Skip to content

Instantly share code, notes, and snippets.

@fnielsen

fnielsen/Nielsen2014Python_kardashianindex.ipynb

Last active August 29, 2015 14:04
Show Gist options
  • Save fnielsen/0db29279cbfc3067aab6 to your computer and use it in GitHub Desktop.
Save fnielsen/0db29279cbfc3067aab6 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Nielsen2014Python_kardashianindex.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:9f4333fdf63aaf1632b7f73e5bf39d0c92e17a844374bee7948dcb504bd04ddd"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kardashian index plot for data available in the Brede Wiki\n",
"====================================================\n",
"\n",
"Around 90 researchers listed in the Brede Wiki have both an Google Scholar identifier and and Twitter identifier \n",
"associated with them. These can be extracted and Google Scholar and Twitter can be queried to get citation count and followers count so the Kardashian index (K-index) can be computed.\n",
"\n",
"See the plot and the Kardashian index at the bottom.\n",
"\n",
"The list of the included researchers is here: http://neuro.compute.dtu.dk/wiki/Category:Researchers_in_Google_Scholar_and_Twitter\n",
"\n",
"Read more about the Kardashian index here: http://neuro.compute.dtu.dk/wiki/Kardashian_index\n",
"\n",
"New coefficients for power law for Google Scholar/Twitter are computed. The power law is 50.5 x citations ^ 0.28.\n",
"\n",
"To run the below code you need to setup a python.cfg configuration file with the four Twitter tokens, user-agent and 'from' email.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division, print_function\n",
"try:\n",
" import ConfigParser as configparser\n",
"except ImportError:\n",
" import configparser\n",
"import copy\n",
"import json\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd\n",
"import re\n",
"import requests\n",
"import scipy.spatial\n",
"import time\n",
"import tweepy\n",
"from urllib import quote"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Read configuration file for request user-agent and Twitter tokens\n",
"config = configparser.ConfigParser()\n",
"config.read(os.path.expanduser('~/etc/python.cfg'))\n",
"\n",
"headers = {\n",
" 'User-Agent': config.get('requests', 'user_agent'),\n",
" 'From': config.get('requests', 'from')\n",
"}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Fetch name of Brede Wiki page with researchers on Google Scholar and Twitter\n",
"url_gst = ('http://neuro.compute.dtu.dk/w/api.php?'\n",
" 'action=query&format=json&list=categorymembers&'\n",
" 'cmtitle=Category:Researchers in Google Scholar and Twitter')\n",
"\n",
"response = requests.get(url_gst, headers=headers).json()\n",
"pagetitles = [page['title'] for page in response['query']['categorymembers']]\n",
"while 'query-continue' in response:\n",
" url_continue = url_gst + '&cmcontinue=' + response['query-continue']['categorymembers']['cmcontinue'].encode('utf-8')\n",
" response = requests.get(url_continue).json()\n",
" pagetitles.extend([page['title'] for page in response['query']['categorymembers']])\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(pagetitles)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'Aaron Clauset', u'Aaron Halfaker', u'Alan Mislove', u'Alan Ruttenberg', u'Albert Gjedde', u'Alex O. Holcombe', u'Alex Verstak', u'Alon Halevy', u'Amy Bruckman', u'Anders Stockmarr', u'Andrea Cuttone', u'Andrea Forte', u'Andreas Roepstorff', u'Andrius Butkus', u'Antonia F. de C. Hamilton', u'Arkadiusz Stopczynski', u'Arto Lanam\\xe4ki', u'Benjamin Mako Hill', u'Bjarne \\xd8rum Fruergaard', u'Brian Keegan', u'C. Lee Giles', u'Carsten Stahlhut', u'Chitu Okoli', u'Chris D. Frith', u'Christian Kohlsch\\xfctter', u'Cliff Lampe', u'Cyril Goutte', u'Dan Jurafsky', u'Daniel M. Romero', u'Daniel Mietchen', u'Dario Taraborelli', u'David D. Cox', u'David Laniado', u'David M. Blei', u'David M. Pennock', u'David N. Kennedy', u'Denny Vrande\\u010di\\u0107', u'Dirk Riehle', u'Dominik Benz', u'Ed H. Chi', u'Edward T. Bullmore', u'Egon Willighagen', u'Elanor Colleoni', u'Emilio Ferrara', u'Eszter Hargittai', u'Felipe Ortega', u'Finn \\xc5rup Nielsen', u'Frank G. A. de Bakker', u'Friederike Schultz', u'Ga\\xebl Varoquaux', u'Geoffrey Karl Aguirre', u'Geraint Rees', u'Haewoon Kwak', u'Henry Markram', u'J. Andreas B\\xe6rentzen', u'J. Douglas Bremner', u'Jakob Eg Larsen', u'James W. Pennebaker', u'Jan Larsen (DTU Compute)', u'Jens Madsen', u'Joseph R. Kiniry', u'Jure Leskovec', u'Kasper Winther Andersen', u'Katrin Weller', u'Lada A. Adamic', u'Lars Kai Hansen', u'Lars Ramkilde Knudsen', u'Lasse L. M\\xf8lgaard', u'Leon French', u'Luca de Alfaro', u'Magnus Manske', u'Marcus Messner', u'Maria Grineva', u'Matthew G. Liptrot', u'Michael Cook', u'Michael Kai Petersen', u'Mikkel N. Schmidt', u'Mikkel Wallentin', u'Morten M\\xf8rup', u'Nikola Milikic', u'Noshir S. Contractor', u'Olaf Sporns', u'Paolo Massa', u'Paul Groth', u'Peter Sheridan Dodds', u'Peter Vuust', u'Petter Holme', u'Phoebe Ayers', u'Piotr Konieczny', u'R. Stuart Geiger', u'Rada Mihalcea', u'Radu Dr\\u0103gu\\u0219in', u'Rapha\\xebl Troncy', u'Renaud Lambiotte', u'Roderick Murray-Smith', u'Russell A. Poldrack', u'Ryota Kanai', u'Ryota Tomioka', u'Sarita Yardi', u'Simon Due Kamronn', u'Stanislas Dehaene', u'Stefania Serafin', u'Stephan Gouws', u'Sue B. Moon', u'Sune Lehmann', u'Taha Yasseri', u'Tal Yarkoni', u'Thomas Steiner', u'Tobias S. Andersen', u'Tom M. Mitchell', u'Trine Julie Abrahamsen', u'Uta Frith', u'Vedran Sekara', u'Vibe Geds\\xf8 Fr\\xf8kj\\xe6r', u'Vince Calhoun', u'Wray Buntine', u'Yong-Yeol Ahn']\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get researcher data from Brede Wiki\n",
"url_pages = \"http://neuro.compute.dtu.dk/w/index.php?action=raw&title=\"\n",
"pattern_researcher = re.compile('{{Researcher(\\s*?\\|.*?)}}', \n",
" re.DOTALL | re.IGNORECASE | re.UNICODE)\n",
"pattern_fields = re.compile(r'\\s*\\|\\s*(\\w+)\\s*=\\s*([^\\|]*\\w)', \n",
" re.DOTALL | re.UNICODE)\n",
"\n",
"researchers = []\n",
"for pagetitle in pagetitles:\n",
" response = requests.get(url_pages + quote(pagetitle.encode('utf-8')), headers=headers)\n",
" print(pagetitle)\n",
" researcher = pattern_researcher.findall(response.text)\n",
" if researcher:\n",
" researchers.append(dict(pattern_fields.findall(researcher[0])))\n",
" else:\n",
" print(\"Problem with \" + pagetitle)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Aaron Clauset\n",
"Aaron Halfaker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alan Mislove"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alan Ruttenberg"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Albert Gjedde"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alex O. Holcombe"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alex Verstak"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alon Halevy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Amy Bruckman"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Anders Stockmarr"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrea Cuttone"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrea Forte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andreas Roepstorff"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrius Butkus"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Antonia F. de C. Hamilton"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Arkadiusz Stopczynski"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Arto Lanam\u00e4ki"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Benjamin Mako Hill"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Bjarne \u00d8rum Fruergaard"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Brian Keegan"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"C. Lee Giles"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Carsten Stahlhut"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chitu Okoli"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chris D. Frith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Christian Kohlsch\u00fctter"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Cliff Lampe"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Cyril