1st plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlopen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import output_notebook, show\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.models import ColumnDataSource, LabelSet, HoverTool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"bc4c926d-5e9a-4643-9f50-b6ba9f9f953a\">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",
       "      }\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(\"bc4c926d-5e9a-4643-9f50-b6ba9f9f953a\");\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) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"bc4c926d-5e9a-4643-9f50-b6ba9f9f953a\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'bc4c926d-5e9a-4643-9f50-b6ba9f9f953a' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\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(\"bc4c926d-5e9a-4643-9f50-b6ba9f9f953a\")).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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"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(\"bc4c926d-5e9a-4643-9f50-b6ba9f9f953a\");\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) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"bc4c926d-5e9a-4643-9f50-b6ba9f9f953a\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'bc4c926d-5e9a-4643-9f50-b6ba9f9f953a' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\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(\"bc4c926d-5e9a-4643-9f50-b6ba9f9f953a\")).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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "h0 = pd.read_stata(\"H0_cpy_all.dta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comnapes = urlopen(\"http://unstats.un.org/unsd/tradekb/Attachment439.aspx?AttachmentType=1\")\n",
    "\n",
    "comnames = pd.read_excel(\"UN Comtrade Commodity Classifications.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting columns to explore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "h0 = h0.loc[:,['year',\n",
    "               'exporter',\n",
    "               'commoditycode',\n",
    "               'export_value',\n",
    "               'population',\n",
    "               'rca',\n",
    "               'mcp',\n",
    "               'eci',\n",
    "               'pci',\n",
    "               'oppgain',\n",
    "               'distance',\n",
    "               'import_value']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The attributes of the dataset and its data types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6181542 entries, 0 to 6181541\n",
      "Data columns (total 12 columns):\n",
      "year             int16\n",
      "exporter         object\n",
      "commoditycode    object\n",
      "export_value     float32\n",
      "population       float32\n",
      "rca              float32\n",
      "mcp              int8\n",
      "eci              float32\n",
      "pci              float32\n",
      "oppgain          float32\n",
      "distance         float32\n",
      "import_value     float64\n",
      "dtypes: float32(7), float64(1), int16(1), int8(1), object(2)\n",
      "memory usage: 371.4+ MB\n"
     ]
    }
   ],
   "source": [
    "h0.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>export_value</th>\n",
       "      <th>population</th>\n",
       "      <th>rca</th>\n",
       "      <th>mcp</th>\n",
       "      <th>eci</th>\n",
       "      <th>pci</th>\n",
       "      <th>oppgain</th>\n",
       "      <th>distance</th>\n",
       "      <th>import_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "      <td>6.181542e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.005684e+03</td>\n",
       "      <td>3.868108e+07</td>\n",
       "      <td>2.866923e+07</td>\n",
       "      <td>2.128131e+00</td>\n",
       "      <td>1.070479e-01</td>\n",
       "      <td>-1.888869e-06</td>\n",
       "      <td>9.767827e-01</td>\n",
       "      <td>4.326566e-01</td>\n",
       "      <td>9.107793e-01</td>\n",
       "      <td>3.880376e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.302178e+00</td>\n",
       "      <td>8.094731e+08</td>\n",
       "      <td>1.179689e+08</td>\n",
       "      <td>4.633954e+02</td>\n",
       "      <td>3.091741e-01</td>\n",
       "      <td>9.968324e-01</td>\n",
       "      <td>1.957165e+00</td>\n",
       "      <td>5.355933e-01</td>\n",
       "      <td>1.113930e-01</td>\n",
       "      <td>7.738980e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.995000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.168124e+00</td>\n",
       "      <td>-5.713369e+00</td>\n",
       "      <td>-1.127975e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.842170e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.514231e-01</td>\n",
       "      <td>-4.648689e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.404849e-01</td>\n",
       "      <td>1.986000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.006000e+03</td>\n",
       "      <td>1.546000e+03</td>\n",
       "      <td>4.628400e+06</td>\n",
       "      <td>2.469272e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.020429e-02</td>\n",
       "      <td>1.101224e+00</td>\n",
       "      <td>3.929624e-01</td>\n",
       "      <td>9.314370e-01</td>\n",
       "      <td>1.694730e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.011000e+03</td>\n",
       "      <td>4.058420e+05</td>\n",
       "      <td>1.727914e+07</td>\n",
       "      <td>1.755035e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.239327e-01</td>\n",
       "      <td>2.465013e+00</td>\n",
       "      <td>8.403433e-01</td>\n",
       "      <td>9.688715e-01</td>\n",
       "      <td>3.171623e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>3.496526e+11</td>\n",
       "      <td>1.371220e+09</td>\n",
       "      <td>7.623724e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.935766e+00</td>\n",
       "      <td>7.687447e+00</td>\n",
       "      <td>2.116711e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.513117e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year  export_value    population           rca           mcp  \\\n",
       "count  6.181542e+06  6.181542e+06  6.181542e+06  6.181542e+06  6.181542e+06   \n",
       "mean   2.005684e+03  3.868108e+07  2.866923e+07  2.128131e+00  1.070479e-01   \n",
       "std    6.302178e+00  8.094731e+08  1.179689e+08  4.633954e+02  3.091741e-01   \n",
       "min    1.995000e+03  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    2.000000e+03  0.000000e+00  2.842170e+05  0.000000e+00  0.000000e+00   \n",
       "50%    2.006000e+03  1.546000e+03  4.628400e+06  2.469272e-03  0.000000e+00   \n",
       "75%    2.011000e+03  4.058420e+05  1.727914e+07  1.755035e-01  0.000000e+00   \n",
       "max    2.016000e+03  3.496526e+11  1.371220e+09  7.623724e+05  1.000000e+00   \n",
       "\n",
       "                eci           pci       oppgain      distance  import_value  \n",
       "count  6.181542e+06  6.181542e+06  6.181542e+06  6.181542e+06  6.181542e+06  \n",
       "mean  -1.888869e-06  9.767827e-01  4.326566e-01  9.107793e-01  3.880376e+07  \n",
       "std    9.968324e-01  1.957165e+00  5.355933e-01  1.113930e-01  7.738980e+08  \n",
       "min   -3.168124e+00 -5.713369e+00 -1.127975e+00  0.000000e+00  0.000000e+00  \n",
       "25%   -7.514231e-01 -4.648689e-01  0.000000e+00  8.404849e-01  1.986000e+03  \n",
       "50%    2.020429e-02  1.101224e+00  3.929624e-01  9.314370e-01  1.694730e+05  \n",
       "75%    7.239327e-01  2.465013e+00  8.403433e-01  9.688715e-01  3.171623e+06  \n",
       "max    2.935766e+00  7.687447e+00  2.116711e+00  1.000000e+00  3.513117e+11  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h0.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data contains more than 6M rows.\n",
    "\n",
    "Explanation for some of the data created by the Atlas researchers:\n",
    "- Distance ('distance'): \"The extent of a location's existing capabilities to make the product\" based on the products distance to current exports as measured by co-export probabilities.\n",
    "- Economic complexity index ('eci'): Country rank base on its export basket's diversification and complexity.\n",
    "- Opportunity gain ('oppgai'): How much a location could benefit by deveoping a particular product.\n",
    "- Product complexity index ('pci'): \"Ranks the diversity and sophistication of productio know-how required to produce a product\" based on other number of countries producing that product and their economic complexity.\n",
    "- Revealed comparative advantage ('rca'): Whether a country is an 'effective' exporter of a product (i.e. exports more than its 'fair share'). The bigger the value, the more important exporter the country is.\n",
    "- Country-Product connection ('mcp'): Marks whether the particular country export the specific product with an `rca` greater than 1. This also allows us to measure country diversity and product ubiquity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A row stands for an exporter country-commodity-year summary data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>exporter</th>\n",
       "      <th>commoditycode</th>\n",
