{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crossref Citation Stats\n",
    "\n",
    "## Data Provenance\n",
    "\n",
    "The data was retrieved in August 2019 via the [Crossref API](http://api.crossref.org/works) (the download should include everything at least up to 05 Aug 2019, having started on the 06th taking over a week). ([The raw responses LZMA zipped](https://doi.org/10.6084/m9.figshare.9751865) take about 49 GB disk space)\n",
    "\n",
    "## Data Preprocessing\n",
    "\n",
    "The data was then preprocessed to get calculate some high level stats over the data which are then analysed in this notebook.\n",
    "\n",
    "The files are available here:\n",
    "* [crossref-works-summaries-stat.tsv.gz](https://storage.googleapis.com/elife-ml/citations/by-date/2019-08-06/crossref-works-summaries-stat.tsv.gz) - Overall summary stats\n",
    "* [crossref-works-summaries-by-type-and-publisher-stat.tsv.gz](https://storage.googleapis.com/elife-ml/citations/by-date/2019-08-06/crossref-works-summaries-by-type-and-publisher-stat.tsv.gz) - Summary stats grouped by _type_ and _publisher_ \n",
    "* [crossref-works-reference-stat.tsv.gz](https://storage.googleapis.com/elife-ml/citations/by-date/2019-08-06/crossref-works-reference-stat.tsv.gz) - Stats relating to open access (oa) references\n",
    "\n",
    "Related, but not used for the stats in this notebook:\n",
    "* [citation links](https://doi.org/10.6084/m9.figshare.9751988)\n",
    "\n",
    "## Notes\n",
    "\n",
    "The *reference\\_count* and *referenced\\_by\\_count* are counts provided by Crossref. *num\\_references* are the number of references that are actually accessible via the API (i.e. oa).\n",
    "\n",
    "## Scripts\n",
    "\n",
    "The scripts can be found in [this repository](https://github.com/elifesciences/datacapsule-crossref), see [README](https://github.com/elifesciences/datacapsule-crossref/blob/analysis/README.md). Using those scripts the data can be updated (but it will take some time to retrieve all of the Crossref data).\n",
    "\n",
    "Link to this notebook: https://elifesci.org/crossref-data-notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "def printmd(string):\n",
    "    display(Markdown(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../data'\n",
    "summary_stats_filename = 'crossref-works-summaries-stat.tsv.gz'\n",
    "summary_by_type_and_publisher_stats_filename = 'crossref-works-summaries-by-type-and-publisher-stat.tsv.gz'\n",
    "reference_stats_filename = 'crossref-works-reference-stat.tsv.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>doi</th>\n",
       "      <th>title</th>\n",
       "      <th>reference_count</th>\n",
       "      <th>referenced_by_count</th>\n",
       "      <th>created</th>\n",
       "      <th>type</th>\n",
       "      <th>publisher</th>\n",
       "      <th>container_title</th>\n",
       "      <th>author_count</th>\n",
       "      <th>first_subject_area</th>\n",
       "      <th>subject_areas</th>\n",
       "      <th>has_references</th>\n",
       "      <th>num_references</th>\n",
       "      <th>num_citations_without_doi</th>\n",
       "      <th>num_duplicate_citation_dois</th>\n",
       "      <th>cited_dois</th>\n",
       "      <th>debug</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_non_zero</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.899232e+07</td>\n",
       "      <td>4.892371e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.794741e+07</td>\n",
       "      <td>2.794741e+07</td>\n",
       "      <td>2.497571e+07</td>\n",
       "      <td>1.190243e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_numeric</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_valid</th>\n",
       "      <td>109738648.0</td>\n",
       "      <td>105033354.0</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109715020.0</td>\n",
       "      <td>109736842.0</td>\n",
       "      <td>103339230.0</td>\n",
       "      <td>90402300.0</td>\n",
       "      <td>34224140.0</td>\n",
       "      <td>34224140.0</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>1.097386e+08</td>\n",
       "      <td>109738648.0</td>\n",
       "      <td>109738648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_zero</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.074632e+07</td>\n",
       "      <td>6.081493e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.179123e+07</td>\n",
       "      <td>8.179123e+07</td>\n",
       "      <td>8.476294e+07</td>\n",
       "      <td>1.085484e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.581400e+04</td>\n",
       "      <td>1.465760e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.579800e+04</td>\n",
       "      <td>1.158400e+04</td>\n",
       "      <td>3.908000e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.318010e+01</td>\n",
       "      <td>9.056722e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.546725e-01</td>\n",
       "      <td>7.755687e+00</td>\n",
       "      <td>2.582804e+00</td>\n",
       "      <td>2.745796e-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_non_zero</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.952230e+01</td>\n",
       "      <td>2.031474e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.045357e+01</td>\n",
       "      <td>1.134836e+01</td>\n",
       "      <td>2.531583e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.446366e+09</td>\n",
       "      <td>9.938724e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.794741e+07</td>\n",
       "      <td>8.510986e+08</td>\n",
       "      <td>2.834334e+08</td>\n",
       "      <td>3.013199e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        doi        title  reference_count  \\\n",
       "stat                                                        \n",
       "count           109738648.0  109738648.0     1.097386e+08   \n",
       "count_non_zero          NaN          NaN     4.899232e+07   \n",
       "count_numeric           NaN          NaN     1.097386e+08   \n",
       "count_valid     109738648.0  105033354.0     1.097386e+08   \n",
       "count_zero              NaN          NaN     6.074632e+07   \n",
       "max                     NaN          NaN     2.581400e+04   \n",
       "mean                    NaN          NaN     1.318010e+01   \n",
       "mean_non_zero           NaN          NaN     2.952230e+01   \n",
       "min                     NaN          NaN     0.000000e+00   \n",
       "sum                     NaN          NaN     1.446366e+09   \n",
       "\n",
       "                referenced_by_count      created         type    publisher  \\\n",
       "stat                                                                         \n",
       "count                  1.097386e+08  109738648.0  109738648.0  109738648.0   \n",
       "count_non_zero         4.892371e+07          NaN          NaN          NaN   \n",
       "count_numeric          1.097386e+08          NaN          NaN          NaN   \n",
       "count_valid            1.097386e+08  109738648.0  109715020.0  109736842.0   \n",
       "count_zero             6.081493e+07          NaN          NaN          NaN   \n",
       "max                    1.465760e+05          NaN          NaN          NaN   \n",
       "mean                   9.056722e+00          NaN          NaN          NaN   \n",
       "mean_non_zero          2.031474e+01          NaN          NaN          NaN   \n",
       "min                    0.000000e+00          NaN          NaN          NaN   \n",
       "sum                    9.938724e+08          NaN          NaN          NaN   \n",
       "\n",
       "                container_title  author_count  first_subject_area  \\\n",
       "stat                                                                \n",
       "count               109738648.0   109738648.0         109738648.0   \n",
       "count_non_zero              NaN           NaN                 NaN   \n",
       "count_numeric               NaN           NaN                 NaN   \n",
       "count_valid         103339230.0    90402300.0          34224140.0   \n",
       "count_zero                  NaN           NaN                 NaN   \n",
       "max                         NaN           NaN                 NaN   \n",
       "mean                        NaN           NaN                 NaN   \n",
       "mean_non_zero               NaN           NaN                 NaN   \n",
       "min                         NaN           NaN                 NaN   \n",
       "sum                         NaN           NaN                 NaN   \n",
       "\n",
       "                subject_areas  has_references  num_references  \\\n",
       "stat                                                            \n",
       "count             109738648.0    1.097386e+08    1.097386e+08   \n",
       "count_non_zero            NaN    2.794741e+07    2.794741e+07   \n",
       "count_numeric             NaN    1.097386e+08    1.097386e+08   \n",
