PubMed.ipynb

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   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from bokeh.charts import Bar, Donut, output_file, show\n",
    "from bokeh.io import output_notebook\n",
    "output_notebook()\n",
    "from collections import defaultdict\n",
    "\n",
    "os.chdir('C:/Users/Poto/Desktop')\n",
    "d = pd.read_csv('pubmed82.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['_id', 'author', 'date', 'journal', 'keywords', 'org', 'country',\n",
       "       'title', 'url', 'Unnamed: 9'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data preprocessing (Since the org data is quite messy, I only picked the firt 300 article information)\n",
    "city = defaultdict(int)\n",
    "city_list = set([])\n",
    "for x in d['country'][:296]:\n",
    "    # Get rid of '.'\n",
    "    x = x.replace('.','')\n",
    "    for y in x.split(';'):\n",
    "        city[y.strip(' ')] += 1\n",
    "        city_list.add(y.strip(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Boston, MA, USA', 40),\n",
       " ('Salt Lake City, UT, USA', 25),\n",
       " ('New York, NY, USA', 23),\n",
       " ('Rochester, MN, USA', 17),\n",
       " ('Houston, TX, USA', 17),\n",
       " ('Seattle, WA, USA', 15),\n",
       " ('Chicago, IL, USA', 15),\n",
       " ('Bethesda, MD, USA', 14),\n",
       " ('London, UK', 12),\n",
       " ('Nashville, TN, USA', 11),\n",
       " ('ORGANIZATION_NA', 11),\n",
       " ('Minneapolis, MN, USA', 10),\n",
       " ('Pittsburgh, PA, USA', 10),\n",
       " ('Philadelphia, PA, USA', 9),\n",
       " ('Beijing, China', 7),\n",
       " ('Ann Arbor, MI, USA', 7),\n",
       " ('Washington, DC, USA', 7),\n",
       " ('Stanford, CA, USA', 7),\n",
       " ('Los Angeles, CA, USA', 7),\n",
       " ('Atlanta, GA, USA', 6),\n",
       " ('Silver Spring, MD, USA', 6),\n",
       " ('Albany, NY, USA', 6),\n",
       " ('Cincinnati, OH, USA', 6),\n",
       " ('Birmingham, AL, USA', 6),\n",
       " ('Lebanon, NH, USA', 5),\n",
       " ('Sydney, Australia', 5),\n",
       " ('Baltimore, MD, USA', 5),\n",
       " ('Columbus, OH, USA', 5),\n",
       " ('Paris, France', 5),\n",
       " ('Charleston, SC, USA', 4),\n",
       " ('Hanover, NH, USA', 4),\n",
       " ('Palo Alto, CA, USA', 4),\n",
       " ('Cambridge, MA, USA', 4),\n",
       " ('Shenzhen, China', 4),\n",
       " ('Portland, OR, USA', 4),\n",
       " ('Cambridge, UK', 4),\n",
       " ('Marshfield, WI, USA', 4),\n",
       " ('Seoul, Korea', 3),\n",
       " ('West Haven, CT, USA', 3),\n",
       " ('New Haven, CT, USA', 3),\n",
       " ('Detroit, MI, USA', 3),\n",
       " ('Shanghai, China', 3),\n",
       " ('Cleveland, OH, USA', 3),\n",
       " ('Denver, CO, USA', 3),\n",
       " ('Lexington, KY, USA', 3),\n",
       " ('Madrid, Spain', 3),\n",
       " ('Singapore', 3),\n",
       " ('Providence, RI, USA', 3),\n",
       " ('Orsay, France', 3),\n",
       " ('Austin, TX, USA', 3),\n",
       " ('Buffalo, NY, USA', 3),\n",
       " ('San Diego, CA, USA', 3),\n",
       " ('Taoyuan, Taiwan', 3),\n",
       " ('Taipei, Taiwan', 3)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort first 300 cities and pick cities with the top appearance (appreace > 2 times)\n",
    "sorted_city_list = sorted(city.items(), key=lambda x: x[1], reverse= True)\n",
    "city_list_top = [x for x in sorted_city_list if x[1] > 2]\n",
    "\n",
    "city_list_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get selected city list\n",
    "unique_list =[x[0] for x in city_list_top]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Find relationship between each cities\n",
    "# To avoid duplicate, cities will only list cities in an alphabetical order\n",
    "relation = {}\n",
    "\n",
    "for x in unique_list:\n",
    "    relation[x] = defaultdict(int)\n",
    "\n",
    "for x in d['country'][:296]:\n",
    "    x = x.replace('.','')\n",
    "    temp = []\n",
    "    for y in x.split(';'):\n",
    "        temp.append(y.strip())\n",
    "    \n",
    "    for z in temp:\n",
    "        if z in unique_list:\n",
    "            for m in temp:\n",
    "                if m != z and m > z and m in unique_list:\n",
    "                    relation[z][m] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Adapt a little bit above code to get all the cities with their cooperation cities\n",