       "      <th>export_value</th>\n",
       "      <th>population</th>\n",
       "      <th>rca</th>\n",
       "      <th>mcp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>493941</th>\n",
       "      <td>1996</td>\n",
       "      <td>TCD</td>\n",
       "      <td>4001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7241134.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.566722</td>\n",
       "      <td>-5.478979</td>\n",
       "      <td>-0.627303</td>\n",
       "      <td>0.980609</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2740191</th>\n",
       "      <td>2005</td>\n",
       "      <td>AND</td>\n",
       "      <td>5401</td>\n",
       "      <td>359.0</td>\n",
       "      <td>78867.0</td>\n",
       "      <td>0.025582</td>\n",
       "      <td>0</td>\n",
       "      <td>1.037972</td>\n",
       "      <td>0.024828</td>\n",
       "      <td>0.075600</td>\n",
       "      <td>0.880197</td>\n",
       "      <td>5323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360495</th>\n",
       "      <td>2003</td>\n",
       "      <td>OMN</td>\n",
       "      <td>0506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2385255.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.694384</td>\n",
       "      <td>-1.146205</td>\n",
       "      <td>0.034685</td>\n",
       "      <td>0.966367</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4736678</th>\n",
       "      <td>2011</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>5902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14386649.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.042156</td>\n",
       "      <td>1.042988</td>\n",
       "      <td>0.365409</td>\n",
       "      <td>0.939681</td>\n",
       "      <td>1025178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610377</th>\n",
       "      <td>1997</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0803</td>\n",
       "      <td>73483.0</td>\n",
       "      <td>5139835.0</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>0</td>\n",
       "      <td>2.041037</td>\n",
       "      <td>-2.548701</td>\n",
       "      <td>-0.357520</td>\n",
       "      <td>0.810972</td>\n",
       "      <td>66149618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2903546</th>\n",
       "      <td>2005</td>\n",
       "      <td>MNG</td>\n",
       "      <td>9002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2526446.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.135494</td>\n",
       "      <td>0.295085</td>\n",
       "      <td>0.496638</td>\n",
       "      <td>0.958825</td>\n",
       "      <td>21170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3113795</th>\n",
       "      <td>2006</td>\n",
       "      <td>GBR</td>\n",
       "      <td>1804</td>\n",
       "      <td>44147904.0</td>\n",
       "      <td>60846820.0</td>\n",
       "      <td>0.461570</td>\n",
       "      <td>0</td>\n",
       "      <td>1.741314</td>\n",
       "      <td>-2.642344</td>\n",
       "      <td>-0.371485</td>\n",
       "      <td>0.683411</td>\n",
       "      <td>197970506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3869976</th>\n",
       "      <td>2008</td>\n",
       "      <td>VUT</td>\n",
       "      <td>2205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225340.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.545303</td>\n",
       "      <td>-0.031700</td>\n",
       "      <td>0.210628</td>\n",
       "      <td>0.976525</td>\n",
       "      <td>451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3295605</th>\n",
       "      <td>2006</td>\n",
       "      <td>VNM</td>\n",
       "      <td>5509</td>\n",
       "      <td>105710176.0</td>\n",
       "      <td>83311200.0</td>\n",
       "      <td>6.867027</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.579564</td>\n",
       "      <td>-0.594978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.705866</td>\n",
       "      <td>31235915.0</td>\n",
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       "    <tr>\n",
       "      <th>4140546</th>\n",
       "      <td>2009</td>\n",
       "      <td>TWN</td>\n",
       "      <td>2516</td>\n",
       "      <td>1942731.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054488</td>\n",
       "      <td>0</td>\n",
       "      <td>1.586017</td>\n",
       "      <td>-1.071775</td>\n",
       "      <td>-0.334435</td>\n",
       "      <td>0.797147</td>\n",
       "      <td>50513093.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year exporter commoditycode  export_value  population       rca  mcp  \\\n",
       "493941   1996      TCD          4001           0.0   7241134.0  0.000000    0   \n",
       "2740191  2005      AND          5401         359.0     78867.0  0.025582    0   \n",
       "2360495  2003      OMN          0506           0.0   2385255.0  0.000000    0   \n",
       "4736678  2011      ZWE          5902           0.0  14386649.0  0.000000    0   \n",
       "610377   1997      FIN          0803       73483.0   5139835.0  0.001684    0   \n",
       "2903546  2005      MNG          9002           0.0   2526446.0  0.000000    0   \n",
       "3113795  2006      GBR          1804    44147904.0  60846820.0  0.461570    0   \n",
       "3869976  2008      VUT          2205           0.0    225340.0  0.000000    0   \n",
       "3295605  2006      VNM          5509   105710176.0  83311200.0  6.867027    1   \n",
       "4140546  2009      TWN          2516     1942731.0         0.0  0.054488    0   \n",
       "\n",
       "              eci       pci   oppgain  distance  import_value  \n",
       "493941  -0.566722 -5.478979 -0.627303  0.980609           0.0  \n",
       "2740191  1.037972  0.024828  0.075600  0.880197        5323.0  \n",
       "2360495 -0.694384 -1.146205  0.034685  0.966367           0.0  \n",
       "4736678 -1.042156  1.042988  0.365409  0.939681     1025178.0  \n",
       "610377   2.041037 -2.548701 -0.357520  0.810972    66149618.0  \n",
       "2903546 -1.135494  0.295085  0.496638  0.958825       21170.0  \n",
       "3113795  1.741314 -2.642344 -0.371485  0.683411   197970506.0  \n",
       "3869976 -1.545303 -0.031700  0.210628  0.976525         451.0  \n",
       "3295605 -0.579564 -0.594978  0.000000  0.705866    31235915.0  \n",
       "4140546  1.586017 -1.071775 -0.334435  0.797147    50513093.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h0.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>mcp</th>\n",
       "      <th>eci</th>\n",
       "      <th>pci</th>\n",
       "      <th>oppgain</th>\n",
       "      <th>distance</th>\n",
       "      <th>import_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79583</th>\n",
       "      <td>1995</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>175124.0</td>\n",
       "      <td>5107790.0</td>\n",
       "      <td>0.052195</td>\n",
       "      <td>0</td>\n",
       "      <td>2.248170</td>\n",
       "      <td>-0.864998</td>\n",
       "      <td>-0.463275</td>\n",
       "      <td>0.819706</td>\n",
       "      <td>1551880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345585</th>\n",
       "      <td>1996</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>141621.0</td>\n",
       "      <td>5124573.0</td>\n",
       "      <td>0.033379</td>\n",
       "      <td>0</td>\n",
       "      <td>2.330003</td>\n",
       "      <td>-1.000540</td>\n",
       "      <td>-0.466982</td>\n",
       "      <td>0.818454</td>\n",
       "      <td>1377644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610344</th>\n",
       "      <td>1997</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>27852.0</td>\n",
       "      <td>5139835.0</td>\n",
       "      <td>0.007341</td>\n",
       "      <td>0</td>\n",
       "      <td>2.041037</td>\n",
       "      <td>-0.922908</td>\n",
       "      <td>-0.524194</td>\n",
       "      <td>0.807045</td>\n",
       "      <td>1661241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872617</th>\n",
       "      <td>1998</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>28995.0</td>\n",
       "      <td>5153498.0</td>\n",
       "      <td>0.007679</td>\n",
       "      <td>0</td>\n",
       "      <td>2.236066</td>\n",
       "      <td>-1.020411</td>\n",
       "      <td>-0.530623</td>\n",
       "      <td>0.814752</td>\n",
       "      <td>2100349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133647</th>\n",
       "      <td>1999</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>11324.0</td>\n",
       "      <td>5165474.0</td>\n",
       "      <td>0.003380</td>\n",
       "      <td>0</td>\n",
       "      <td>2.233593</td>\n",
       "      <td>-1.154626</td>\n",
       "      <td>-0.487669</td>\n",
       "      <td>0.829371</td>\n",
       "      <td>2033317.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400892</th>\n",
       "      <td>2000</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>16580.0</td>\n",
       "      <td>5176209.0</td>\n",
       "      <td>0.005058</td>\n",
       "      <td>0</td>\n",
       "      <td>2.168545</td>\n",
       "      <td>-1.147362</td>\n",