       "count_valid        34224140.0    1.097386e+08    1.097386e+08   \n",
       "count_zero                NaN    8.179123e+07    8.179123e+07   \n",
       "max                       NaN    1.000000e+00    2.579800e+04   \n",
       "mean                      NaN    2.546725e-01    7.755687e+00   \n",
       "mean_non_zero             NaN    1.000000e+00    3.045357e+01   \n",
       "min                       NaN    0.000000e+00    0.000000e+00   \n",
       "sum                       NaN    2.794741e+07    8.510986e+08   \n",
       "\n",
       "                num_citations_without_doi  num_duplicate_citation_dois  \\\n",
       "stat                                                                     \n",
       "count                        1.097386e+08                 1.097386e+08   \n",
       "count_non_zero               2.497571e+07                 1.190243e+06   \n",
       "count_numeric                1.097386e+08                 1.097386e+08   \n",
       "count_valid                  1.097386e+08                 1.097386e+08   \n",
       "count_zero                   8.476294e+07                 1.085484e+08   \n",
       "max                          1.158400e+04                 3.908000e+03   \n",
       "mean                         2.582804e+00                 2.745796e-02   \n",
       "mean_non_zero                1.134836e+01                 2.531583e+00   \n",
       "min                          0.000000e+00                 0.000000e+00   \n",
       "sum                          2.834334e+08                 3.013199e+06   \n",
       "\n",
       "                 cited_dois        debug  \n",
       "stat                                      \n",
       "count           109738648.0  109738648.0  \n",
       "count_non_zero          NaN          NaN  \n",
       "count_numeric           NaN          NaN  \n",
       "count_valid     109738648.0  109738648.0  \n",
       "count_zero              NaN          NaN  \n",
       "max                     NaN          NaN  \n",
       "mean                    NaN          NaN  \n",
       "mean_non_zero           NaN          NaN  \n",
       "min                     NaN          NaN  \n",
       "sum                     NaN          NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary = pd.read_csv(os.path.join(data_path, summary_stats_filename), sep='\\t')\n",
    "\n",
    "# drop non-numeric stat value 'type' and convert to numeric values\n",
    "df_summary = df_summary[\n",
    "    df_summary['stat'] != 'type'\n",
    "].apply(pd.to_numeric, errors='ignore')\n",
    "\n",
    "df_summary = df_summary.set_index('stat')\n",
    "df_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total work count: 109,738,648\n"
     ]
    }
   ],
   "source": [
    "print('total work count: {:,}'.format(int(df_summary['reference_count']['count'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEFAULT_NUMBER_FORMAT = '{:,.0f}'\n",
    "DEFAULT_PERCENTAGE_FORMAT = '{:,.2f}%'\n",
    "format_count = lambda c, number_format=DEFAULT_NUMBER_FORMAT: number_format.format(c)\n",
    "format_percentage = lambda c, number_format=DEFAULT_PERCENTAGE_FORMAT: number_format.format(c)\n",
    "\n",
    "def apply_columns(df, columns, f):\n",
    "    for c in columns:\n",
    "        df[c] = df[c].apply(f)\n",
    "    return df\n",
    "\n",
    "def format_columns(df):\n",
    "    for c in df.columns:\n",
    "        if str(c).startswith('p_'):\n",
    "            df[c] = df[c].apply(format_percentage)\n",
    "        else:\n",
    "            df[c] = df[c].apply(format_count)\n",
    "    return df\n",
    "\n",
    "def show_counts(counts, title='', number_format=DEFAULT_NUMBER_FORMAT, **kwargs):\n",
    "    if isinstance(counts, list):\n",
    "        df = pd.DataFrame(counts, columns=['what', 'count']).set_index('what')['count']\n",
    "    else:\n",
    "        df = counts\n",
    "    ax = df.plot(kind='bar', **kwargs)\n",
    "    for p in ax.patches:\n",
    "        ax.annotate(\n",
    "            format_count(p.get_height(), number_format=number_format),\n",
    "            xy=(p.get_x() + p.get_width() / 2, p.get_height()), xycoords=\"data\",\n",
    "            va=\"bottom\", ha=\"center\"\n",
    "        )\n",
    "    ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: format_count(y))) \n",
    "    ax.axes.get_xaxis().set_label_text(title)\n",
    "    ax.margins(y=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Work counts"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *48,992,325* works with references in Crossref, **57.04%** are open (*27,947,414*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Work counts')\n",
    "printmd('Out of the *{:,}* works with references in Crossref, **{:.2f}%** are open (*{:,}*).'.format(\n",
    "    int(df_summary['reference_count']['count_non_zero']),\n",
    "    100.0 * df_summary['has_references']['count_non_zero'] / df_summary['reference_count']['count_non_zero'],\n",
    "    int(df_summary['has_references']['count_non_zero'])\n",
    "))\n",
    "\n",
    "show_counts([\n",
    "    ['extracted', df_summary['has_references']['count']],\n",
    "    ['has ref count', df_summary['reference_count']['count_non_zero']],\n",
    "    ['oa references', df_summary['has_references']['count_non_zero']]\n",
    "], 'works')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Reference counts"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *1,446,365,933* references in Crossref, **58.84%** are open (*851,098,649*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Reference counts')\n",
    "printmd('Out of the *{:,}* references in Crossref, **{:.2f}%** are open (*{:,}*).'.format(\n",
    "    int(df_summary['reference_count']['sum']),\n",
    "    100.0 * df_summary['num_references']['sum'] / df_summary['reference_count']['sum'],\n",
    "    int(df_summary['num_references']['sum'])\n",
    "))\n",
    "\n",
    "show_counts([\n",
    "    ['as per Crossref', df_summary['reference_count']['sum']],\n",
    "    ['oa available', df_summary['num_references']['sum']],\n",
    "    ['with dois', df_summary['num_references']['sum'] - df_summary['num_citations_without_doi']['sum']],\n",
    "    ['missing dois', df_summary['num_citations_without_doi']['sum']],\n",
    "    ['duplicate dois', df_summary['num_duplicate_citation_dois']['sum']]\n",
    "], 'references')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Reference stats"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>type</th>\n",
       "      <th>key</th>\n",
       "      <th>count</th>\n",
       "      <th>examples_without_source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>921552</th>\n",
       "      <td>year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79233328</td>\n",
       "      <td>[{'key': 'S1935789300003219_ref042', 'unstruct...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864147</th>\n",
       "      <td>total_first_subject_area</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75514508</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>key_combination</td>\n",
       "      <td>key|unstructured</td>\n",
       "      <td>70930410</td>\n",
       "      <td>[{'key': 'S1935789300003219_ref042', 'unstruct...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>key_combination</td>\n",
       "      <td>author|key|volume-title|year</td>\n",
       "      <td>39450593</td>\n",
       "      <td>[{'key': 'S1935789313000591_ref17', 'author': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>key_combination</td>\n",
       "      <td>author|first-page|journal-title|key|volume|year</td>\n",
       "      <td>29013838</td>\n",
       "      <td>[{'key': 'S1935789300003165_ref020', 'author':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>key_combination</td>\n",
       "      <td>author|first-page|journal-title|key|unstructur...</td>\n",
       "      <td>25505471</td>\n",
       "      <td>[{'key': '10.1002/(SICI)1096-8628(20000207)96:...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>key_combination</td>\n",
       "      <td>author|key|unstructured|volume-title|year</td>\n",
       "      <td>19137676</td>\n",
       "      <td>[{'key': '10.1002/(SICI)1096-8628(19960102)61:...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>key_combination</td>\n",
       "      <td>article-title|author|first-page|journal-title|...</td>\n",
       "      <td>18917337</td>\n",
       "      <td>[{'key': 'S1935789300003219_ref014', 'first-pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864470</th>\n",
       "      <td>total_publisher</td>\n",
       "      <td>Elsevier BV</td>\n",
       "      <td>16342614</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72522</th>\n",
       "      <td>non_oa_ref_first_subject_area</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12150971</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 type  \\\n",
       "921552                           year   \n",
       "864147       total_first_subject_area   \n",
