Data about particular researchers is interesting to us. We are interested in their career path, disciplinarity, affilitations. So far we have two datasets we can work with. One comes from highlycited.com (provided by Thompson Reuters) and another comes from American Philosophical Society.
In [1]:
import requests
In [2]:
rxls = requests.get('http://highlycited.com/highly_cited_2014.xlsx')
rcsv = requests.get('http://highlycited.com/highly_cited_2014.csv')
In [1]:
import pandas as pd
url = 'd:\Desktop\highly_cited_2014_june_2014.xlsx'
df = pd.read_excel(url)
In [2]:
df.columns
Out[2]:
Index([u'First_Name', u'Middle_Name', u'Family_Name', u'Category', u'Primary Affiliation', u'Secondary Affiliations'], dtype='object')
Let's see what kind of disciplines/fields to they have. They use very neutral name of 'category'.
In [46]:
all_categories = list(df['Category'].unique())
all_categories
Out[46]:
[u'Agricultural Sciences',
u'Biology & Biochemistry',
u'Chemistry',
u'Clinical Medicine',
u'Computer Science',
u'Economics & Business',
u'Engineering',
u'Environment/Ecology',
u'Geosciences',
u'Immunology',
u'Materials Science',
u'Mathematics',
u'Microbiology',
u'Molecular Biology & Genetics',
u'Neuroscience & Behavior',
u'Pharmacology & Toxicology',
u'Physics',
u'Plant & Animal Science',
u'Psychiatry/Psychology',
u'Social Sciences, general',
u'Space Science']
The list we got here is quite nice. We can say something about it. First, some disciplines are grouped together. For example: 'Biology & Biochemistry', 'Economics & Business', 'Environment/Ecology'. Second, 'Environment/Ecology' and 'Psychiatry/Psychology' are separated with "/" rathern than "&" like most other categories. Does this refer to some kind of different relations between the disciplines? Third, 'Social Sciences, general' - this cattegorie refers to social sciences in general, that means we should expect overal.
In [56]:
[x.replace('/', '&').split('&') for x in all_categories]
Out[56]:
[[u'Agricultural Sciences'],
[u'Biology ', u' Biochemistry'],
[u'Chemistry'],
[u'Clinical Medicine'],
[u'Computer Science'],
[u'Economics ', u' Business'],
[u'Engineering'],
[u'Environment', u'Ecology'],
[u'Geosciences'],
[u'Immunology'],
[u'Materials Science'],
[u'Mathematics'],
[u'Microbiology'],
[u'Molecular Biology ', u' Genetics'],
[u'Neuroscience ', u' Behavior'],
[u'Pharmacology ', u' Toxicology'],
[u'Physics'],
[u'Plant ', u' Animal Science'],
[u'Psychiatry', u'Psychology'],
[u'Social Sciences, general'],
[u'Space Science']]
From here we might choose few paths. One of paths might be connecting these fields with the classifications we already have. We might think of:
However, we will not be doing this here. Later we will have a link to another notebook that will do that. Comparing classifications <- not working yet
For now, lets look inti some simple stats.
In [62]:
df.groupby('Category').size().order(ascending=False)
Out[62]:
Category
Clinical Medicine 402
Molecular Biology & Genetics 200
Chemistry 198
Biology & Biochemistry 196
Engineering 187
Social Sciences, general 177
Plant & Animal Science 176
Geosciences 159
Materials Science 147
Physics 144
Environment/Ecology 137
Pharmacology & Toxicology 133
Neuroscience & Behavior 129
Computer Science 117
Microbiology 114
Agricultural Sciences 112
Space Science 106
Psychiatry/Psychology 100
Mathematics 99
Economics & Business 95
Immunology 87
dtype: int64
In [63]:
grpd_by_prim_afil = df.groupby('Primary Affiliation').size().order(ascending=False)
grpd_by_prim_afil.head()
Out[63]:
Primary Affiliation
Harvard University, USA 105
Stanford University, USA 56
Chinese Academy of Sciences, China 46
University of California, Berkeley, USA 41
NIH, USA 33
dtype: int64
In [64]:
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(grpd_by_prim_afil)
Out[64]:
[<matplotlib.lines.Line2D at 0x7fb0e90>]
In [65]:
df.groupby('Secondary Affiliations').size().order(ascending=False).head()
Out[65]:
Secondary Affiliations
King Abdulaziz University, Saudi Arabia 122
Harvard University, USA 27
The University of Tokyo, Japan 10
King Abdulaziz University (50%), Saudi Arabia 8
Massachusetts Gen Hosp, USA 8
dtype: int64
Why so many researchers have King Abdulaziz University as their secondary affiliation??? This seems like very straighforward finding that can easily attract attention. To whom and how should I communicate it. We could think of posting this data right into some social media or my blog.
Let's look into institutions that lead different Categories.