    "relat = {}\n",
    "\n",
    "for x in unique_list:\n",
    "    relat[x] = defaultdict(int)\n",
    "\n",
    "for x in d['country'][:296]:\n",
    "    x = x.replace('.','')\n",
    "    temp = []\n",
    "    for y in x.split(';'):\n",
    "        temp.append(y.strip())\n",
    "    \n",
    "    for z in temp:\n",
    "        if z in unique_list:\n",
    "            for m in temp:\n",
    "                if m != z and m in unique_list:\n",
    "                    relat[z][m] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston, MA, USA 25\n",
      "New York, NY, USA 25\n",
      "Seattle, WA, USA 23\n",
      "Rochester, MN, USA 20\n",
      "Salt Lake City, UT, USA 18\n",
      "Chicago, IL, USA 18\n",
      "Atlanta, GA, USA 16\n",
      "Houston, TX, USA 15\n",
      "Nashville, TN, USA 15\n",
      "Philadelphia, PA, USA 15\n",
      "Stanford, CA, USA 14\n",
      "Bethesda, MD, USA 12\n",
      "Minneapolis, MN, USA 11\n",
      "Ann Arbor, MI, USA 11\n",
      "Palo Alto, CA, USA 11\n",
      "Cincinnati, OH, USA 10\n",
      "Marshfield, WI, USA 10\n",
      "Pittsburgh, PA, USA 9\n",
      "Lebanon, NH, USA 9\n",
      "Charleston, SC, USA 9\n",
      "Detroit, MI, USA 8\n",
      "Beijing, China 7\n",
      "Birmingham, AL, USA 7\n",
      "Baltimore, MD, USA 7\n",
      "Cambridge, UK 7\n",
      "Cleveland, OH, USA 7\n",
      "Madrid, Spain 7\n",
      "London, UK 6\n",
      "Los Angeles, CA, USA 6\n",
      "Sydney, Australia 6\n",
      "Singapore 6\n",
      "Portland, OR, USA 5\n",
      "Seoul, Korea 5\n",
      "West Haven, CT, USA 4\n",
      "Denver, CO, USA 4\n",
      "San Diego, CA, USA 4\n",
      "Washington, DC, USA 3\n",
      "Cambridge, MA, USA 3\n",
      "Shanghai, China 3\n",
      "Buffalo, NY, USA 3\n",
      "Albany, NY, USA 2\n",
      "Paris, France 2\n",
      "Hanover, NH, USA 2\n",
      "New Haven, CT, USA 2\n",
      "Providence, RI, USA 2\n",
      "Orsay, France 2\n",
      "Austin, TX, USA 2\n",
      "Taipei, Taiwan 2\n",
      "Silver Spring, MD, USA 1\n",
      "Columbus, OH, USA 1\n",
      "Shenzhen, China 1\n",
      "Taoyuan, Taiwan 1\n",
      "ORGANIZATION_NA 0\n",
      "Lexington, KY, USA 0\n"
     ]
    }
   ],
   "source": [
    "# Calcualte how many cooperation cities each city has\n",
    "temp = sorted(relat.items(), key = lambda x:len(x[1]), reverse = True)\n",
    "for v in temp:\n",
    "    print(v[0], len(v[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {'Australia': 6,\n",
       "             'China': 10,\n",
       "             'Korea': 5,\n",
       "             'Singapore': 6,\n",
       "             'Spain': 7,\n",
       "             'UK': 13,\n",
       "             'USA': 357})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all the country names and their counts > 2\n",
    "country_count = defaultdict(int)\n",
    "count_list = [(x[0], len(x[1])) for x in temp if len(x[1]) > 2]\n",
    "\n",
    "for x in count_list:\n",
    "    cname = x[0].split(',')[-1:]\n",
    "    temp_count = x[1]\n",
    "    country_count[cname[0].strip()] += temp_count\n",
    "\n",
    "country_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plot pie chart\n",
    "data = pd.Series(list(dict(country_count).values()), index = list(dict(country_count).keys()))\n",
    "pie_chart = Donut(data, title = 'Number of Papers Published by Countries in Clinical NLP')\n",
    "show(pie_chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>120</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>135</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>248</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2010</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2006</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2005</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2004</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2003</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1994</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2001</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1998</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1997</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1996</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1995</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1993</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1992</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1991</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1990</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1988</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Count\n",