       "0                     key_combination   \n",
       "1                     key_combination   \n",
       "2                     key_combination   \n",
       "3                     key_combination   \n",
       "4                     key_combination   \n",
       "5                     key_combination   \n",
       "864470                total_publisher   \n",
       "72522   non_oa_ref_first_subject_area   \n",
       "\n",
       "                                                      key     count  \\\n",
       "921552                                                NaN  79233328   \n",
       "864147                                                NaN  75514508   \n",
       "0                                        key|unstructured  70930410   \n",
       "1                            author|key|volume-title|year  39450593   \n",
       "2         author|first-page|journal-title|key|volume|year  29013838   \n",
       "3       author|first-page|journal-title|key|unstructur...  25505471   \n",
       "4               author|key|unstructured|volume-title|year  19137676   \n",
       "5       article-title|author|first-page|journal-title|...  18917337   \n",
       "864470                                        Elsevier BV  16342614   \n",
       "72522                                                 NaN  12150971   \n",
       "\n",
       "                                  examples_without_source  \n",
       "921552  [{'key': 'S1935789300003219_ref042', 'unstruct...  \n",
       "864147                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  \n",
       "0       [{'key': 'S1935789300003219_ref042', 'unstruct...  \n",
       "1       [{'key': 'S1935789313000591_ref17', 'author': ...  \n",
       "2       [{'key': 'S1935789300003165_ref020', 'author':...  \n",
       "3       [{'key': '10.1002/(SICI)1096-8628(20000207)96:...  \n",
       "4       [{'key': '10.1002/(SICI)1096-8628(19960102)61:...  \n",
       "5       [{'key': 'S1935789300003219_ref014', 'first-pa...  \n",
       "864470                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  \n",
       "72522                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Reference stats')\n",
    "df_reference_stat = pd.read_csv(os.path.join(data_path, reference_stats_filename), sep='\\t')\n",
    "df_reference_stat['examples'] = df_reference_stat['examples'].map(lambda x: json.loads(x))\n",
    "df_reference_stat['examples_without_source'] = df_reference_stat['examples'].map(lambda x: [y[1] for y in x])\n",
    "df_reference_stat = df_reference_stat.sort_values('count', ascending=False)\n",
    "df_reference_stat.drop('examples', axis=1).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Reference stats are aggregated by different 'types'"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_combination\n",
      "non_oa_ref_countainer_title\n",
      "non_oa_ref_created\n",
      "non_oa_ref_first_subject_area\n",
      "non_oa_ref_publisher\n",
      "total_countainer_title\n",
      "total_created\n",
      "total_first_subject_area\n",
      "total_publisher\n",
      "year\n"
     ]
    }
   ],
   "source": [
    "printmd('### Reference stats are aggregated by different \\'types\\'')\n",
    "print('\\n'.join(sorted(set(df_reference_stat['type'].values))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Count of referenced work by key combinations (populated fields in reference)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Count of referenced work by key combinations (populated fields in reference)')\n",
    "\n",
    "df_reference_key_combination_stat = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'key_combination']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "show_counts(df_reference_key_combination_stat['count'].head(10), figsize=(20, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Count of referenced work by year, sorted descending by year"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Count of referenced work by year, sorted descending by year')\n",
    "\n",
    "df_reference_year_stat = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'year']\n",
    "    .drop('type', axis=1)\n",
    "    .sort_values('key', ascending=False)\n",
    "    .set_index('key')\n",
    ")\n",
    "df_reference_year_stat = df_reference_year_stat[df_reference_year_stat['count'] > 1000]\n",
    "show_counts(df_reference_year_stat['count'].head(150), figsize=(20, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key_combination',\n",
       " 'non_oa_ref_countainer_title',\n",
       " 'non_oa_ref_created',\n",
       " 'non_oa_ref_first_subject_area',\n",
       " 'non_oa_ref_publisher',\n",
       " 'total_countainer_title',\n",
       " 'total_created',\n",
       " 'total_first_subject_area',\n",
       " 'total_publisher',\n",
       " 'year'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df_reference_stat['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Count of non-oa work by publisher, sorted descending"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key\n",
      "Elsevier BV                                                 0.690575\n",
      "IEEE                                                        0.828127\n",
      "Ovid Technologies (Wolters Kluwer Health)                   0.354177\n",
      "American Chemical Society (ACS)                             0.346757\n",
      "Institute of Electrical and Electronics Engineers (IEEE)    0.658336\n",
      "IOP Publishing                                              0.776109\n",
      "Elsevier                                                    0.559011\n",
      "Mary Ann Liebert Inc                                        0.836376\n",
      "Medknow                                                     0.663512\n",
      "Institution of Engineering and Technology (IET)             0.586324\n",
      "Name: count, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Count of non-oa work by publisher, sorted descending')\n",
    "\n",
    "df_temp = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'non_oa_ref_publisher']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "\n",
    "df_temp_total = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'total_publisher']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "# print(df_temp_total.head())\n",
    "print((df_temp['count'].head(10) / df_temp_total['count']).dropna())\n",
    "show_counts(df_temp['count'].head(10), figsize=(20, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Percentage of non-oa work by publisher (>10k works), sorted descending by percentage"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key\n",
      "Mary Ann Liebert Inc    0.836376\n",
      "IEEE                    0.828127\n",
      "IOP Publishing          0.776109\n",
      "Elsevier BV             0.690575\n",
      "Medknow                 0.663512\n",
      "Name: count, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Percentage of non-oa work by publisher (>10k works), sorted descending by percentage')\n",
    "\n",
    "df_temp = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'non_oa_ref_publisher']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "df_temp2 = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'total_publisher']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "df_temp = df_temp[df_temp['count'] > 100000]\n",
    "# df_temp = df_temp[df_temp2['count'] > 10000]\n",
    "# df_temp2 = df_temp2[df_temp2['count'] > 10000]\n",
    "df_temp3 = df_temp.copy()\n",
    "df_temp3['count'] = df_temp3['count'] / df_temp2['count']\n",
    "print(df_temp3['count'].sort_values(ascending=False).head())\n",
    "show_counts(df_temp3['count'].sort_values(ascending=False).head(10), number_format='{:,.5f}', figsize=(20, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Non-oa works by container title"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Non-oa works by container title')\n",
    "\n",
    "df_temp = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'non_oa_ref_countainer_title']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "show_counts(df_temp['count'].head(10), figsize=(20, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Non-oa works by first subject area"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Non-oa works by first subject area')\n",
    "\n",
    "df_temp = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'non_oa_ref_first_subject_area']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "show_counts(df_temp['count'].head(10), figsize=(20, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Non-oa works by creation date (year)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Non-oa works by creation date (year)')\n",
    "\n",
    "df_temp = (\n",
    "    df_reference_stat[df_reference_stat['type'] == 'non_oa_ref_created']\n",
    "    .drop('type', axis=1)\n",
    "    .set_index('key')\n",
    ")\n",