In [45]:
for x in df['Category'].unique():
grouped = df[df['Category'] == x].groupby('Primary Affiliation').size()
ordered = grouped.order(ascending=False).head(3)
print (x, ordered), '\n'
(u'Agricultural Sciences', Primary Affiliation
USDA, USA 8
University of Southampton, UK 6
Cornell University, USA 4
dtype: int64)
(u'Biology & Biochemistry', Primary Affiliation
NIH, USA 26
University of California, Santa Cruz, USA 18
European Bioinformat Inst, UK 16
dtype: int64)
(u'Chemistry', Primary Affiliation
Northwestern University, USA 11
University of California, Berkeley, USA 10
Chinese Academy of Sciences, China 9
dtype: int64)
(u'Clinical Medicine', Primary Affiliation
Brigham & Womens Hosp, USA 21
Harvard University, USA 14
The University of Texas M. D. Anderson Cancer Center, USA 10
dtype: int64)
(u'Computer Science', Primary Affiliation
Stanford University, USA 8
Massachusetts Institute of Technology (MIT), USA 4
The University of Texas at Austin, USA 4
dtype: int64)
(u'Economics & Business', Primary Affiliation
Harvard University, USA 12
University of Chicago, USA 8
Northwestern University, USA 4
dtype: int64)
(u'Engineering', Primary Affiliation
Aalborg University, Denmark 4
Aerodyne Res Inc, USA 4
Chinese Academy of Sciences, China 4
dtype: int64)
(u'Environment/Ecology', Primary Affiliation
University of Minnesota, Twin Cities, USA 4
CNRS, France 3
University College London, UK 3
dtype: int64)
(u'Geosciences', Primary Affiliation
Natl Ctr Atmospher Res, USA 11
NOAA, USA 9
NASA, USA 7
dtype: int64)
(u'Immunology', Primary Affiliation
NIAID, USA 6
The University of Tokyo, Japan 5
Osaka University, Japan 5
dtype: int64)
(u'Materials Science', Primary Affiliation
Chinese Academy of Sciences, China 15
National University of Singapore, Singapore 4
Imperial College London, UK 4
dtype: int64)
(u'Mathematics', Primary Affiliation
Stanford University, USA 7
King Abdulaziz University, Saudi Arabia 5
University of California, Berkeley, USA 3
dtype: int64)
(u'Microbiology', Primary Affiliation
Wellcome Trust Sanger Inst, UK 8
J Craig Venter Inst, USA 7
University of Maryland, Baltimore County, USA 4
dtype: int64)
(u'Molecular Biology & Genetics', Primary Affiliation
deCODE Genet, Iceland 10
Wellcome Trust Sanger Inst, UK 9
Harvard University, USA 8
dtype: int64)
(u'Neuroscience & Behavior', Primary Affiliation
Harvard University, USA 11
Mayo Medical School, USA 10
Stanford University, USA 7
dtype: int64)
(u'Pharmacology & Toxicology', Primary Affiliation
University of Washington, USA 4
Imperial College London, UK 3
University of North Carolina at Chapel Hill, USA 3
dtype: int64)
(u'Physics', Primary Affiliation
University of California, Berkeley, USA 9
Chinese Academy of Sciences, China 6
Stanford University, USA 5
dtype: int64)
(u'Plant & Animal Science', Primary Affiliation
Ghent University, Belgium 8
King Saud University, Saudi Arabia 7
Max Planck Inst Mol Plant Physiol, Germany 6
dtype: int64)
(u'Psychiatry/Psychology', Primary Affiliation
Harvard University, USA 10
Columbia University, USA 5
Duke University, USA 5
dtype: int64)
(u'Social Sciences, general', Primary Affiliation
Harvard University, USA 13
VU University Amsterdam, Netherlands 4
Indonesian Ctr Archaeol, Indonesia 4
dtype: int64)
(u'Space Science', Primary Affiliation
Princeton University, USA 8
The Johns Hopkins University, USA 5
Apache Point Observ, USA 5
dtype: int64)
We are also able to retrieve what institutions are best in what field.
In [75]:
for x in df['Primary Affiliation'].unique():
grouped = df[df['Primary Affiliation'] == x].groupby('Category').size()
ordered = grouped.order(ascending=False)
if len(ordered) > 8:
print (x, ordered), '\n'
(u'University of California, Berkeley, USA', Category
Chemistry 10
Physics 9
Plant & Animal Science 4
Mathematics 3
Environment/Ecology 3
Biology & Biochemistry 3
Materials Science 2
Economics & Business 2
Computer Science 2
Space Science 1
Molecular Biology & Genetics 1
Agricultural Sciences 1
dtype: int64)
(u'Cornell University, USA', Category
Agricultural Sciences 4
Physics 3
Plant & Animal Science 2
Clinical Medicine 2
Social Sciences, general 1
Psychiatry/Psychology 1
Pharmacology & Toxicology 1
Molecular Biology & Genetics 1
Materials Science 1
Geosciences 1
Environment/Ecology 1
Economics & Business 1
Computer Science 1
Biology & Biochemistry 1
dtype: int64)
(u'Pennsylvania State University - University Park, USA', Category
Space Science 2
Plant & Animal Science 2
Engineering 2
Agricultural Sciences 2
Molecular Biology & Genetics 1
Microbiology 1
Mathematics 1
Geosciences 1
Environment/Ecology 1
Economics & Business 1
Computer Science 1
dtype: int64)