       "      <td>-0.487177</td>\n",
       "      <td>0.827328</td>\n",
       "      <td>1962472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684296</th>\n",
       "      <td>2001</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>7285.0</td>\n",
       "      <td>5188008.0</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>0</td>\n",
       "      <td>2.060467</td>\n",
       "      <td>-0.934955</td>\n",
       "      <td>-0.414810</td>\n",
       "      <td>0.814347</td>\n",
       "      <td>1484333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967700</th>\n",
       "      <td>2002</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>40274.0</td>\n",
       "      <td>5200598.0</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0</td>\n",
       "      <td>2.132359</td>\n",
       "      <td>-0.951882</td>\n",
       "      <td>-0.533304</td>\n",
       "      <td>0.824454</td>\n",
       "      <td>2171525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251104</th>\n",
       "      <td>2003</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>4193.0</td>\n",
       "      <td>5213014.0</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0</td>\n",
       "      <td>2.236961</td>\n",
       "      <td>-0.948723</td>\n",
       "      <td>-0.560999</td>\n",
       "      <td>0.825958</td>\n",
       "      <td>3404777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2534508</th>\n",
       "      <td>2004</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>35397.0</td>\n",
       "      <td>5228172.0</td>\n",
       "      <td>0.005789</td>\n",
       "      <td>0</td>\n",
       "      <td>2.197879</td>\n",
       "      <td>-0.892554</td>\n",
       "      <td>-0.527219</td>\n",
       "      <td>0.830410</td>\n",
       "      <td>3634920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819155</th>\n",
       "      <td>2005</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>246290.0</td>\n",
       "      <td>5246096.0</td>\n",
       "      <td>0.051438</td>\n",
       "      <td>0</td>\n",
       "      <td>2.121061</td>\n",
       "      <td>-0.589868</td>\n",
       "      <td>-0.436269</td>\n",
       "      <td>0.799083</td>\n",
       "      <td>2786910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3104975</th>\n",
       "      <td>2006</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>131924.0</td>\n",
       "      <td>5266268.0</td>\n",
       "      <td>0.022621</td>\n",
       "      <td>0</td>\n",
       "      <td>1.938468</td>\n",
       "      <td>-0.935902</td>\n",
       "      <td>-0.466189</td>\n",
       "      <td>0.822722</td>\n",
       "      <td>2759685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3391877</th>\n",
       "      <td>2007</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>229578.0</td>\n",
       "      <td>5288720.0</td>\n",
       "      <td>0.037837</td>\n",
       "      <td>0</td>\n",
       "      <td>2.013963</td>\n",
       "      <td>-0.747688</td>\n",
       "      <td>-0.424995</td>\n",
       "      <td>0.809319</td>\n",
       "      <td>3446895.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3678709</th>\n",
       "      <td>2008</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>425565.0</td>\n",
       "      <td>5313399.0</td>\n",
       "      <td>0.051771</td>\n",
       "      <td>0</td>\n",
       "      <td>1.845256</td>\n",
       "      <td>-1.244461</td>\n",
       "      <td>-0.466939</td>\n",
       "      <td>0.805160</td>\n",
       "      <td>3951317.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3965380</th>\n",
       "      <td>2009</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>152205.0</td>\n",
       "      <td>5338871.0</td>\n",
       "      <td>0.022401</td>\n",
       "      <td>0</td>\n",
       "      <td>2.031959</td>\n",
       "      <td>-1.075058</td>\n",
       "      <td>-0.482462</td>\n",
       "      <td>0.802171</td>\n",
       "      <td>5087300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4250810</th>\n",
       "      <td>2010</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>117663.0</td>\n",
       "      <td>5363352.0</td>\n",
       "      <td>0.015949</td>\n",
       "      <td>0</td>\n",
       "      <td>1.980341</td>\n",
       "      <td>-1.148247</td>\n",
       "      <td>-0.554206</td>\n",
       "      <td>0.793536</td>\n",
       "      <td>6591699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4537481</th>\n",
       "      <td>2011</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>124962.0</td>\n",
       "      <td>5388272.0</td>\n",
       "      <td>0.015799</td>\n",
       "      <td>0</td>\n",
       "      <td>1.837719</td>\n",
       "      <td>-1.283972</td>\n",
       "      <td>-0.623035</td>\n",
       "      <td>0.788548</td>\n",
       "      <td>7951285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4824152</th>\n",
       "      <td>2012</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>35980.0</td>\n",
       "      <td>5413971.0</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0</td>\n",
       "      <td>1.691105</td>\n",
       "      <td>-0.895717</td>\n",
       "      <td>-0.499165</td>\n",
       "      <td>0.778146</td>\n",
       "      <td>7476927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5114546</th>\n",
       "      <td>2013</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>60214.0</td>\n",
       "      <td>5438972.0</td>\n",
       "      <td>0.006909</td>\n",
       "      <td>0</td>\n",
       "      <td>1.699357</td>\n",
       "      <td>-0.768054</td>\n",
       "      <td>-0.476912</td>\n",
       "      <td>0.775970</td>\n",
       "      <td>8539311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5404940</th>\n",
       "      <td>2014</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>38603.0</td>\n",
       "      <td>5461512.0</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>0</td>\n",
       "      <td>1.754628</td>\n",
       "      <td>-1.079929</td>\n",
       "      <td>-0.548020</td>\n",
       "      <td>0.784188</td>\n",
       "      <td>8911872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695262</th>\n",
       "      <td>2015</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>109341.0</td>\n",
       "      <td>5479531.0</td>\n",
       "      <td>0.011980</td>\n",
       "      <td>0</td>\n",
       "      <td>1.618825</td>\n",
       "      <td>-0.987223</td>\n",
       "      <td>-0.497193</td>\n",
       "      <td>0.784486</td>\n",
       "      <td>8894293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5983013</th>\n",
       "      <td>2016</td>\n",
       "      <td>FIN</td>\n",
       "      <td>0409</td>\n",
       "      <td>180391.0</td>\n",
       "      <td>5488275.0</td>\n",
       "      <td>0.022149</td>\n",
       "      <td>0</td>\n",
       "      <td>1.550109</td>\n",
       "      <td>-0.862451</td>\n",
       "      <td>-0.446703</td>\n",
       "      <td>0.780560</td>\n",
       "      <td>9603562.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year exporter commoditycode  export_value  population       rca  mcp  \\\n",
       "79583    1995      FIN          0409      175124.0   5107790.0  0.052195    0   \n",
       "345585   1996      FIN          0409      141621.0   5124573.0  0.033379    0   \n",
       "610344   1997      FIN          0409       27852.0   5139835.0  0.007341    0   \n",
       "872617   1998      FIN          0409       28995.0   5153498.0  0.007679    0   \n",
       "1133647  1999      FIN          0409       11324.0   5165474.0  0.003380    0   \n",
       "1400892  2000      FIN          0409       16580.0   5176209.0  0.005058    0   \n",
       "1684296  2001      FIN          0409        7285.0   5188008.0  0.002142    0   \n",
       "1967700  2002      FIN          0409       40274.0   5200598.0  0.007588    0   \n",
       "2251104  2003      FIN          0409        4193.0   5213014.0  0.000598    0   \n",
       "2534508  2004      FIN          0409       35397.0   5228172.0  0.005789    0   \n",
       "2819155  2005      FIN          0409      246290.0   5246096.0  0.051438    0   \n",
       "3104975  2006      FIN          0409      131924.0   5266268.0  0.022621    0   \n",
       "3391877  2007      FIN          0409      229578.0   5288720.0  0.037837    0   \n",
       "3678709  2008      FIN          0409      425565.0   5313399.0  0.051771    0   \n",
       "3965380  2009      FIN          0409      152205.0   5338871.0  0.022401    0   \n",
       "4250810  2010      FIN          0409      117663.0   5363352.0  0.015949    0   \n",
       "4537481  2011      FIN          0409      124962.0   5388272.0  0.015799    0   \n",
       "4824152  2012      FIN          0409       35980.0   5413971.0  0.004785    0   \n",
       "5114546  2013      FIN          0409       60214.0   5438972.0  0.006909    0   \n",
       "5404940  2014      FIN          0409       38603.0   5461512.0  0.004004    0   \n",
       "5695262  2015      FIN          0409      109341.0   5479531.0  0.011980    0   \n",
       "5983013  2016      FIN          0409      180391.0   5488275.0  0.022149    0   \n",