Goutte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dan Jurafsky"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Daniel M. Romero"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Daniel Mietchen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dario Taraborelli"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David D. Cox"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David Laniado"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David M. Blei"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David M. Pennock"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David N. Kennedy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Denny Vrande\u010di\u0107"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dirk Riehle"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dominik Benz"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ed H. Chi"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Edward T. Bullmore"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Egon Willighagen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Elanor Colleoni"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Emilio Ferrara"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Eszter Hargittai"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Felipe Ortega"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Finn \u00c5rup Nielsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Frank G. A. de Bakker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Friederike Schultz"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ga\u00ebl Varoquaux"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Geoffrey Karl Aguirre"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Geraint Rees"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Haewoon Kwak"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Henry Markram"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"J. Andreas B\u00e6rentzen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"J. Douglas Bremner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jakob Eg Larsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"James W. Pennebaker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jan Larsen (DTU Compute)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jens Madsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Joseph R. Kiniry"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jure Leskovec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Kasper Winther Andersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Katrin Weller"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lada A. Adamic"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lars Kai Hansen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lars Ramkilde Knudsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lasse L. M\u00f8lgaard"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Leon French"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Luca de Alfaro"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Magnus Manske"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Marcus Messner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Maria Grineva"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Matthew G. Liptrot"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Michael Cook"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Michael Kai Petersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Mikkel N. Schmidt"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Mikkel Wallentin"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Morten M\u00f8rup"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Nikola Milikic"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Noshir S. Contractor"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Olaf Sporns"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Paolo Massa"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Paul Groth"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Peter Sheridan Dodds"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Peter Vuust"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Petter Holme"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Phoebe Ayers"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Piotr Konieczny"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"R. Stuart Geiger"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Rada Mihalcea"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Radu Dr\u0103gu\u0219in"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Rapha\u00ebl Troncy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Renaud Lambiotte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Roderick Murray-Smith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Russell A. Poldrack"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ryota Kanai"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ryota Tomioka"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sarita Yardi"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Simon Due Kamronn"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stanislas Dehaene"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stefania Serafin"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stephan Gouws"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sue B. Moon"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sune Lehmann"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Taha Yasseri"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tal Yarkoni"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Thomas Steiner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tobias S. Andersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tom M. Mitchell"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Trine Julie Abrahamsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Uta Frith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vedran Sekara"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vibe Geds\u00f8 Fr\u00f8kj\u00e6r"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vince Calhoun"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Wray Buntine"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Yong-Yeol Ahn"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"researchers[14]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"{u'affiliation1': u'Institute of Cognitive Neuroscience',\n",
" u'affiliation2': u'School of Psychology, University of Nottingham',\n",
" u'affiliation3': u'Sobell Department of Motor Neuroscience and Movement Disorders',\n",
" u'country': u'United Kingdom',\n",
" u'family_name': u'Hamilton',\n",
" u'googlescholar': u'vNqtGKcAAAAJ',\n",
" u'initials': u'AF',\n",
" u'name': u'Antonia Hamilton',\n",
" u'position': u'Senior Lecturer',\n",
" u'scopus': u'7202618686',\n",
" u'scopus1': u'38060964400',\n",
" u'twitter': u'antoniahamilton',\n",
" u'url1': u'http://www.antoniahamilton.com/people.html'}"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"url_gs = 'http://scholar.google.com/citations?user='\n",
"\n",
"headers = {\n",
" 'User-Agent': config.get('requests', 'user_agent'),\n",
" 'From': config.get('requests', 'from')\n",
"}\n",
"\n",
"# <td class=\"cit-borderleft cit-data\">2537</td>\n",
"pattern_gscount = re.compile('<td class=\"gsc_rsb_std\">(\\d+)</td>')\n",
"\n",
"def get_google_scholar_counts(google_scholar_id):\n",
" response = requests.get(url_gs + google_scholar_id, \n",
" headers=headers)\n",
" counts = dict(zip(['citations', 'citations5', 'h-index', 'h-index5',\n",
" 'i10-index', 'i10-index5'],\n",
" map(int, pattern_gscount.findall(response.text))))\n",
" return counts\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Yong-Yeol Ahn check\n",
"get_google_scholar_counts('US7OSNgAAAAJ')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"{'citations': 3379,\n",
" 'citations5': 3181,\n",
" 'h-index': 16,\n",
" 'h-index5': 16,\n",
" 'i10-index': 18,\n",
" 'i10-index5': 17}"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get data from Google Scholar\n",
"for researcher in researchers:\n",
" if 'citations' not in researcher:\n",
" print(researcher['name'])\n",
" researcher.update(get_google_scholar_counts(researcher['googlescholar']))\n",
