Step_5_Visualisation_Bokeh.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Visualisation with Bokeh\n",
    "This is just a simple piece of visualisation code to display the text data which has been processed in other notebooks.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Step 5:  Creating visualisation\n",
      "2018-06-12 15:31:49.053012\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('------------------------------------------------------')\n",
    "print('Step 5:  Creating visualisation')\n",
    "from datetime import datetime as dt\n",
    "print(dt.now())\n",
    "print('------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from bokeh.plotting import *\n",
    "# from bokeh.models import ColumnDataSource, OpenURL, TapTool\n",
    "from bokeh.models import *\n",
    "from bokeh.io import output_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "from config import Config as c\n",
    "tfidf_filename = c.tfidf_filename\n",
    "bar_filename = c.bar_filename\n",
    "plot_title = c.title\n",
    "doi_datapath = c.dois_pkl\n",
    "working_data = c.working_data\n",
    "set_name = c.set_name\n",
    "# test_set = c.test_set\n",
    "n_clusters = c.n_clusters\n",
    "journal_of_interest = c.joi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# global variable\n",
    "dois = pickle.load(open(doi_datapath,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DI</th>\n",
       "      <th>PY</th>\n",
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       "      <th>...</th>\n",
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       "      <th>highly_cited_5</th>\n",
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       "      <th>relative_citation_ratio</th>\n",
       "      <th>times_cited</th>\n",
       "      <th>Citations</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Cluster_no</th>\n",
       "      <th>Article_kws</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>10.1016/j.ijedudev.2012.08.005</td>\n",
       "      <td>2013</td>\n",
       "      <td>Promoting access to education for disabled chi...</td>\n",
       "      <td>Poor people are most likely to be disabled, an...</td>\n",
       "      <td>Promoting access to education for disabled chi...</td>\n",
       "      <td>Croft, A</td>\n",
       "      <td>a.m.croft@sussex.ac.uk</td>\n",
       "      <td>Croft, Alison</td>\n",
       "      <td>INTERNATIONAL JOURNAL OF EDUCATIONAL DEVELOPMENT</td>\n",
       "      <td>Education &amp; Educational Research</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>['disabl', 'children', 'vaccin']</td>\n",
       "      <td>9</td>\n",
       "      <td>['disabl', 'educ', 'inclus']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>10.1111/cch.12165</td>\n",
       "      <td>2015</td>\n",
       "      <td>Definitions of sleeplessness in children with ...</td>\n",
       "      <td>BackgroundSleep disturbances are common in chi...</td>\n",
       "      <td>Definitions of sleeplessness in children with ...</td>\n",
       "      <td>Montgomery, P; Wiggs, L</td>\n",
       "      <td>paul.montgomery@spi.ox.ac.uk</td>\n",
       "      <td>Montgomery, P.; Wiggs, L.</td>\n",
       "      <td>CHILD CARE HEALTH AND DEVELOPMENT</td>\n",
       "      <td>Psychology; Pediatrics</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>0.18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>['sleep', 'adhd', 'problem']</td>\n",
       "      <td>7</td>\n",
       "      <td>['sleepless', 'definit', 'adhd']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>10.1111/cch.12050</td>\n",
       "      <td>2013</td>\n",
       "      <td>The youth report version of the Child and Adol...</td>\n",
       "      <td>Objective The Child and Adolescent Scale of Pa...</td>\n",
       "      <td>The youth report version of the Child and Adol...</td>\n",
       "      <td>McDougall, J; Bedell, G; Wright, V</td>\n",
       "      <td>janette.mcdougall@tvcc.on.ca</td>\n",
       "      <td>McDougall, J.; Bedell, G.; Wright, V.</td>\n",
       "      <td>CHILD CARE HEALTH AND DEVELOPMENT</td>\n",
       "      <td>Psychology; Pediatrics</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>['motor', 'children', 'development']</td>\n",
       "      <td>3</td>\n",
       "      <td>['casp', 'youth', 'report']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>10.1177/1367493511403953</td>\n",
       "      <td>2011</td>\n",
       "      <td>Polyunsaturated fatty acids, cognition and lit...</td>\n",
       "      <td>Suboptimal omega-3 polyunsaturated fatty acid ...</td>\n",
       "      <td>Polyunsaturated fatty acids, cognition and lit...</td>\n",