(u'University of California, Los Angeles, USA', Category
Chemistry 4
Mathematics 3
Space Science 2
Psychiatry/Psychology 2
Physics 2
Neuroscience & Behavior 2
Materials Science 2
Engineering 2
Computer Science 2
Molecular Biology & Genetics 1
Environment/Ecology 1
Clinical Medicine 1
Biology & Biochemistry 1
Agricultural Sciences 1
dtype: int64)
(u'Harvard University, USA', Category
Clinical Medicine 14
Social Sciences, general 13
Economics & Business 12
Neuroscience & Behavior 11
Psychiatry/Psychology 10
Molecular Biology & Genetics 8
Chemistry 5
Biology & Biochemistry 4
Physics 4
Computer Science 3
Agricultural Sciences 3
Immunology 3
Microbiology 3
Geosciences 2
Engineering 2
Mathematics 2
Pharmacology & Toxicology 2
Plant & Animal Science 2
Materials Science 2
dtype: int64)
(u'Chinese Academy of Sciences, China', Category
Materials Science 15
Chemistry 9
Physics 6
Geosciences 4
Engineering 4
Pharmacology & Toxicology 3
Plant & Animal Science 2
Environment/Ecology 1
Computer Science 1
Agricultural Sciences 1
dtype: int64)
(u'Swiss Federal Institute of Technology Zurich, Switzerland', Category
Mathematics 3
Geosciences 3
Plant & Animal Science 2
Physics 2
Pharmacology & Toxicology 2
Social Sciences, general 1
Materials Science 1
Immunology 1
Computer Science 1
Biology & Biochemistry 1
dtype: int64)
(u'University of Cambridge, UK', Category
Molecular Biology & Genetics 5
Neuroscience & Behavior 4
Biology & Biochemistry 4
Physics 2
Materials Science 2
Clinical Medicine 2
Plant & Animal Science 1
Geosciences 1
Environment/Ecology 1
Computer Science 1
dtype: int64)
(u'University of Toronto, Canada', Category
Neuroscience & Behavior 2
Environment/Ecology 2
Economics & Business 2
Computer Science 2
Space Science 1
Social Sciences, general 1
Psychiatry/Psychology 1
Plant & Animal Science 1
Molecular Biology & Genetics 1
Clinical Medicine 1
Chemistry 1
Biology & Biochemistry 1
dtype: int64)
(u'University of Washington, USA', Category
Molecular Biology & Genetics 5
Pharmacology & Toxicology 4
Materials Science 4
Space Science 3
Geosciences 3
Clinical Medicine 3
Microbiology 1
Mathematics 1
Immunology 1
Economics & Business 1
Biology & Biochemistry 1
dtype: int64)
(u'Yale University, USA', Category
Psychiatry/Psychology 3
Physics 3
Clinical Medicine 3
Immunology 2
Environment/Ecology 2
Biology & Biochemistry 2
Plant & Animal Science 1
Neuroscience & Behavior 1
Molecular Biology & Genetics 1
Chemistry 1
dtype: int64)
(u'Imperial College London, UK', Category
Materials Science 4
Pharmacology & Toxicology 3
Molecular Biology & Genetics 3
Clinical Medicine 3
Physics 2
Plant & Animal Science 1
Neuroscience & Behavior 1
Environment/Ecology 1
Engineering 1
Biology & Biochemistry 1
dtype: int64)
(u'Stanford University, USA', Category
Computer Science 8
Neuroscience & Behavior 7
Mathematics 7
Clinical Medicine 7
Physics 5
Chemistry 4
Biology & Biochemistry 4
Social Sciences, general 2
Psychiatry/Psychology 2
Materials Science 2
Environment/Ecology 2
Engineering 2
Pharmacology & Toxicology 1
Molecular Biology & Genetics 1
Immunology 1
Economics & Business 1
dtype: int64)
(u'University of Oxford, UK', Category
Clinical Medicine 8
Neuroscience & Behavior 6
Molecular Biology & Genetics 5
Social Sciences, general 4
Geosciences 2
Psychiatry/Psychology 1
Plant & Animal Science 1
Microbiology 1
Mathematics 1
Immunology 1
Engineering 1
Chemistry 1
Biology & Biochemistry 1
dtype: int64)
(u'University of California, San Diego, USA', Category
Psychiatry/Psychology 3
Neuroscience & Behavior 3
Molecular Biology & Genetics 3
Computer Science 3
Clinical Medicine 3
Biology & Biochemistry 3
Social Sciences, general 2
Plant & Animal Science 2
Immunology 2
Environment/Ecology 2
Geosciences 1
Engineering 1
Economics & Business 1
Chemistry 1
dtype: int64)
(u'University of Michigan - Ann Arbor, USA', Category
Molecular Biology & Genetics 7
Clinical Medicine 4
Space Science 2
Social Sciences, general 2
Physics 2
Mathematics 2
Materials Science 2
Immunology 2
Economics & Business 2
Chemistry 2
Psychiatry/Psychology 1
Environment/Ecology 1
Computer Science 1
Biology & Biochemistry 1
dtype: int64)
(u'Massachusetts Institute of Technology (MIT), USA', Category
Chemistry 7
Molecular Biology & Genetics 6
Computer Science 4
Materials Science 3
Economics & Business 3
Physics 2
Pharmacology & Toxicology 1
Neuroscience & Behavior 1
Mathematics 1
Geosciences 1
Clinical Medicine 1
Biology & Biochemistry 1
dtype: int64)
(u'The Johns Hopkins University, USA', Category
Space Science 5
Clinical Medicine 4
Social Sciences, general 3
Neuroscience & Behavior 3
Microbiology 2
Pharmacology & Toxicology 1