       "0   2017    120\n",
       "1   2016    135\n",
       "2   2015    248\n",
       "3   2014    167\n",
       "4   2013    179\n",
       "5   2012    113\n",
       "6   2011     69\n",
       "7   2010     81\n",
       "8   2009     73\n",
       "9   2008    141\n",
       "10  2007     48\n",
       "11  2006     43\n",
       "12  2005     35\n",
       "13  2004     22\n",
       "14  2003     19\n",
       "15  2002     14\n",
       "16  1994      8\n",
       "17  2001     13\n",
       "18  2000     14\n",
       "19  1999     13\n",
       "20  1998     17\n",
       "21  1997     12\n",
       "22  1996      8\n",
       "23  1995     24\n",
       "24  1993      6\n",
       "25  1992      3\n",
       "26  1991      6\n",
       "27  1990      4\n",
       "28  1989      1\n",
       "29  1988      2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot number of papers published each year\n",
    "yearPub = [x.split()[0] for x in d['date']]\n",
    "\n",
    "yearCount = defaultdict(int)\n",
    "for y in yearPub:\n",
    "    if y.isdigit():\n",
    "        yearCount[y] += 1\n",
    "\n",
    "countFrame = pd.DataFrame()\n",
    "countFrame['Year'] = [int(x) for x in yearCount.keys()]\n",
    "countFrame['Count'] = [int(x) for x in yearCount.values()]\n",
    "countFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Plot bar chart\n",
    "p = Bar(countFrame, 'Year', values='Count', title=\"Yearly Published Clinical NLP Papers\")\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numbers of Researchers: 4725\n"
     ]
    }
   ],
   "source": [
    "# Processing author name\n",
    "author_name_all = [phrase.split(',') for phrase in d['author']]\n",
    "author_list = []\n",
    "for x in author_name_all:\n",
    "    author_list += x\n",
    "\n",
    "author_set = set()\n",
    "author_collect = []\n",
    "for y in author_list:\n",
    "    edit = y.strip().replace('.', '')\n",
    "    author_set.add(edit)\n",
    "    author_collect.append(edit)\n",
    "\n",
    "print('Numbers of Researchers:', len(author_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Xu H', 57),\n",
       " ('Friedman C', 55),\n",
       " ('Denny JC', 51),\n",
       " ('Liu H', 48),\n",
       " ('Chute CG', 41),\n",
       " ('Hripcsak G', 36),\n",
       " ('Chapman WW', 35),\n",
       " ('Melton GB', 26),\n",
       " ('Fiszman M', 24),\n",
       " ('Savova GK', 23),\n",
       " ('Sohn S', 22),\n",
       " ('Haug PJ', 21),\n",
       " ('Wang Y', 21),\n",
       " ('Johnson SB', 21),\n",
       " ('Zhou L', 20),\n",
       " ('South BR', 19),\n",
       " ('Elhadad N', 18),\n",
       " ('Solti I', 18),\n",
       " ('Rindflesch TC', 17),\n",
       " ('Pakhomov S', 17),\n",
       " ('Shen S', 17),\n",
       " ('Cimino JJ', 16),\n",
       " ('Jiang M', 16),\n",
       " ('Meystre SM', 16),\n",
       " ('Lingren T', 15),\n",
       " ('Demner-Fushman D', 15),\n",
       " ('Chen ES', 15),\n",
       " ('Zweigenbaum P', 15),\n",
       " ('Elkin PL', 15),\n",
       " ('Uzuner O', 15)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count appearance of each authors and pick top 30\n",
    "author_count = defaultdict(int)\n",
    "for x in author_collect:\n",
    "    author_count[x] += 1\n",
    "\n",
    "author30 = sorted(author_count.items(), key=lambda x: x[1], reverse=True)[:30]\n",