       "      <td>Milte, CM; Sinn, N; Buckley, JD; Coates, AM; Y...</td>\n",
       "      <td>natalie.sinn@unisa.edu.au</td>\n",
       "      <td>Milte, Catherine M.; Sinn, Natalie; Buckley, J...</td>\n",
       "      <td>JOURNAL OF CHILD HEALTH CARE</td>\n",
       "      <td>Nursing; Pediatrics</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>1.49</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>['sleep', 'adhd', 'problem']</td>\n",
       "      <td>7</td>\n",
       "      <td>['pufa', 'beta', 'acid']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>10.1111/cch.12061</td>\n",
       "      <td>2014</td>\n",
       "      <td>Early parental physical punishment and emotion...</td>\n",
       "      <td>ObjectiveTo determine whether there is an asso...</td>\n",
       "      <td>Early parental physical punishment and emotion...</td>\n",
       "      <td>Scott, S; Lewsey, J; Thompson, L; Wilson, P</td>\n",
       "      <td>sonyascott@nhs.net</td>\n",
       "      <td>Scott, S.; Lewsey, J.; Thompson, L.; Wilson, P.</td>\n",
       "      <td>CHILD CARE HEALTH AND DEVELOPMENT</td>\n",
       "      <td>Psychology; Pediatrics</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>['depress', 'infant', 'behaviour']</td>\n",
       "      <td>15</td>\n",
       "      <td>['smack', 'caregiv', 'sdq']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                                  DI    PY  \\\n",
       "867   10.1016/j.ijedudev.2012.08.005  2013   \n",
       "196                10.1111/cch.12165  2015   \n",
       "323                10.1111/cch.12050  2013   \n",
       "1244        10.1177/1367493511403953  2011   \n",
       "242                10.1111/cch.12061  2014   \n",
       "\n",
       "                                                     TI  \\\n",
       "867   Promoting access to education for disabled chi...   \n",
       "196   Definitions of sleeplessness in children with ...   \n",
       "323   The youth report version of the Child and Adol...   \n",
       "1244  Polyunsaturated fatty acids, cognition and lit...   \n",
       "242   Early parental physical punishment and emotion...   \n",
       "\n",
       "                                                     AB  \\\n",
       "867   Poor people are most likely to be disabled, an...   \n",
       "196   BackgroundSleep disturbances are common in chi...   \n",
       "323   Objective The Child and Adolescent Scale of Pa...   \n",
       "1244  Suboptimal omega-3 polyunsaturated fatty acid ...   \n",
       "242   ObjectiveTo determine whether there is an asso...   \n",
       "\n",
       "                                                     WD  \\\n",
       "867   Promoting access to education for disabled chi...   \n",
       "196   Definitions of sleeplessness in children with ...   \n",
       "323   The youth report version of the Child and Adol...   \n",
       "1244  Polyunsaturated fatty acids, cognition and lit...   \n",
       "242   Early parental physical punishment and emotion...   \n",
       "\n",
       "                                                     AU  \\\n",
       "867                                            Croft, A   \n",
       "196                             Montgomery, P; Wiggs, L   \n",
       "323                  McDougall, J; Bedell, G; Wright, V   \n",
       "1244  Milte, CM; Sinn, N; Buckley, JD; Coates, AM; Y...   \n",
       "242         Scott, S; Lewsey, J; Thompson, L; Wilson, P   \n",
       "\n",
       "                                EM  \\\n",
       "867         a.m.croft@sussex.ac.uk   \n",
       "196   paul.montgomery@spi.ox.ac.uk   \n",
       "323   janette.mcdougall@tvcc.on.ca   \n",
       "1244     natalie.sinn@unisa.edu.au   \n",
       "242             sonyascott@nhs.net   \n",
       "\n",
       "                                                     AF  \\\n",
       "867                                       Croft, Alison   \n",
       "196                           Montgomery, P.; Wiggs, L.   \n",
       "323               McDougall, J.; Bedell, G.; Wright, V.   \n",
       "1244  Milte, Catherine M.; Sinn, Natalie; Buckley, J...   \n",
       "242     Scott, S.; Lewsey, J.; Thompson, L.; Wilson, P.   \n",
       "\n",
       "                                                    SO  \\\n",
       "867   INTERNATIONAL JOURNAL OF EDUCATIONAL DEVELOPMENT   \n",
       "196                  CHILD CARE HEALTH AND DEVELOPMENT   \n",
       "323                  CHILD CARE HEALTH AND DEVELOPMENT   \n",
       "1244                      JOURNAL OF CHILD HEALTH CARE   \n",