       "\n",
       "              eci       pci   oppgain  distance  import_value  \n",
       "79583    2.248170 -0.864998 -0.463275  0.819706     1551880.0  \n",
       "345585   2.330003 -1.000540 -0.466982  0.818454     1377644.0  \n",
       "610344   2.041037 -0.922908 -0.524194  0.807045     1661241.0  \n",
       "872617   2.236066 -1.020411 -0.530623  0.814752     2100349.0  \n",
       "1133647  2.233593 -1.154626 -0.487669  0.829371     2033317.0  \n",
       "1400892  2.168545 -1.147362 -0.487177  0.827328     1962472.0  \n",
       "1684296  2.060467 -0.934955 -0.414810  0.814347     1484333.0  \n",
       "1967700  2.132359 -0.951882 -0.533304  0.824454     2171525.0  \n",
       "2251104  2.236961 -0.948723 -0.560999  0.825958     3404777.0  \n",
       "2534508  2.197879 -0.892554 -0.527219  0.830410     3634920.0  \n",
       "2819155  2.121061 -0.589868 -0.436269  0.799083     2786910.0  \n",
       "3104975  1.938468 -0.935902 -0.466189  0.822722     2759685.0  \n",
       "3391877  2.013963 -0.747688 -0.424995  0.809319     3446895.0  \n",
       "3678709  1.845256 -1.244461 -0.466939  0.805160     3951317.0  \n",
       "3965380  2.031959 -1.075058 -0.482462  0.802171     5087300.0  \n",
       "4250810  1.980341 -1.148247 -0.554206  0.793536     6591699.0  \n",
       "4537481  1.837719 -1.283972 -0.623035  0.788548     7951285.0  \n",
       "4824152  1.691105 -0.895717 -0.499165  0.778146     7476927.0  \n",
       "5114546  1.699357 -0.768054 -0.476912  0.775970     8539311.0  \n",
       "5404940  1.754628 -1.079929 -0.548020  0.784188     8911872.0  \n",
       "5695262  1.618825 -0.987223 -0.497193  0.784486     8894293.0  \n",
       "5983013  1.550109 -0.862451 -0.446703  0.780560     9603562.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h0[(h0.commoditycode == '0409') & (h0.exporter == 'FIN')] # The annual summary data for Finland and 'natural honey'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a cleaned dataset and, therefore, there are no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>year</th>\n",
       "      <th>exporter</th>\n",
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       "      <th>export_value</th>\n",
       "      <th>population</th>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "      <td>6181542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year exporter commoditycode export_value population      rca  \\\n",
       "count   6181542  6181542       6181542      6181542    6181542  6181542   \n",
       "unique        1        1             1            1          1        1   \n",
       "top       False    False         False        False      False    False   \n",
       "freq    6181542  6181542       6181542      6181542    6181542  6181542   \n",
       "\n",
       "            mcp      eci      pci  oppgain distance import_value  \n",
       "count   6181542  6181542  6181542  6181542  6181542      6181542  \n",
       "unique        1        1        1        1        1            1  \n",
       "top       False    False    False    False    False        False  \n",
       "freq    6181542  6181542  6181542  6181542  6181542      6181542  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h0.isna().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attribute distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the distributions of the variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f689b296240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b583198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b70dc88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f689b2f2550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b240320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b240358>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f689b548c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b6582b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b7889b0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f689b7bf940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b1d59b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f689b176080>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f689b296f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h0.hist(figsize=(24, 24))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this is a summary dataset which also tries to be consistent, a number of  attributes contains lots of zero values (e.g. import/export values, rce and mcp). These rows, nevertheless, give information about the country's distance and possible opportunity gain in relation that particular products and therefore we do not drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>year</th>\n",
       "      <th>exporter</th>\n",
       "      <th>commoditycode</th>\n",
       "      <th>export_value</th>\n",
       "      <th>population</th>\n",
       "      <th>rca</th>\n",
       "      <th>mcp</th>\n",
       "      <th>eci</th>\n",
       "      <th>pci</th>\n",
       "      <th>oppgain</th>\n",
       "      <th>distance</th>\n",
       "      <th>import_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>1.310403</td>\n",
       "      <td>0.533991</td>\n",
       "      <td>0.987476</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>1.574714</td>\n",
       "      <td>0.411058</td>\n",
       "      <td>0.992502</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-0.187467</td>\n",
       "      <td>0.173810</td>\n",
       "      <td>0.988796</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>1.058983</td>\n",
       "      <td>0.378973</td>\n",
       "      <td>0.987044</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-1.286610</td>\n",
       "      <td>-0.379686</td>\n",
       "      <td>0.991840</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-0.693909</td>\n",
       "      <td>-0.016578</td>\n",
       "      <td>0.991208</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-0.479478</td>\n",
       "      <td>0.118833</td>\n",
       "      <td>0.993193</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-0.354880</td>\n",
       "      <td>0.150833</td>\n",
       "      <td>0.992543</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-1.986722</td>\n",
       "      <td>-0.483823</td>\n",
       "      <td>0.989773</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-2.529573</td>\n",
       "      <td>-0.573510</td>\n",
       "      <td>0.987096</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-2.370713</td>\n",
       "      <td>-0.566450</td>\n",
       "      <td>0.988905</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-0.894733</td>\n",
       "      <td>0.159218</td>\n",
       "      <td>0.990571</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-0.839016</td>\n",
       "      <td>-0.376120</td>\n",
       "      <td>0.989374</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-0.801242</td>\n",
       "      <td>-0.316387</td>\n",
       "      <td>0.990439</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>0.571152</td>\n",
       "      <td>0.186703</td>\n",
       "      <td>0.995693</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>0.172906</td>\n",
       "      <td>0.096287</td>\n",
       "      <td>0.988968</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-1.670636</td>\n",
       "      <td>-0.586423</td>\n",
       "      <td>0.989337</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-2.497406</td>\n",
       "      <td>-0.670671</td>\n",
       "      <td>0.988390</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-1.589862</td>\n",
       "      <td>-0.414900</td>\n",
       "      <td>0.989201</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>0.162322</td>\n",
       "      <td>0.057715</td>\n",
       "      <td>0.991403</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0814</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-1.033420</td>\n",
       "      <td>-0.152415</td>\n",
       "      <td>0.991076</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-2.351748</td>\n",
       "      <td>-0.177832</td>\n",
       "      <td>0.985668</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-1.997972</td>\n",
       "      <td>-0.705909</td>\n",
       "      <td>0.990607</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-2.784066</td>\n",
       "      <td>-0.271292</td>\n",
       "      <td>0.986342</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-4.000634</td>\n",
       "      <td>-0.515360</td>\n",
       "      <td>0.988577</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0907</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-4.066315</td>\n",
       "      <td>-0.482716</td>\n",
       "      <td>0.986222</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-3.449054</td>\n",
       "      <td>-0.637791</td>\n",
       "      <td>0.989201</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>-1.611782</td>\n",
       "      <td>-0.419733</td>\n",
       "      <td>0.988289</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>1001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>0.593483</td>\n",
       "      <td>0.246283</td>\n",