Molecular Biology & Genetics 1
Materials Science 1
Computer Science 1
Biology & Biochemistry 1
dtype: int64)
(u'Georgia Institute of Technology, USA', Category
Chemistry 4
Materials Science 3
Psychiatry/Psychology 1
Physics 1
Pharmacology & Toxicology 1
Geosciences 1
Engineering 1
Economics & Business 1
Computer Science 1
dtype: int64)
(u'Northwestern University, USA', Category
Chemistry 11
Clinical Medicine 5
Economics & Business 4
Social Sciences, general 2
Materials Science 2
Neuroscience & Behavior 1
Microbiology 1
Engineering 1
Computer Science 1
dtype: int64)
(u'California Institute of Technology, USA', Category
Physics 3
Geosciences 3
Engineering 3
Space Science 2
Chemistry 2
Pharmacology & Toxicology 1
Neuroscience & Behavior 1
Economics & Business 1
Computer Science 1
dtype: int64)
(u'University of North Carolina at Chapel Hill, USA', Category
Clinical Medicine 4
Social Sciences, general 3
Pharmacology & Toxicology 3
Molecular Biology & Genetics 2
Psychiatry/Psychology 1
Plant & Animal Science 1
Microbiology 1
Economics & Business 1
Chemistry 1
dtype: int64)
(u'Princeton University, USA', Category
Space Science 8
Physics 3
Mathematics 3
Economics & Business 3
Computer Science 3
Engineering 2
Social Sciences, general 1
Neuroscience & Behavior 1
Geosciences 1
Chemistry 1
dtype: int64)
(u'University of Pennsylvania, USA', Category
Social Sciences, general 3
Neuroscience & Behavior 2
Microbiology 2
Immunology 2
Economics & Business 2
Chemistry 2
Psychiatry/Psychology 1
Pharmacology & Toxicology 1
Molecular Biology & Genetics 1
Clinical Medicine 1
dtype: int64)
(u'University of Minnesota, Twin Cities, USA', Category
Environment/Ecology 4
Mathematics 3
Social Sciences, general 2
Engineering 2
Computer Science 2
Plant & Animal Science 1
Neuroscience & Behavior 1
Materials Science 1
Geosciences 1
Economics & Business 1
Clinical Medicine 1
Chemistry 1
dtype: int64)
(u'Duke University, USA', Category
Psychiatry/Psychology 5
Social Sciences, general 3
Plant & Animal Science 3
Microbiology 3
Economics & Business 3
Clinical Medicine 3
Mathematics 2
Environment/Ecology 2
Physics 1
Pharmacology & Toxicology 1
Neuroscience & Behavior 1
Materials Science 1
Chemistry 1
dtype: int64)
(u'Columbia University, USA', Category
Psychiatry/Psychology 5
Geosciences 3
Clinical Medicine 3
Social Sciences, general 2
Neuroscience & Behavior 2
Environment/Ecology 2
Space Science 1
Physics 1
Mathematics 1
Economics & Business 1
dtype: int64)
(u'University of Melbourne, Australia', Category
Environment/Ecology 3
Neuroscience & Behavior 2
Social Sciences, general 1
Psychiatry/Psychology 1
Microbiology 1
Mathematics 1
Materials Science 1
Engineering 1
Economics & Business 1
dtype: int64)
In [106]:
#Do the same with: affiliation.contains(pycountry names)
In [84]:
import networkx as nx
g = nx.Graph()
for unique_affiliation in df['Primary Affiliation'].unique():
grouped = df[df['Primary Affiliation'] == unique_affiliation].groupby('Category').size()
ordered = grouped.order(ascending=False)
for CAT, EGORY in ordered.iteritems():
g.add_edge(unique_affiliation, CAT, {'Weight':EGORY})
In [99]:
print(len(g.nodes()), len(g.edges()))
(1038, 1980)
In [113]:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-113-3bc49a33bab8> in <module>()
1 pos = nx.fruchterman_reingold_layout(g)
----> 2 nx.draw_nodes(g, pos)
AttributeError: 'module' object has no attribute 'draw_nodes'
In [214]:
pos = nx.fruchterman_reingold_layout(g)
plt.figure(figsize=(10,10))
nx.draw_networkx_nodes(g, pos, nodelist=[x for x,y in g.nodes_iter(data=True) if x in all_categories], alpha=0.1)
nx.draw_networkx_labels(g, pos, labels = {key: key for (key, value) in g.nodes_iter(data=True) if key in all_categories}, alpha=0.3)
nx.draw_networkx_nodes(g, pos, nodelist=[x for x,y in g.nodes_iter(data=True) if x not in all_categories], alpha=0.05, node_color='b')
nx.draw_networkx_edges(g, pos, alpha=0.05)
Out[214]:
<matplotlib.collections.LineCollection at 0x169c8a50>
In [215]:
g1 = nx.subgraph(g, [x for x in g.nodes() if (len(g.neighbors(x)) > 10)])
plt.figure(figsize=(10,10))
pos = nx.fruchterman_reingold_layout(g1)
nx.draw_networkx_nodes(g1, pos, nodelist=[x for x,y in g1.nodes_iter(data=True) if x in all_categories], alpha=0.1)
nx.draw_networkx_labels(g1, pos, labels = {key: key for (key, value) in g1.nodes_iter(data=True) if key in all_categories}, alpha=0.3)
nx.draw_networkx_nodes(g1, pos, nodelist=[x for x,y in g1.nodes_iter(data=True) if x not in all_categories], alpha=0.05, node_color='b')
nx.draw_networkx_edges(g1, pos, alpha=0.05)
Out[215]:
<matplotlib.collections.LineCollection at 0x16bd03b0>
In [105]:
nx.closeness_centrality(g)