       "242                  CHILD CARE HEALTH AND DEVELOPMENT   \n",
       "\n",
       "                                    SC                ...                 \\\n",
       "867   Education & Educational Research                ...                  \n",
       "196             Psychology; Pediatrics                ...                  \n",
       "323             Psychology; Pediatrics                ...                  \n",
       "1244               Nursing; Pediatrics                ...                  \n",
       "242             Psychology; Pediatrics                ...                  \n",
       "\n",
       "     highly_cited_1 highly_cited_10  highly_cited_5  recent_citations  \\\n",
       "867           False           False           False                 7   \n",
       "196           False           False           False                 4   \n",
       "323           False           False           False                10   \n",
       "1244          False           False           False                 6   \n",
       "242           False           False           False                 1   \n",
       "\n",
       "     relative_citation_ratio  times_cited  Citations  \\\n",
       "867                      NaN           12         12   \n",
       "196                     0.18            4          4   \n",
       "323                     2.10           18         18   \n",
       "1244                    1.49           23         23   \n",
       "242                     0.20            2          2   \n",
       "\n",
       "                                   Cluster Cluster_no  \\\n",
       "867       ['disabl', 'children', 'vaccin']          9   \n",
       "196           ['sleep', 'adhd', 'problem']          7   \n",
       "323   ['motor', 'children', 'development']          3   \n",
       "1244          ['sleep', 'adhd', 'problem']          7   \n",
       "242     ['depress', 'infant', 'behaviour']         15   \n",
       "\n",
       "                           Article_kws  \n",
       "867       ['disabl', 'educ', 'inclus']  \n",
       "196   ['sleepless', 'definit', 'adhd']  \n",
       "323        ['casp', 'youth', 'report']  \n",
       "1244          ['pufa', 'beta', 'acid']  \n",
       "242        ['smack', 'caregiv', 'sdq']  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data\n",
    "data = pd.read_csv(c.working_data,index_col = 0)\n",
    "# data[\"Cluster_no\"] = data[\"Cluster_no\"].astype('category')\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Set NaNs in citations col to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "data['Citations'] = data['Citations'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing data for the following journals:\n",
      "CHILD CARE HEALTH AND DEVELOPMENT                   559\n",
      "INTERNATIONAL JOURNAL OF EDUCATIONAL DEVELOPMENT    485\n",
      "JOURNAL OF CHILD HEALTH CARE                        232\n",
      "JOURNAL OF RESEARCH IN CHILDHOOD EDUCATION           76\n",
      "Name: SO, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Showing data for the following journals:')\n",
    "print(data['SO'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# for col in data.columns:\n",
    "#     print(col,': ',sum(data[col].isnull()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Bokeh code\n",
    "The first step is to define a few things that will go into the plot.\n",
    "\n",
    "#### Hover tool\n",
    "The hover tool defines what happens when you hover your mouse over the plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "#             (\"index\", \"$index\"),\n",
    "#             (\"(x,y)\", \"($x, $y)\"),\n",
    "            (\"Journal\", \"@SO\"),\n",
    "            (\"DOI\", \"@DI\"),\n",
    "            (\"Article Keywords\",\"@Article_kws\"),\n",
    "            (\"Citations\", \"@Citations\"),\n",
    "            (\"Cluster_no\", \"@Cluster_no\"),\n",
    "            (\"Cluster Keywords\",\"@Cluster\")\n",
    "                ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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=\"87280ab2-271b-4e4c-bcd1-b0b144870170\">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 !== undefined) {\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 element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\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[0].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",
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       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