       "      <td>0.987287</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1995</td>\n",
       "      <td>ABW</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80324.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.219401</td>\n",
       "      <td>1.847445</td>\n",
       "      <td>0.629317</td>\n",
       "      <td>0.988292</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181180</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>6.520512</td>\n",
       "      <td>0.587297</td>\n",
       "      <td>0.979000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181182</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>2.619017</td>\n",
       "      <td>0.692352</td>\n",
       "      <td>0.971391</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181183</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>3.357888</td>\n",
       "      <td>0.545048</td>\n",
       "      <td>0.971906</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181188</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.805145</td>\n",
       "      <td>0.803709</td>\n",
       "      <td>0.941116</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181189</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>2.150766</td>\n",
       "      <td>0.816979</td>\n",
       "      <td>0.946181</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181211</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>-0.901468</td>\n",
       "      <td>0.112412</td>\n",
       "      <td>0.921056</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181212</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>2.766239</td>\n",
       "      <td>0.736787</td>\n",
       "      <td>0.965786</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181214</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>6.520512</td>\n",
       "      <td>0.587297</td>\n",
       "      <td>0.979000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181221</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>7906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>2.904048</td>\n",
       "      <td>0.675732</td>\n",
       "      <td>0.974431</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181224</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>0.932046</td>\n",
       "      <td>0.380629</td>\n",
       "      <td>0.933035</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181225</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.239528</td>\n",
       "      <td>0.777561</td>\n",
       "      <td>0.951901</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181226</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>2.226621</td>\n",
       "      <td>0.648447</td>\n",
       "      <td>0.967989</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181227</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>4.922174</td>\n",
       "      <td>0.183020</td>\n",
       "      <td>0.990215</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181228</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>4.538086</td>\n",
       "      <td>0.678716</td>\n",
       "      <td>0.967251</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181232</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.985815</td>\n",
       "      <td>1.018930</td>\n",
       "      <td>0.956690</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181236</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>0.944428</td>\n",
       "      <td>0.517497</td>\n",
       "      <td>0.938818</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181238</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.784700</td>\n",
       "      <td>0.724872</td>\n",
       "      <td>0.945000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181240</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>0.396482</td>\n",
       "      <td>0.389589</td>\n",
       "      <td>0.951141</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181241</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.451161</td>\n",
       "      <td>0.489575</td>\n",
       "      <td>0.939572</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181242</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>4.264668</td>\n",
       "      <td>1.140968</td>\n",
       "      <td>0.970118</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181406</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.911010</td>\n",
       "      <td>0.778884</td>\n",
       "      <td>0.948864</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181433</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>0.245823</td>\n",
       "      <td>0.157118</td>\n",
       "      <td>0.938794</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181435</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>0.359145</td>\n",
       "      <td>0.202458</td>\n",
       "      <td>0.943104</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181448</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>9009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.569144</td>\n",
       "      <td>0.608354</td>\n",
       "      <td>0.971313</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181483</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>9111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>0.895827</td>\n",
       "      <td>0.426697</td>\n",
       "      <td>0.965731</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181489</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>9203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>4.531119</td>\n",
       "      <td>0.725733</td>\n",
       "      <td>0.967687</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181490</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>9204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>0.826840</td>\n",
       "      <td>0.120654</td>\n",
       "      <td>0.966605</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181496</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>9301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.904771</td>\n",
       "      <td>0.653363</td>\n",
       "      <td>0.942991</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181509</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>9501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>1.996369</td>\n",
       "      <td>0.184221</td>\n",
       "      <td>0.977714</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181510</th>\n",
       "      <td>2016</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>9502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15429829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.918227</td>\n",
       "      <td>-0.561856</td>\n",
       "      <td>0.138823</td>\n",
       "      <td>0.964595</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1158122 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         year exporter commoditycode  export_value  population  rca  mcp  \\\n",
       "1        1995      ABW          0102           0.0     80324.0  0.0    0   \n",
       "2        1995      ABW          0103           0.0     80324.0  0.0    0   \n",
       "3        1995      ABW          0104           0.0     80324.0  0.0    0   \n",
       "10       1995      ABW          0205           0.0     80324.0  0.0    0   \n",
       "32       1995      ABW          0410           0.0     80324.0  0.0    0   \n",
       "33       1995      ABW          0501           0.0     80324.0  0.0    0   \n",
       "34       1995      ABW          0502           0.0     80324.0  0.0    0   \n",
       "35       1995      ABW          0503           0.0     80324.0  0.0    0   \n",
       "38       1995      ABW          0506           0.0     80324.0  0.0    0   \n",
       "39       1995      ABW          0507           0.0     80324.0  0.0    0   \n",
       "40       1995      ABW          0508           0.0     80324.0  0.0    0   \n",
       "41       1995      ABW          0509           0.0     80324.0  0.0    0   \n",
       "42       1995      ABW          0510           0.0     80324.0  0.0    0   \n",
       "43       1995      ABW          0511           0.0     80324.0  0.0    0   \n",
       "44       1995      ABW          0601           0.0     80324.0  0.0    0   \n",
       "54       1995      ABW          0707           0.0     80324.0  0.0    0   \n",
       "58       1995      ABW          0711           0.0     80324.0  0.0    0   \n",
       "65       1995      ABW          0804           0.0     80324.0  0.0    0   \n",
       "68       1995      ABW          0807           0.0     80324.0  0.0    0   \n",
       "73       1995      ABW          0812           0.0     80324.0  0.0    0   \n",
       "75       1995      ABW          0814           0.0     80324.0  0.0    0   \n",
       "78       1995      ABW          0903           0.0     80324.0  0.0    0   \n",
       "79       1995      ABW          0904           0.0     80324.0  0.0    0   \n",
       "80       1995      ABW          0905           0.0     80324.0  0.0    0   \n",
       "81       1995      ABW          0906           0.0     80324.0  0.0    0   \n",
       "82       1995      ABW          0907           0.0     80324.0  0.0    0   \n",