" time.sleep(5) \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Aaron Clauset\n",
"Aaron Halfaker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alan Mislove"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alan Ruttenberg"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Albert Gjedde"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alex O. Holcombe"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alex Verstak"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alon Halevy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Amy Bruckman"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Anders Stockmarr"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrea Cuttone"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrea Forte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andreas Roepstorff"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrius Butkus"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Antonia Hamilton"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Arkadiusz Stopczynski"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Arto Lanam\u00e4ki"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Benjamin Mako Hill"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Bjarne \u00d8rum Fruergaard"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Brian Keegan"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"C. Lee Giles"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Carsten Stahlhut"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chitu Okoli"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chris D. Frith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Christian Kohlsch\u00fctter"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Cliff Lampe"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Cyril Goutte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dan Jurafsky"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Daniel M. Romero"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Daniel Mietchen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dario Taraborelli"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David D. Cox"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David Laniado"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David M. Blei"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David M. Pennock"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David N. Kennedy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Denny Vrande\u010di\u0107"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dirk Riehle"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dominik Benz"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ed H. Chi"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Edward T. Bullmore"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Egon Willighagen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Elanor Colleoni"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Emilio Ferrara"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Eszter Hargittai"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Felipe Ortega"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Finn \u00c5rup Nielsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Frank G. A. de Bakker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Friederike Schultz"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ga\u00ebl Varoquaux"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Geoffrey Karl Aguirre"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Geraint Rees"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Haewoon Kwak"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Henry Markram"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"J. Andreas B\u00e6rentzen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"J. Douglas Bremner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jakob Eg Larsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"James W. Pennebaker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jan Larsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jens Madsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Joseph R Kiniry"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jure Leskovec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Kasper Winther Andersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Katrin Weller"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lada A. Adamic"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lars Kai Hansen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lars Ramkilde Knudsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lasse L. M\u00f8lgaard"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Leon French"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Luca de Alfaro"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Heinrich Magnus Manske"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Marcus Messner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Maria Grineva"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Matthew G. Liptrot"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Michael Cook"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Michael Kai Petersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Mikkel N. Schmidt"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Mikkel Wallentin"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Morten M\u00f8rup"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Nikola Milikic"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Noshir S. Contractor"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Olaf Sporns"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Paolo Massa"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Paul Groth"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Peter Sheridan Dodds"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Peter Vuust"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Petter Holme"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Phoebe Ayers"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Piotr Konieczny"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"R. Stuart Geiger"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Rada Mihalcea"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Radu Dr\u0103gu\u0219in"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Rapha\u00ebl Troncy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Renaud Lambiotte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Roderick Murray-Smith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Russell A. Poldrack"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ryota Kanai"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ryota Tomioka"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sarita Yardi Schoenebeck"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Simon Due Kamronn"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stanislas Dehaene"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stefania Serafin"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stephan Gouws"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sue B. Moon"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sune Lehmann"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Taha Yasseri"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tal Yarkoni"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Thomas Steiner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tobias S. Andersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tom M. Mitchell"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Trine Julie Abrahamsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Uta Frith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vedran Sekara"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vibe Geds\u00f8 Fr\u00f8kj\u00e6r"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vince Calhoun"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Wray Buntine"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Yong-Yeol Ahn"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Saving just in case\n",