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       "  }\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[0]);\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",
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       "  function display_loaded() {\n",
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       "      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",
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       "        if (root._bokeh_is_loading === 0) {\n",
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       "          run_callbacks()\n",
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       "      };\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",
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       "  if (element == null) {\n",
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       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.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-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.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(\"87280ab2-271b-4e4c-bcd1-b0b144870170\")).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(\"87280ab2-271b-4e4c-bcd1-b0b144870170\");\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(\"87280ab2-271b-4e4c-bcd1-b0b144870170\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '87280ab2-271b-4e4c-bcd1-b0b144870170' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.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-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.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(\"87280ab2-271b-4e4c-bcd1-b0b144870170\")).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": [
    "#### Other tools\n",
    "Other tools to add to the right hand side of the plot can be selected from a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "TOOLS = [BoxSelectTool(), hover, 'tap','box_zoom','reset', 'crosshair'] #,HoverTool()] # just say 'HoverTool()' for the default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Add Alpha and size data\n",
    "\n",
    "'Alpha' is the transparency or 'brightness' of the dots.  The formula below ensures that low-cited articles are dimmer than the bright ones.  This has quite a subtle effect on the final plot and can be removed, but it does help to make individual articles stand out , even if their coloring puts then in a group that has low citations.\n",
    "\n",
    "'Sizes' is our setting for the size of the dots.  I used to use this to help accentuate highly cited papers, but I decided that it made the plot look cluttered.  Perhaps it's worth uncommenting this line if you are using a small dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# data['Sizes'] = 3+(1.5*(np.log(1+data.Citations)))\n",
    "\n",
    "# normalize citations to max 1 and attenuate with log so that we don't have a spike\n",
    "normed = np.log10(1+data.Citations)/np.log10(1+np.max(data.Citations))\n",
    "# set minimum alpha\n",
    "min_alpha = 0.2\n",
    "# set max alpha\n",
    "max_alpha = 1.0\n",
    "# set alpha\n",
    "data['Alpha'] = min_alpha + ( (max_alpha-min_alpha)*normed )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Colour clusters by average citation rates\n",
    "This is where we define our colour scheme. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "sns.palplot(sns.color_palette(\"YlOrRd\", 50)[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DI</th>\n",
       "      <th>PY</th>\n",
       "      <th>TI</th>\n",
       "      <th>AB</th>\n",
       "      <th>WD</th>\n",
       "      <th>AU</th>\n",
       "      <th>EM</th>\n",
       "      <th>AF</th>\n",
       "      <th>SO</th>\n",
       "      <th>SC</th>\n",
       "      <th>...</th>\n",
       "      <th>highly_cited_10</th>\n",
       "      <th>highly_cited_5</th>\n",
       "      <th>recent_citations</th>\n",
       "      <th>relative_citation_ratio</th>\n",
       "      <th>times_cited</th>\n",
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       "      <th>Cluster_no</th>\n",
       "      <th>Article_kws</th>\n",
       "      <th>Alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>10.1111/j.1365-2214.2011.01282.x</td>\n",
       "      <td>2011</td>\n",
       "      <td>A social-ecological model of readiness for tra...</td>\n",
       "      <td>Background Policy and research related to tran...</td>\n",
       "      <td>A social-ecological model of readiness for tra...</td>\n",
       "      <td>Schwartz, LA; Tuchman, LK; Hobbie, WL; Ginsber...</td>\n",
       "      <td>schwartzl@email.chop.edu</td>\n",
       "      <td>Schwartz, L. A.; Tuchman, L. K.; Hobbie, W. L....</td>\n",