    "show_counts(df_temp['count'].head(10), figsize=(20, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More detailed analysis on the summaries by type and publisher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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>type</th>\n",
       "      <th>publisher</th>\n",
       "      <th>stat</th>\n",
       "      <th>doi</th>\n",
       "      <th>title</th>\n",
       "      <th>reference_count_crossref</th>\n",
       "      <th>referenced_by_count</th>\n",
       "      <th>created</th>\n",
       "      <th>container_title</th>\n",
       "      <th>author_count</th>\n",
       "      <th>first_subject_area</th>\n",
       "      <th>subject_areas</th>\n",
       "      <th>has_references</th>\n",
       "      <th>reference_count_oa</th>\n",
       "      <th>num_citations_without_doi</th>\n",
       "      <th>num_duplicate_citation_dois</th>\n",
       "      <th>cited_dois</th>\n",
       "      <th>debug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>count</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>count_non_zero</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>count_numeric</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type publisher            stat  doi  title  reference_count_crossref  \\\n",
       "0                          count  3.0    3.0                       3.0   \n",
       "1                 count_non_zero  NaN    NaN                       0.0   \n",
       "2                  count_numeric  NaN    NaN                       3.0   \n",
       "\n",
       "   referenced_by_count  created  container_title  author_count  \\\n",
       "0                  3.0      3.0              3.0           3.0   \n",
       "1                  1.0      NaN              NaN           NaN   \n",
       "2                  3.0      NaN              NaN           NaN   \n",
       "\n",
       "   first_subject_area  subject_areas  has_references  reference_count_oa  \\\n",
       "0                 3.0            3.0             3.0                 3.0   \n",
       "1                 NaN            NaN             0.0                 0.0   \n",
       "2                 NaN            NaN             3.0                 3.0   \n",
       "\n",
       "   num_citations_without_doi  num_duplicate_citation_dois  cited_dois  debug  \n",
       "0                        3.0                          3.0         3.0    3.0  \n",
       "1                        0.0                          0.0         NaN    NaN  \n",
       "2                        3.0                          3.0         NaN    NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary_by_type_and_publisher = pd.read_csv(\n",
    "    os.path.join(data_path, summary_by_type_and_publisher_stats_filename), sep='\\t'\n",
    ").rename(columns={\n",
    "    'reference_count': 'reference_count_crossref',\n",
    "    'num_references': 'reference_count_oa'\n",
    "})\n",
    "\n",
    "# fill na type and publisher with blank (otherwise causes issues with groupby)\n",
    "df_summary_by_type_and_publisher[['type', 'publisher']] = (\n",
    "    df_summary_by_type_and_publisher[['type', 'publisher']].fillna('')\n",
    ")\n",
    "\n",
    "# drop non-numeric stat value 'type' and convert to numeric values\n",
    "df_summary_by_type_and_publisher = df_summary_by_type_and_publisher[\n",
    "    df_summary_by_type_and_publisher['stat'] != 'type'\n",
    "].apply(pd.to_numeric, errors='ignore')\n",
    "\n",
    "df_summary_by_type_and_publisher.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Work counts by type and publisher"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>work_count</th>\n",
       "      <th>has_nonzero_reference_count_crossref</th>\n",
       "      <th>has_nonzero_reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>publisher</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">journal-article</th>\n",
       "      <th>Elsevier BV</th>\n",
       "      <td>16,339,851</td>\n",
       "      <td>11,285,796</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiley</th>\n",
       "      <td>8,047,211</td>\n",
       "      <td>5,583,310</td>\n",
       "      <td>5,583,310</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>4,464,416</td>\n",
       "      <td>3,469,369</td>\n",
       "      <td>3,465,542</td>\n",
       "      <td>99.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>4,281,233</td>\n",
       "      <td>2,792,654</td>\n",
       "      <td>2,776,404</td>\n",
       "      <td>99.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>3,281,604</td>\n",
       "      <td>1,846,267</td>\n",
       "      <td>1,844,492</td>\n",
       "      <td>99.90%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         work_count  \\\n",
       "type            publisher                                             \n",
       "journal-article Elsevier BV                              16,339,851   \n",
       "                Wiley                                     8,047,211   \n",
       "                Springer Science and Business Media LLC   4,464,416   \n",
       "                Informa UK Limited                        4,281,233   \n",
       "                Springer Nature                           3,281,604   \n",
       "\n",
       "                                                        has_nonzero_reference_count_crossref  \\\n",
       "type            publisher                                                                      \n",
       "journal-article Elsevier BV                                                       11,285,796   \n",
       "                Wiley                                                              5,583,310   \n",
       "                Springer Science and Business Media LLC                            3,469,369   \n",
       "                Informa UK Limited                                                 2,792,654   \n",
       "                Springer Nature                                                    1,846,267   \n",
       "\n",
       "                                                        has_nonzero_reference_count_oa  \\\n",
       "type            publisher                                                                \n",
       "journal-article Elsevier BV                                                          0   \n",
       "                Wiley                                                        5,583,310   \n",
       "                Springer Science and Business Media LLC                      3,465,542   \n",
       "                Informa UK Limited                                           2,776,404   \n",
       "                Springer Nature                                              1,844,492   \n",
       "\n",
       "                                                            p_oa  \n",
       "type            publisher                                         \n",
       "journal-article Elsevier BV                                0.00%  \n",
       "                Wiley                                    100.00%  \n",
       "                Springer Science and Business Media LLC   99.89%  \n",
       "                Informa UK Limited                        99.42%  \n",
       "                Springer Nature                           99.90%  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Work counts by type and publisher')\n",
    "\n",
    "df_summary_by_type_and_publisher_work_counts = df_summary_by_type_and_publisher[\n",
    "    df_summary_by_type_and_publisher['stat'] == 'count'\n",
    "].groupby(['type', 'publisher'])['doi'].sum().sort_values(ascending=False).to_frame('work_count')\n",
    "\n",
    "df_summary_by_type_and_publisher_non_zero_counts = df_summary_by_type_and_publisher[\n",
    "    df_summary_by_type_and_publisher['stat'] == 'count_non_zero'\n",
    "].groupby(['type', 'publisher'])[[\n",
    "    'reference_count_crossref', 'reference_count_oa'\n",
    "]].sum().rename(columns={\n",
    "    'reference_count_crossref': 'has_nonzero_reference_count_crossref',\n",
    "    'reference_count_oa': 'has_nonzero_reference_count_oa'\n",
    "})\n",
    "\n",
    "df_summary_by_type_and_publisher_work_counts = df_summary_by_type_and_publisher_work_counts.merge(\n",
    "    df_summary_by_type_and_publisher_non_zero_counts, how='outer', left_index=True, right_index=True\n",
    ").astype(int)\n",
    "\n",
    "# sanity check\n",
    "assert int(df_summary_by_type_and_publisher_work_counts.sum()[0]) == int(df_summary['has_references']['count'])\n",
    "\n",
    "df_summary_by_type_and_publisher_work_counts['p_oa'] = (\n",
    "    100.0 * df_summary_by_type_and_publisher_work_counts['has_nonzero_reference_count_oa'] /\n",
    "    df_summary_by_type_and_publisher_work_counts['has_nonzero_reference_count_crossref']\n",
    ")\n",
    "\n",
    "format_columns(df_summary_by_type_and_publisher_work_counts.sort_values(\n",
    "    'work_count', ascending=False\n",
    ").head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Total work counts"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>work_count</th>\n",