"json.dump(researchers, open('researchers.json', 'w'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Twitter authentication\n",
"auth = tweepy.OAuthHandler(config.get('twitter', 'consumer_key'),\n",
" config.get('twitter', 'consumer_secret'))\n",
"auth.set_access_token(config.get('twitter', 'access_token'),\n",
" config.get('twitter', 'access_secret'))\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Function to download data from Twitter profiles\n",
"api = tweepy.API(auth)\n",
"\n",
"def get_twitter_count(twitter_id):\n",
" try:\n",
" user = api.get_user(twitter_id)\n",
" counts = {\n",
" 'Followers count': user.followers_count,\n",
" 'Friends count': user.friends_count,\n",
" 'Statuses count': user.statuses_count\n",
" }\n",
" return counts\n",
" except Exception:\n",
" print('Problem with ' + twitter_id)\n",
" return {}\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Testing with Finn Aarup Nielsen (fnielsen)\n",
"get_twitter_count('fnielsen')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"{'Followers count': 411, 'Friends count': 351, 'Statuses count': 2569}"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Download data from Twitter\n",
"for researcher in researchers:\n",
" researcher.update(get_twitter_count(researcher['twitter']))\n",
" print(researcher['name'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Aaron Clauset\n",
"Aaron Halfaker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alan Mislove"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alan Ruttenberg"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Albert Gjedde"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alex O. Holcombe"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alex Verstak"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Alon Halevy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Amy Bruckman"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Anders Stockmarr"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrea Cuttone"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrea Forte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andreas Roepstorff"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Andrius Butkus"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Antonia Hamilton"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Arkadiusz Stopczynski"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Arto Lanam\u00e4ki"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Benjamin Mako Hill"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Bjarne \u00d8rum Fruergaard"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Brian Keegan"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"C. Lee Giles"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Carsten Stahlhut"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chitu Okoli"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Chris D. Frith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Christian Kohlsch\u00fctter"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Cliff Lampe"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Cyril Goutte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dan Jurafsky"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Daniel M. Romero"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Daniel Mietchen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dario Taraborelli"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David D. Cox"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David Laniado"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David M. Blei"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David M. Pennock"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"David N. Kennedy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Denny Vrande\u010di\u0107"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dirk Riehle"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Dominik Benz"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ed H. Chi"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Edward T. Bullmore"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Egon Willighagen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Elanor Colleoni"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Emilio Ferrara"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Eszter Hargittai"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Felipe Ortega"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Finn \u00c5rup Nielsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Frank G. A. de Bakker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Friederike Schultz"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ga\u00ebl Varoquaux"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Geoffrey Karl Aguirre"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Geraint Rees"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Haewoon Kwak"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Henry Markram"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"J. Andreas B\u00e6rentzen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"J. Douglas Bremner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jakob Eg Larsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"James W. Pennebaker"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jan Larsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jens Madsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Joseph R Kiniry"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Jure Leskovec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Kasper Winther Andersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Katrin Weller"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lada A. Adamic"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lars Kai Hansen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lars Ramkilde Knudsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Lasse L. M\u00f8lgaard"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Leon French"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Luca de Alfaro"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Heinrich Magnus Manske"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Marcus Messner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Maria Grineva"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Matthew G. Liptrot"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Michael Cook"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Michael Kai Petersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Mikkel N. Schmidt"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Mikkel Wallentin"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Morten M\u00f8rup"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Nikola Milikic"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Noshir S. Contractor"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Olaf Sporns"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Paolo Massa"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Paul Groth"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Peter Sheridan Dodds"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Peter Vuust"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Petter Holme"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Phoebe Ayers"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Piotr Konieczny"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"R. Stuart Geiger"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Rada Mihalcea"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Radu Dr\u0103gu\u0219in"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Rapha\u00ebl Troncy"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Renaud Lambiotte"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Roderick Murray-Smith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Russell A. Poldrack"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ryota Kanai"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Ryota Tomioka"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sarita Yardi Schoenebeck"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Simon Due Kamronn"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stanislas Dehaene"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stefania Serafin"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Stephan Gouws"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sue B. Moon"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Sune Lehmann"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Taha Yasseri"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tal Yarkoni"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Thomas Steiner"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tobias S. Andersen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Tom M. Mitchell"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Trine Julie Abrahamsen"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Uta Frith"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vedran Sekara"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vibe Geds\u00f8 Fr\u00f8kj\u00e6r"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Vince Calhoun"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Wray Buntine"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Yong-Yeol Ahn"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Save just in case\n",