       "      <td>CHILD CARE HEALTH AND DEVELOPMENT</td>\n",
       "      <td>Psychology; Pediatrics</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>59</td>\n",
       "      <td>5.52</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>['transit', 'adult', 'care']</td>\n",
       "      <td>17</td>\n",
       "      <td>['smart', 'transit', 'readi']</td>\n",
       "      <td>0.985472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   DI    PY  \\\n",
       "477  10.1111/j.1365-2214.2011.01282.x  2011   \n",
       "\n",
       "                                                    TI  \\\n",
       "477  A social-ecological model of readiness for tra...   \n",
       "\n",
       "                                                    AB  \\\n",
       "477  Background Policy and research related to tran...   \n",
       "\n",
       "                                                    WD  \\\n",
       "477  A social-ecological model of readiness for tra...   \n",
       "\n",
       "                                                    AU  \\\n",
       "477  Schwartz, LA; Tuchman, LK; Hobbie, WL; Ginsber...   \n",
       "\n",
       "                           EM  \\\n",
       "477  schwartzl@email.chop.edu   \n",
       "\n",
       "                                                    AF  \\\n",
       "477  Schwartz, L. A.; Tuchman, L. K.; Hobbie, W. L....   \n",
       "\n",
       "                                    SO                      SC    ...     \\\n",
       "477  CHILD CARE HEALTH AND DEVELOPMENT  Psychology; Pediatrics    ...      \n",
       "\n",
       "    highly_cited_10 highly_cited_5  recent_citations  relative_citation_ratio  \\\n",
       "477            True           True                59                     5.52   \n",
       "\n",
       "    times_cited  Citations                       Cluster Cluster_no  \\\n",
       "477         100        100  ['transit', 'adult', 'care']         17   \n",
       "\n",
       "                       Article_kws     Alpha  \n",
       "477  ['smart', 'transit', 'readi']  0.985472  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Citations']==100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "cit_col_ls = []\n",
    "\n",
    "av_years = c.av_years # this won't work if you have zero articles from these years in a particular cluster.  Use with care or just set to c.years\n",
    "\n",
    "avcits = data[data['PY'].isin(av_years)].groupby('Cluster_no')['Citations'].mean()\n",
    "maxav = avcits.max()\n",
    "minav = avcits.min()\n",
    "avrange = maxav-minav\n",
    "\n",
    "# define the palette\n",
    "palette = sns.color_palette(\"YlOrRd\", n_clusters+1).as_hex()[::-1] # note +1 to fix rounding errors\n",
    "# palette2 = ['#%02x%02x%02x'%(int(y) for y in x) for x in palette]\n",
    "# palette2\n",
    "i=0\n",
    "so = data['SO'].tolist()\n",
    "color_numerics=[]\n",
    "for cl_no in data['Cluster_no'].tolist():\n",
    "    \n",
    "    if so[i] == journal_of_interest:\n",
    "        cit_col_ls.append('#7ec0ee') # this colour is a bright sky-blue.  'green' or maybe purple would show up well, too\n",
    "    else:\n",
    "        cl_avcit = n_clusters*(avcits[cl_no]-minav)/avrange # gets the relative position of the cluster's average cites in the distribution\n",
    "        color_numerics.append(cl_avcit)\n",
    "        colr = palette[int(cl_avcit)]\n",
    "        cit_col_ls.append(colr)\n",
    "    i+=1\n",
    "data['Cit_col'] = cit_col_ls   \n",
    "# cit_col_ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the distribution of the colours.  In some datasets, you'll find a poor distribution of red/orange/yellow simply due to the distribution of citations in the dataset.  Worth fiddling with the code to make the diferences stand out.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28368f5ac18>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(color_numerics).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Build plot\n",
    "This is where we define the figure itself using the Bokeh package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "from bokeh.io import show\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.palettes import RdBu3\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "# https://stackoverflow.com/questions/41856999/bokeh-plots-just-bring-up-a-blank-window\n",
    "# BOKEH_RESOURCES=inline\n",
    "\n",
    "\n",
    "# plotting\n",
    "p = figure(plot_width=950, plot_height=600,\n",
    "           title=plot_title,  # specified in the config file!\n",
    "           tools=TOOLS,\n",
