows():\n",
    "    if party == 'NA':\n",
    "        continue\n",
    "    ax.text(\n",
    "        row.MCA0,\n",
    "        row.MCA1,\n",
    "        party_ideology.get(party),\n",
    "        ha='center',\n",
    "        va='center',\n",
    "        fontsize=10,\n",
    "        path_effects=[PathEffects.withStroke(linewidth=3, foreground=\"w\")],\n",
    "    )\n",
    "\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1.05, 1), ncol=1, title='Party')\n",
    "ax.set_title('MCA of MEP Voting Patterns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "registered-slovak",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Interactive visualization\n",
    "\n",
    "You can zoom and pan the visualization. Hovering over each point (corresponding to a MEP) will display relevant information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "pressing-struggle",
   "metadata": {
    "tags": []
   },
   "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>MCA0</th>\n",
       "      <th>MCA1</th>\n",
       "      <th>group</th>\n",
       "      <th>name</th>\n",
       "      <th>birthday</th>\n",
       "      <th>active</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mepid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96750</th>\n",
       "      <td>-0.371809</td>\n",
       "      <td>0.631197</td>\n",
       "      <td>Verts/ALE</td>\n",
       "      <td>François Alfonsi</td>\n",
       "      <td>1953-09-14</td>\n",
       "      <td>True</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4746</th>\n",
       "      <td>0.593274</td>\n",
       "      <td>-0.471376</td>\n",
       "      <td>ECR</td>\n",
       "      <td>Sergio Berlato</td>\n",
       "      <td>1959-07-27</td>\n",
       "      <td>True</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23788</th>\n",
       "      <td>0.398250</td>\n",
       "      <td>-0.753624</td>\n",
       "      <td>ECR</td>\n",
       "      <td>Adam Bielan</td>\n",
       "      <td>1974-09-12</td>\n",
       "      <td>True</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96810</th>\n",
       "      <td>0.604044</td>\n",
       "      <td>-0.488594</td>\n",
       "      <td>ECR</td>\n",
       "      <td>Carlo Fidanza</td>\n",
       "      <td>1976-09-21</td>\n",
       "      <td>True</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96808</th>\n",
       "      <td>-0.231881</td>\n",
       "      <td>-0.476251</td>\n",
       "      <td>PPE</td>\n",
       "      <td>Pablo Arias Echeverría</td>\n",
       "      <td>1970-06-30</td>\n",
       "      <td>True</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MCA0      MCA1      group                    name    birthday  \\\n",
       "mepid                                                                      \n",
       "96750 -0.371809  0.631197  Verts/ALE        François Alfonsi  1953-09-14   \n",