       "83       1995      ABW          0908           0.0     80324.0  0.0    0   \n",
       "84       1995      ABW          0909           0.0     80324.0  0.0    0   \n",
       "86       1995      ABW          1001           0.0     80324.0  0.0    0   \n",
       "87       1995      ABW          1002           0.0     80324.0  0.0    0   \n",
       "...       ...      ...           ...           ...         ...  ...  ...   \n",
       "6181180  2016      ZWE          7414           0.0  15429829.0  0.0    0   \n",
       "6181182  2016      ZWE          7416           0.0  15429829.0  0.0    0   \n",
       "6181183  2016      ZWE          7417           0.0  15429829.0  0.0    0   \n",
       "6181188  2016      ZWE          7503           0.0  15429829.0  0.0    0   \n",
       "6181189  2016      ZWE          7504           0.0  15429829.0  0.0    0   \n",
       "6181211  2016      ZWE          7802           0.0  15429829.0  0.0    0   \n",
       "6181212  2016      ZWE          7803           0.0  15429829.0  0.0    0   \n",
       "6181214  2016      ZWE          7805           0.0  15429829.0  0.0    0   \n",
       "6181221  2016      ZWE          7906           0.0  15429829.0  0.0    0   \n",
       "6181224  2016      ZWE          8002           0.0  15429829.0  0.0    0   \n",
       "6181225  2016      ZWE          8003           0.0  15429829.0  0.0    0   \n",
       "6181226  2016      ZWE          8004           0.0  15429829.0  0.0    0   \n",
       "6181227  2016      ZWE          8005           0.0  15429829.0  0.0    0   \n",
       "6181228  2016      ZWE          8006           0.0  15429829.0  0.0    0   \n",
       "6181232  2016      ZWE          8103           0.0  15429829.0  0.0    0   \n",
       "6181236  2016      ZWE          8107           0.0  15429829.0  0.0    0   \n",
       "6181238  2016      ZWE          8109           0.0  15429829.0  0.0    0   \n",
       "6181240  2016      ZWE          8111           0.0  15429829.0  0.0    0   \n",
       "6181241  2016      ZWE          8112           0.0  15429829.0  0.0    0   \n",
       "6181242  2016      ZWE          8113           0.0  15429829.0  0.0    0   \n",
       "6181406  2016      ZWE          8605           0.0  15429829.0  0.0    0   \n",
       "6181433  2016      ZWE          8902           0.0  15429829.0  0.0    0   \n",
       "6181435  2016      ZWE          8904           0.0  15429829.0  0.0    0   \n",
       "6181448  2016      ZWE          9009           0.0  15429829.0  0.0    0   \n",
       "6181483  2016      ZWE          9111           0.0  15429829.0  0.0    0   \n",
       "6181489  2016      ZWE          9203           0.0  15429829.0  0.0    0   \n",
       "6181490  2016      ZWE          9204           0.0  15429829.0  0.0    0   \n",
       "6181496  2016      ZWE          9301           0.0  15429829.0  0.0    0   \n",
       "6181509  2016      ZWE          9501           0.0  15429829.0  0.0    0   \n",
       "6181510  2016      ZWE          9502           0.0  15429829.0  0.0    0   \n",
       "\n",
       "              eci       pci   oppgain  distance  import_value  \n",
       "1       -0.219401  1.310403  0.533991  0.987476           0.0  \n",
       "2       -0.219401  1.574714  0.411058  0.992502           0.0  \n",
       "3       -0.219401 -0.187467  0.173810  0.988796           0.0  \n",
       "10      -0.219401  1.058983  0.378973  0.987044           0.0  \n",
       "32      -0.219401 -1.286610 -0.379686  0.991840           0.0  \n",
       "33      -0.219401 -0.693909 -0.016578  0.991208           0.0  \n",
       "34      -0.219401 -0.479478  0.118833  0.993193           0.0  \n",
       "35      -0.219401 -0.354880  0.150833  0.992543           0.0  \n",
       "38      -0.219401 -1.986722 -0.483823  0.989773           0.0  \n",
       "39      -0.219401 -2.529573 -0.573510  0.987096           0.0  \n",
       "40      -0.219401 -2.370713 -0.566450  0.988905           0.0  \n",
       "41      -0.219401 -0.894733  0.159218  0.990571           0.0  \n",
       "42      -0.219401 -0.839016 -0.376120  0.989374           0.0  \n",
       "43      -0.219401 -0.801242 -0.316387  0.990439           0.0  \n",
       "44      -0.219401  0.571152  0.186703  0.995693           0.0  \n",
       "54      -0.219401  0.172906  0.096287  0.988968           0.0  \n",
       "58      -0.219401 -1.670636 -0.586423  0.989337           0.0  \n",
       "65      -0.219401 -2.497406 -0.670671  0.988390           0.0  \n",
       "68      -0.219401 -1.589862 -0.414900  0.989201           0.0  \n",
       "73      -0.219401  0.162322  0.057715  0.991403           0.0  \n",
       "75      -0.219401 -1.033420 -0.152415  0.991076           0.0  \n",
       "78      -0.219401 -2.351748 -0.177832  0.985668           0.0  \n",
       "79      -0.219401 -1.997972 -0.705909  0.990607           0.0  \n",
       "80      -0.219401 -2.784066 -0.271292  0.986342           0.0  \n",
       "81      -0.219401 -4.000634 -0.515360  0.988577           0.0  \n",
       "82      -0.219401 -4.066315 -0.482716  0.986222           0.0  \n",
       "83      -0.219401 -3.449054 -0.637791  0.989201           0.0  \n",
       "84      -0.219401 -1.611782 -0.419733  0.988289           0.0  \n",
       "86      -0.219401  0.593483  0.246283  0.987287           0.0  \n",
       "87      -0.219401  1.847445  0.629317  0.988292           0.0  \n",
       "...           ...       ...       ...       ...           ...  \n",
       "6181180 -0.918227  6.520512  0.587297  0.979000           0.0  \n",
       "6181182 -0.918227  2.619017  0.692352  0.971391           0.0  \n",
       "6181183 -0.918227  3.357888  0.545048  0.971906           0.0  \n",
       "6181188 -0.918227  1.805145  0.803709  0.941116           0.0  \n",
       "6181189 -0.918227  2.150766  0.816979  0.946181           0.0  \n",
       "6181211 -0.918227 -0.901468  0.112412  0.921056           0.0  \n",
       "6181212 -0.918227  2.766239  0.736787  0.965786           0.0  \n",
       "6181214 -0.918227  6.520512  0.587297  0.979000           0.0  \n",
       "6181221 -0.918227  2.904048  0.675732  0.974431           0.0  \n",
       "6181224 -0.918227  0.932046  0.380629  0.933035           0.0  \n",
       "6181225 -0.918227  1.239528  0.777561  0.951901           0.0  \n",
       "6181226 -0.918227  2.226621  0.648447  0.967989           0.0  \n",
       "6181227 -0.918227  4.922174  0.183020  0.990215           0.0  \n",
       "6181228 -0.918227  4.538086  0.678716  0.967251           0.0  \n",
       "6181232 -0.918227  1.985815  1.018930  0.956690           0.0  \n",
       "6181236 -0.918227  0.944428  0.517497  0.938818           0.0  \n",
       "6181238 -0.918227  1.784700  0.724872  0.945000           0.0  \n",
       "6181240 -0.918227  0.396482  0.389589  0.951141           0.0  \n",
       "6181241 -0.918227  1.451161  0.489575  0.939572           0.0  \n",
       "6181242 -0.918227  4.264668  1.140968  0.970118           0.0  \n",
       "6181406 -0.918227  1.911010  0.778884  0.948864           0.0  \n",
       "6181433 -0.918227  0.245823  0.157118  0.938794           0.0  \n",
       "6181435 -0.918227  0.359145  0.202458  0.943104           0.0  \n",
       "6181448 -0.918227  1.569144  0.608354  0.971313           0.0  \n",
       "6181483 -0.918227  0.895827  0.426697  0.965731           0.0  \n",
       "6181489 -0.918227  4.531119  0.725733  0.967687           0.0  \n",
       "6181490 -0.918227  0.826840  0.120654  0.966605           0.0  \n",
       "6181496 -0.918227  1.904771  0.653363  0.942991           0.0  \n",
       "6181509 -0.918227  1.996369  0.184221  0.977714           0.0  \n",
       "6181510 -0.918227 -0.561856  0.138823  0.964595           0.0  \n",
       "\n",
       "[1158122 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h0[(h0.export_value == 0) & (h0.import_value == 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "comtoexp = h0[(h0.year == 2016)].drop(columns=['year', 'population']).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\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>exporter</th>\n",
       "      <th>commoditycode</th>\n",
       "      <th>export_value</th>\n",
       "      <th>rca</th>\n",
       "      <th>mcp</th>\n",
       "      <th>eci</th>\n",
       "      <th>pci</th>\n",
       "      <th>oppgain</th>\n",
       "      <th>distance</th>\n",
       "      <th>import_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6015309</th>\n",
       "      <td>IDN</td>\n",
       "      <td>0714</td>\n",
       "      <td>13483019.0</td>\n",
       "      <td>0.491027</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.306436</td>\n",
       "      <td>-3.686064</td>\n",
       "      <td>-0.262815</td>\n",
       "      <td>0.652393</td>\n",
       "      <td>2680025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6015857</th>\n",
       "      <td>IDN</td>\n",
       "      <td>5303</td>\n",
       "      <td>39514.0</td>\n",
       "      <td>0.014981</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.306436</td>\n",
       "      <td>-4.248095</td>\n",
       "      <td>-0.200430</td>\n",