       "      <td>109,738,648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_nonzero_reference_count_crossref</th>\n",
       "      <td>48,992,325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_nonzero_reference_count_oa</th>\n",
       "      <td>27,947,414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_oa</th>\n",
       "      <td>192,206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            total\n",
       "work_count                            109,738,648\n",
       "has_nonzero_reference_count_crossref   48,992,325\n",
       "has_nonzero_reference_count_oa         27,947,414\n",
       "p_oa                                      192,206"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Total work counts')\n",
    "\n",
    "(\n",
    "    df_summary_by_type_and_publisher_work_counts.reset_index()\n",
    "    .drop(['type', 'publisher'], axis=1)\n",
    "    .sum(axis=0)\n",
    "    .to_frame('total')\n",
    "    .applymap(format_count)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Work counts by type"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *40,291,216* works of type *journal-article* with references in Crossref, **56.98%** are open (*22,956,661*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *4,966,411* works of type *book-chapter* with references in Crossref, **85.16%** are open (*4,229,448*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *3,372,899* works of type *proceedings-article* with references in Crossref, **14.31%** are open (*482,620*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *11* works of type *component* with references in Crossref, **18.18%** are open (*2*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *75,416* works of type *dataset* with references in Crossref, **0.04%** are open (*30*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>work_count</th>\n",
       "      <th>has_nonzero_reference_count_crossref</th>\n",
       "      <th>has_nonzero_reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>journal-article</th>\n",
       "      <td>79,960,807</td>\n",
       "      <td>40,291,216</td>\n",
       "      <td>22,956,661</td>\n",
       "      <td>56.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book-chapter</th>\n",
       "      <td>13,361,218</td>\n",
       "      <td>4,966,411</td>\n",
       "      <td>4,229,448</td>\n",
       "      <td>85.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proceedings-article</th>\n",
       "      <td>6,003,592</td>\n",
       "      <td>3,372,899</td>\n",
       "      <td>482,620</td>\n",
       "      <td>14.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>component</th>\n",
       "      <td>3,863,919</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>18.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <td>1,814,101</td>\n",
       "      <td>75,416</td>\n",
       "      <td>30</td>\n",
       "      <td>0.04%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     work_count has_nonzero_reference_count_crossref  \\\n",
       "type                                                                   \n",
       "journal-article      79,960,807                           40,291,216   \n",
       "book-chapter         13,361,218                            4,966,411   \n",
       "proceedings-article   6,003,592                            3,372,899   \n",
       "component             3,863,919                                   11   \n",
       "dataset               1,814,101                               75,416   \n",
       "\n",
       "                    has_nonzero_reference_count_oa    p_oa  \n",
       "type                                                        \n",
       "journal-article                         22,956,661  56.98%  \n",
       "book-chapter                             4,229,448  85.16%  \n",
       "proceedings-article                        482,620  14.31%  \n",
       "component                                        2  18.18%  \n",
       "dataset                                         30   0.04%  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Work counts by type')\n",
    "\n",
    "df_temp = (\n",
    "    df_summary_by_type_and_publisher_work_counts.reset_index().groupby('type')\n",
    "    .sum().sort_values(by='work_count', ascending=False).head()\n",
    ")\n",
    "\n",
    "def show_oa_ref_work_percentages_by_type(df):\n",
    "    for work_type, has_nonzero_reference_count_crossref, has_nonzero_reference_count_oa in zip(\n",
    "        df.index,\n",
    "        df['has_nonzero_reference_count_crossref'],\n",
    "        df['has_nonzero_reference_count_oa']\n",
    "    ):\n",
    "        if not has_nonzero_reference_count_crossref:\n",
    "            continue\n",
    "        printmd('Out of the *{:,}* works of type *{:}* with references in Crossref, **{:.2f}%** are open (*{:,}*).'.format(\n",
    "            int(has_nonzero_reference_count_crossref),\n",
    "            work_type,\n",
    "            100.0 * has_nonzero_reference_count_oa / has_nonzero_reference_count_crossref,\n",
    "            int(has_nonzero_reference_count_oa)\n",
    "        ))\n",
    "\n",
    "df_temp['p_oa'] = (\n",
    "    100.0 * df_temp['has_nonzero_reference_count_oa'] / df_temp['has_nonzero_reference_count_crossref']\n",
    ")\n",
    "\n",
    "show_oa_ref_work_percentages_by_type(df_temp)\n",
    "\n",
    "format_columns(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Journal vs non-journal-article type"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *40,291,216* works of type *journal-article* with references in Crossref, **56.98%** are open (*22,956,661*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of the *8,701,109* works of type *non-journal-article* with references in Crossref, **57.36%** are open (*4,990,753*)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>work_count</th>\n",
       "      <th>has_nonzero_reference_count_crossref</th>\n",
       "      <th>has_nonzero_reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>journal-article</th>\n",
       "      <td>79,960,807</td>\n",
       "      <td>40,291,216</td>\n",
       "      <td>22,956,661</td>\n",
       "      <td>56.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-journal-article</th>\n",
       "      <td>29,777,841</td>\n",
       "      <td>8,701,109</td>\n",
       "      <td>4,990,753</td>\n",
       "      <td>57.36%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     work_count has_nonzero_reference_count_crossref  \\\n",
       "type                                                                   \n",
       "journal-article      79,960,807                           40,291,216   \n",
       "non-journal-article  29,777,841                            8,701,109   \n",
       "\n",
       "                    has_nonzero_reference_count_oa    p_oa  \n",
       "type                                                        \n",
       "journal-article                         22,956,661  56.98%  \n",
       "non-journal-article                      4,990,753  57.36%  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Journal vs non-journal-article type')\n",
    "\n",
    "def type_to_journal_non_journal_article(df):\n",
    "    df = df.copy()\n",
    "    df['type'] = df['type'].map(lambda x: 'non-journal-article' if x != 'journal-article' else x)\n",
    "    return df\n",
    "\n",
    "df_temp = (\n",
    "    type_to_journal_non_journal_article(\n",
    "        df_summary_by_type_and_publisher_work_counts.reset_index()\n",
    "    )\n",
    "    .groupby('type')\n",
    "    .sum().sort_values(by='work_count', ascending=False).head()\n",
    ")\n",
    "\n",
    "df_temp['p_oa'] = 100.0 * df_temp['has_nonzero_reference_count_oa'] / df_temp['has_nonzero_reference_count_crossref']\n",
    "\n",
    "show_oa_ref_work_percentages_by_type(df_temp)\n",
    "\n",
    "format_columns(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### By publisher"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>work_count</th>\n",
       "      <th>has_nonzero_reference_count_crossref</th>\n",
       "      <th>has_nonzero_reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>publisher</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">journal-article</th>\n",
       "      <th>Elsevier BV</th>\n",
       "      <td>16,339,851</td>\n",
       "      <td>11,285,796</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiley</th>\n",
       "      <td>8,047,211</td>\n",
       "      <td>5,583,310</td>\n",
       "      <td>5,583,310</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>4,464,416</td>\n",
       "      <td>3,469,369</td>\n",
       "      <td>3,465,542</td>\n",
       "      <td>99.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>4,281,233</td>\n",
       "      <td>2,792,654</td>\n",
       "      <td>2,776,404</td>\n",
       "      <td>99.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>3,281,604</td>\n",
       "      <td>1,846,267</td>\n",
       "      <td>1,844,492</td>\n",