       "4746   0.593274 -0.471376        ECR          Sergio Berlato  1959-07-27   \n",
       "23788  0.398250 -0.753624        ECR             Adam Bielan  1974-09-12   \n",
       "96810  0.604044 -0.488594        ECR           Carlo Fidanza  1976-09-21   \n",
       "96808 -0.231881 -0.476251        PPE  Pablo Arias Echeverría  1970-06-30   \n",
       "\n",
       "       active  age  \n",
       "mepid               \n",
       "96750    True   67  \n",
       "4746     True   61  \n",
       "23788    True   46  \n",
       "96810    True   44  \n",
       "96808    True   50  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate data for each tooltip\n",
    "hover_data = df_meps.loc[df_mca.index]\n",
    "\n",
    "hover_data['name'] = hover_data['name'].str.title()\n",
    "hover_data['birthday'] = hover_data['birthday'].apply(\n",
    "    lambda x: x.strftime(\"%Y-%m-%d\") if not pd.isnull(x) else 'undef'\n",
    ")\n",
    "hover_data['age'] = hover_data['age'].apply(\n",
    "    lambda x: int(x) if not pd.isnull(x) else -1\n",
    ")\n",
    "\n",
    "df_data = df_mca.merge(\n",
    "    hover_data.drop('group', axis=1), left_index=True, right_index=True\n",
    ")\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "focal-prisoner",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set up point colors\n",
    "palette = d3['Category10'][df_data['group'].nunique()]\n",
    "color_map = bmo.CategoricalColorMapper(\n",
    "    factors=df_data['group'].unique(), palette=palette\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "occupied-contract",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"b4244b0c-d299-4ae4-8240-6125ec3245cc\" data-root-id=\"1003\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Gy\\u00fcrk\",\"Brice Hortefeux\",\"Margrete Auken\",\"Younous Omarjee\",\"Ryszard Czarnecki\",\"Pascal Arimont\",\"Laura Ferrara\",\"Piernicola Pedicini\",\"Tiziana Beghin\",\"Ernest Urtasun\",\"Nicola Danti\",\"Krzysztof Hetman\",\"Georg Mayer\",\"Csaba Moln\\u00e1r\",\"Ska Keller\",\"Malin Bj\\u00f6rk\",\"Fredrick Federley\",\"Roberta Metsola\",\"Inma Rodr\\u00edguez-Pi\\u00f1ero\",\"Caterina Chinnici\",\"Ev\\u017een To\\u0161enovsk\\u00fd\",\"Jeroen Lenaers\",\"Maria Arena\",\"Sophia In 'T Veld\",\"Nadine Morano\",\"Martin Sonneborn\",\"Dominique Riquet\",\"Jo\\u00eblle M\\u00e9lin\",\"Christian Ehler\",\"Peter Liese\",\"Simona Bonaf\\u00e8\",\"Markus Ferber\",\"Angelo Ciocca\",\"Christophe Hansen\",\"Se\\u00e1n Kelly\",\"Emil Radev\",\"Juan Fernando L\\u00f3pez Aguilar\",\"Petras Au\\u0161trevi\\u010dius\",\"Eva Maydell\",\"Iratxe Garc\\u00eda P\\u00e9rez\",\"Elissavet Vozemberg-Vrionidi\",\"Maite Pagazaurtund\\u00faa\",\"Guy Verhofstadt\",\"Michaela 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Papadakis\",\"Emmanuel Maurel\",\"Ilhan Kyuchyuk\",\"Eric Andrieu\",\"Franc Bogovi\\u010d\",\"Tam\\u00e1s Deutsch\",\"Kinga