"json.dump(researchers, open('researchers.json', 'w'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"researchers = json.load(open('researchers.json'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"researchers[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"{u'Followers count': 1733,\n",
" u'Friends count': 95,\n",
" u'Statuses count': 575,\n",
" u'affiliation1': u'University of Colorado at Boulder',\n",
" u'citations': 8346,\n",
" u'citations5': 7446,\n",
" u'country': u'United States of America',\n",
" u'family_name': u'Clauset',\n",
" u'googlescholar': u'e7VI_HcAAAAJ',\n",
" u'h-index': 20,\n",
" u'h-index5': 18,\n",
" u'i10-index': 28,\n",
" u'i10-index5': 27,\n",
" u'initials': u'A',\n",
" u'name': u'Aaron Clauset',\n",
" u'position': u'Assistant Professor',\n",
" u'scopus': u'8298347800',\n",
" u'twitter': u'aaronclauset',\n",
" u'url1': u'http://tuvalu.santafe.edu/~aaronc',\n",
" u'url2': u'http://cs.unm.edu/~aaron/blog',\n",
" u'videolectures': u'aaron_clauset'}"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Pandas!\n",
"df = pd.DataFrame(researchers)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# isnan: Houston, we've had a problem\n",
"indices = (~np.isnan(df['citations'])) & (df['citations'] != 0)\n",
"reverse_index = indices[indices].index.values\n",
"\n",
"# Plot the data\n",
"matplotlib.rc('font', family='DejaVu Sans')\n",
"fig = df.plot(x='citations', y='Followers count',\n",
" kind='scatter', figsize=(15, 10), \n",
" marker='*', s=df['Statuses count']/10,\n",
" linewidth=2, color=(0.8, 0.8, 0.8))\n",
"ax = plt.gca()\n",
"ax.set_xscale('log')\n",
"ax.set_yscale('log')\n",
"plt.xlabel('Google Scholar citations')\n",
"plt.ylabel('Twitter followers count')\n",
"plt.title('Kardashian index for Brede Wiki researchers on Google Scholar and Twitter')\n",
"\n",
"# Power law fit\n",
"p = np.polyfit(np.log(df.ix[indices, 'citations']), np.log(df.ix[indices, 'Followers count']), 1)\n",
"powerlaw = np.frompyfunc(lambda x: np.exp(p[1]) * x ** p[0], 1, 1)\n",
"plt.plot([1, 200000], powerlaw([1, 200000]), linewidth=5, color=(0.5, 1, 0.5))\n",
"plt.text(10, 5000, '{:.3} x citations^{:0.2}'.format(np.exp(p[1]), p[0]), fontsize=20)\n",
"\n",
"# Annotation of some of the points with researcher names\n",
"hull = scipy.spatial.ConvexHull(df.ix[indices, ['citations', 'Followers count']])\n",
"for index in hull.vertices:\n",
" x, y, name = df.ix[reverse_index[index], ['citations', 'Followers count', 'name']].values\n",
" try:\n",
" plt.text(x, y, name, horizontalalignment='center', verticalalignment='center')\n",
" except:\n",
" pass\n",
" \n",
"# Myself and Ryoto and et al.\n",
"family_names = ['Nielsen', 'Tomioka', 'Willighagen']\n",
"for family_name in family_names:\n",
" x, y, name = df.ix[df['family_name'] == family_name, ['citations', 'Followers count', 'name']].values.flatten()\n",
" plt.text(x, y, name, horizontalalignment='center', verticalalignment='center')\n",
"\n",
"dummy = plt.axis((1, 200000, 1, 20000))\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAJuCAYAAAANVMtCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x4678fd0>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Followers count</th>\n",
" <th>Friends count</th>\n",
" <th>Statuses count</th>\n",
" <th>citations</th>\n",
" <th>citations5</th>\n",
" <th>h-index</th>\n",
" <th>h-index5</th>\n",
" <th>i10-index</th>\n",
" <th>i10-index5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 117.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 1388.213675</td>\n",
" <td> 374.042735</td>\n",
" <td> 2564.042735</td>\n",
" <td> 7694.487179</td>\n",
" <td> 4507.735043</td>\n",
" <td> 27.393162</td>\n",
" <td> 22.649573</td>\n",
" <td> 56.606838</td>\n",
" <td> 47.188034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 2407.753202</td>\n",
" <td> 673.039777</td>\n",
" <td> 4933.751046</td>\n",
" <td> 15717.475907</td>\n",
" <td> 7870.026290</td>\n",
" <td> 29.289859</td>\n",
" <td> 21.741156</td>\n",
" <td> 82.538910</td>\n",
" <td> 67.952561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 73.000000</td>\n",
" <td> 52.000000</td>\n",
" <td> 40.000000</td>\n",
" <td> 348.000000</td>\n",
" <td> 308.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 504.000000</td>\n",
" <td> 189.000000</td>\n",
" <td> 551.000000</td>\n",
" <td> 1815.000000</td>\n",
" <td> 1393.000000</td>\n",
" <td> 17.000000</td>\n",
" <td> 16.000000</td>\n",
" <td> 24.000000</td>\n",
" <td> 20.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 1733.000000</td>\n",
" <td> 435.000000</td>\n",
" <td> 3053.000000</td>\n",
" <td> 7424.000000</td>\n",
" <td> 5018.000000</td>\n",
" <td> 34.000000</td>\n",
" <td> 30.000000</td>\n",
" <td> 66.000000</td>\n",
" <td> 57.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 15224.000000</td>\n",
" <td> 5915.000000</td>\n",
" <td> 31695.000000</td>\n",
" <td> 124042.000000</td>\n",
" <td> 51905.000000</td>\n",
" <td> 179.000000</td>\n",
" <td> 117.000000</td>\n",
" <td> 522.000000</td>\n",
" <td> 415.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows \u00d7 9 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
" Followers count Friends count Statuses count citations \\\n",
"count 117.000000 117.000000 117.000000 117.000000 \n",
"mean 1388.213675 374.042735 2564.042735 7694.487179 \n",
"std 2407.753202 673.039777 4933.751046 15717.475907 \n",
"min 1.000000 0.000000 0.000000 0.000000 \n",
"25% 73.000000 52.000000 40.000000 348.000000 \n",
"50% 504.000000 189.000000 551.000000 1815.000000 \n",
"75% 1733.000000 435.000000 3053.000000 7424.000000 \n",
"max 15224.000000 5915.000000 31695.000000 124042.000000 \n",
"\n",
" citations5 h-index h-index5 i10-index i10-index5 \n",
"count 117.000000 117.000000 117.000000 117.000000 117.000000 \n",
"mean 4507.735043 27.393162 22.649573 56.606838 47.188034 \n",
"std 7870.026290 29.289859 21.741156 82.538910 67.952561 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 308.000000 9.000000 9.000000 9.000000 8.000000 \n",
"50% 1393.000000 17.000000 16.000000 24.000000 20.000000 \n",
"75% 5018.000000 34.000000 30.000000 66.000000 57.000000 \n",
"max 51905.000000 179.000000 117.000000 522.000000 415.000000 \n",
"\n",
"[8 rows x 9 columns]"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['K-index'] = df['Followers count'] / powerlaw(df['citations'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Identify the 'scientific Kardashians'\n",
"high_score = df[indices].sort(columns='K-index', ascending=False)[['name', 'K-index', 'Statuses count']]\n",
"high_score"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>K-index</th>\n",
" <th>Statuses count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>104</th>\n",
" <td> Sune Lehmann</td>\n",
" <td> 43.35202</td>\n",
" <td> 1994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43 </th>\n",
" <td> Emilio Ferrara</td>\n",
" <td> 29.38693</td>\n",
" <td> 4680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61 </th>\n",