       "      <td>99.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">non-journal-article</th>\n",
       "      <th>Wiley</th>\n",
       "      <td>367,169</td>\n",
       "      <td>3,661</td>\n",
       "      <td>3,661</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>21,202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>3,907</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elsevier BV</th>\n",
       "      <td>2,763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>552</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             work_count  \\\n",
       "type                publisher                                             \n",
       "journal-article     Elsevier BV                              16,339,851   \n",
       "                    Wiley                                     8,047,211   \n",
       "                    Springer Science and Business Media LLC   4,464,416   \n",
       "                    Informa UK Limited                        4,281,233   \n",
       "                    Springer Nature                           3,281,604   \n",
       "non-journal-article Wiley                                       367,169   \n",
       "                    Springer Nature                              21,202   \n",
       "                    Informa UK Limited                            3,907   \n",
       "                    Elsevier BV                                   2,763   \n",
       "                    Springer Science and Business Media LLC         552   \n",
       "\n",
       "                                                            has_nonzero_reference_count_crossref  \\\n",
       "type                publisher                                                                      \n",
       "journal-article     Elsevier BV                                                       11,285,796   \n",
       "                    Wiley                                                              5,583,310   \n",
       "                    Springer Science and Business Media LLC                            3,469,369   \n",
       "                    Informa UK Limited                                                 2,792,654   \n",
       "                    Springer Nature                                                    1,846,267   \n",
       "non-journal-article Wiley                                                                  3,661   \n",
       "                    Springer Nature                                                            0   \n",
       "                    Informa UK Limited                                                         0   \n",
       "                    Elsevier BV                                                                0   \n",
       "                    Springer Science and Business Media LLC                                    0   \n",
       "\n",
       "                                                            has_nonzero_reference_count_oa  \\\n",
       "type                publisher                                                                \n",
       "journal-article     Elsevier BV                                                          0   \n",
       "                    Wiley                                                        5,583,310   \n",
       "                    Springer Science and Business Media LLC                      3,465,542   \n",
       "                    Informa UK Limited                                           2,776,404   \n",
       "                    Springer Nature                                              1,844,492   \n",
       "non-journal-article Wiley                                                            3,661   \n",
       "                    Springer Nature                                                      0   \n",
       "                    Informa UK Limited                                                   0   \n",
       "                    Elsevier BV                                                          0   \n",
       "                    Springer Science and Business Media LLC                              0   \n",
       "\n",
       "                                                                p_oa  \n",
       "type                publisher                                         \n",
       "journal-article     Elsevier BV                                0.00%  \n",
       "                    Wiley                                    100.00%  \n",
       "                    Springer Science and Business Media LLC   99.89%  \n",
       "                    Informa UK Limited                        99.42%  \n",
       "                    Springer Nature                           99.90%  \n",
       "non-journal-article Wiley                                    100.00%  \n",
       "                    Springer Nature                             nan%  \n",
       "                    Informa UK Limited                          nan%  \n",
       "                    Elsevier BV                                 nan%  \n",
       "                    Springer Science and Business Media LLC     nan%  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### By publisher')\n",
    "\n",
    "def select_top_publishers(df, count=5):\n",
    "    top_publishers = (\n",
    "        df.groupby('publisher').sum()\n",
    "        .sort_values(by='work_count', ascending=False)\n",
    "        .index.values\n",
    "    )\n",
    "    return df[df['publisher'].isin(top_publishers[:count])]\n",
    "\n",
    "df_temp = (\n",
    "    type_to_journal_non_journal_article(\n",
    "        select_top_publishers(df_summary_by_type_and_publisher_work_counts.reset_index())\n",
    "    )\n",
    "    .groupby(['type', 'publisher'])\n",
    "    .sum().sort_values(by='work_count', ascending=False)\n",
    ")\n",
    "\n",
    "df_temp['p_oa'] = (\n",
    "    100.0 * df_temp['has_nonzero_reference_count_oa'] / df_temp['has_nonzero_reference_count_crossref']\n",
    ")\n",
    "\n",
    "format_columns(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### By normalised publisher"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>work_count</th>\n",
       "      <th>has_nonzero_reference_count_crossref</th>\n",
       "      <th>has_nonzero_reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>publisher</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">journal-article</th>\n",
       "      <th>Elsevier (all)</th>\n",
       "      <td>16,339,851</td>\n",
       "      <td>11,285,796</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiley</th>\n",
       "      <td>8,047,211</td>\n",
       "      <td>5,583,310</td>\n",
       "      <td>5,583,310</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>4,464,416</td>\n",
       "      <td>3,469,369</td>\n",
       "      <td>3,465,542</td>\n",
       "      <td>99.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>4,281,233</td>\n",
       "      <td>2,792,654</td>\n",
       "      <td>2,776,404</td>\n",
       "      <td>99.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>3,281,604</td>\n",
       "      <td>1,846,267</td>\n",
       "      <td>1,844,492</td>\n",
       "      <td>99.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">non-journal-article</th>\n",
       "      <th>Elsevier (all)</th>\n",
       "      <td>1,210,213</td>\n",
       "      <td>675,293</td>\n",
       "      <td>421</td>\n",
       "      <td>0.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiley</th>\n",
       "      <td>367,169</td>\n",
       "      <td>3,661</td>\n",
       "      <td>3,661</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>21,202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>3,907</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>552</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             work_count  \\\n",
       "type                publisher                                             \n",
       "journal-article     Elsevier (all)                           16,339,851   \n",
       "                    Wiley                                     8,047,211   \n",
       "                    Springer Science and Business Media LLC   4,464,416   \n",
       "                    Informa UK Limited                        4,281,233   \n",
       "                    Springer Nature                           3,281,604   \n",
       "non-journal-article Elsevier (all)                            1,210,213   \n",
       "                    Wiley                                       367,169   \n",
       "                    Springer Nature                              21,202   \n",
       "                    Informa UK Limited                            3,907   \n",
       "                    Springer Science and Business Media LLC         552   \n",
       "\n",
       "                                                            has_nonzero_reference_count_crossref  \\\n",