# Next, add this to all nodes
# Remove 50 percent things.
# Also, we do not extract country names. this can be solved with pycountry module
# import pycountry
# len(pycountry.countries)
Out[105]:
{u', Germany': 0.26233240576777134,
u', USA': 0.2697009102730819,
u'3M Co, USA': 0.2648786717752235,
u'ARS, USA': 0.2690012970168612,
u'ASTAR, Singapore': 0.26233240576777134,
u'AT&T Labs Res, USA': 0.2648786717752235,
u'Aalborg University (80%), Denmark': 0.2690012970168612,
u'Aalborg University, Denmark': 0.29893341020466996,
u'Aarhus University, Denmark': 0.2834107679693905,
u'Aaron Diamond AIDS Research Center, USA': 0.2606182457903996,
u'Acad Sci Czech Republic, Czech': 0.2661021298434693,
u'Academy of Sciences of the Czech Republic, Czech': 0.2648786717752235,
u'Addenbrookes Hosp, UK': 0.28893842295904154,
u'Adimab LLC, USA': 0.2606182457903996,
u'Aerodyne Res Inc, USA': 0.2795901860339714,
u'Agenzia Spaziale Italiana ASI Sci Data Ctr, Italy': 0.2591852036990752,
u'Agr & Agri Food Canada, Canada': 0.26074930852401307,
u'Agricultural Sciences': 0.35260115606936415,
u'Agricultural University of Athens, Greece': 0.26074930852401307,
u'Aix Marseille University, France': 0.28449931412894375,
u'Akad Pedog Krakowie, Poland': 0.2591852036990752,
u'Al Ain Wildlife Pk & Resort, United Arab Emirates': 0.2648786717752235,
u'Alan Guttmacher Inst, USA': 0.26569305662311044,
u'AlertMe, UK': 0.2633985267970536,
u'Allegheny Gen Hosp, USA': 0.27943950417677177,
u'Alliance for a Healthier Generation, USA': 0.26569305662311044,
u'Amer Canc Soc, USA': 0.27943950417677177,
u'Amirkabir University of Technology, Iran': 0.2758712423516893,
u'Apache Point Observ, USA': 0.2591852036990752,
u'Argonne Natl Lab, USA': 0.28403177211722813,
u'Arizona State University - Tempe, USA': 0.28187007338950804,
u'Assoc Nazl Med Cardiol Osped ANMCO Res Ctr, Italy': 0.27943950417677177,
u'Associates of Cape Cod, USA': 0.26074930852401307,
u'Astex Pharmaceut, UK': 0.2648786717752235,
u'Atmospher Chem Serv, UK': 0.2632647880172633,
u'Auburn University, USA': 0.2690012970168612,
u'Auckland City Hosp, New Zealand': 0.27943950417677177,
u'Austin Peay State University, USA': 0.2591852036990752,
u'Autonomous University of Barcelona, Spain': 0.2648786717752235,
u'Autonomous University of Madrid, Spain': 0.2625981261078754,
u'Avid Radiopharmaceuticals, USA': 0.2594445834375782,
u'BGI (60%), China': 0.2648786717752235,
u'BGI Shenzhen, China': 0.2648786717752235,
u'BNLMS, China': 0.2633985267970536,
u'Babol Noshirvani University of Technology, Iran': 0.2690012970168612,
u'Babraham Institute Cambridge, UK': 0.2648786717752235,
u'Babson College, USA': 0.2581528503858601,
u'Baker IDI Heart & Diabet Inst, Australia': 0.27943950417677177,
u'Bangor University, UK': 0.26569305662311044,
u'Bauhaus University, Weimar, Germany': 0.2777926600589338,
u'Bavarian Ctr Appl Energy Res ZAE Bayern, Germany': 0.2633985267970536,
u'Baylor College of Medicine, USA': 0.2975609756097561,
u'Baylor University, USA': 0.25789604575976127,
u'Beaujon Hosp, France': 0.27943950417677177,
u'Beaumont Hosp, Ireland': 0.25957446808510637,
u'Beihang University, China': 0.2625981261078754,
u'Beijing Normal University, China': 0.2625981261078754,
u'Beijing University of Posts and Telecommunications, China': 0.2625981261078754,
u'Bern Univ Hosp, Switzerland': 0.27943950417677177,
u'Beth Israel Deaconess Med Ctr, USA': 0.2849683979115141,
u'Betty & Guy Beatty Ctr Integrat Res, USA': 0.27943950417677177,
u'Bilkent University, Turkey': 0.2754316069057105,
u'Biobyte Solut GmbH, Germany': 0.2633985267970536,
u'Biogen Idec Inc, USA': 0.2648786717752235,
u'Biogen Idec, USA': 0.25789604575976127,
u'Biology & Biochemistry': 0.35746294381247845,
u'Biotechnology industry, USA': 0.2606182457903996,
u'BlackRock, USA': 0.2581528503858601,
u'Boise State University, USA': 0.2591852036990752,
u"Boston Children's Hospital & Harvard Medical School, USA": 0.27943950417677177,
u'Boston College, USA': 0.2690012970168612,
u'Boston Med Ctr, USA': 0.27943950417677177,
u'Boston University, USA': 0.3128205128205128,
u'Boston Vet Affairs Healthcare Syst, USA': 0.27943950417677177,
u'Brigham & Womens Hosp, USA': 0.31471927162367225,
u'Brigham &Womens Hosp, USA': 0.27943950417677177,
u'Brigham Young University, USA': 0.26233240576777134,
u'British Columbia Canc Agcy, Canada': 0.27943950417677177,
u'British Columbia Canc Res Ctr, Canada': 0.2648786717752235,
u'Broad Inst Harvard & MIT, USA': 0.2975609756097561,
u'Broad Inst Harvard & Massachusetts Inst Technol M, USA': 0.2648786717752235,
u'Broad Inst Harvard & Massachusetts Inst Technol, USA': 0.2648786717752235,
u'Broad Inst MIT & Harvard, USA': 0.28893842295904154,