       "      <td>0.665689</td>\n",
       "      <td>1065658.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6015858</th>\n",
       "      <td>IDN</td>\n",
       "      <td>5304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.306436</td>\n",
       "      <td>-3.626167</td>\n",
       "      <td>-0.115842</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6015864</th>\n",
       "      <td>IDN</td>\n",
       "      <td>5310</td>\n",
       "      <td>140443.0</td>\n",
       "      <td>0.070955</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.306436</td>\n",
       "      <td>-3.800158</td>\n",
       "      <td>-0.182509</td>\n",
       "      <td>0.677161</td>\n",
       "      <td>705712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6015312</th>\n",
       "      <td>IDN</td>\n",
       "      <td>0803</td>\n",
       "      <td>9774218.0</td>\n",
       "      <td>0.067245</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.306436</td>\n",
       "      <td>-3.399886</td>\n",
       "      <td>-0.170962</td>\n",
       "      <td>0.686601</td>\n",
       "      <td>188689.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        exporter commoditycode  export_value       rca  mcp       eci  \\\n",
       "6015309      IDN          0714    13483019.0  0.491027    0 -0.306436   \n",
       "6015857      IDN          5303       39514.0  0.014981    0 -0.306436   \n",
       "6015858      IDN          5304           0.0  0.000000    0 -0.306436   \n",
       "6015864      IDN          5310      140443.0  0.070955    0 -0.306436   \n",
       "6015312      IDN          0803     9774218.0  0.067245    0 -0.306436   \n",
       "\n",
       "              pci   oppgain  distance  import_value  \n",
       "6015309 -3.686064 -0.262815  0.652393     2680025.0  \n",
       "6015857 -4.248095 -0.200430  0.665689     1065658.0  \n",
       "6015858 -3.626167 -0.115842  0.675000           0.0  \n",
       "6015864 -3.800158 -0.182509  0.677161      705712.0  \n",
       "6015312 -3.399886 -0.170962  0.686601      188689.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = comtoexp[(comtoexp.exporter == 'IDN') & (comtoexp.mcp == 0)]\n",
    "query.sort_values(by='distance').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Classification</th>\n",
       "      <th>Code</th>\n",
       "      <th>Description</th>\n",
       "      <th>Code Parent</th>\n",
       "      <th>Level</th>\n",
       "      <th>isLeaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BE</td>\n",
       "      <td>1</td>\n",
       "      <td>Food and beverages</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>11</td>\n",
       "      <td>Food and beverages, primary</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>111</td>\n",
       "      <td>Food and beverages, primary, mainly for industry</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BE</td>\n",
       "      <td>112</td>\n",
       "      <td>Food and beverages, primary, mainly for househ...</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE</td>\n",
       "      <td>12</td>\n",
       "      <td>Food and beverages, processed</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Classification Code                                        Description  \\\n",
       "0             BE    1                                 Food and beverages   \n",
       "1             BE   11                        Food and beverages, primary   \n",
       "2             BE  111   Food and beverages, primary, mainly for industry   \n",
       "3             BE  112  Food and beverages, primary, mainly for househ...   \n",
       "4             BE   12                      Food and beverages, processed   \n",
       "\n",
       "  Code Parent  Level  isLeaf  \n",
       "0       TOTAL      1       0  \n",
       "1           1      2       0  \n",
       "2          11      3       1  \n",
       "3          11      3       1  \n",
       "4           1      2       0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comnames.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "comnames = comnames[(comnames.Classification == 'H5')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = pd.merge(\n",
    "                comnames[comnames.isLeaf ==  0].loc[:,['Code',\n",
    "                                                       'Description']],\n",
    "                query.loc[:,['commoditycode',\n",
    "                             'mcp',\n",
    "                             'distance',\n",
    "                             'pci',\n",
    "                             'oppgain']], \n",
    "    \n",
    "                left_on='Code',\n",
    "                right_on='commoditycode'\n",
    "    \n",
    "                ).drop(columns=['Code', 'commoditycode'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>mcp</th>\n",
       "      <th>distance</th>\n",
       "      <th>pci</th>\n",
       "      <th>oppgain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Manioc, arrowroot, salep, Jerusalem artichokes...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.652393</td>\n",
       "      <td>-3.686064</td>\n",
       "      <td>-0.262815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Jute and other textile bast fibres (not flax, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.665689</td>\n",
       "      <td>-4.248095</td>\n",
       "      <td>-0.200430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>Woven fabrics of jute, other textile bast fibr...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.677161</td>\n",
       "      <td>-3.800158</td>\n",
       "      <td>-0.182509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Bananas, including plantains; fresh or dried</td>\n",
       "      <td>0</td>\n",
       "      <td>0.686601</td>\n",
       "      <td>-3.399886</td>\n",
       "      <td>-0.170962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>Underpants, briefs, nightshirts, pyjamas, bath...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.695909</td>\n",
       "      <td>-1.540036</td>\n",
       "      <td>-0.018677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Petroleum oils and oils obtained from bitumino...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.696705</td>\n",
       "      <td>-3.855992</td>\n",
       "      <td>-0.172183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Hats and other headgear; knitted or crocheted,...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.701737</td>\n",
       "      <td>-2.557624</td>\n",
       "      <td>0.037690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dates, figs, pineapples, avocados, guavas, man...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.706207</td>\n",
       "      <td>-2.575540</td>\n",
       "      <td>-0.234240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Ores and concentrates; n.e.c. in heading no. 2601</td>\n",
       "      <td>0</td>\n",
       "      <td>0.708489</td>\n",
       "      <td>-3.100494</td>\n",
       "      <td>-0.213986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Cane or beet sugar and chemically pure sucrose...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.712296</td>\n",
       "      <td>-2.123256</td>\n",
       "      <td>-0.174438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Yarn of jute or of other textile bast fibres o...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.712508</td>\n",
       "      <td>-2.849410</td>\n",
       "      <td>-0.021837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Cocoa beans; whole or broken, raw or roasted</td>\n",
       "      <td>0</td>\n",
       "      <td>0.713871</td>\n",
       "      <td>-3.978386</td>\n",
       "      <td>-0.068918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Bed linen, table linen, toilet linen and kitch...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.719002</td>\n",
       "      <td>-2.239327</td>\n",
       "      <td>-0.079319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>Gold (including gold plated with platinum) unw...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720216</td>\n",
       "      <td>-3.518152</td>\n",
       "      <td>-0.221922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Aluminium ores and concentrates</td>\n",
       "      <td>0</td>\n",
       "      <td>0.722630</td>\n",
       "      <td>-2.487854</td>\n",
       "      <td>-0.090249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Vegetables, leguminous; shelled, whether or no...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.725538</td>\n",
       "      <td>-2.398730</td>\n",
       "      <td>-0.233350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Portland cement, aluminous cement (ciment fond...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.726017</td>\n",
       "      <td>-1.572682</td>\n",
       "      <td>-0.109451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Tanned or crust skins of sheep and lambs, with...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.729556</td>\n",
       "      <td>-2.687297</td>\n",
       "      <td>-0.184050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Chromium ores and concentrates</td>\n",