G\\u00e1l\",\"Dita Charanzov\\u00e1\",\"Andrea Bocskor\",\"Pascal Durand\",\"Luke Ming Flanagan\",\"Gy\\u00f6rgy H\\u00f6lv\\u00e9nyi\",\"Gilles Lebreton\",\"Jos\\u00e9 Manuel Fernandes\",\"Angel Dzhambazki\",\"Jean-Fran\\u00e7ois Jalkh\",\"Ismail Ertug\",\"Nikos Androulakis\",\"Eleonora Evi\",\"Vilija Blinkevi\\u010di\\u016bt\\u0117\",\"Andor Deli\",\"Clara Aguilera\",\"Dominique Bilde\",\"Nicolas Bay\",\"Daniel Buda\",\"Lefteris Christoforou\",\"Bas Eickhout\",\"Jens Gieseke\",\"Raffaele Fitto\",\"Monika Be\\u0148ov\\u00e1\",\"David Casa\",\"Jaros\\u0142aw Kalinowski\",\"Cristian-Silviu Bu\\u015foi\",\"Biljana Borzan\",\"Matteo Adinolfi\",\"Magdalena Adamowicz\",\"Alex Agius Saliba\",\"Mazaly Aguilar\",\"Alexander Alexandrov Yordanov\",\"Atidzhe Alieva-Veli\",\"Abir Al-Sahlani\",\"\\u00c1lvaro Amaro\",\"Andris Ameriks\",\"Christine Anderson\",\"Rasmus Andresen\",\"Mathilde Androu\\u00ebt\",\"Attila Ara-Kov\\u00e1cs\",\"Clotilde Armand\",\"Bartosz Ar\\u0142ukowicz\",\"Konstantinos Arvanitis\",\"Anna-Michelle Asimakopoulou\",\"Manon Aubry\",\"Carmen Avram\",\"Malik Azmani\",\"Simona Baldassarre\",\"Marek Pawe\\u0142 Balt\",\"Jordan Bardella\",\"Katarina Barley\",\"Pernando Barrena Arza\",\"Pietro Bartolo\",\"Traian B\\u0103sescu\",\"Alessandra Basso\",\"Jos\\u00e9 Ram\\u00f3n Bauz\\u00e1 D\\u00edaz\",\"Gunnar Beck\",\"Nicola Beer\",\"Aurelia Beigneux\",\"Marek Belka\",\"Fran\\u00e7ois-Xavier Bellamy\",\"Adrian-Drago\\u015f Benea\",\"Isabel Benjumea Benjumea\",\"Hildegard Bentele\",\"Lars Patrick Berg\",\"Tom Berendsen\",\"Stefan Berger\",\"Erik Bergkvist\",\"Alexander Bernhuber\",\"Robert Biedro\\u0144\",\"St\\u00e9phane Bijoux\",\"Vladim\\u00edr Bil\\u010d\\u00edk\",\"Gabriele Bischoff\",\"Beno\\u00eet Biteau\",\"Vasile Blaga\",\"Hynek Bla\\u0161ko\",\"Michael Bloss\",\"Damian Boeselager\",\"Ioan-Rare\\u015f Bogdan\",\"Manuel Bompard\",\"Anna Bonfrisco\",\"Paolo Borchia\",\"Marc Botenga\",\"Geert Bourgeois\",\"Vlad-Marius Boto\\u015f\",\"Gilles Boyer\",\"Patrick Breyer\",\"Milan Brglez\",\"Saskia Bricmont\",\"Joachim Stanis\\u0142aw Brudzi\\u0144ski\",\"Annika Bruna\",\"Sylvie Brunet\",\"Markus Buchheit\",\"Delara Burkhardt\",\"Martin Buschmann\",\"Jorge Buxad\\u00e9 Villalba\",\"Carlo Calenda\",\"Marco Campomenosi\",\"Jordi Ca\\u00f1as\",\"Damien Car\\u00eame\",\"Andrea Caroppo\",\"Massimo Casanova\",\"Anna Cavazzini\",\"Susanna Ceccardi\",\"Sara Cerdas\",\"Catherine Chabaud\",\"Leila Chaibi\",\"Mohammed Chahim\",\"Olivier Chastel\",\"Asger Christensen\",\"W\\u0142odzimierz Cimoszewicz\",\"Dacian Ciolo\\u015f\",\"Tudor Ciuhodaru\",\"Miroslav \\u010c\\u00ed\\u017e\",\"Nathalie Colin-Oesterl\\u00e9\",\"Gilbert Collard\",\"Rosanna Conte\",\"David Cormand\",\"Katalin Cseh\",\"Ciar\\u00e1n Cuffe\",\"Josianne Cutajar\",\"Clare Daly\",\"Johan Danielsson\",\"Gianantonio Da Re\",\"Ivan David\",\"J\\u00e9r\\u00e9my Decerle\",\"Gwendoline Delbos-Corfield\",\"Filip