u'Broad Inst Massachusetts Inst Technol & Harvard, USA': 0.2648786717752235,
u'Broad Inst Massachusetts Inst Technol MIT & Harva, USA': 0.2594445834375782,
u'Broad Inst, USA': 0.3067139899438036,
u'Brookhaven Natl Lab, USA': 0.2690012970168612,
u'Brown University, USA': 0.29893341020466996,
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u'University Libre Bruxelles, Belgium': 0.2690012970168612,
u'University Mah, Turkey': 0.2690012970168612,
u'University Rovira i Virgili, Spain': 0.26074930852401307,
u'University Sains Malaysia, Malaysia': 0.2690012970168612,
u'University of Aberdeen, UK': 0.274556526343659,
u'University of Alaska - Fairbanks, USA': 0.2648786717752235,
u'University of Alberta, Canada': 0.3107581660173809,
u'University of Alicante, Spain': 0.2661021298434693,
u'University of Amsterdam, Netherlands': 0.3006668599594085,
u'University of Aquila, Italy': 0.2632647880172633,
u'University of Arizona, USA': 0.30617065249483316,
u'University of Arkansas at Fayetteville, USA': 0.27082789240010446,
u'University of Arkansas at Little Rock, USA': 0.28893842295904154,
u'University of Auvergne, France': 0.2654210391604812,
u'University of Barcelona (75%), Spain': 0.27943950417677177,
u'University of Barcelona, Spain': 0.2863849765258216,
u'University of Basel, Switzerland': 0.2654210391604812,
u'University of Bayreuth, Germany': 0.26074930852401307,
u'University of Bergen, Norway': 0.2908835904628331,
u'University of Bern, Switzerland': 0.29203041396789636,
u'University of Bielefeld, Germany': 0.2654210391604812,
u'University of Birmingham, UK': 0.31471927162367225,
u'University of Bochum, Germany': 0.2736869886513592,
u'University of Bologna, Italy': 0.28733721252424493,
u'University of Bonn, Germany': 0.2764596107704612,
u'University of Bordeaux 1, France': 0.26569305662311044,
u'University of Bordeaux, France': 0.2908835904628331,
u'University of Bristol, UK': 0.33397745571658616,
u'University of British Columbia, Canada': 0.3185867895545315,
u'University of Cagliari, Italy': 0.25957446808510637,
u'University of Calabria, Italy': 0.2600953097567093,
u'University of California, Berkeley, USA': 0.36475553992261694,
u'University of California, Davis, USA': 0.33570734865652313,
u'University of California, Irvine, USA': 0.3278533038254821,
u'University of California, Los Angeles, USA': 0.41397205588822356,
u'University of California, Riverside, USA': 0.3115049564433764,
u'University of California, San Diego, USA': 0.4156312625250501,
u'University of California, San Francisco, USA': 0.3348401679044236,
u'University of California, Santa Barbara, USA': 0.3100149476831091,
u'University of California, Santa Cruz, USA': 0.29962438601560243,
u'University of Cambridge, UK': 0.3736936936936937,
u'University of Cape Town, South Africa': 0.2755779962795642,
u'University of Central Florida, USA': 0.2625981261078754,
u'University of Chicago, USA': 0.32174992243251627,
u'University of Cincinnati, USA': 0.3063515509601182,
u'University of Colorado at Boulder, USA': 0.3370165745856354,
u'University of Colorado at Denver, USA': 0.26788943425471456,
u'University of Copenhagen, Denmark': 0.28512510310695627,
u'University of Delaware, USA': 0.28465550370573706,
u'University of Denver, USA': 0.2690012970168612,
u'University of Duisburg-Essen, Germany': 0.27943950417677177,
u'University of Durham, UK': 0.2591852036990752,
u'University of East Anglia, UK': 0.2977318403674993,
u'University of Eastern Finland, Finland': 0.2761651131824234,
u'University of Erlangen-Nuremberg, Germany': 0.27311035027653413,
u'University of Essex, UK': 0.26569305662311044,
u'University of Evry, France': 0.2654210391604812,
u'University of Exeter, UK': 0.28733721252424493,
u'University of Ferrara, Italy': 0.26569305662311044,
u'University of Florence, Italy': 0.2752853729758428,
u'University of Florida, USA': 0.3284764016471334,
u'University of Frankfurt, Germany': 0.27943950417677177,
u'University of Freiburg, Germany': 0.2594445834375782,
u'University of Fribourg, Switzerland': 0.2606182457903996,
u'University of Geneva, Switzerland': 0.2835657642876675,
u'University of Giessen, Germany': 0.3038382654556109,
u'University of Goettingen, Germany': 0.2661021298434693,
u'University of Gothenburg, Sweden': 0.2908835904628331,
u'University of Granada, Spain': 0.2777926600589338,
u'University of Groningen, Netherlands': 0.3324783584482206,
u'University of Guelph, Canada': 0.2736869886513592,
u'University of Hamburg, Germany': 0.2661021298434693,
u'University of Hannover, Germany': 0.2600953097567093,
u'University of Hasselt, Belgium': 0.26569305662311044,
u'University of Hawaii at Manoa, USA': 0.26474342609139645,
u'University of Heidelberg, Germany': 0.3365790327815644,
u'University of Helsinki, Finland': 0.32016054337758565,