       "type                publisher                                                                      \n",
       "journal-article     Elsevier (all)                                                    11,285,796   \n",
       "                    Wiley                                                              5,583,310   \n",
       "                    Springer Science and Business Media LLC                            3,469,369   \n",
       "                    Informa UK Limited                                                 2,792,654   \n",
       "                    Springer Nature                                                    1,846,267   \n",
       "non-journal-article Elsevier (all)                                                       675,293   \n",
       "                    Wiley                                                                  3,661   \n",
       "                    Springer Nature                                                            0   \n",
       "                    Informa UK Limited                                                         0   \n",
       "                    Springer Science and Business Media LLC                                    0   \n",
       "\n",
       "                                                            has_nonzero_reference_count_oa  \\\n",
       "type                publisher                                                                \n",
       "journal-article     Elsevier (all)                                                       0   \n",
       "                    Wiley                                                        5,583,310   \n",
       "                    Springer Science and Business Media LLC                      3,465,542   \n",
       "                    Informa UK Limited                                           2,776,404   \n",
       "                    Springer Nature                                              1,844,492   \n",
       "non-journal-article Elsevier (all)                                                     421   \n",
       "                    Wiley                                                            3,661   \n",
       "                    Springer Nature                                                      0   \n",
       "                    Informa UK Limited                                                   0   \n",
       "                    Springer Science and Business Media LLC                              0   \n",
       "\n",
       "                                                                p_oa  \n",
       "type                publisher                                         \n",
       "journal-article     Elsevier (all)                             0.00%  \n",
       "                    Wiley                                    100.00%  \n",
       "                    Springer Science and Business Media LLC   99.89%  \n",
       "                    Informa UK Limited                        99.42%  \n",
       "                    Springer Nature                           99.90%  \n",
       "non-journal-article Elsevier (all)                             0.06%  \n",
       "                    Wiley                                    100.00%  \n",
       "                    Springer Nature                             nan%  \n",
       "                    Informa UK Limited                          nan%  \n",
       "                    Springer Science and Business Media LLC     nan%  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### By normalised publisher')\n",
    "\n",
    "def normalise_publisher(df):\n",
    "    elsevier_names = [\n",
    "        s for s in df['publisher'].unique()\n",
    "        if not pd.isnull(s) and 'elsevier' in s.lower()\n",
    "    ]\n",
    "    return df.replace({\n",
    "        'publisher': {s: 'Elsevier (all)' for s in elsevier_names}\n",
    "    })\n",
    "\n",
    "df_temp = (\n",
    "    select_top_publishers(normalise_publisher(type_to_journal_non_journal_article(\n",
    "        df_summary_by_type_and_publisher_work_counts.reset_index()\n",
    "    )))\n",
    "    .groupby(['type', 'publisher'])\n",
    "    .sum().sort_values(by='work_count', ascending=False)\n",
    ")\n",
    "\n",
    "df_temp['p_oa'] = (\n",
    "    100.0 * df_temp['has_nonzero_reference_count_oa'] / df_temp['has_nonzero_reference_count_crossref']\n",
    ")\n",
    "\n",
    "format_columns(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Sanity check, the grouped summary should equal the ungrouped summary"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>reference_count_crossref</th>\n",
       "      <td>1,446,365,933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reference_count_oa</th>\n",
       "      <td>851,098,649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    sum\n",
       "reference_count_crossref  1,446,365,933\n",
       "reference_count_oa          851,098,649"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Sanity check, the grouped summary should equal the ungrouped summary')\n",
    "\n",
    "df_summary_by_type_and_publisher_sums = df_summary_by_type_and_publisher[\n",
    "    df_summary_by_type_and_publisher['stat'] == 'sum'\n",
    "]\n",
    "total_reference_count_according_to_crossref = df_summary_by_type_and_publisher_sums['reference_count_crossref'].sum()\n",
    "assert total_reference_count_according_to_crossref == df_summary['reference_count']['sum']\n",
    "(\n",
    "    df_summary_by_type_and_publisher_sums[['reference_count_crossref', 'reference_count_oa']]\n",
    "    .sum()\n",
    "    .map(format_count)\n",
    "    .to_frame('sum')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Total references by type"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>reference_count_crossref</th>\n",
       "      <th>reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>journal-article</th>\n",
       "      <td>1,199,588,788</td>\n",
       "      <td>685,022,593</td>\n",
       "      <td>57.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book-chapter</th>\n",
       "      <td>190,323,744</td>\n",
       "      <td>147,448,104</td>\n",
       "      <td>77.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proceedings-article</th>\n",
       "      <td>44,855,360</td>\n",
       "      <td>7,342,649</td>\n",
       "      <td>16.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reference-entry</th>\n",
       "      <td>6,599,007</td>\n",
       "      <td>6,598,789</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posted-content</th>\n",
       "      <td>2,706,066</td>\n",
       "      <td>2,705,849</td>\n",
       "      <td>99.99%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    reference_count_crossref reference_count_oa     p_oa\n",
       "type                                                                    \n",
       "journal-article                1,199,588,788        685,022,593   57.10%\n",
       "book-chapter                     190,323,744        147,448,104   77.47%\n",
       "proceedings-article               44,855,360          7,342,649   16.37%\n",
       "reference-entry                    6,599,007          6,598,789  100.00%\n",
       "posted-content                     2,706,066          2,705,849   99.99%"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Total references by type')\n",
    "\n",
    "df_temp = (\n",
    "    df_summary_by_type_and_publisher_sums.groupby('type')\n",
    "    [['reference_count_crossref', 'reference_count_oa']]\n",
    "    .sum()\n",
    "    .sort_values('reference_count_crossref', ascending=False)\n",
    "    .head()\n",
    ")\n",
    "\n",
    "df_temp['p_oa'] = (\n",
    "    100.0 * df_temp['reference_count_oa'] / df_temp['reference_count_crossref']\n",
    ")\n",
    "\n",
    "format_columns(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Publishers with the highest amount of references"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>reference_count_crossref</th>\n",
       "      <th>reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publisher</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Elsevier BV</th>\n",
       "      <td>355,713,781</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiley</th>\n",
       "      <td>155,877,856</td>\n",
       "      <td>155,877,856</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>100,336,210</td>\n",
       "      <td>100,193,985</td>\n",
       "      <td>99.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>81,434,261</td>\n",
       "      <td>81,057,438</td>\n",
       "      <td>99.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>56,198,393</td>\n",
       "      <td>56,129,361</td>\n",
       "      <td>99.88%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        reference_count_crossref  \\\n",
       "publisher                                                          \n",
       "Elsevier BV                                          355,713,781   \n",
       "Wiley                                                155,877,856   \n",
