Acadmic_tree_statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dj/webScrapper/env/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/home/dj/webScrapper/env/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "people=pd.read_csv('./neurotree/people.tsv', sep='\\t', encoding='utf-8',  escapechar='\\\\')\n",
    "connect=pd.read_csv('./neurotree/connect.tsv', sep='\\t', encoding='latin-1',  escapechar='\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(697713, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pid', 'firstname', 'middlename', 'lastname', 'degrees', 'location',\n",
       "       'locid', 'area', 'majorarea', 'award', 'orcid_id', 'homepage',\n",
       "       'addedby', 'dateadded'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_list=['Computer Science','Neuroscience','Mathematics','Physiology','Robotics','Chemistry',\n",
    "          'Physics','Linguistics','Meteorology','Astronomy']\n",
    "res=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "people_cs=people[people['majorarea'].str.contains('computerscience')]\n",
    "people_neuro=people[people['majorarea'].str.contains('neuro')]\n",
    "people_maths=people[people['majorarea'].str.contains('math')]\n",
    "people_physiology=people[people['majorarea'].str.contains('physiology')]\n",
    "people_robotics=people[people['majorarea'].str.contains('robotics')]\n",
    "people_chem=people[people['majorarea'].str.contains('chemistry')]\n",
    "people_physics=people[people['majorarea'].str.contains('physics')]\n",
    "people_linguistic=people[people['majorarea'].str.contains('ling')]\n",
    "people_meteorology=people[people['majorarea'].str.contains('meteorology')]\n",
    "people_astronomy=people[people['majorarea'].str.contains('astronomy')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.append(len(people_cs))\n",
    "res.append(len(people_neuro))\n",
    "res.append(len(people_maths))\n",
    "res.append(len(people_physiology))\n",
    "res.append(len(people_robotics))\n",
    "res.append(len(people_chem))\n",
    "res.append(len(people_physics))\n",
    "res.append(len(people_linguistic))\n",
    "res.append(len(people_meteorology))\n",
    "res.append(len(people_astronomy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21313"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(people_cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.arange(len(sub_list))\n",
    "plt.bar(index, reser)\n",
    "plt.xlabel('Academic Tree Disciplines', fontsize=10)\n",
    "plt.ylabel('No of Researchers', fontsize=10)\n",
    "plt.xticks(index, sub_list, fontsize=10, rotation=90)\n",
    "plt.title('Researcher distribution across different disciplines')\n",
    "plt.gcf().subplots_adjust(bottom=0.15)\n",
    "plt.savefig('domains.png',bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "people_cs_int=pd.read_csv('./neurotree/people_cs.tsv', sep='\\t', encoding='utf-8',  escapechar='\\\\')\n",
    "connect_cs=pd.read_csv('./neurotree/connect_cs.tsv', sep='\\t', encoding='latin-1',  escapechar='\\\\')\n",
    "inst=pd.read_csv('./neurotree/location.tsv', sep='\\t', encoding='latin-1',  escapechar='\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attr_statistics(tab):\n",
    "    total_attr=[]\n",
    "    for row in tab.values:\n",
    "        attr=0\n",
    "        for col in row:\n",
    "            if str(col)=='nan' or str(col)=='N':\n",
    "                continue\n",
    "            else:\n",
    "                attr+=1\n",
    "        total_attr.append(attr)\n",
    "    return total_attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28704"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_attr = attr_statistics(inst)\n",
    "len(total_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28704, 12)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "3\n",
      "6.436803232998885\n"
     ]
    }
   ],
   "source": [
    "max_attr=max(total_attr)\n",
    "print(max_attr)\n",
    "min_attr=min(total_attr)\n",
    "print(min_attr)\n",
    "avg_attr=sum(total_attr)/len(total_attr)\n",
    "print(avg_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.30235067798996"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([179132, 'Srinivasan', nan, 'Parthasarathy', 'Ph.D.',\n",
       "       'University of Maryland, College Park', 398,\n",
       "       'Theoretical Computer Science,Bioinformatics and Computational Biology',\n",
       "       'computerscience', 'N', 'N', nan, 'pq', '2016-01-07 23:52:45'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_cs_int.values[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22081, 14)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_cs_int.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([318, 'University of California, San Francisco', 'UCSF',\n",
       "       'San Francisco', 'CA', nan, 0, '-122.45747', '37.762982', nan, 'N',\n",
       "       0], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst.values[317]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27181, 11)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connect_cs[connect_cs['department'].astype(str)=='nan'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27181"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([x for x in connect_cs['department'].values if str(x) == 'nan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.from_pandas_edgelist(connect_cs, 'pid1', 'pid2',create_using=nx.DiGraph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "degree_sequence = sorted([d for n, d in G.degree()], reverse=True)  # degree sequence\n",
    "# print \"Degree sequence\", degree_sequence\n",
    "degreeCount = collections.Counter(degree_sequence)\n",
    "deg, cnt = zip(*degreeCount.items())\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(deg, cnt, color='b')\n",
    "\n",
    "plt.title(\"Degree Histogram\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Degree\")\n",
    "plt.ylim((0,2000))\n",
    "plt.xlim(0,50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21311"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.degree()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21443"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(connect_cs[connect_cs['pid2']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>cid</th>\n",
       "      <th>pid1</th>\n",
       "      <th>pid2</th>\n",
       "      <th>relation</th>\n",
       "      <th>locid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.829600e+04</td>\n",
       "      <td>38296.000000</td>\n",
       "      <td>38296.000000</td>\n",
       "      <td>38296.000000</td>\n",
       "      <td>38296.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.506714e+05</td>\n",
       "      <td>427137.056037</td>\n",
       "      <td>179249.370013</td>\n",
       "      <td>0.447279</td>\n",
       "      <td>3910.718874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.290542e+05</td>\n",
       "      <td>122472.789775</td>\n",
       "      <td>219463.483225</td>\n",
       "      <td>0.524066</td>\n",
       "      <td>6898.311357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.680000e+02</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.281048e+05</td>\n",
       "      <td>419577.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>314.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.457945e+05</td>\n",
       "      <td>428270.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>481.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.883282e+05</td>\n",
       "      <td>450484.000000</td>\n",
       "      <td>416281.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.483880e+06</td>\n",
       "      <td>754245.000000</td>\n",
       "      <td>754194.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30156.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cid           pid1           pid2      relation         locid\n",
       "count  3.829600e+04   38296.000000   38296.000000  38296.000000  38296.000000\n",
       "mean   8.506714e+05  427137.056037  179249.370013      0.447279   3910.718874\n",
       "std    2.290542e+05  122472.789775  219463.483225      0.524066   6898.311357\n",
       "min    2.680000e+02      84.000000       0.000000      0.000000      1.000000\n",
       "25%    8.281048e+05  419577.750000       0.000000      0.000000    314.000000\n",
       "50%    8.457945e+05  428270.000000       0.000000      0.000000    481.000000\n",
       "75%    8.883282e+05  450484.000000  416281.250000      1.000000   3495.000000\n",
       "max    1.483880e+06  754245.000000  754194.000000      4.000000  30156.000000"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connect_cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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