       "      <td>0</td>\n",
       "      <td>0.729639</td>\n",
       "      <td>-2.619961</td>\n",
       "      <td>-0.160982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Seeds of anise, badian, fennel, coriander, cum...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.731101</td>\n",
       "      <td>-1.427963</td>\n",
       "      <td>-0.123540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Textiles; made up articles n.e.c. in chapter 6...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.732166</td>\n",
       "      <td>-0.768375</td>\n",
       "      <td>0.134883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Wheat or meslin flour</td>\n",
       "      <td>0</td>\n",
       "      <td>0.733392</td>\n",
       "      <td>-1.081669</td>\n",
       "      <td>-0.060603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Manganese ores and concentrates, including fer...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.733573</td>\n",
       "      <td>-3.161647</td>\n",
       "      <td>-0.121734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Rice</td>\n",
       "      <td>0</td>\n",
       "      <td>0.733656</td>\n",
       "      <td>-1.634416</td>\n",
       "      <td>-0.017067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Raw hides and skins n.e.c in headings no. 4101...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.734457</td>\n",
       "      <td>-2.865018</td>\n",
       "      <td>-0.018379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>Twine, cordage, ropes and cables, whether or n...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.735464</td>\n",
       "      <td>-0.601092</td>\n",
       "      <td>0.174418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Vegetables, dried; whole, cut, sliced, broken ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.735684</td>\n",
       "      <td>-1.622323</td>\n",
       "      <td>-0.064108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Fruit, fresh; n.e.c. in chapter 08</td>\n",
       "      <td>0</td>\n",
       "      <td>0.737276</td>\n",
       "      <td>-2.335409</td>\n",
       "      <td>-0.109024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Melons (including watermelons) and papaws (pap...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.737543</td>\n",
       "      <td>-1.924755</td>\n",
       "      <td>-0.106647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>Antimony; articles thereof, including waste an...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.738693</td>\n",
       "      <td>-1.506133</td>\n",
       "      <td>0.121194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Description  mcp  distance  \\\n",
       "48   Manioc, arrowroot, salep, Jerusalem artichokes...    0  0.652393   \n",
       "457  Jute and other textile bast fibres (not flax, ...    0  0.665689   \n",
       "462  Woven fabrics of jute, other textile bast fibr...    0  0.677161   \n",
       "49        Bananas, including plantains; fresh or dried    0  0.686601   \n",
       "505  Underpants, briefs, nightshirts, pyjamas, bath...    0  0.695909   \n",
       "186  Petroleum oils and oils obtained from bitumino...    0  0.696705   \n",
       "522  Hats and other headgear; knitted or crocheted,...    0  0.701737   \n",
       "50   Dates, figs, pineapples, avocados, guavas, man...    0  0.706207   \n",
       "178  Ores and concentrates; n.e.c. in heading no. 2601    0  0.708489   \n",
       "106  Cane or beet sugar and chemically pure sucrose...    0  0.712296   \n",
       "459  Yarn of jute or of other textile bast fibres o...    0  0.712508   \n",
       "108       Cocoa beans; whole or broken, raw or roasted    0  0.713871   \n",
       "513  Bed linen, table linen, toilet linen and kitch...    0  0.719002   \n",
       "579  Gold (including gold plated with platinum) unw...    0  0.720216   \n",
       "170                    Aluminium ores and concentrates    0  0.722630   \n",
       "47   Vegetables, leguminous; shelled, whether or no...    0  0.725538   \n",
       "159  Portland cement, aluminous cement (ciment fond...    0  0.726017   \n",
       "378  Tanned or crust skins of sheep and lambs, with...    0  0.729556   \n",
       "173                     Chromium ores and concentrates    0  0.729639   \n",
       "62   Seeds of anise, badian, fennel, coriander, cum...    0  0.731101   \n",
       "516  Textiles; made up articles n.e.c. in chapter 6...    0  0.732166   \n",
       "71                               Wheat or meslin flour    0  0.733392   \n",
       "167  Manganese ores and concentrates, including fer...    0  0.733573   \n",
       "68                                                Rice    0  0.733656   \n",
       "376  Raw hides and skins n.e.c in headings no. 4101...    0  0.734457   \n",
       "479  Twine, cordage, ropes and cables, whether or n...    0  0.735464   \n",
       "46   Vegetables, dried; whole, cut, sliced, broken ...    0  0.735684   \n",
       "56                  Fruit, fresh; n.e.c. in chapter 08    0  0.737276   \n",
       "53   Melons (including watermelons) and papaws (pap...    0  0.737543   \n",
       "689  Antimony; articles thereof, including waste an...    0  0.738693   \n",
       "\n",
       "          pci   oppgain  \n",
       "48  -3.686064 -0.262815  \n",
       "457 -4.248095 -0.200430  \n",
       "462 -3.800158 -0.182509  \n",
       "49  -3.399886 -0.170962  \n",
       "505 -1.540036 -0.018677  \n",
       "186 -3.855992 -0.172183  \n",
       "522 -2.557624  0.037690  \n",
       "50  -2.575540 -0.234240  \n",
       "178 -3.100494 -0.213986  \n",
       "106 -2.123256 -0.174438  \n",
       "459 -2.849410 -0.021837  \n",
       "108 -3.978386 -0.068918  \n",
       "513 -2.239327 -0.079319  \n",
       "579 -3.518152 -0.221922  \n",
       "170 -2.487854 -0.090249  \n",
       "47  -2.398730 -0.233350  \n",
       "159 -1.572682 -0.109451  \n",
       "378 -2.687297 -0.184050  \n",
       "173 -2.619961 -0.160982  \n",
       "62  -1.427963 -0.123540  \n",
       "516 -0.768375  0.134883  \n",
       "71  -1.081669 -0.060603  \n",
       "167 -3.161647 -0.121734  \n",
       "68  -1.634416 -0.017067  \n",
       "376 -2.865018 -0.018379  \n",
       "479 -0.601092  0.174418  \n",
       "46  -1.622323 -0.064108  \n",
       "56  -2.335409 -0.109024  \n",
       "53  -1.924755 -0.106647  \n",
       "689 -1.506133  0.121194  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query.sort_values(by='distance').head(30)\n",
    "query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rendering the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = ColumnDataSource(data = dict(names = list(query.Description),\n",
    "                                      opg = query.oppgain,                                      \n",
    "                                      pci = query.pci, \n",
    "                                      dist=query.distance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "hover = HoverTool(\n",
    "    tooltips=[\n",
    "        (\"Desc\", \"@names\"),\n",
    "        (\"PCI\", \"$x\"),\n",
    "        (\"Oppgain\", \"$y\"),\n",
    "        (\"Distance\", \"@dist\")\n",
    "],\n",
    "    formatters={\n",
    "        '@names' : 'printf',\n",
    "    },)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = figure(plot_width = 600,\n",
    "           plot_height = 600,\n",
    "           tools=['pan', hover, 'zoom_out', 'zoom_in', 'reset'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 26,
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    "p.scatter(x = 'pci',\n",
    "          y = 'opg',\n",
    "          size = 15,\n",
    "          color = 'indigo',\n",
    "          alpha = 0.6,\n",
    "          source = source)"
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    "p.xaxis.axis_label = 'Product Complexity Index'\n",
    "p.yaxis.axis_label = 'Opportunity Gain'"
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    "## Plot 1\n",
    "The graph can help users to identify opportunities for production within a country.\n",
    "\n",
    "It shows the least 30 least 'distant' but yet not produced prodcuts in a country (in this case, Indonesia). That is, starting to produce them would be relatively easy (in relation to the whole product universe), but nonetheless the country is not an 'effective' exporter of them.\n",
    "\n",
    "Axes:\n",
    "* X: 'Product complexity index': An index showing the relative complexity of that particular product as based on the diversity of countries producing it and the ubiquity of countries these countries make. That is, products with high PCI are typically produced by only a few countries with a wide procuction line.\n",
    "* Y: 'Opportunity gain': The degree with which new opporunities emerge to more complex countries when producing that particular product.\n",
    "\n",
    "Accordingly, the graph can help users to see those product types which are easy to produce, not produced within the country but can lead to novel valuable skills and know-how."
   ]
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