u'University of Hohenheim, Germany': 0.26074930852401307,
u'University of Houston, USA': 0.2633985267970536,
u'University of Iceland, Iceland': 0.2648786717752235,
u'University of Illes Balears, Spain': 0.2654210391604812,
u'University of Illinois at Chicago, USA': 0.25957446808510637,
u'University of Illinois at Urbana Champaign, USA': 0.2654210391604812,
u'University of Illinois at Urbana-Champaign, USA': 0.31206740896780016,
u'University of Innsbruck, Austria': 0.2698412698412698,
u'University of Iowa, USA': 0.2632647880172633,
u'University of Jaen, Spain': 0.26233240576777134,
u'University of Kansas - Lawrence, USA': 0.2648786717752235,
u'University of Karlsruhe, Germany': 0.2625981261078754,
u'University of Kiel, Germany': 0.29807415924116126,
u'University of Koeln, Germany': 0.2633985267970536,
u'University of Konstanz, Germany': 0.2581528503858601,
u'University of Lausanne, Switzerland': 0.28325594099972684,
u'University of Laval, Canada': 0.2606182457903996,
u'University of Leeds, UK': 0.30853912526033916,
u'University of Leicester, UK': 0.2648786717752235,
u'University of Leipzig, Germany': 0.26233240576777134,
u'University of Liege, Belgium': 0.26074930852401307,
u'University of Limerick, Ireland': 0.2661021298434693,
u'University of Lisbon, Portugal': 0.2690012970168612,
u'University of Liverpool, UK': 0.29335219236209337,
u'University of Maastricht (95%), Netherlands': 0.25957446808510637,
u'University of Maastricht, Netherlands': 0.27943950417677177,
u'University of Malaya, Malaysia': 0.26569305662311044,
u'University of Marburg, Germany': 0.2758712423516893,
u'University of Maribor (50%), Slovenia': 0.2625981261078754,
u'University of Maryland, Baltimore County, USA': 0.26788943425471456,
u'University of Maryland, Baltimore, USA': 0.2716793293162169,
u'University of Maryland, College Park, USA': 0.2890995260663507,
u'University of Massachusetts Amherst, USA': 0.33922145894667977,
u'University of Massachusetts Medical School - Worcester, USA': 0.2651495781130146,
u'University of Melbourne, Australia': 0.33636068764190724,
u'University of Miami, USA': 0.28233052001089026,
u'University of Michigan - Ann Arbor, USA': 0.4027184466019417,
u'University of Milan (93%), Italy': 0.25789604575976127,
u'University of Milan - Bicocca, Italy': 0.27943950417677177,
u'University of Minho, Portugal': 0.2625981261078754,
u'University of Minnesota, Twin Cities, USA': 0.3977752205600307,
u'University of Missouri - Kansas City, USA': 0.27943950417677177,
u'University of Montreal, Canada': 0.28845618915159943,
u'University of Muenster, Germany': 0.28095367109184505,
u'University of Munich (50%), Germany': 0.2625981261078754,
u'University of Munich, Germany': 0.29023229778897286,
u'University of Naples Federico II, Italy': 0.26074930852401307,
u'University of Nebraska - Lincoln, USA': 0.27111111111111114,
u'University of Neuchatel, Switzerland': 0.2654210391604812,
u'University of New England, Australia': 0.26569305662311044,
u'University of New Hampshire - Durham, USA': 0.26569305662311044,
u'University of New South Wales, Australia': 0.28279247341150804,
u'University of Newcastle, Australia': 0.27943950417677177,
u'University of Nis, Serbia': 0.2600953097567093,
u'University of North Carolina at Chapel Hill, USA': 0.35944540727902946,
u'University of North Texas - Denton, USA': 0.2654210391604812,
u'University of Notre Dame, USA': 0.2748476013782136,
u'University of Nottingham, UK': 0.2661021298434693,
u'University of Oldenburg, Germany': 0.2648786717752235,
u'University of Ontario Institute of Technology, Canada': 0.2690012970168612,
u'University of Oregon, USA': 0.26569305662311044,
u'University of Oslo, Norway': 0.28893842295904154,
u'University of Otago, New Zealand': 0.25957446808510637,
u'University of Ottawa, Canada': 0.2661021298434693,
u'University of Oulu, Finland': 0.2648786717752235,
u'University of Oxford, UK': 0.4046039797112759,
u'University of Padua, Italy': 0.2648786717752235,
u'University of Paris Descartes (Paris 5), France': 0.3008413112851755,
u'University of Paris Diderot (Paris 7), France': 0.2758712423516893,
u'University of Paris Sud (Paris 11), France': 0.3052693553135119,
u'University of Parma, Italy': 0.26928070631004936,
u'University of Patras, Greece': 0.2632647880172633,
u'University of Pavia, Italy': 0.26233240576777134,
u'University of Pennsylvania, USA': 0.35260115606936415,
u'University of Pittsburgh, USA': 0.3348401679044236,
u'University of Poitiers, France': 0.27943950417677177,
u'University of Pompeu Fabra, Spain': 0.2581528503858601,
u'University of Portsmouth, UK': 0.2591852036990752,
u'University of Puerto Rico, USA': 0.2594445834375782,
u'University of Rhode Island, USA': 0.26074930852401307,