" <td> Jure Leskovec</td>\n",
" <td> 14.17319</td>\n",
" <td> 575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td> Uta Frith</td>\n",
" <td> 13.85914</td>\n",
" <td> 3760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> Brian Keegan</td>\n",
" <td> 13.52433</td>\n",
" <td> 17571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74 </th>\n",
" <td> Michael Cook</td>\n",
" <td> 13.20721</td>\n",
" <td> 24813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71 </th>\n",
" <td> Marcus Messner</td>\n",
" <td> 10.85944</td>\n",
" <td> 3086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44 </th>\n",
" <td> Eszter Hargittai</td>\n",
" <td> 10.48633</td>\n",
" <td> 3050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> Daniel Mietchen</td>\n",
" <td> 10.21248</td>\n",
" <td> 18336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72 </th>\n",
" <td> Maria Grineva</td>\n",
" <td> 10.20603</td>\n",
" <td> 15734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64 </th>\n",
" <td> Lada A. Adamic</td>\n",
" <td> 9.837668</td>\n",
" <td> 278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98 </th>\n",
" <td> Sarita Yardi Schoenebeck</td>\n",
" <td> 8.830271</td>\n",
" <td> 15314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55 </th>\n",
" <td> J. Douglas Bremner</td>\n",
" <td> 7.902007</td>\n",
" <td> 9638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td> Thomas Steiner</td>\n",
" <td> 7.387838</td>\n",
" <td> 6546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49 </th>\n",
" <td> Ga\u00ebl Varoquaux</td>\n",
" <td> 7.323572</td>\n",
" <td> 1326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39 </th>\n",
" <td> Ed H. Chi</td>\n",
" <td> 7.263597</td>\n",
" <td> 7197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30 </th>\n",
" <td> Dario Taraborelli</td>\n",
" <td> 7.039645</td>\n",
" <td> 4027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td> Tal Yarkoni</td>\n",
" <td> 6.59077</td>\n",
" <td> 2586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51 </th>\n",
" <td> Geraint Rees</td>\n",
" <td> 6.533561</td>\n",
" <td> 1246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87 </th>\n",
" <td> Phoebe Ayers</td>\n",
" <td> 6.198842</td>\n",
" <td> 2682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> Yong-Yeol Ahn</td>\n",
" <td> 6.043176</td>\n",
" <td> 8292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> Benjamin Mako Hill</td>\n",
" <td> 5.815526</td>\n",
" <td> 197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80 </th>\n",
" <td> Noshir S. Contractor</td>\n",
" <td> 5.768472</td>\n",
" <td> 899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> Alex O. Holcombe</td>\n",
" <td> 5.398742</td>\n",
" <td> 6329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41 </th>\n",
" <td> Egon Willighagen</td>\n",
" <td> 5.352792</td>\n",
" <td> 31695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89 </th>\n",
" <td> R. Stuart Geiger</td>\n",
" <td> 5.329288</td>\n",
" <td> 3053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> Amy Bruckman</td>\n",
" <td> 4.750928</td>\n",
" <td> 3101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> Antonia Hamilton</td>\n",
" <td> 4.601453</td>\n",
" <td> 1778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63 </th>\n",
" <td> Katrin Weller</td>\n",
" <td> 4.519743</td>\n",
" <td> 3682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37 </th>\n",
" <td> Dirk Riehle</td>\n",
" <td> 4.273715</td>\n",
" <td> 4747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92 </th>\n",
" <td> Rapha\u00ebl Troncy</td>\n",
" <td> 4.229026</td>\n",
" <td> 5685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> Cliff Lampe</td>\n",
" <td> 3.944443</td>\n",
" <td> 3606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56 </th>\n",
" <td> Jakob Eg Larsen</td>\n",
" <td> 3.93527</td>\n",
" <td> 2468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31 </th>\n",
" <td> David D. Cox</td>\n",
" <td> 3.797303</td>\n",
" <td> 2709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td> Taha Yasseri</td>\n",
" <td> 3.666922</td>\n",
" <td> 723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96 </th>\n",
" <td> Ryota Kanai</td>\n",
" <td> 3.658165</td>\n",
" <td> 2230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> Dan Jurafsky</td>\n",
" <td> 3.499375</td>\n",
" <td> 68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> Arkadiusz Stopczynski</td>\n",
" <td> 3.370665</td>\n",
" <td> 4556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95 </th>\n",
" <td> Russell A. Poldrack</td>\n",
" <td> 3.091737</td>\n",
" <td> 551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> Arto Lanam\u00e4ki</td>\n",
" <td> 3.045272</td>\n",
" <td> 1050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> Aaron Clauset</td>\n",
" <td> 2.984011</td>\n",
" <td> 575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36 </th>\n",
" <td> Denny Vrande\u010di\u0107</td>\n",
" <td> 2.847603</td>\n",
" <td> 1250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45 </th>\n",
" <td> Felipe Ortega</td>\n",
" <td> 2.768661</td>\n",
" <td> 1733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47 </th>\n",
" <td> Frank G. A. de Bakker</td>\n",
" <td> 2.726936</td>\n",
" <td> 4753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> Andrea Forte</td>\n",
" <td> 2.69998</td>\n",
" <td> 1428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83 </th>\n",
" <td> Paul Groth</td>\n",
" <td> 2.444785</td>\n",
" <td> 4791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td> Sue B. Moon</td>\n",
" <td> 2.39438</td>\n",
" <td> 3346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> Alon Halevy</td>\n",
" <td> 2.314019</td>\n",
" <td> 328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> Chris D. Frith</td>\n",
" <td> 2.212742</td>\n",
" <td> 107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52 </th>\n",
" <td> Haewoon Kwak</td>\n",
" <td> 2.030797</td>\n",
" <td> 11376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81 </th>\n",
" <td> Olaf Sporns</td>\n",
" <td> 1.851942</td>\n",
" <td> 483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> Christian Kohlsch\u00fctter</td>\n",
" <td> 1.684115</td>\n",
" <td> 5203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70 </th>\n",
" <td> Heinrich Magnus Manske</td>\n",
" <td> 1.650236</td>\n",
" <td> 2723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34 </th>\n",
" <td> David M. Pennock</td>\n",
" <td> 1.610894</td>\n",
" <td> 834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86 </th>\n",
" <td> Petter Holme</td>\n",
" <td> 1.593025</td>\n",
" <td> 3053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> Aaron Halfaker</td>\n",
" <td> 1.566837</td>\n",
" <td> 692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84 </th>\n",
" <td> Peter Sheridan Dodds</td>\n",
" <td> 1.534204</td>\n",
" <td> 5482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td> Stanislas Dehaene</td>\n",
" <td> 1.496988</td>\n",
" <td> 40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97 </th>\n",
" <td> Ryota Tomioka</td>\n",
" <td> 1.421273</td>\n",
" <td> 4815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td> Stephan Gouws</td>\n",
" <td> 1.345312</td>\n",
" <td> 1428</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>116 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
" name K-index Statuses count\n",
"104 Sune Lehmann 43.35202 1994\n",
"43 Emilio Ferrara 29.38693 4680\n",
"61 Jure Leskovec 14.17319 575\n",
"111 Uta Frith 13.85914 3760\n",
"19 Brian Keegan 13.52433 17571\n",
"74 Michael Cook 13.20721 24813\n",
"71 Marcus Messner 10.85944 3086\n",
"44 Eszter Hargittai 10.48633 3050\n",
"29 Daniel Mietchen 10.21248 18336\n",
"72 Maria Grineva 10.20603 15734\n",
"64 Lada A. Adamic 9.837668 278\n",
"98 Sarita Yardi Schoenebeck 8.830271 15314\n",
"55 J. Douglas Bremner 7.902007 9638\n",
"107 Thomas Steiner 7.387838 6546\n",
"49 Ga\u00ebl Varoquaux 7.323572 1326\n",
"39 Ed H. Chi 7.263597 7197\n",
"30 Dario Taraborelli 7.039645 4027\n",
"106 Tal Yarkoni 6.59077 2586\n",
"51 Geraint Rees 6.533561 1246\n",
"87 Phoebe Ayers 6.198842 2682\n",
"116 Yong-Yeol Ahn 6.043176 8292\n",