De Man\",\"\\u00d6zlem Demirel\",\"Anna Deparnay-Grunenberg\",\"Petra De Sutter\",\"Kl\\u00e1ra Dobrev\",\"Christian Doleschal\",\"Anna J\\u00falia Don\\u00e1th\",\"Francesca Donato\",\"Marco Dreosto\",\"Jaros\\u0142aw Duda\",\"Lena D\\u00fcpont\",\"Estrella Dur\\u00e1 Ferrandis\",\"Lucia \\u010euri\\u0161 Nicholsonov\\u00e1\",\"Engin Eroglu\",\"Agn\\u00e8s Evren\",\"Gheorghe Falc\\u0103\",\"Laurence Farreng\",\"Nicolaus Fest\",\"Pietro Fiocchi\",\"Frances Fitzgerald\",\"Valter Flego\",\"Loucas Fourlas\",\"Tomasz Frankowski\",\"Cindy Franssen\",\"Daniel Freund\",\"Hel\\u00e9ne Fritzon\",\"Niels Fuglsang\",\"Mario Furore\",\"S\\u00f8ren Gade\",\"Lina G\\u00e1lvez Mu\\u00f1oz\",\"Claudia Gamon\",\"Gianna Gancia\",\"Ib\\u00e1n Garc\\u00eda Del Blanco\",\"Isabel Garc\\u00eda Mu\\u00f1oz\",\"Luis Garicano\",\"Jean-Paul Garraud\",\"Alexandra Geese\",\"Chiara Gemma\",\"Giorgos Georgiou\",\"Alexis Georgoulis\",\"Helmut Geuking\",\"Cristian Ghinea\",\"Dino Giarrusso\",\"Andreas Gl\\u00fcck\",\"Rapha\\u00ebl Glucksmann\",\"M\\u00f3nica Silvana Gonz\\u00e1lez\",\"Nicol\\u00e1s Gonz\\u00e1lez Casares\",\"Valentino Grant\",\"Mark\\u00e9ta Gregorov\\u00e1\",\"Catherine Griset\",\"Christophe Grudler\",\"Klemen Gro\\u0161elj\",\"Elisabetta Gualmini\",\"Francisco Guerreiro\",\"Bernard Guetta\",\"Jos\\u00e9 Gusm\\u00e3o\",\"M\\u00e1rton Gy\\u00f6ngy\\u00f6si\",\"Henrike Hahn\",\"Svenja Hahn\",\"Roman Haider\",\"Andrzej Halicki\",\"Teuvo Hakkarainen\",\"Mircea-Gheorghe Hava\",\"Val\\u00e9rie Hayer\",\"Hannes Heide\",\"Eero Hein\\u00e4luoma\",\"Niclas Herbst\",\"Pierrette Herzberger-Fofana\",\"Martin Hlav\\u00e1\\u010dek\",\"Martin Hojs\\u00edk\",\"P\\u00e4r Holmgren\",\"Alicia Homs Ginel\",\"Ivo Hristov\",\"Laura Huhtasaari\",\"Ivars Ijabs\",\"Evin Incir\",\"Stasys Jakeli\\u016bnas\",\"Patryk Jaki\",\"Adam Jarubas\",\"Virginie Joron\",\"Rasa Juknevi\\u010dien\\u0117\",\"Irena Joveva\",\"Krzysztof Jurgiel\",\"Eugen Jurzyca\",\"Herve Juvin\",\"Marina Kaljurand\",\"Radan Kanev\",\"Assita Kanko\",\"Pierre Karleskind\",\"Karin Karlsbro\",\"Billy Kelleher\",\"Beata Kempa\",\"Niyazi Kizily\\u00fcrek\",\"Izabela-Helena Kloc\",\"Ond\\u0159ej Knotek\",\"Peter Kofod\",\"\\u0141ukasz Kohut\",\"Arba Kokalari\",\"Marcel Kolaja\",\"Petros Kokkalis\",\"Mislav Kolaku\\u0161i\\u0107\",\"Athanasios Konstantinou\",\"Ewa Kopacz\",\"Joanna Kopci\\u0144ska\",\"Moritz K\\u00f6rner\",\"Elena Kountoura\",\"Maximilian Krah\",\"El\\u017cbieta Kruk\",\"Andrius Kubilius\",\"Alice Kuhnke\",\"Joachim Kuhs\",\"Stelios Kympouropoulos\",\"Ioannis Lagos\",\"Sergey Lagodinsky\",\"Aurore Lalucq\",\"Katrin Langensiepen\",\"H\\u00e9l\\u00e8ne Laporte\",\"Pierre Larrouturou\",\"Julie Lechanteux\",\"David Lega\",\"Maria-Manuel Leit\\u00e3o-Marques\",\"Sylvia Limmer\",\"Elena Lizzi\",\"Nathalie Loiseau\",\"Leopoldo L\\u00f3pez Gil\",\"C\\u00e9sar Luena\",\"Beno\\u00eet Lutgen\",\"Jaak Madison\",\"Cristina