    "          x_axis_label = \"Textual similarity axis_1 (arbitrary units)\",\n",
    "          y_axis_label = 'Textual similarity axis_2 (arbitrary units)') # , active_inspect=None)\n",
    "\n",
    "# p.toolbar.active_inspect = ['crosshair', hover]\n",
    "\n",
    "p.background_fill_color = \"black\"\n",
    "\n",
    "p.circle(x = 'TSNE1', \n",
    "         y = 'TSNE2', \n",
    "#           legend = 'Division',\n",
    "         size = 5, # 'Sizes',\n",
    "         color = 'Cit_col', # Cit_col',#'j_col', #'cit_colr', # #841F27', #'Color',\n",
    "         alpha = 'Alpha', \n",
    "         line_alpha = 0,\n",
    "         source = ColumnDataSource(data))  # This conversion to ColumnDataSource is crucial.\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "# p.legend.text = div_colors\n",
    "\n",
    "output_file(tfidf_filename,\n",
    "           mode = 'inline')  # toggle for write-to-file\n",
    "\n",
    "\n",
    "# add links\n",
    "url = \"@Link\"\n",
    "taptool = p.select(type=TapTool)\n",
    "taptool.callback = OpenURL(url=url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"443a0ac5-cb05-4015-a8ce-036d7df062b4\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    }
   ],
   "source": [
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### Add bar plots\n",
    "Now that the plot is completed, there are a few simple images that might help to describe the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "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>Cluster</th>\n",
       "      <th>cites</th>\n",
       "      <th>dois_ls</th>\n",
       "      <th>mean_cites</th>\n",
       "      <th>nz_cites</th>\n",
       "      <th>len_cites</th>\n",
       "      <th>Cluster_no</th>\n",
       "      <th>nz_pc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>['educ', 'school', 'enrol']</td>\n",
       "      <td>[14, 5, 2, 0, 1, 3, 0, 0, 0, 1, 3, 0, 1, 1, 2,...</td>\n",
       "      <td>['10.1111/j.1365-2214.2011.01327.x', '10.1111/...</td>\n",
       "      <td>4.904762</td>\n",
       "      <td>72.0</td>\n",
       "      <td>84</td>\n",
       "      <td>5</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Cluster  \\\n",
       "5  ['educ', 'school', 'enrol']   \n",
       "\n",
       "                                               cites  \\\n",
       "5  [14, 5, 2, 0, 1, 3, 0, 0, 0, 1, 3, 0, 1, 1, 2,...   \n",
       "\n",
       "                                             dois_ls  mean_cites  nz_cites  \\\n",
       "5  ['10.1111/j.1365-2214.2011.01327.x', '10.1111/...    4.904762      72.0   \n",
       "\n",
       "   len_cites  Cluster_no     nz_pc  \n",
       "5         84           5  0.857143  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % matplotlib inline\n",
    "df = pd.read_csv('data/cluster_data.csv', index_col=0)\n",
    "df.sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster        object\n",
       "cites          object\n",
       "dois_ls        object\n",
       "mean_cites    float64\n",
       "nz_cites      float64\n",
       "len_cites       int64\n",
       "Cluster_no      int64\n",
       "nz_pc         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aday\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Cluster       category\n",
       "cites           object\n",
       "dois_ls         object\n",
       "mean_cites     float64\n",
       "nz_cites       float64\n",
       "len_cites        int64\n",
       "Cluster_no       int64\n",
       "nz_pc          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert Cluster to category data type\n",
    "df['Cluster'] = df['Cluster'].astype(str).astype('category', ordered = True)\n",
    "#check\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "### With Seaborn\n",
    "Means first"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 'Cluster'\n",
    "y = 'mean_cites'\n",
    "\n",
    "df.Cluster = df.sort_values(y).Cluster\n",
    "df = df.sort_values(y, ascending = False)\n",
    "df.Cluster.cat.ordered\n",
    "# df['Cluster'] = df['Cluster'].cat.reorder_categories(list(df['Cluster']), ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['['activ', 'sibl', 'food']',\n",
       "                  '['children', 'health', 'adolesc']',\n",
       "                  '['depress', 'infant', 'behaviour']',\n",
       "                  '['disabl', 'children', 'vaccin']',\n",
       "                  '['dropout', 'hiv', 'migrat']',\n",
       "                  '['educ', 'develop', 'learn']',\n",
       "                  '['educ', 'higher', 'univers']',\n",
