mromanello/AWOL-index language distribution.ipynb

## AWOL-index language distribution.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requirements to run this notebook:\n",
    "\n",
    "    pip install pandas iso639 matplotlib requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import requests\n",
    "import pandas as pd\n",
    "import zipfile\n",
    "import glob\n",
    "import json\n",
    "import codecs\n",
    "import collections\n",
    "import iso639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basedir = \"../tmp/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname = 'awol-index-json.zip'\n",
    "url = 'http://isaw.nyu.edu/publications/awol-index/' + fname\n",
    "r = requests.get(url)\n",
    "open(basedir + fname , 'wb').write(r.content)\n",
    "awol_zip = zipfile.ZipFile(basedir + fname)\n",
    "awol_zip.extractall(basedir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "awol_json_files = glob.glob(\"%s/json/*/*.json\"%basedir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52006"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(awol_json_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "awol_records = [json.load(codecs.open(file_path,\"r\",\"utf-8\")) for file_path in awol_json_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52006"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(awol_records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's keep only the records that correspond to journals.\n",
    "\n",
    "Here's the heuristic I've applied:\n",
    "- the record should have at least one subordinate resource\n",
    "- the list of identifiers should contain at least one \"ISSN\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "journal_records = [record for record in awol_records \n",
    "                                     if len(record[\"subordinate_resources\"])>0 and \"issn\" in record[\"identifiers\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "661"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(journal_records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "languages = [language for record in journal_records for language in record[\"languages\"] \n",
    "                                                                 if len(record[\"languages\"])>0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "639"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(languages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(dict(collections.Counter(languages)), orient=\"index\")\n",
    "df.columns = [\"lang\"]\n",