Maestre Mart\\u00edn De Almagro\",\"Pierfrancesco Majorino\",\"Au\\u0161ra Maldeikien\\u0117\",\"Adriana Maldonado L\\u00f3pez\",\"Claudiu Manda\",\"Thierry Mariani\",\"Erik Marquardt\",\"Margarida Marques\",\"Pedro Marques\",\"Predrag Fred Mati\\u0107\",\"Radka Maxov\\u00e1\",\"Beata Mazurek\",\"Liudas Ma\\u017eylis\",\"Vangelis Meimarakis\",\"Karen Melchior\",\"Dace Melb\\u0101rde\",\"Sven Mikser\",\"Giuseppe Milazzo\",\"Leszek Miller\",\"Silvia Modig\",\"Dolors Montserrat\",\"Marlene Mortler\",\"Dan-\\u0218tefan Motreanu\",\"And\\u017celika Anna Mo\\u017cd\\u017canowska\",\"Hannah Neumann\",\"Niklas Nienass\",\"Ville Niinist\\u00f6\",\"Lefteris Nikolaou-Alavanos\",\"Grace O'Sullivan\",\"Janina Ochojska\",\"Jan-Christoph Oetjen\",\"Juozas Olekas\",\"Philippe Olivier\",\"Alessandro Panza\",\"Jutta Paulus\",\"Kris Peeters\",\"Mauri Pekkarinen\",\"Mikul\\u00e1\\u0161 Peksa\",\"Tsvetelina Penkova\",\"Anne-Sophie Pelletier\",\"Sandra Pereira\",\"L\\u00eddia Pereira\",\"Kira Marie Peter-Hansen\",\"Sabrina Pignedoli\",\"Manu Pineda\",\"Maxette Pirbakas\",\"Giuliano Pisapia\",\"Drago\\u0219 P\\u00eeslaru\",\"Manuel Pizarro\",\"Jessica Polfj\\u00e4rd\",\"Peter Poll\\u00e1k\",\"Nicola Procaccini\",\"Miroslav Rada\\u010dovsk\\u00fd\",\"Samira Rafaela\",\"El\\u017cbieta Rafalska\",\"Luisa Regimenti\",\"Sira Rego\",\"Guido Reil\",\"Karlo Ressler\",\"Diana Riba I Giner\",\"Antonio Maria Rinaldi\",\"J\\u00e9r\\u00f4me Rivi\\u00e8re\",\"Franco Roberti\",\"Eugenia Rodr\\u00edguez Palop\",\"S\\u00e1ndor R\\u00f3nai\",\"Rob Rooken\",\"Daniela Rondinelli\",\"Caroline Roose\",\"Robert Roos\",\"Andr\\u00e9 Roug\\u00e9\",\"Bert-Jan Ruissen\",\"Dom\\u00e8nec Ruiz Devesa\",\"Bogdan Rzo\\u0144ca\",\"Nacho S\\u00e1nchez Amor\",\"Isabel Santos\",\"Silvia Sardone\",\"Mounir Satouri\",\"Andreas Schieder\",\"Simone Schmiedtbauer\",\"Christine Schneider\",\"Liesje Schreinemacher\",\"Ralf Seekatz\",\"St\\u00e9phane S\\u00e9journ\\u00e9\",\"Nico Semsrott\",\"Monica Semedo\",\"G\\u00fcnther Sidl\",\"Rados\\u0142aw Sikorski\",\"Michal \\u0160ime\\u010dka\",\"Sven Simon\",\"Ivan Vilibor Sin\\u010di\\u0107\",\"Sara Skyttedal\",\"Andrey Slabakov\",\"Massimiliano Smeriglio\",\"Susana Sol\\u00eds P\\u00e9rez\",\"Tomislav Sokol\",\"Sylwia Spurek\",\"Raffaele Stancanelli\",\"Nicolae \\u015etef\\u0103nu\\u021b\\u0103\",\"Jessica Stegrud\",\"Tineke Strik\",\"Ramona Strugariu\",\"Beata Szyd\\u0142o\",\"Annalisa Tardino\",\"Vera Tax\",\"Cristian Terhe\\u015f\",\"Hermann Tertsch\",\"Barbara Thaler\",\"Irene Tinagli\",\"Tomas Tob\\u00e9\",\"Grzegorz Tobiszowski\",\"Ir\\u00e8ne Tolleret\",\"Eugen Tomac\",\"Edina T\\u00f3th\",\"Marie Toussaint\",\"Isabella Tovaglieri\",\"V\\u00e9ronique Trillet-Lenoir\",\"L\\u00e1szl\\u00f3 Tr\\u00f3cs\\u00e1nyi\",\"Drago\\u015f Tudorache\",\"Mihai Tudose\",\"Milan Uhr\\u00edk\",\"Nils U\\u0161akovs\",\"Tom Vandendriessche\",\"Kim Van Sparrentak\",\"Marie-Pierre