u'University of Rochester, USA': 0.2905575791538246,
u'University of Rostock, Germany': 0.2661021298434693,
u'University of Santiago Compostela, Spain': 0.2600953097567093,
u'University of Sao Paulo, Brazil': 0.2795901860339714,
u'University of Science Ho Chi Minh City, Vietnam': 0.26233240576777134,
u'University of Science and Technology of China, China': 0.2835657642876675,
u'University of South Carolina - Columbia, USA': 0.31263189629182997,
u'University of South Florida (80%), USA': 0.2633985267970536,
u'University of South Florida, USA': 0.28279247341150804,
u'University of Southampton, UK': 0.3107581660173809,
u'University of Southern California, USA': 0.34055829228243023,
u'University of Strasbourg, France': 0.2648786717752235,
u'University of Stuttgart, Germany': 0.2625981261078754,
u'University of Surrey, UK': 0.26074930852401307,
u'University of Sussex, UK': 0.2591852036990752,
u'University of Tasmania, Australia': 0.2648786717752235,
u'University of Technology, Sydney, Australia': 0.28325594099972684,
u'University of Tehran, Iran': 0.2690012970168612,
u'University of Tennessee - Knoxville, USA': 0.2690012970168612,
u'University of Tennessee Health Science Center, USA': 0.3041947785274274,
u'University of Toronto, Canada': 0.3847866419294991,
u'University of Trieste, Italy': 0.2661021298434693,
u'University of Tuebingen, Germany': 0.2654210391604812,
u'University of Turin, Italy': 0.27943950417677177,
u'University of Ulm, Germany': 0.29603197259491865,
u'University of Ulster, Ireland': 0.26569305662311044,
u'University of Utah, USA': 0.3052693553135119,
u'University of Valencia, Spain': 0.2594445834375782,
u'University of Vermont, USA': 0.2648786717752235,
u'University of Versailles, France': 0.2661021298434693,
u'University of Victoria, Canada': 0.28591122139509234,
u'University of Vienna, Austria': 0.27943950417677177,
u'University of Vigo, Spain': 0.26233240576777134,
u'University of Virginia, USA': 0.28942227183924085,
u'University of Wageningen, Netherlands': 0.28528198074277855,
u'University of Warwick, UK': 0.2938509492774157,
u'University of Washington, USA': 0.3681221157259496,
u'University of Waterloo, Canada': 0.2633985267970536,
u'University of Wisconsin - Madison, USA': 0.3401115119711381,
u'University of Wollongong, Australia': 0.26569305662311044,
u'University of Wuerzburg, Germany': 0.2940175786787638,
u'University of Wuppertal, Germany': 0.2633985267970536,
u'University of Yasuj, Iran': 0.2690012970168612,
u'University of York, UK': 0.2648786717752235,
u'University of Zaragoza, Spain': 0.2625981261078754,
u'University of Zurich, Switzerland': 0.3370165745856354,
...}
Based on this we can see what institutions target very specific group of sciences and what institutions are more spread-out. To do that we will use something. :)
We might also thing of providing relative values according to total representatives of Category. Category should be value of $$S / S_t$$
American Philosophial Society that has rather big membership. Their database of members is rather big.
In [13]:
def get_all():
#gets many websites that have scientists in them.
count = 1
mylist = []
for x in range(1, 2000, 20):
payload = {'browse-all':'yes', 'sort':'creator', 'startDoc':'{}'.format(x)}
r = requests.get('http://www.amphilsoc.org/memhist/search?', params=payload)
mylist.append(r)
return mylist
In [11]:
from bs4 import BeautifulSoup
soup = BeautifulSoup(r.content)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-11-cbbca10cb554> in <module>()
1 from bs4 import BeautifulSoup
----> 2 soup = BeautifulSoup(r.content)
NameError: name 'r' is not defined
In [9]:
# will use them with regex
tags = ['Name:',
'Institution:',
'Year Elected:',
'Class:' ,
'Subdivision:',
'Residency:',
'Living?:',
'Birth Date:']
In [27]:
import re
res = soup.findAll('td', {'class':'docHit'})[0]
len(res.contents[0].contents[0])
len(res.contents[1].contents[0])
len(res.contents[2].contents[0])
len(res.contents[4].contents[0])
len(res.contents[5].contents[0])
for x in res.contents[5].contents[0].contents[0].children:
print x
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-27-405ee752da02> in <module>()
1 import re
----> 2 res = soup.findAll('td', {'class':'docHit'})[0]
3 len(res.contents[0].contents[0])
4 len(res.contents[1].contents[0])
5 len(res.contents[2].contents[0])
NameError: name 'soup' is not defined
In [154]:
#alternative
res = soup.findAll('td', {'class':'docHit'})
df = pd.read_html(str(res))
#not working properly
In [156]:
#alternative, reads one table
res = soup.findAll('div', {'id':'main_5'})
df = pd.read_html(str(res))
#not working properly
Content source: aidiss/disciplines
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