       "Springer Science and Business Media LLC              100,336,210   \n",
       "Informa UK Limited                                    81,434,261   \n",
       "Springer Nature                                       56,198,393   \n",
       "\n",
       "                                        reference_count_oa     p_oa  \n",
       "publisher                                                            \n",
       "Elsevier BV                                              0    0.00%  \n",
       "Wiley                                          155,877,856  100.00%  \n",
       "Springer Science and Business Media LLC        100,193,985   99.86%  \n",
       "Informa UK Limited                              81,057,438   99.54%  \n",
       "Springer Nature                                 56,129,361   99.88%  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Publishers with the highest amount of references')\n",
    "\n",
    "df_temp = (\n",
    "    df_summary_by_type_and_publisher_sums.groupby('publisher')\n",
    "    .sum()[['reference_count_crossref', 'reference_count_oa']]\n",
    "    .sort_values(by='reference_count_crossref', ascending=False)\n",
    ").head()\n",
    "\n",
    "df_temp['p_oa'] = (\n",
    "    100.0 * df_temp['reference_count_oa'] / df_temp['reference_count_crossref']\n",
    ")\n",
    "\n",
    "format_columns(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Elsevier uses mutliple names, treat them as one"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* Elsevier\n",
       "* Elsevier Inc.\n",
       "* Elsevier BV\n",
       "* \"Elsevier\",\n",
       "* Elsevier Masson (programme ReLIRE)\n",
       "* Elsevier Pub. Co.\n",
       "* Elsevier Pub. Co.,\n",
       "* Elsevier,\n",
       "* Publishing company \"Elsevier,\",\n",
       "* Elsevier IFAC\n",
       "* Elsevier, IFAC\n",
       "* IFAC, Elsevier"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printmd('### Elsevier uses mutliple names, treat them as one')\n",
    "\n",
    "elsevier_names = [\n",
    "    s for s in df_summary_by_type_and_publisher_sums['publisher'].unique()\n",
    "    if not pd.isnull(s) and 'elsevier' in s.lower()\n",
    "]\n",
    "printmd('* %s' % '\\n* '.join(elsevier_names))\n",
    "df_summary_by_type_and_publisher_sums_merged_publisher = (\n",
    "    df_summary_by_type_and_publisher_sums.replace({\n",
    "        'publisher': {s: 'Elsevier (all)' for s in elsevier_names}\n",
    "    })\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Publishers with the highest amount of references - this time with merged publisher names"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>reference_count_crossref</th>\n",
       "      <th>reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publisher</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Elsevier (all)</th>\n",
       "      <td>396,031,893</td>\n",
       "      <td>14,984</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiley</th>\n",
       "      <td>155,877,856</td>\n",
       "      <td>155,877,856</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>100,336,210</td>\n",
       "      <td>100,193,985</td>\n",
       "      <td>99.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>81,434,261</td>\n",
       "      <td>81,057,438</td>\n",
       "      <td>99.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>56,198,393</td>\n",
       "      <td>56,129,361</td>\n",
       "      <td>99.88%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        reference_count_crossref  \\\n",
       "publisher                                                          \n",
       "Elsevier (all)                                       396,031,893   \n",
       "Wiley                                                155,877,856   \n",
       "Springer Science and Business Media LLC              100,336,210   \n",
       "Informa UK Limited                                    81,434,261   \n",
       "Springer Nature                                       56,198,393   \n",
       "\n",
       "                                        reference_count_oa     p_oa  \n",
       "publisher                                                            \n",
       "Elsevier (all)                                      14,984    0.00%  \n",
       "Wiley                                          155,877,856  100.00%  \n",
       "Springer Science and Business Media LLC        100,193,985   99.86%  \n",
       "Informa UK Limited                              81,057,438   99.54%  \n",
       "Springer Nature                                 56,129,361   99.88%  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Publishers with the highest amount of references - this time with merged publisher names')\n",
    "\n",
    "df_temp = (\n",
    "    df_summary_by_type_and_publisher_sums_merged_publisher.groupby('publisher')\n",
    "    .sum()[['reference_count_crossref', 'reference_count_oa']]\n",
    "    .sort_values(by='reference_count_crossref', ascending=False)\n",
    ").head()\n",
    "\n",
    "df_temp['p_oa'] = (\n",
    "    100.0 * df_temp['reference_count_oa'] / df_temp['reference_count_crossref']\n",
    ")\n",
    "\n",
    "format_columns(df_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Publishers with the highest amount of references (by type) - this time with merged publisher names"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>reference_count_crossref</th>\n",
       "      <th>reference_count_oa</th>\n",
       "      <th>p_oa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>publisher</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">journal-article</th>\n",
       "      <th>Elsevier (all)</th>\n",
       "      <td>355,713,781</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiley</th>\n",
       "      <td>155,663,089</td>\n",
       "      <td>155,663,089</td>\n",
       "      <td>100.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Science and Business Media LLC</th>\n",
       "      <td>100,336,210</td>\n",
       "      <td>100,193,985</td>\n",
       "      <td>99.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Informa UK Limited</th>\n",
       "      <td>81,434,261</td>\n",
       "      <td>81,057,438</td>\n",
       "      <td>99.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Springer Nature</th>\n",
       "      <td>56,198,393</td>\n",
       "      <td>56,129,361</td>\n",
       "      <td>99.88%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        reference_count_crossref  \\\n",
       "type            publisher                                                          \n",
       "journal-article Elsevier (all)                                       355,713,781   \n",
       "                Wiley                                                155,663,089   \n",
       "                Springer Science and Business Media LLC              100,336,210   \n",
       "                Informa UK Limited                                    81,434,261   \n",
       "                Springer Nature                                       56,198,393   \n",
       "\n",
       "                                                        reference_count_oa  \\\n",
       "type            publisher                                                    \n",
       "journal-article Elsevier (all)                                           0   \n",
       "                Wiley                                          155,663,089   \n",
       "                Springer Science and Business Media LLC        100,193,985   \n",
       "                Informa UK Limited                              81,057,438   \n",
       "                Springer Nature                                 56,129,361   \n",
       "\n",
       "                                                            p_oa  \n",
       "type            publisher                                         \n",
       "journal-article Elsevier (all)                             0.00%  \n",
       "                Wiley                                    100.00%  \n",
       "                Springer Science and Business Media LLC   99.86%  \n",
       "                Informa UK Limited                        99.54%  \n",
       "                Springer Nature                           99.88%  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "printmd('### Publishers with the highest amount of references (by type) - this time with merged publisher names')\n",
    "\n",
    "df_temp = (\n",
    "    df_summary_by_type_and_publisher_sums_merged_publisher.groupby(['type', 'publisher'])\n",
    "    .sum()[['reference_count_crossref', 'reference_count_oa']]\n",
    "    .sort_values(by='reference_count_crossref', ascending=False)\n",
    ").head()\n",
    "\n",
    "df_temp['p_oa'] = (\n",
    "    100.0 * df_temp['reference_count_oa'] / df_temp['reference_count_crossref']\n",
    ")\n",
    "\n",
    "format_columns(df_temp)"
   ]
  }
 ],
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