       "                  '['educ', 'school', 'enrol']', '['gender', 'girl', 'educ']',\n",
       "                  '['hospit', 'nurs', 'asthma']',\n",
       "                  '['literaci', 'read', 'instruct']',\n",
       "                  '['motor', 'children', 'development']',\n",
       "                  '['obes', 'weight', 'overweight']',\n",
       "                  '['pain', 'children', 'adolesc']',\n",
       "                  '['parent', 'child', 'famili']',\n",
       "                  '['school', 'student', 'privat']',\n",
       "                  '['servic', 'care', 'famili']',\n",
       "                  '['sleep', 'adhd', 'problem']',\n",
       "                  '['teacher', 'educ', 'student']',\n",
       "                  '['transit', 'adult', 'care']'],\n",
       "                 ordered=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cluster'] = data['Cluster'].astype('category')\n",
    "data['Cluster'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   0,   4,   7,   3,   5,  11,   8,   9,   6,  10,  12,\n",
       "        13,  16,  14,  20,  19,  15,  41,  21,  26,  25,  18,  17,  35,\n",
       "        29,  23,  32,  33,  45,  27,  43,  52,  34,  38,  28,  30,  22,\n",
       "        42, 109,  37, 100,  50,  39,  51,  48,  24,  31,  72,  46,  68,\n",
       "        40,  44,  49,  58], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['times_cited'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aday\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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07YAfmtnd/j6Pwu3k2R24FNgUt1X6feALfsfNIOBXvt5VwIlmttDXuwIYBnwU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\njuM4juPUlf8fdQrsJWgyuW8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2836e1db588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# df.Cluster = df.sort_values('times_cited').Cluster\n",
    "ax = sns.boxplot('times_cited',\n",
    "                 'Cluster', \n",
    "                 orient='h', \n",
    "                 order = list(df.Cluster),\n",
    "                 data = data).set_title('Boxplots for citation rates of various k-means clusters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 'Cluster'\n",
    "y = 'mean_cites'\n",
    "\n",
    "df.Cluster = df.sort_values(y).Cluster\n",
    "df = df.sort_values(y, ascending = False)\n",
    "df.Cluster.cat.ordered\n",
    "# df['Cluster'] = df['Cluster'].cat.reorder_categories(list(df['Cluster']), ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19            ['transit', 'adult', 'care']\n",
       "17            ['sleep', 'adhd', 'problem']\n",
       "12        ['obes', 'weight', 'overweight']\n",
       "9             ['hospit', 'nurs', 'asthma']\n",
       "11    ['motor', 'children', 'development']\n",
       "0                ['activ', 'sibl', 'food']\n",
       "5             ['educ', 'develop', 'learn']\n",
       "2       ['depress', 'infant', 'behaviour']\n",
       "1        ['children', 'health', 'adolesc']\n",
       "4             ['dropout', 'hiv', 'migrat']\n",
       "13         ['pain', 'children', 'adolesc']\n",
       "16            ['servic', 'care', 'famili']\n",
       "14           ['parent', 'child', 'famili']\n",
       "6            ['educ', 'higher', 'univers']\n",
       "7              ['educ', 'school', 'enrol']\n",
       "3         ['disabl', 'children', 'vaccin']\n",
       "8               ['gender', 'girl', 'educ']\n",
       "15         ['school', 'student', 'privat']\n",
       "18          ['teacher', 'educ', 'student']\n",
       "10        ['literaci', 'read', 'instruct']\n",
       "Name: Cluster, dtype: category\n",
       "Categories (20, object): [['activ', 'sibl', 'food'], ['children', 'health', 'adolesc'], ['depress', 'infant', 'behaviour'], ['disabl', 'children', 'vaccin'], ..., ['servic', 'care', 'famili'], ['sleep', 'adhd', 'problem'], ['teacher', 'educ', 'student'], ['transit', 'adult', 'care']]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.Cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DI', 'PY', 'TI', 'AB', 'WD', 'AU', 'EM', 'AF', 'SO', 'SC', 'SN', 'EI',\n",
       "       'TC', 'Z9', 'C1', 'TSNE1', 'TSNE2', 'DOI', 'Link',\n",
       "       'field_citation_ratio', 'highly_cited_1', 'highly_cited_10',\n",
       "       'highly_cited_5', 'recent_citations', 'relative_citation_ratio',\n",
       "       'times_cited', 'Citations', 'Cluster', 'Cluster_no', 'Article_kws',\n",
       "       'Alpha', 'Cit_col'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aday\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Citation rates for Continuity in education split into 20 K-Means clusters')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "f, ax = plt.subplots(figsize=(20, 12))\n",
    "p = sns.barplot(data = df, \n",
    "            x=df[y],\n",
    "            y=df[x],\n",
    "            color = 'b',\n",
    "            orient='h',\n",
    "            order = df[x]).set_title('Citation rates for {} split into {} K-Means clusters'.format(c.set_name,c.n_clusters))\n",
    "# p.set_xticklabels(labels = df[x],rotation=90)\n",
    "ax.set(xlabel='Mean citations', ylabel='Cluster')\n",
    "plt.savefig('outputs/K_Means_Barplot_means_k{}.png'.format(n_clusters))\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the medians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df = pd.DataFrame(data.groupby('Cluster')['Citations'].median())\n",
    "df.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "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>Cluster</th>\n",
       "      <th>Median citations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['activ', 'sibl', 'food']</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['children', 'health', 'adolesc']</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['depress', 'infant', 'behaviour']</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['disabl', 'children', 'vaccin']</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['dropout', 'hiv', 'migrat']</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Cluster  Median citations\n",
       "0           ['activ', 'sibl', 'food']               6.0\n",
       "1   ['children', 'health', 'adolesc']               5.0\n",
       "2  ['depress', 'infant', 'behaviour']               6.0\n",
       "3    ['disabl', 'children', 'vaccin']               4.0\n",
       "4        ['dropout', 'hiv', 'migrat']               5.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['Cluster', 'Median citations']\n",
    "df.Cluster = df.Cluster.astype('category')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "x = 'Cluster'\n",
    "y = 'Median citations'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "df[x] = df.sort_values(y).Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(y, ascending = False)\n",
    "df.Cluster.cat.ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aday\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Citation rates for Continuity in education split into 20 K-Means clusters')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "f, ax = plt.subplots(figsize=(20, 12))\n",
    "p = sns.barplot(data = df, \n",
    "            x=df[y],\n",
    "            y=df[x],\n",
    "            color = 'b',\n",
    "            orient='h',\n",
    "            order = df[x]).set_title('Citation rates for {} split into {} K-Means clusters'.format(c.set_name, c.n_clusters))\n",
    "# p.set_xticklabels(labels = df[x],rotation=90)\n",
    "ax.set(xlabel='Median citations', ylabel='Cluster')\n",
    "plt.savefig('outputs/K_Means_Barplot_medians_k{}.png'.format(n_clusters))\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Show relative sizes of journals over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false,
    "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>SO</th>\n",
       "      <th>PY</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>INTERNATIONAL JOURNAL OF EDUCATIONAL DEVELOPMENT</td>\n",
       "      <td>2015</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  SO    PY  Count\n",
       "10  INTERNATIONAL JOURNAL OF EDUCATIONAL DEVELOPMENT  2015    118"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see clusters 15, 47, 10\n",
    "\n",
    "bar_df = pd.DataFrame(data.groupby(['SO',\n",
    "#                                     'Cluster',\n",
    "                                    'PY']).size().reset_index(name=\"Count\"))\n",
    "\n",
    "# bar_df.columns\n",
    "bar_df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aday\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\aday\\\\AppData\\\\Roaming\\\\Python\\\\Python36\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 12))\n",
    "p = sns.factorplot(data = bar_df, \n",
    "                   ax =ax,\n",
    "            x='PY',\n",
    "            y='Count',\n",
    "#                     fit_reg=False,\n",
    "                   kind='bar',\n",
    "            hue = 'SO')\n",
    "# plt.title('Citations per year for cluster 15')\n",
    "# p.set_xticklabels(labels = df[x],rotation=90)\n",
    "# plt.savefig('outputs/Cluster_15_Barplot.png') #image not saving correctly\n",
    "plt"
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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