    "author30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Xu H</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Friedman C</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denny JC</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Liu H</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chute CG</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hripcsak G</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chapman WW</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Melton GB</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fiszman M</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Savova GK</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sohn S</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Haug PJ</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wang Y</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Johnson SB</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zhou L</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>South BR</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Elhadad N</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Solti I</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rindflesch TC</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pakhomov S</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Shen S</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cimino JJ</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Jiang M</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Meystre SM</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lingren T</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Demner-Fushman D</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Chen ES</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Zweigenbaum P</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Elkin PL</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Uzuner O</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Author  Count\n",
       "0               Xu H     57\n",
       "1         Friedman C     55\n",
       "2           Denny JC     51\n",
       "3              Liu H     48\n",
       "4           Chute CG     41\n",
       "5         Hripcsak G     36\n",
       "6         Chapman WW     35\n",
       "7          Melton GB     26\n",
       "8          Fiszman M     24\n",
       "9          Savova GK     23\n",
       "10            Sohn S     22\n",
       "11           Haug PJ     21\n",
       "12            Wang Y     21\n",
       "13        Johnson SB     21\n",
       "14            Zhou L     20\n",
       "15          South BR     19\n",
       "16         Elhadad N     18\n",
       "17           Solti I     18\n",
       "18     Rindflesch TC     17\n",
       "19        Pakhomov S     17\n",
       "20            Shen S     17\n",
       "21         Cimino JJ     16\n",
       "22           Jiang M     16\n",
       "23        Meystre SM     16\n",
       "24         Lingren T     15\n",
       "25  Demner-Fushman D     15\n",
       "26           Chen ES     15\n",
       "27     Zweigenbaum P     15\n",
       "28          Elkin PL     15\n",
       "29          Uzuner O     15"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authorFrame = pd.DataFrame()\n",
    "authorFrame['Author'] = [x[0] for x in author30]\n",
    "authorFrame['Count'] = [int(x[1]) for x in author30]\n",
    "authorFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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    }
   ],
   "source": [
    "# Plot bar chart\n",
    "p = Bar(authorFrame, 'Author', values='Count', title=\"Number of Papers Published by Authors\")\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Journals: 278\n"
     ]
    }
   ],
   "source": [
    "# Without much data cleaning, it seems there are 276 different journals\n",
    "print('Number of Journals:', len(set(d['journal'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Studies in health technology and informatics', 302),\n",
       " ('AMIA ... Annual Symposium proceedings. AMIA Symposium', 263),\n",
       " ('Journal of the American Medical Informatics Association : JAMIA', 201),\n",
       " ('Journal of biomedical informatics', 169),\n",
       " ('Methods of information in medicine', 41),\n",
       " ('International journal of medical informatics', 38),\n",
       " ('BMC medical informatics and decision making', 34),\n",
       " ('Proceedings. AMIA Symposium', 25),\n",
       " ('AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science',\n",
       "  21),\n",
       " ('BMC bioinformatics', 21)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick journals by its number of appearance\n",
    "jour = defaultdict(int)\n",
    "for x in d['journal']:\n",
    "    jour[x] += 1\n",
    "sorted(jour.items(), key=lambda x: x[1], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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