    "df[\"pct\"] = (df[\"lang\"]*100)/df[\"lang\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "639"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"lang\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top10_languages = df.sort_values(by=\"pct\",ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lang</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>315</td>\n",
       "      <td>49.295775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French</th>\n",
       "      <td>87</td>\n",
       "      <td>13.615023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spanish; Castilian</th>\n",
       "      <td>81</td>\n",
       "      <td>12.676056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italian</th>\n",
       "      <td>36</td>\n",
       "      <td>5.633803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portuguese</th>\n",
       "      <td>24</td>\n",
       "      <td>3.755869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>German</th>\n",
       "      <td>24</td>\n",
       "      <td>3.755869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin</th>\n",
       "      <td>10</td>\n",
       "      <td>1.564945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catalan; Valencian</th>\n",
       "      <td>10</td>\n",
       "      <td>1.564945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romanian; Moldavian; Moldovan</th>\n",
       "      <td>6</td>\n",
       "      <td>0.938967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greek, Modern (1453-); Greek</th>\n",
       "      <td>6</td>\n",
       "      <td>0.938967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               lang        pct\n",
       "English                         315  49.295775\n",
       "French                           87  13.615023\n",
       "Spanish; Castilian               81  12.676056\n",
       "Italian                          36   5.633803\n",
       "Portuguese                       24   3.755869\n",
       "German                           24   3.755869\n",
       "Latin                            10   1.564945\n",
       "Catalan; Valencian               10   1.564945\n",
       "Romanian; Moldavian; Moldovan     6   0.938967\n",
       "Greek, Modern (1453-); Greek      6   0.938967"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_languages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "top10_languages.index = [iso639.to_name(lang) for lang in top10_languages.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x36cfcf10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AREREMHnyZGw2Gz169CgyhX/8+HFeeOEFpkyZwr///W8KCwuBX4+ILRYL8fHxTJs2jRdf\nfJGFCxe6+qampnLnnXcyY8YM/P39XdPtffr0KbGAuOjGG29kw4YN/PTTT4SHh+Pj4+N67uKpgKSk\nJJo3b86yZcuK9C0oKODNN98kMTGRadOmufYDQFhYGC+99BI2m42YmBgWL14M/PqBDpCZmUlmZibh\n4eFFxnVnH40fP77EAgKgUaNGZGVlcf78edavX8/NN9/sem7//v18/fXXTJ06lVdeeYUvv/ySgwcP\nFum/ceNGUlNTmTVrFrGxsezZs8e1/fPmzWPMmDEkJSVht9v5/PPP6dixo6tQANiwYYNrnQ8//DBT\np04lKSmJlJQUDh8+7FpPYGAgNpuNPn36sHLlytJeMhER8bByZyLy8/NJSEjg1KlThIaG0qdPHwB2\n797t+kAIDAykXbt27Nu3jzp16tCqVSsCAwOBCx9cnTt3BqB58+akpKQAkJGRwXvvvceZM2dwOByE\nhoa61tmlSxdMJhNWq5XAwEAyMzOLTLs7nU4WL17Mrl27MBgMnDlzhrNnzwIQGhpK8+bNAQgPDyct\nLa3cnWAwGLjpppuYNWsWx48f5+abb+ann34CICcnh5ycHNq2bQvAbbfdxqxZs4r0P3bsGNdccw3X\nXHMNALfccgtffvklAOfPn2fOnDmcOHECg8GAw+EALhQtkydPJiYmhg0bNtC9e/diua5kH5W2nd26\ndWP9+vXs3buXp556ylUk7t69m65du2KxWADo1q0bu3fvLnJaZ9euXa7XPCgoiPbt2wMXCpprrrmG\nRo0aAdCzZ08+++wz+vXrR6dOndi8eTM33ngjW7duZdCgQcCFU0ZffvklhYWFZGZmcvToUdfr1q1b\nN+DC67dp06YStyU5OZnk5GTX45iYmBLbmUxm/K3WMvdLTWCxWLB6Qc6SeHN2UH5PU37PW7p0qevv\nyMhIIiMjK9y33CLC19cXm81Gfn4+kydPZvPmza5/5Esd1PzrsEaj0fXYaDS6PkTnz5/PPffcQ5cu\nXUhJSSlydH/pLIDBYHAdZV+0bt06srKysNlsGI1GYmNjKSgoKNbXaDS6lpcnMDAQk8nEjh07ePzx\nx11FBPw6G1OW0tosWbKE9u3bExcXR3p6OpMmTQIgODgYq9XK4cOH+fbbb/nTn/5UrO+V7KPS9OjR\ng4SEBH7/+99f8bUOl25zadvfo0cPPv30U+rVq0fLli3x8/MjLS2Njz/+mGnTpuHv78+8efOKvE4X\nt+3S98vlKvpGdzjsZGdnV2azPMJqtXpFzpJ4c3ZQfk9Tfs+yWq2lHoRVRIVPZ1gsFh5//HH++c9/\nAhem2jds2EBhYSFZWVns2rWLVq1aVXjFOTk5BAUFAbBmzZoK9bmYJScnh8DAQIxGIzt37iQjI6NY\nm8t9+umnfPbZZ2WOP3DgQB599NEiH67+/v7Uq1eP3bt3A7B27VratWtXpF/Tpk1d1yjAhaPsS7fz\n4gzB6tWri/S7eIoiJyfHdRR+KXf20csvv8yZM2dKfb5hw4Y8/PDD3HHHHUWWR0RE8P3335Ofn09u\nbi6bNm0iIiIC+HWftmvXzvWanzlzxjUT0KRJEzIyMjh58iRQdB+1a9eOAwcO8MUXX7hmMX755Rf8\n/PyoU6cOmZmZ/PDDDxXaNhERqVnKnYm49AO1RYsWNG7cmA0bNtCjRw/27NlDfHw8RqORwYMHExgY\nyLFjxyq04gEDBjBz5kzq1atHZGQk6eklfxXv0vVf/PuWW27BZrMRHx9PeHg4TZs2LbH9pY4fP+46\nJVGa1q1bl7h8xIgRxS6svJSPjw9/+tOfmDp1Kr6+vrRt29b1Vcr+/fszd+5cPvjggyIXawJ0796d\n+fPn8+CDD5a43sruI6fTSWpqKvXq1StzOy+9NuRi3+uuu46ePXsyfvx41wWo1157bZE23bp1Y+fO\nnYwdO5aGDRu6LiD18fFh+PDhzJw503Vh5cXTXkajkRtuuIGvv/6aZ599FoBrr72WFi1aMGbMGBo0\naFDkddE3QUREvIfBWZG5+lrAZrMRFxdX5BsFtc2RI0dYvXp1qfd1qO2O3BVVbJkl0YajZYQH0lSO\nN0+JenN2UH5PU37PutJvv/1m7liZkJDg6QjVrlmzZr/ZAkJERK6+30wRIbWfJdFWfGFwyNUPIiLy\nG6EiQmoNbzhtISJSm+hXPEVERMQtKiJERETELSoiRERExC0qIkRERMQtKiJERETELSoiRERExC0q\nIkRERMQtKiJERETELSoiRERExC26Y6XUGqZ9u8puEByCI6jh1QkjIvIboCLCDZmZmSxYsID9+/dT\nt25dAgMDeeyxx2jUqFGJ7XNycvjmm2+44447yh17yJAhLFy4sMqy5ufnM3z4cObOnYufn59reVJS\nEtHR0dx0000l9ktJSWHlypUkJiZWWZbNmzdz7Ngx7r333iob81L508r+kTVLog1URIiIVBmdznDD\n9OnTad++PX/961+ZOnUqjzzyCJmZmaW2P3fuHJ999lmFxjYYDFUVEwCLxUKnTp3YtGmTa1lOTg4/\n/fQTN9xww1XNEhUVVW0FhIiIXH2aiaiknTt3Yjab6d27t2tZ8+bNAcjNzSUpKYnz58/jcDgYOHAg\nUVFRLF68mLS0NBISEujQoQMPPvhgie0uVdpY6enpTJkyhbZt27Jnzx6Cg4MZN24cPj4+rFq1CoPB\nUCQbwM0338znn3/OrbfeCsCmTZvo1KkTFouFvXv3smDBAgoKCrBYLIwYMYLGjRsX6Z+Xl8c777zD\n0aNHsdvtDBgwgKioKNasWcPmzZvJz8/n5MmTdO3alUGDBgGwbds2/vnPf1JYWEhAQAATJkxgzZo1\n7N+/n2HDhrFlyxaWL1+O3W7HarUyatQoAgICWLZsGRkZGaSlpZGRkUG/fv248847q/x1FBGRK6ci\nopKOHDlCeHh4ic9ZLBbi4+Px8/MjOzub559/nqioKB599FGOHj2KzXbhp6oLCwtLbFeRsQBSU1MZ\nM2YMTz/9NLNmzWLjxo1ER0fTp0+fEnN16tSJN998k3PnzlGvXj02bNjAH/7wBwDCwsJ46aWXMBqN\n7Nixg8WLFzN27Ngi/ZcvX06HDh0YPnw4OTk5jB8/no4dOwJw6NAhkpKSMJlMjB49mn79+mE2m3nz\nzTd5+eWXadiwIefPny+WKSIigsmTJwPw1Vdf8eGHHzJ48GAAjh8/zl/+8hdycnIYPXo0ffv2xWjU\npJmISE2jIqIKOZ1OFi9ezK5duzAYDJw5c4azZ89WuF1gYGCFxgoNDXXNfoSHh5OWllZmLrPZTFRU\nFN999x3du3fn4MGDdO7cGYDz588zZ84cTpw4gcFgwOFwFOu/fft2tmzZwsqVKwGw2+1kZGQA0KFD\nB9e1FmFhYaSnp3Pu3DnatWtHw4YXrj+oW7dusTEzMjJ47733OHPmDA6Hg9DQUNdzXbp0wWQyYbVa\nCQwMJDMzk+Dg4CL9k5OTSU5Odj2OiYkpcx8AmExm/K3Wctt5gsViwVpDs5XHm7OD8nua8nve0qVL\nXX9HRkYSGRlZ4b4qIiqpWbNmfPfddyU+t27dOrKysrDZbBiNRmJjYykoKHCrXVltfHx8XO2MRmOJ\n67hcjx49+OCDD3A6nURFRbmO7JcsWUL79u2Ji4sjPT2dSZMmFevrdDoZO3ZssdMce/bswWz+9S10\naRHidDrLzDN//nzuueceunTpQkpKCsuWLXM9d+n2GQwGCgsLi/Wv7BsdwOGwk52dXak+V4vVaq2x\n2crjzdlB+T1N+T3LarVW6CCsNJojrqT27dtjt9v58ssvXcsOHz7M7t27ycnJITAwEKPRyM6dO11H\n63Xq1OGXX35xtS+tHfz64VuRNpf79NNPS72AMzIyktTUVD7//HOio6OLZLl4lL969eoS+3bq1IlP\nPvnE9fjgwYMltrvo+uuvZ/fu3aSnpwMXLiy9XE5ODkFBQQCsWbOmzPFERKRmUhHhhvj4eLZv387I\nkSMZO3Ysixcvpn79+txyyy3s27eP+Ph41q1bR9OmTQGoV68ebdq0IS4ujkWLFnHrrbeW2A5+/UZE\naWNd2uZyx48fL3VazWAw0L17d9ephov69+/P+++/T0JCQqnFyR//+EfsdjtxcXGMHTuWJUuWlLoO\ngICAAJ566immT5/OuHHjmD17drG2AwYMYObMmYwfP56AgIASxytrW0VExPMMzvLmncVr2Gw24uLi\nMJlMno7iEUfuiirzeUuiDUfLiKuUpnK8eUrUm7OD8nua8ntWkyZNrqi/romoRRISyr7ZkoiISFVS\nESG1hiXRVnaD4JCrE0RE5DdCRYTUGjX1VIWISG2lCytFRETELSoiRERExC0qIkRERMQtKiJERETE\nLSoiRERExC0qIkRERMQtKiJERETELSoiRERExC0qIkRERMQtumOl1BqmfbvKbhAcgiOo4dUJIyLy\nG6AiQqrEkCFDWLhwYYXapqSkYDabad26NQCrVq3C19eXW2+99Yoy5E8r+wfILIk2UBEhIlJlVERI\nlTAYDBVum5ycjJ+fn6uI6NOnT3XFEhGRaqQiQqrNli1bWL58OXa7HavVyqhRo8jLy2PVqlWYTCa+\n+eYbHn/8cXbs2EGdOnW4++67mTRpEq1atSI5OZmcnByeeeYZ2rZt6+lNERGREqiIkGoTERHB5MmT\nAfjqq6/48MMPGTx4MH369HEVDQA7duwo0q+wsJApU6bwww8/sGzZMiZMmHDVs4uISPlUREi1ycjI\n4L333uPMmTM4HA5CQ0Mr1K979+4AhIeHk5GRUZ0RRUTkCqiIkGozf/587rnnHrp06UJKSgrLli2r\nUD+z+cLb0mg04nA4SmyTnJxMcnKy63FMTEy545pMZvyt1gpluNosFgvWGpqtPN6cHZTf05Tf85Yu\nXer6OzIyksjIyAr3VREhVcLpdBZblpOTQ1BQEABr1qxxLa9Tpw45OTlujwuVf6MDOBx2srOzK9Xn\narFarTU2W3m8OTsov6cpv2dZrdYKHYSVRkWEVIn8/HyGDx/uenz33XczYMAAZs6cSb169YiMjCQ9\nPR2AG264gZkzZ7JlyxYef/zxMr/ZUZlvfYiIyNVlcJZ2qCfiZY7cFVXm85ZEG46WEVcpTeV489GM\nN2cH5fc05fesJk2aXFF/3fZaRERE3KLTGVJrWBJtZTcIDrk6QUREfiNUREitUVNPVYiI1FY6nSEi\nIiJuUREhIiIiblERISIiIm5RESEiIiJuUREhIiIiblERISIiIm5RESEiIiJuUREhIiIiblERISIi\nIm7RHSul1jDt21U9AweH4AhqWD1ji4h4MRURUmvkT0uolnEtiTZQESEiUoyKCHE5e/Ys7777Lj//\n/DP16tXDbDbTv39/unbt6uloIiJSA6mIEJekpCR69uzJqFGjAMjIyGDz5s0V6ltYWIjRqEtsRER+\nS1RECAA7d+7Ex8eH3r17u5Y1bNiQP/zhDxQWFrJ48WJSUlIoKCigb9++9O7dm5SUFJYsWULdunU5\nfvw4zz//PFOmTOH666/np59+omXLlvTs2ZNly5aRlZXFqFGjaNmyJXv37mXBggUUFBRgsVgYMWIE\njRs3Zs2aNWzevJn8/HxOnjxJ165dGTRokAf3ioiIlEWHjgLAkSNHuO6660p87quvvsLf358pU6Yw\ndepUvvzyS9LT0wE4cOAAw4YN47XXXgMgNTWV/v37M3v2bI4fP8769et5+eWXGTx4MMuXLwcgLCyM\nl156CZvNRkxMDIsXL3at69ChQzz33HNMnz6db7/9ltOnT1fzlouIiLs0EyElevvtt9m9ezdms5mQ\nkBAOHz7Md999B8Avv/zCiRMnMJvNtGrVioYNf73oMDQ0lLCwMOBCsdChQwcAmjdvTkZGBgDnz59n\nzpw5nDhxAoPBgMPhcPXv0KEDfn5+rv7p6ekEBwcXy5ecnExycrLrcUxMTBXvgV+ZTGb8rdZqGx/A\nYrFgreZ1VBdvzg7K72nK73lLly51/R0ZGUlkZGSF+6qIEACaNWvGxo0bXY+feOIJzp07R0JCAiEh\nIQwbNoyOHTsW6ZOSkoKvr2+RZT4+Pq6/jUaj6/GlxcKSJUto3749cXFxpKenM2nSJFcfs/nXt+Tl\nBcalKvuVPliqAAAbwklEQVRGvxIOh53s7OxqXYfVaq32dVQXb84Oyu9pyu9ZVqv1ig7CdDpDAGjf\nvj0FBQWsWrXKtSw3NxeDwUCnTp347LPPXB/oJ06cIC8vr8RxnE5nuevKyclxzS6sXr26CtKLiIgn\naCZCXOLj41mwYAErV64kICAAX19fBg0axI033khaWhoJCQk4nU4CAwOJj48vcQyDwVDuevr378/c\nuXP54IMP6NKlS6ntKjKWiIh4jsFZkUNHES9w5K6oahnXkmjD0TKiWsa+yJunRL05Oyi/pym/ZzVp\n0uSK+ut0hoiIiLhFpzOk1rAk2qpn4OCQ6hlXRMTLqYiQWqO6TzmIiEhROp0hIiIiblERISIiIm5R\nESEiIiJuUREhIiIiblERISIiIm5RESEiIiJuUREhIiIiblERISIiIm5RESEiIiJu0R0rpdYw7dtV\nPQMHh+AIalg9Y4uIeDEVEbXEwIEDadGiBXa7nbCwMGJjY7FYLBXuv2LFCu6///5qTFj98qclVMu4\nlkQbqIgQESlGpzNqCT8/P2w2GzNmzMBkMrFq1aoK9y0sLGTFihXVmE5ERGojzUTUQhERERw+fBiA\njz/+mNWrV2MwGOjVqxf9+vUjPT2dyZMn06pVKw4cOEDLli3Jz88nISGBsLAwHnroIaZNm8aMGTMA\n+Oijj8jLy+PBBx9k7969vPnmmxiNRjp06MAPP/zAjBkzWLNmDfv372fYsGEATJs2jf79+9OuXTu2\nb9/O0qVLsdvtXHPNNYwYMQJfX1/ef/99tm7ditFopFOnTgwaNIisrCzeeustTp06BcDQoUNp06aN\nZ3akiIiUSUVELeF0OgFwOBz88MMP/O53v2P//v18/fXXTJ06lcLCQp5//nnatWtH3bp1SU1N5dln\nn6VVq1YAbNy4EZvtwk9pp6enYzAYSlzP66+/zvDhw2nVqhWLFy8utd1F2dnZfPDBB7z44otYLBY+\n/PBDPv74Y/r27cv333/Pa6+9BkBOTg4ACxYs4O6776ZNmzZkZGQwefJkZs2aVSX7SEREqpaKiFri\n4kwCXJiJ6NWrF5999hldu3Z1XRvRrVs3du/ezQ033EBISIirgKionJwccnNzXf2io6PZunVrmX1+\n/vlnjh49yoQJE3A6nTgcDlq3bo2/vz8Wi4U33niDLl260KVLFwB27NjBsWPHXEVRbm4ueXl5+Pr6\nFhk3OTmZ5ORk1+OYmJhKbUtlmExm/K3WahsfwGKxYK3mdVQXb84Oyu9pyu95S5cudf0dGRlJZGRk\nhfuqiKglfH19XTMJFW1/qYsf2gAmk4nCwkLX44KCgnLHM5lMRca42MfpdNKpUydGjRpVrM+UKVPY\nuXMn3377LZ9++ikvvvgiTqeTyZMnYzaX/das7Bv9SjgcdrKzs6t1HVartdrXUV28OTsov6cpv2dZ\nrdYrOgjThZW1xKUf4BdFRETw/fffk5+fT25uLps2baJt27YltjebzTgcDgACAwPJysri3LlzFBQU\nsGXLFgD8/f2pU6cOe/fuBWD9+vWu/iEhIRw8eBCn00lGRoarzfXXX89PP/1EamoqAHl5eZw4cYLc\n3FxycnLo3LkzQ4cO5dChQwB07NiR//3vf65xDx48WBW7R0REqoFmImqJkq5NuO666+jZsyfjx4/H\nYDDQu3dvWrRoUeI1D7179yYuLo7w8HBGjhzJH//4R8aPH0+DBg1o2rSpq90zzzzjurAyIiICf39/\nANq2bUtISAjPPfccTZs2JTw8HICAgABGjBjB7NmzsdvtADz00EPUqVOHV1991TVjMXToUAAef/xx\n3n77beLj4yksLCQiIoInn3yy6neYiIhcMYOzpENYkVLk5ubi5+cHwH/+8x8yMzN57LHHPBvq/xy5\nK6paxrUk2nC0jKiWsS/y5ilRb84Oyu9pyu9ZTZo0uaL+momQStm6dSv/+c9/cDgchISEEBsb6+lI\nIiLiISoipFJ69OhBjx49PB2jRJbEil9YWinBIdUzroiIl1MRIbVGdZ9yEBGRovTtDBEREXGLiggR\nERFxi4oIERERcYuKCBEREXGLiggRERFxi4oIERERcYuKCBEREXGLiggRERFxi4oIERERcYvuWCm1\nhmnfLk9HgOAQHEENPZ1CROSqUBEhlTJkyBAWLlxIeno6P/30E9HR0WW2T09PZ9q0acyYMYP9+/ez\ndu3aavvVz/xpCdUybmVYEm2gIkJEfiNUREilGAwGANLS0vjmm2/KLSIu7RMeHk54eHi15hMRkatH\nRYS4ZfHixRw/fpyEhARuu+02unbtypw5c8jLywNg2LBhtG7dukiflJQUVq5cSWJiInv37mXBggUU\nFBRgsVgYMWIEjRs3Zs2aNWzevJn8/HxOnjxJ165dGTRokCc2UUREyqEiQtzy6KOP8tFHH5GQcOEU\nQn5+PhMmTMBsNpOamsrs2bOZOnVqsX4XZyXCwsJ46aWXMBqN7Nixg8WLFzN27FgADh06RFJSEiaT\nidGjR9OvXz+Cg4Ov3saJiEiFqIiQKmG323nnnXc4ePAgRqOREydOlNn+/PnzzJkzhxMnTmAwGHA4\nHK7nOnTogJ+fH3Ch2EhPT1cRISJSA6mIkCrx3//+l/r16zN9+nQKCwt59NFHy2y/ZMkS2rdvT1xc\nHOnp6UyaNMn1nNn869vy8gLjouTkZJKTk12PY2JiqmArrpzJZMbfaq10P4vFgtWNfjWBN2cH5fc0\n5fe8pUuXuv6OjIwkMjKywn1VREilOJ1OAPz8/MjNzXUtz8nJoUGDBgB8/fXXFBYWljlOTk6Oa3Zh\n9erVlc5R2Tf61eJw2MnOzq50P6vV6la/msCbs4Pye5rye5bVar2igzDdbEoq5eI1Dddeey0Gg4Fx\n48bxv//9j759+/L1118zbtw4Tpw44TodUZr+/fvz/vvvk5CQ4CpMylqfiIjUPAZnWf+Ci3iRI3dF\neToClkQbjpYRle7nzUcz3pwdlN/TlN+zmjRpckX9NRMhIiIibtE1EVJrWBJtno4AwSGeTiAictWo\niJBaw53TCCIi4j6dzhARERG3qIgQERERt6iIEBEREbeoiBARERG3qIgQERERt6iIEBEREbeoiBAR\nERG3qIgQERERt6iIEBEREbfojpVSa5j27fJ0BLflmcyYHParu9LgEBxBDa/uOkWkVlERIbVG/rQE\nT0fwKpZEG6iIEJEroNMZVWT58uWMHTuW+Ph4EhIS2Lt3b5WvY8KECWU+P2TIkEqPeeLECaZOncqf\n//xnEhMTee2118jKyqr0OCtWrCjy+GLW9PR0xo4dC8D+/ftZsGBBpccWEZGaSTMRVWDPnj388MMP\nvPrqq5hMJs6dO4fdXvVT0y+//HKZzxsMhkqNV1BQwLRp0xg6dChdunQBICUlhaysLAICAio11ooV\nK7j//vtLzHoxV3h4OOHh4ZUaV0REai4VEVUgMzMTq9WKyWQCoF69eq7nYmNjuemmm9i2bRu+vr6M\nGjWKa665hi1btrB8+XLsdjtWq5VRo0YREBDAsmXLyMjIIC0tjYyMDPr168edd94JXJhpWLhwIZmZ\nmcyaNYvc3FwcDgdPPvkkbdu2xel08q9//YstW7bg6+vLuHHjCAgIYPPmzezfv5+YmJgiub/55hta\nt27tKiAA2rVrB1yYQZgzZw55eXkADBs2jNatW5e47q1bt5Kfn09CQgJhYWGMHDnSlfVSKSkprFy5\nksTERPbu3cuCBQsoKCjAYrEwYsQIGjduzJo1a9i8eTP5+fmcPHmSrl27MmjQoKp/0URE5IqpiKgC\nHTt25N///jejR4+mffv29OjRw/VhDBeKiunTp7N27Vrmz59PYmIiERERTJ48GYCvvvqKDz/8kMGD\nBwNw/Phx/vKXv5CTk8Po0aPp27cvRqPRdUT/zTff0LlzZ+6//36cTqfrgz4vL4/WrVvz0EMPsWjR\nIr744gseeOABoqKiiIqKKpb7yJEjpc4MBAYGMmHCBMxmM6mpqcyePZupU6eWuO62bdvy2WefYbPZ\nXP1LmxW5uDwsLIyXXnoJo9HIjh07WLx4seu0x6FDh0hKSsJkMjF69Gj69etHcHBwpV4TERGpfioi\nqoCfnx82m41du3axc+dOZs+ezSOPPMJtt90GQI8ePQC4+eabeffddwHIyMjgvffe48yZMzgcDkJD\nQ13jdenSBZPJhNVqJTAwkMzMzCIfoi1btuSNN97A4XAQFRVFixYtADCbza5ZhfDwcHbs2OH2Ntnt\ndt555x0OHjyI0WjkxIkTZa67ss6fP8+cOXM4ceIEBoMBh8Pheq5Dhw74+fkBF4qN9PT0YkVEcnIy\nycnJrseXz7JI+UwmM/5W6xWPY7FYsFbBOJ6i/J6l/J63dOlS19+RkZFERkZWuK+KiCpiMBho164d\n7dq1o3nz5qxdu9ZVRFx6VH7x7/nz53PPPffQpUsXUlJSWLZsmauNj49PkfaFhYVF1hUREcGkSZPY\nunUr8+bN4+677+bWW2/FbP715TQajUU+mEsSFhZGSkpKic/997//pX79+kyfPp3CwkIeffTRMtft\ndDorsptclixZQvv27YmLiyM9PZ1Jkya5nrt0Oy4vMC6q7BtdinM47GRnZ1/xOFartUrG8RTl9yzl\n9yyr1XpFB2H6dkYVOH78OKmpqa7HBw8epGHDX786t2HDBgDWr19P69atAcjJySEoKAiANWvWVGg9\nFz+oMzIyCAgIoFevXvTq1YsDBw4Uef5ymzZtYvHixcWWR0dH8/PPP/PDDz+4lu3atYsjR46Qk5ND\n/fr1Afj6669dhUxp6zabzUU+7MsrKnJyclyzC6tXr67Q9ouISM2imYgqkJuby/z588nJycFoNNKo\nUSOefvpp1/Pnz58nPj4eHx8f/vznPwMwYMAAZs6cSb169YiMjCQ9Pb3EsUuaxUhOTmblypWYzWb8\n/PwYOXJksbaXOnnyJP7+/sWWWywWEhISWLBgAQsWLMBsNtO8eXMef/xx+vbty4wZM1i7di2dO3d2\nnV64fN3PPvssAL179yYuLo7w8HBGjhxZ7jdF+vfvz9y5c/nggw+KXNhZ1vaLiEjNYnBWdh5aKiU2\nNhabzVbkGxtX25w5cxg6dKjXn7crz5G7il88KqWzJNpwtIy44nFqw3Su8nuO8ntWkyZNrqi/ZiKq\nWU04kr44WyAiIlKVVERUszlz5ng6wm+GJdFWfqMaymQy4/DAb2eIiFwJFRFSa1TF1Lyn+Hv5lKiI\n/Dbp2xkiIiLiFhURIiIi4hYVESIiIuIWFREiIiLiFhURIiIi4hYVESIiIuIWFREiIiLiFhURIiIi\n4hYVESIiIuIW3bFSag3Tvl2ejuC2PJMZ09W+7XVpgkNwBDUsv52I/OapiJByDRw4kBYtWuB0OjEY\nDMTHx9OwYfV8yKSkpLBy5UoSExMr3Td/WkI1JPrtsSTaQEWEiFSAiggpl5+fHzZb6T9uVVhYiNFY\ndWfGasIvn4qISPlUREi5nE5nsWVr1qxh06ZN5Obm4nQ6mThxIitXruTbb7/FbrfTrVs3BgwYQHp6\nOlOmTKFt27bs2bOH4OBgxo0bh4+PD6mpqbz11ltkZWVhMpl47rnnAMjNzWXmzJkcOXKE8PBwRo4c\nebU3WUREKkBFhJQrPz+fhIQEnE4noaGhxMXFAXDgwAFmzJiBv78/27dvJzU1lalTp+J0OrHZbOze\nvZsGDRqQmprKmDFjePrpp5k1axYbN24kOjqav/3tb9x///1ERUVht9spLCwkIyODgwcPMnPmTOrX\nr8+ECRP46aefaNOmjYf3goiIXE5FhJTL19e3xNMZHTt2xN/fH4Aff/yR7du3u4qNvLw8Tpw4QYMG\nDQgNDaV58+YAhIeHk5aWRm5uLqdPnyYqKgoAs/nXt2KrVq0ICgoCoEWLFqSnpxcrIpKTk0lOTnY9\njomJqdqN/g0zmcz4W60Vbm+xWLBWon1No/yepfyet3TpUtffkZGRREZGVrivighxm6+vr+tvp9PJ\nfffdR+/evYu0SU9Px8fHx/XYaDRSUFBQ5riXFhRGoxGHw1GsTWXf6FJxDoed7OzsCre3Wq2Val/T\nKL9nKb9nWa3WKzoI030ipFwlXRNxuc6dO7N69Wpyc3MBOH36NFlZWaX29/Pzo0GDBnz//fcA2O12\n8vPzqzC1iIhUN81ESLkq8m2Jjh07cuzYMV544QUA6tSpw8iRIzEYDKX2f/bZZ/n73//O0qVLMZvN\njBkzpkpzi4hI9TI4K3KYKeIFjtwV5ekItYIl0YajZUSF29eG6Vzl9xzl96wmTZpcUX+dzhARERG3\n6HSG1BqWxNJviFXTmUxmHDXottciIhWhIkJqjcpMwdc0/l4+JSoiv006nSEiIiJuUREhIiIiblER\nISIiIm5RESEiIiJuUREhIiIiblERISIiIm5RESEiIiJuUREhIiIiblERISIiIm7RHSul1jDt2+Xp\nCG7LM5kx1ZTbXleSN2cH5fe032z+4BAcQQ2rPtBVpiJCSjRw4EBatGiB0+nEYDDQo0cP7r33XrfG\nGjJkCAsXLuTMmTPMnz+f5557rsR26enpTJs2jRkzZri1nvxpCW71ExG52iyJNlARIbWVn58fNlvV\n/KCVwWAAICgoqNQC4vK2IiJS86mIkBI5nc4Sl8fGxnLbbbexZcsWCgsLGTNmDE2aNCErK4u//vWv\nnDlzhuuvv54dO3Zgs9moV6+eq++lMw1Hjx5l3rx5OBwOCgsLGTt2LCaTCYfDwZtvvsmePXsIDg5m\n3Lhx+Pj4XK3NFhGRSlARISXKz88nISHBdTrjvvvu46abbgIgMDAQm83G559/zkcffcTTTz/Nv//9\nb9q3b899993Htm3bWL16dYnjXpxp+Pzzz+nXrx/R0dGuQiIzM5PU1FTGjBnD008/zaxZs9i4cSPR\n0dFXbbtFRKTiVERIiXx9fUs9ndGtWzcAwsPD2bRpEwC7d+8mPj4egM6dOxeZgShJ69atWbFiBadO\nnaJ79+40atQIgNDQUJo3b+4aPy0trUq2R0REqp6KCKm0i6cXjEYjDoejxDalnQ65KDo6mtatW7Nl\nyxamTp3KU089RWhoaJFTF0ajkYKCghL7Jycnk5yc7HocExNT2c0QEfEYk8mMv9Xq6RgALF261PV3\nZGQkkZGRFe6rIkJKVF4RcLk2bdqwYcMG7r33Xn788UfOnz9f5lhpaWmEhoZy5513kpGRwaFDhwgN\nDa3weiv7RhcRqUkcDjvZ2dmejoHVar2igzAVEVKigoKCItdEdOrUiUceeaTUb08MGDCA2bNns27d\nOlq3bk39+vXx8/MDSv7GxYYNG1i3bh0mk4mgoCAeeOABcnJy9O0MEREvYnBW9pBTpAR2ux2j0YjR\naGTPnj28/fbbVfYV0Yo6clfUVV2fiIi7LIk2HC0jPB2DJk2aXFF/zURIlcjIyGDWrFkUFhbi4+PD\n008/7elIIiJSzTQTIbXGyXVfejqC20wmMw4vvfWvN2cH5fe032z+GnLba81EiPyfmjA16C5/q7VG\nXGTlDm/ODsrvacrv3fQrniIiIuIWFREiIiLiFhURIiIi4hYVESIiIuIWfTtDRERE3KKZCKkVLr33\nuzfy5vzenB2U39OU37OuNL+KCBEREXGLiggRERFxi4oIqRW8/Rc9vTm/N2cH5fc05fesK82vCytF\nRETELZqJEBEREbeoiBARERG36Ae4xKtt27aNBQsW4HQ6+f3vf899993n6Ujlio2Nxd/fH4PBgMlk\nYurUqZw7d47XXnuN9PR0QkNDGTNmDP7+/p6OCsDrr7/O1q1bCQwMZPr06QBl5l2xYgWrV6/GZDLx\n2GOP0alTJ0/GLzH/smXL+PLLLwkMDATg4YcfpnPnzkDNyn/q1CnmzJnD2bNnMRgM3H777fTr189r\n9v/l+Xv37s2dd97pNfu/oKCAiRMnYrfbsdvtREVF8cgjj3jN/i8tf5Xuf6eIl3I4HM5nn33WmZaW\n5iwoKHDGxcU5jx496ulY5YqNjXVmZ2cXWfbee+85//Of/zidTqdzxYoVzkWLFnkiWol27drlPHDg\ngHPs2LGuZaXlPXLkiDM+Pt5pt9udJ0+edD777LPOwsJCj+S+qKT8S5cudX700UfF2ta0/GfOnHEe\nOHDA6XQ6nb/88otz1KhRzqNHj3rN/i8tv7fsf6fT6czNzXU6nRf+vfl//+//OXft2uU1+9/pLDl/\nVe5/nc4Qr7V3714aN25MSEgIZrOZm2++me+//97TscrldDpxXnY98+bNm7ntttsA6NmzZ43ajrZt\n21K3bt0iy0rLu3nzZnr06IHJZCI0NJTGjRuzd+/eq575UiXlB4q9BlDz8tevX58WLVoA4OfnR9Om\nTTl16pTX7P+S8p8+fRrwjv0P4OvrC1w4qi8sLKRevXpes/+h5PxQdftfpzPEa50+fZoGDRq4HgcH\nB3v8P9iKMBgMvPLKKxiNRnr37s3tt9/O2bNnqV+/PnDhH96zZ896OGXZSst7+vRpWrdu7WoXHBzs\n+tCoaT799FPWrl1Ly5YtGTJkCP7+/jU6f1paGocOHaJ169Zeuf8v5r/++uvZvXu31+z/wsJCEhMT\nOXnyJH369CEsLMyr9n9J+aHq3v8qIkSuspdffpmgoCCysrJ45ZVXaNKkSbE2BoPBA8nc5215+/bt\ny4MPPojBYOBf//oXCxcu5JlnnvF0rFLl5uYyc+ZMHnvsMfz8/Io9X9P3/+X5vWn/G41GXn31VXJy\ncpg8eTLJycnF2tTk/X95/pSUlCrd/zqdIV4rODiYjIwM1+PTp08THBzswUQVExQUBEBAQABdu3Zl\n79691K9fn8zMTAAyMzNdFzzVVKXlvfw1OXXqVI18TQICAlz/8N9+++2uGayamN/hcDBjxgxuvfVW\nunbtCnjX/i8pvzft/4v8/f353e9+x759+7xq/190af6q3P8qIsRrtWrVitTUVNLT07Hb7axfv56o\nqChPxypTXl4eubm5wIWjs+3bt9O8eXNuuOEG1qxZA8CaNWtq3HZcfh1HaXmjoqLYsGEDdrudtLQ0\nUlNTadWqlSciF3F5/osfAAAbN26kWbNmQM3M//rrrxMWFka/fv1cy7xp/5eU31v2f1ZWFjk5OQDk\n5+ezY8cOrrvuOq/Z/yXlb9GiRZXuf92xUrzatm3bmD9/Pk6nk169etX4r3impaWRlJSEwWDA4XBw\nyy23cN9993Hu3DlmzZpFRkYGISEhjBkzpsSLAT1h9uzZpKSkkJ2dTWBgIDExMXTt2rXUvCtWrOCr\nr77CbDZ7/CtupeVPTk7m4MGDGAwGQkJCeOqpp1znuGtS/t27dzNx4kSaN2+OwWDAYDDw8MMP06pV\nK6/Y/6Xl/+abb7xi/x8+fJi5c+e6itBbbrmF/v37l/nfqzfknzNnTpXtfxURIiIi4hadzhARERG3\nqIgQERERt6iIEBEREbeoiBARERG3qIgQERERt6iIEBEREbeoiBARERG3qIgQERERt/x/DpcSM1n2\nZ1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3694dbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10_languages[\"lang\"].plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x373cf690>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wbFgJ2Dn7PtUz/oHQT5kBv76D4BfRlBcDENE1YymjOlUyiN90eD/Mny6EKMxX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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37636d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10_languages.plot(kind=\"pie\",y=\"pct\",legend=False,figsize=(8,8),title=\"Language distribution of journals in AWOL Index\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}