"17 Benjamin Mako Hill 5.815526 197\n",
"80 Noshir S. Contractor 5.768472 899\n",
"5 Alex O. Holcombe 5.398742 6329\n",
"41 Egon Willighagen 5.352792 31695\n",
"89 R. Stuart Geiger 5.329288 3053\n",
"8 Amy Bruckman 4.750928 3101\n",
"14 Antonia Hamilton 4.601453 1778\n",
"63 Katrin Weller 4.519743 3682\n",
"37 Dirk Riehle 4.273715 4747\n",
"92 Rapha\u00ebl Troncy 4.229026 5685\n",
"25 Cliff Lampe 3.944443 3606\n",
"56 Jakob Eg Larsen 3.93527 2468\n",
"31 David D. Cox 3.797303 2709\n",
"105 Taha Yasseri 3.666922 723\n",
"96 Ryota Kanai 3.658165 2230\n",
"27 Dan Jurafsky 3.499375 68\n",
"15 Arkadiusz Stopczynski 3.370665 4556\n",
"95 Russell A. Poldrack 3.091737 551\n",
"16 Arto Lanam\u00e4ki 3.045272 1050\n",
"0 Aaron Clauset 2.984011 575\n",
"36 Denny Vrande\u010di\u0107 2.847603 1250\n",
"45 Felipe Ortega 2.768661 1733\n",
"47 Frank G. A. de Bakker 2.726936 4753\n",
"11 Andrea Forte 2.69998 1428\n",
"83 Paul Groth 2.444785 4791\n",
"103 Sue B. Moon 2.39438 3346\n",
"7 Alon Halevy 2.314019 328\n",
"23 Chris D. Frith 2.212742 107\n",
"52 Haewoon Kwak 2.030797 11376\n",
"81 Olaf Sporns 1.851942 483\n",
"24 Christian Kohlsch\u00fctter 1.684115 5203\n",
"70 Heinrich Magnus Manske 1.650236 2723\n",
"34 David M. Pennock 1.610894 834\n",
"86 Petter Holme 1.593025 3053\n",
"1 Aaron Halfaker 1.566837 692\n",
"84 Peter Sheridan Dodds 1.534204 5482\n",
"100 Stanislas Dehaene 1.496988 40\n",
"97 Ryota Tomioka 1.421273 4815\n",
"102 Stephan Gouws 1.345312 1428\n",
" ... ... ...\n",
"\n",
"[116 rows x 3 columns]"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Not all is shown above\n",
"# The below code will give the full list:\n",
"# https://stackoverflow.com/questions/23388810/ipython-notebook-output-cell-is-truncating-contents-of-my-list\n",
"from IPython.display import HTML\n",
"HTML(high_score.to_html())\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>K-index</th>\n",
" <th>Statuses count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>104</th>\n",
" <td> Sune Lehmann</td>\n",
" <td> 43.35202</td>\n",
" <td> 1994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43 </th>\n",
" <td> Emilio Ferrara</td>\n",
" <td> 29.38693</td>\n",
" <td> 4680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61 </th>\n",
" <td> Jure Leskovec</td>\n",
" <td> 14.17319</td>\n",
" <td> 575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td> Uta Frith</td>\n",
" <td> 13.85914</td>\n",
" <td> 3760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> Brian Keegan</td>\n",
" <td> 13.52433</td>\n",
" <td> 17571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74 </th>\n",
" <td> Michael Cook</td>\n",
" <td> 13.20721</td>\n",
" <td> 24813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71 </th>\n",
" <td> Marcus Messner</td>\n",
" <td> 10.85944</td>\n",
" <td> 3086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44 </th>\n",
" <td> Eszter Hargittai</td>\n",
" <td> 10.48633</td>\n",
" <td> 3050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> Daniel Mietchen</td>\n",
" <td> 10.21248</td>\n",
" <td> 18336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72 </th>\n",
" <td> Maria Grineva</td>\n",
" <td> 10.20603</td>\n",
" <td> 15734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64 </th>\n",
" <td> Lada A. Adamic</td>\n",
" <td> 9.837668</td>\n",
" <td> 278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98 </th>\n",
" <td> Sarita Yardi Schoenebeck</td>\n",
" <td> 8.830271</td>\n",
" <td> 15314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55 </th>\n",
" <td> J. Douglas Bremner</td>\n",
" <td> 7.902007</td>\n",
" <td> 9638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td> Thomas Steiner</td>\n",
" <td> 7.387838</td>\n",
" <td> 6546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49 </th>\n",
" <td> Ga\u00ebl Varoquaux</td>\n",
" <td> 7.323572</td>\n",
" <td> 1326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39 </th>\n",
" <td> Ed H. Chi</td>\n",
" <td> 7.263597</td>\n",
" <td> 7197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30 </th>\n",
" <td> Dario Taraborelli</td>\n",
" <td> 7.039645</td>\n",
" <td> 4027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td> Tal Yarkoni</td>\n",
" <td> 6.59077</td>\n",
" <td> 2586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51 </th>\n",
" <td> Geraint Rees</td>\n",
" <td> 6.533561</td>\n",
" <td> 1246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87 </th>\n",
" <td> Phoebe Ayers</td>\n",
" <td> 6.198842</td>\n",
" <td> 2682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> Yong-Yeol Ahn</td>\n",
" <td> 6.043176</td>\n",
" <td> 8292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> Benjamin Mako Hill</td>\n",
" <td> 5.815526</td>\n",
" <td> 197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80 </th>\n",
" <td> Noshir S. Contractor</td>\n",
" <td> 5.768472</td>\n",
" <td> 899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> Alex O. Holcombe</td>\n",
" <td> 5.398742</td>\n",
" <td> 6329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41 </th>\n",
" <td> Egon Willighagen</td>\n",
" <td> 5.352792</td>\n",
" <td> 31695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89 </th>\n",
" <td> R. Stuart Geiger</td>\n",
" <td> 5.329288</td>\n",
" <td> 3053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> Amy Bruckman</td>\n",
" <td> 4.750928</td>\n",
" <td> 3101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> Antonia Hamilton</td>\n",
" <td> 4.601453</td>\n",
" <td> 1778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63 </th>\n",
" <td> Katrin Weller</td>\n",
" <td> 4.519743</td>\n",
" <td> 3682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37 </th>\n",
" <td> Dirk Riehle</td>\n",
" <td> 4.273715</td>\n",
" <td> 4747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92 </th>\n",
" <td> Rapha\u00ebl Troncy</td>\n",
" <td> 4.229026</td>\n",
" <td> 5685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> Cliff Lampe</td>\n",
" <td> 3.944443</td>\n",
" <td> 3606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56 </th>\n",
" <td> Jakob Eg Larsen</td>\n",
" <td> 3.93527</td>\n",
" <td> 2468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31 </th>\n",
" <td> David D. Cox</td>\n",
" <td> 3.797303</td>\n",
" <td> 2709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td> Taha Yasseri</td>\n",
" <td> 3.666922</td>\n",
" <td> 723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96 </th>\n",
" <td> Ryota Kanai</td>\n",
" <td> 3.658165</td>\n",
" <td> 2230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> Dan Jurafsky</td>\n",
" <td> 3.499375</td>\n",
" <td> 68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> Arkadiusz Stopczynski</td>\n",
" <td> 3.370665</td>\n",
" <td> 4556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95 </th>\n",
" <td> Russell A. Poldrack</td>\n",
" <td> 3.091737</td>\n",
" <td> 551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> Arto Lanam\u00e4ki</td>\n",
" <td> 3.045272</td>\n",
" <td> 1050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> Aaron Clauset</td>\n",
" <td> 2.984011</td>\n",
" <td> 575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36 </th>\n",
" <td> Denny Vrande\u010di\u0107</td>\n",
" <td> 2.847603</td>\n",
" <td> 1250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45 </th>\n",
" <td> Felipe Ortega</td>\n",
" <td> 2.768661</td>\n",
" <td> 1733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47 </th>\n",
" <td> Frank G. A. de Bakker</td>\n",
" <td> 2.726936</td>\n",
" <td> 4753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> Andrea Forte</td>\n",
" <td> 2.69998</td>\n",
" <td> 1428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83 </th>\n",
" <td> Paul Groth</td>\n",
" <td> 2.444785</td>\n",
" <td> 4791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td> Sue B. Moon</td>\n",
" <td> 2.39438</td>\n",
" <td> 3346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> Alon Halevy</td>\n",
" <td> 2.314019</td>\n",
" <td> 328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> Chris D. Frith</td>\n",
" <td> 2.212742</td>\n",
" <td> 107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52 </th>\n",
" <td> Haewoon Kwak</td>\n",