Vedrenne\",\"Idoia Villanueva Ruiz\",\"Nikolaj Villumsen\",\"Lor\\u00e1nt Vincze\",\"Petar Vitanov\",\"Viola Von Cramon-Taubadel\",\"Bettina Vollath\",\"Alexandr Vondra\",\"Lucia Vuolo\",\"Veronika Vrecionov\\u00e1\",\"Maria Walsh\",\"Mick Wallace\",\"Marion Walsmann\",\"J\\u00f6rgen Warborn\",\"Witold Jan Waszczykowski\",\"Charlie Weimers\",\"Pernille Weiss\",\"Sarah Wiener\",\"Michal Wiezik\",\"Angelika Winzig\",\"Isabel Wiseler-Lima\",\"Salima Yenbou\",\"Elena Yoncheva\",\"St\\u00e9phanie Yon-Courtin\",\"Chrysoula Zacharopoulou\",\"Anna Zalewska\",\"Stefania Zambelli\",\"Javier Zarzalejos\",\"Bernhard Zimniok\",\"Juan Ignacio Zoido \\u00c1lvarez\",\"Robert Haj\\u0161el\",\"Ond\\u0159ej Kova\\u0159\\u00edk\",\"Lara Wolters\",\"Emmanouil Fragkos\",\"Marianne Vind\",\"Isabel Carvalhais\",\"Sun\\u010dana Glavak\",\"Gheorghe-Vlad Nistor\",\"Marc Angel\",\"Antoni Com\\u00edn I Oliveres\",\"Carles Puigdemont I Casamaj\\u00f3\",\"Christian Sagartz\",\"Barry Andrews\",\"Alviina Alamets\\u00e4\",\"Ilana Cicurel\",\"Margarita De La Pisa Carri\\u00f3n\",\"Salvatore De Meo\",\"Claude Gruffat\",\"Sandro Gozi\",\"Miriam Lexmann\",\"Jean-Lin Lacapelle\",\"Romana Jerkovi\\u0107\",\"Nora Mebarek\",\"Clara Ponsat\\u00ed Obiols\",\"Marcos Ros Sempere\",\"Vincenzo Sofo\",\"Linea S\\u00f8gaard-Lidell\",\"Dominik Tarczy\\u0144ski\",\"Riho Terras\",\"Adri\\u00e1n V\\u00e1zquez L\\u00e1zara\",\"Bart Groothuis\",\"Dorien Rookmaker\",\"Chris Macmanus\",\"Manuela Ripa\"]},\"selected\":{\"id\":\"1045\"},\"selection_policy\":{\"id\":\"1044\"}},\"id\":\"1032\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"axis\":{\"id\":\"1012\"},\"ticker\":null,\"visible\":false},\"id\":\"1015\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1004\",\"type\":\"DataRange1d\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.3\"}};\n",
       "  var render_items = [{\"docid\":\"75fa2651-e4e4-4933-a9fb-c317f86f5160\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"b4244b0c-d299-4ae4-8240-6125ec3245cc\"}}];\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": "1003"
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     "output_type": "display_data"
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   ],
   "source": [
    "# create figure\n",
    "p = bpl.figure(\n",
    "    tools='hover,pan,reset,wheel_zoom,box_zoom',\n",
    "    active_scroll='wheel_zoom',\n",
    "    tooltips=[(col, f'@{col}') for col in hover_data],\n",
    "    background_fill_color='black',\n",
    ")\n",
    "\n",
    "p.scatter(\n",
    "    x='MCA0',\n",
    "    y='MCA1',\n",
    "    color={'field': 'group', 'transform': color_map},\n",
    "    legend_field='group',\n",
    "    size=5,\n",
    "    source=df_data,\n",
    ")\n",
    "\n",
    "p.grid.visible = False\n",
    "p.axis.visible = False\n",
    "\n",
    "bpl.show(p)"
   ]
  }
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