" <td> 2.030797</td>\n",
" <td> 11376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81 </th>\n",
" <td> Olaf Sporns</td>\n",
" <td> 1.851942</td>\n",
" <td> 483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> Christian Kohlsch\u00fctter</td>\n",
" <td> 1.684115</td>\n",
" <td> 5203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70 </th>\n",
" <td> Heinrich Magnus Manske</td>\n",
" <td> 1.650236</td>\n",
" <td> 2723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34 </th>\n",
" <td> David M. Pennock</td>\n",
" <td> 1.610894</td>\n",
" <td> 834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86 </th>\n",
" <td> Petter Holme</td>\n",
" <td> 1.593025</td>\n",
" <td> 3053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> Aaron Halfaker</td>\n",
" <td> 1.566837</td>\n",
" <td> 692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84 </th>\n",
" <td> Peter Sheridan Dodds</td>\n",
" <td> 1.534204</td>\n",
" <td> 5482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td> Stanislas Dehaene</td>\n",
" <td> 1.496988</td>\n",
" <td> 40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97 </th>\n",
" <td> Ryota Tomioka</td>\n",
" <td> 1.421273</td>\n",
" <td> 4815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td> Stephan Gouws</td>\n",
" <td> 1.345312</td>\n",
" <td> 1428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79 </th>\n",
" <td> Nikola Milikic</td>\n",
" <td> 1.339853</td>\n",
" <td> 107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td> Tom M. Mitchell</td>\n",
" <td> 1.313142</td>\n",
" <td> 21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82 </th>\n",
" <td> Paolo Massa</td>\n",
" <td> 1.306788</td>\n",
" <td> 465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> Andreas Roepstorff</td>\n",
" <td> 1.143615</td>\n",
" <td> 2042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> Daniel M. Romero</td>\n",
" <td> 1.042678</td>\n",
" <td> 339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60 </th>\n",
" <td> Joseph R Kiniry</td>\n",
" <td> 1.036185</td>\n",
" <td> 1480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75 </th>\n",
" <td> Michael Kai Petersen</td>\n",
" <td> 1.030835</td>\n",
" <td> 154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46 </th>\n",
" <td> Finn \u00c5rup Nielsen</td>\n",
" <td> 0.9939271</td>\n",
" <td> 2569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91 </th>\n",
" <td> Radu Dr\u0103gu\u0219in</td>\n",
" <td> 0.9096936</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57 </th>\n",
" <td> James W. Pennebaker</td>\n",
" <td> 0.8613386</td>\n",
" <td> 39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50 </th>\n",
" <td> Geoffrey Karl Aguirre</td>\n",
" <td> 0.7240992</td>\n",
" <td> 357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32 </th>\n",
" <td> David Laniado</td>\n",
" <td> 0.6778401</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> Alan Ruttenberg</td>\n",
" <td> 0.6029182</td>\n",
" <td> 145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 </th>\n",
" <td> Friederike Schultz</td>\n",
" <td> 0.5622809</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38 </th>\n",
" <td> Dominik Benz</td>\n",
" <td> 0.5512532</td>\n",
" <td> 1151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68 </th>\n",
" <td> Leon French</td>\n",
" <td> 0.4934968</td>\n",
" <td> 162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> Bjarne \u00d8rum Fruergaard</td>\n",
" <td> 0.4660093</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td> Stefania Serafin</td>\n",
" <td> 0.4615673</td>\n",
" <td> 351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65 </th>\n",
" <td> Lars Kai Hansen</td>\n",
" <td> 0.4436704</td>\n",
" <td> 1251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42 </th>\n",
" <td> Elanor Colleoni</td>\n",
" <td> 0.4111292</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94 </th>\n",
" <td> Roderick Murray-Smith</td>\n",
" <td> 0.380659</td>\n",
" <td> 554</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67 </th>\n",
" <td> Lasse L. M\u00f8lgaard</td>\n",
" <td> 0.3693654</td>\n",
" <td> 156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58 </th>\n",
" <td> Jan Larsen</td>\n",
" <td> 0.3333603</td>\n",
" <td> 207</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77 </th>\n",
" <td> Mikkel Wallentin</td>\n",
" <td> 0.3172575</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73 </th>\n",
" <td> Matthew G. Liptrot</td>\n",
" <td> 0.3059698</td>\n",
" <td> 506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td> Vedran Sekara</td>\n",
" <td> 0.2842159</td>\n",
" <td> 43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85 </th>\n",
" <td> Peter Vuust</td>\n",
" <td> 0.2595274</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33 </th>\n",
" <td> David M. Blei</td>\n",
" <td> 0.242567</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54 </th>\n",
" <td> J. Andreas B\u00e6rentzen</td>\n",
" <td> 0.2377323</td>\n",
" <td> 104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93 </th>\n",
" <td> Renaud Lambiotte</td>\n",
" <td> 0.2345374</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td> Trine Julie Abrahamsen</td>\n",
" <td> 0.222549</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69 </th>\n",
" <td> Luca de Alfaro</td>\n",
" <td> 0.2064739</td>\n",
" <td> 85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59 </th>\n",
" <td> Jens Madsen</td>\n",
" <td> 0.2003703</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> Andrea Cuttone</td>\n",
" <td> 0.1989996</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> Andrius Butkus</td>\n",
" <td> 0.1978109</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76 </th>\n",
" <td> Mikkel N. Schmidt</td>\n",
" <td> 0.1591836</td>\n",
" <td> 25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62 </th>\n",
" <td> Kasper Winther Andersen</td>\n",
" <td> 0.1481508</td>\n",
" <td> 88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88 </th>\n",
" <td> Piotr Konieczny</td>\n",
" <td> 0.1426637</td>\n",
" <td> 158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53 </th>\n",
" <td> Henry Markram</td>\n",
" <td> 0.1372832</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> Carsten Stahlhut</td>\n",
" <td> 0.110743</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78 </th>\n",
" <td> Morten M\u00f8rup</td>\n",
" <td> 0.1098451</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td> Wray Buntine</td>\n",
" <td> 0.1090059</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> Alex Verstak</td>\n",
" <td> 0.09618637</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> Albert Gjedde</td>\n",
" <td> 0.0933135</td>\n",
" <td> 162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66 </th>\n",
" <td> Lars Ramkilde Knudsen</td>\n",
" <td> 0.08555802</td>\n",
" <td> 40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> Chitu Okoli</td>\n",
" <td> 0.08295347</td>\n",
" <td> 376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td> Tobias S. Andersen</td>\n",
" <td> 0.05471306</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> Alan Mislove</td>\n",
" <td> 0.04338656</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40 </th>\n",
" <td> Edward T. Bullmore</td>\n",
" <td> 0.03679437</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td> Vince Calhoun</td>\n",
" <td> 0.02110989</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> C. Lee Giles</td>\n",
" <td> 0.02110068</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td> Vibe Geds\u00f8 Fr\u00f8kj\u00e6r</td>\n",
" <td> 0.0203848</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35 </th>\n",
" <td> David N. Kennedy</td>\n",
" <td> 0.01667231</td>\n",
" <td> 70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90 </th>\n",
" <td> Rada Mihalcea</td>\n",
" <td> 0.01475713</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> Cyril Goutte</td>\n",
" <td> 0.004659881</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Anders Stockmarr</td>\n",
" <td> 0.003353294</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"<IPython.core.display.HTML at 0x4449110>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment