In [4]:
import pandas as pd
In [6]:
!ls
01 Pandas-Übung (Sven).ipynb P3_GrantExport.csv
02 pandas II, dates & plotting.ipynb geckodriver.log
03 BeautifulSoup Übung.ipynb scraped_and_cleand_six.csv
04 Selenium.ipynb
In [7]:
df = pd.read_csv('P3_GrantExport.csv', sep=';', error_bad_lines=False)
b'Skipping line 15384: expected 18 fields, saw 19\nSkipping line 19118: expected 18 fields, saw 22\nSkipping line 20104: expected 18 fields, saw 19\nSkipping line 22178: expected 18 fields, saw 23\nSkipping line 22426: expected 18 fields, saw 23\nSkipping line 24491: expected 18 fields, saw 24\nSkipping line 25196: expected 18 fields, saw 23\nSkipping line 25210: expected 18 fields, saw 25\nSkipping line 25782: expected 18 fields, saw 22\nSkipping line 30599: expected 18 fields, saw 24\nSkipping line 31698: expected 18 fields, saw 19\nSkipping line 31798: expected 18 fields, saw 20\nSkipping line 32463: expected 18 fields, saw 21\nSkipping line 32727: expected 18 fields, saw 19\nSkipping line 32743: expected 18 fields, saw 19\n'
b'Skipping line 32845: expected 18 fields, saw 25\nSkipping line 33518: expected 18 fields, saw 21\nSkipping line 33579: expected 18 fields, saw 21\nSkipping line 34695: expected 18 fields, saw 19\nSkipping line 34935: expected 18 fields, saw 25\nSkipping line 36780: expected 18 fields, saw 22\nSkipping line 37615: expected 18 fields, saw 21\nSkipping line 38179: expected 18 fields, saw 20\nSkipping line 38885: expected 18 fields, saw 21\nSkipping line 39205: expected 18 fields, saw 20\nSkipping line 39613: expected 18 fields, saw 19\nSkipping line 39714: expected 18 fields, saw 25\nSkipping line 40801: expected 18 fields, saw 20\nSkipping line 41887: expected 18 fields, saw 28\nSkipping line 42401: expected 18 fields, saw 19\nSkipping line 42723: expected 18 fields, saw 22\nSkipping line 43654: expected 18 fields, saw 19\nSkipping line 43714: expected 18 fields, saw 19\nSkipping line 44624: expected 18 fields, saw 28\nSkipping line 45468: expected 18 fields, saw 19\nSkipping line 45533: expected 18 fields, saw 19\nSkipping line 46015: expected 18 fields, saw 24\nSkipping line 46769: expected 18 fields, saw 20\nSkipping line 46965: expected 18 fields, saw 21\nSkipping line 48350: expected 18 fields, saw 23\nSkipping line 49503: expected 18 fields, saw 20\nSkipping line 49643: expected 18 fields, saw 19\nSkipping line 50113: expected 18 fields, saw 19\nSkipping line 51851: expected 18 fields, saw 19\nSkipping line 53013: expected 18 fields, saw 22\nSkipping line 56012: expected 18 fields, saw 25\nSkipping line 56597: expected 18 fields, saw 22\nSkipping line 57118: expected 18 fields, saw 21\nSkipping line 57673: expected 18 fields, saw 19\nSkipping line 57718: expected 18 fields, saw 23\nSkipping line 59877: expected 18 fields, saw 20\nSkipping line 60084: expected 18 fields, saw 19\nSkipping line 61668: expected 18 fields, saw 22\nSkipping line 62231: expected 18 fields, saw 20\nSkipping line 62581: expected 18 fields, saw 23\nSkipping line 63395: expected 18 fields, saw 19\nSkipping line 65217: expected 18 fields, saw 20\nSkipping line 65453: expected 18 fields, saw 22\nSkipping line 65549: expected 18 fields, saw 20\n'
b'Skipping line 65703: expected 18 fields, saw 22\nSkipping line 66263: expected 18 fields, saw 23\nSkipping line 66863: expected 18 fields, saw 20\nSkipping line 66867: expected 18 fields, saw 22\n'
In [9]:
df.info() #Aufgabe 2
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 67162 entries, 0 to 67161
Data columns (total 18 columns):
Project Number 67162 non-null int64
Project Number String 67162 non-null object
Project Title 67161 non-null object
Project Title English 26872 non-null object
Responsible Applicant 67162 non-null object
Funding Instrument 67162 non-null object
Funding Instrument Hierarchy 65362 non-null object
Institution 61766 non-null object
Institution Country 61701 non-null object
University 66893 non-null object
Discipline Number 67162 non-null int64
Discipline Name 67162 non-null object
Discipline Name Hierarchy 66671 non-null object
All disciplines 67161 non-null object
Start Date 67160 non-null object
End Date 67160 non-null object
Approved Amount 67162 non-null object
Keywords 42110 non-null object
dtypes: int64(2), object(16)
memory usage: 9.2+ MB
In [ ]:
In [19]:
df["Approved Amount"]
Out[19]:
0 11619.00
1 41022.00
2 79732.00
3 52627.00
4 120042.00
5 53009.00
6 25403.00
7 47100.00
8 25814.00
9 360000.00
10 153886.00
11 862200.00
12 116991.00
13 112664.00
14 5000.00
15 204018.00
16 149485.00
17 83983.00
18 38152.00
19 14138.00
20 164602.00
21 147795.00
22 24552.00
23 44802.00
24 56000.00
25 152535.00
26 225000.00
27 179124.00
28 20000.00
29 445198.00
...
67132 4500.00
67133 8900.00
67134 21500.00
67135 12150.00
67136 7300.00
67137 5050.00
67138 10000.00
67139 4400.00
67140 12000.00
67141 180000.00
67142 180000.00
67143 8432.00
67144 5600.00
67145 8900.00
67146 180000.00
67147 180000.00
67148 13130.00
67149 10080.00
67150 3000.00
67151 9960.00
67152 2725.00
67153 4500.00
67154 11050.00
67155 15700.00
67156 180000.00
67157 180000.00
67158 25000.00
67159 1544165.00
67160 3360.00
67161 7575.00
Name: Approved Amount, Length: 67162, dtype: object
In [30]:
def comma(elem):
try:
elem = elem.split('.')[0]
return int(elem)
except:
return 0
In [31]:
df["Approved Amount 2"] = df["Approved Amount"].apply(comma)
In [32]:
df["Approved Amount 2"].astype(int)
Out[32]:
0 11619
1 41022
2 79732
3 52627
4 120042
5 53009
6 25403
7 47100
8 25814
9 360000
10 153886
11 862200
12 116991
13 112664
14 5000
15 204018
16 149485
17 83983
18 38152
19 14138
20 164602
21 147795
22 24552
23 44802
24 56000
25 152535
26 225000
27 179124
28 20000
29 445198
...
67132 4500
67133 8900
67134 21500
67135 12150
67136 7300
67137 5050
67138 10000
67139 4400
67140 12000
67141 180000
67142 180000
67143 8432
67144 5600
67145 8900
67146 180000
67147 180000
67148 13130
67149 10080
67150 3000
67151 9960
67152 2725
67153 4500
67154 11050
67155 15700
67156 180000
67157 180000
67158 25000
67159 1544165
67160 3360
67161 7575
Name: Approved Amount 2, Length: 67162, dtype: int64
In [33]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 67162 entries, 0 to 67161
Data columns (total 19 columns):
Project Number 67162 non-null int64
Project Number String 67162 non-null object
Project Title 67161 non-null object
Project Title English 26872 non-null object
Responsible Applicant 67162 non-null object
Funding Instrument 67162 non-null object
Funding Instrument Hierarchy 65362 non-null object
Institution 61766 non-null object
Institution Country 61701 non-null object
University 66893 non-null object
Discipline Number 67162 non-null int64
Discipline Name 67162 non-null object
Discipline Name Hierarchy 66671 non-null object
All disciplines 67161 non-null object
Start Date 67160 non-null object
End Date 67160 non-null object
Approved Amount 67162 non-null object
Keywords 42110 non-null object
Approved Amount 2 67162 non-null int64
dtypes: int64(3), object(16)
memory usage: 9.7+ MB
In [38]:
df.groupby('Responsible Applicant')['Approved Amount 2'].sum().sort_values(ascending=False)
Out[38]:
Responsible Applicant
Ensslin Klaus 44841313
Keller Ursula 44391463
Francioli Patrick Bernard 40765287
Verrey François 38762922
Stocker Thomas 38631453
Riezman Howard 38251010
Duboule Denis 38240961
Aberer Karl 33543850
Floreano Dario 33257764
Fischer Øystein 31732220
Benz Willy 30527457
Spini Dario 29674325
Aguet Michel 26851256
Geiss Johannes 25032714
Vuilleumier Jean-Luc 24736514
Magistretti Pierre 24126777
Schönenberger Christian 24109956
Clark Allan Geoffrey 24015445
Günthard Huldrych Fritz 23690716
Grütter Markus Gerhard 22510238
Pretzl Klaus 22300705
Wernli Boris 22255073
Schneider Olivier 22117196
Bourlard Hervé 22022112
Ward Thomas R. 21900741
Troyon Francis 21087566
Mühlemann Oliver 21027099
Güntherodt Hans-Joachim 20400977
Scherer Klaus 19987580
Dayer Alexandre 19883931
...
Ioannidou Dimitra 0
Inversini Alessandro 0
Invernizzi Cédric 0
Invernizzi Antonella 0
Internicola Antonina 0
Interlandi Gianluca 0
Unseld Sigrid 0
Unal Kerem 0
Isenring Giang Ly 0
Umstätter Mamedova Lada 0
Iyer Vijay Mahadevan 0
Jacob Francis 0
Uldry Marc 0
Jackson Christopher B. 0
Jackett Sarah-Jane 0
Jaccard Yves 0
Ullrich Hannes 0
Jaccard Ivan 0
Jabes Adeline 0
Ivanova Petya 0
Ising Alexander 0
Ittensohn Mark 0
Itten Anatol 0
Ulrich Thomas 0
Itel Fabian 0
Istrate Alena 0
Ismail Sascha Asif 0
Isliker Heinz 0
Isliker Franziska 0
Hueber Frédéric 0
Name: Approved Amount 2, Length: 25442, dtype: int64
In [ ]:
In [29]:
string = '1544165.00'
In [26]:
string.split('.')[0]
Out[26]:
'1544165'
In [22]:
df["Approved Amount 2"]
Out[22]:
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
..
67132 0
67133 0
67134 0
67135 0
67136 0
67137 0
67138 0
67139 0
67140 0
67141 0
67142 0
67143 0
67144 0
67145 0
67146 0
67147 0
67148 0
67149 0
67150 0
67151 0
67152 0
67153 0
67154 0
67155 0
67156 0
67157 0
67158 0
67159 0
67160 0
67161 0
Name: Approved Amount 2, Length: 67162, dtype: object
In [ ]:
In [18]:
df["Approved Amount"]
Out[18]:
0 11619
1 41022
2 79732
3 52627
4 120042
5 53009
6 25403
7 47100
8 25814
9 360000
10 153886
11 862200
12 116991
13 112664
14 5000
15 204018
16 149485
17 83983
18 38152
19 14138
20 164602
21 147795
22 24552
23 44802
24 56000
25 152535
26 225000
27 179124
28 20000
29 445198
...
67132 4500
67133 8900
67134 21500
67135 12150
67136 7300
67137 5050
67138 10000
67139 4400
67140 12000
67141 180000
67142 180000
67143 8432
67144 5600
67145 8900
67146 180000
67147 180000
67148 13130
67149 10080
67150 3000
67151 9960
67152 2725
67153 4500
67154 11050
67155 15700
67156 180000
67157 180000
67158 25000
67159 1544165
67160 3360
67161 7575
Name: Approved Amount, Length: 67162, dtype: object
In [19]:
df["Approved Amount"].astype(int)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-46d97dc39a4f> in <module>()
----> 1 df["Approved Amount"].astype(int)
~/.virtualenvs/investigativ/lib/python3.6/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
89 else:
90 kwargs[new_arg_name] = new_arg_value
---> 91 return func(*args, **kwargs)
92 return wrapper
93 return _deprecate_kwarg
~/.virtualenvs/investigativ/lib/python3.6/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs)
3408 # else, only a single dtype is given
3409 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,
-> 3410 **kwargs)
3411 return self._constructor(new_data).__finalize__(self)
3412
~/.virtualenvs/investigativ/lib/python3.6/site-packages/pandas/core/internals.py in astype(self, dtype, **kwargs)
3222
3223 def astype(self, dtype, **kwargs):
-> 3224 return self.apply('astype', dtype=dtype, **kwargs)
3225
3226 def convert(self, **kwargs):
~/.virtualenvs/investigativ/lib/python3.6/site-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
3089
3090 kwargs['mgr'] = self
-> 3091 applied = getattr(b, f)(**kwargs)
3092 result_blocks = _extend_blocks(applied, result_blocks)
3093
~/.virtualenvs/investigativ/lib/python3.6/site-packages/pandas/core/internals.py in astype(self, dtype, copy, errors, values, **kwargs)
469 def astype(self, dtype, copy=False, errors='raise', values=None, **kwargs):
470 return self._astype(dtype, copy=copy, errors=errors, values=values,
--> 471 **kwargs)
472
473 def _astype(self, dtype, copy=False, errors='raise', values=None,
~/.virtualenvs/investigativ/lib/python3.6/site-packages/pandas/core/internals.py in _astype(self, dtype, copy, errors, values, klass, mgr, raise_on_error, **kwargs)
519
520 # _astype_nansafe works fine with 1-d only
--> 521 values = astype_nansafe(values.ravel(), dtype, copy=True)
522 values = values.reshape(self.shape)
523
~/.virtualenvs/investigativ/lib/python3.6/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy)
623 elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
624 # work around NumPy brokenness, #1987
--> 625 return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
626
627 if dtype.name in ("datetime64", "timedelta64"):
pandas/_libs/lib.pyx in pandas._libs.lib.astype_intsafe (pandas/_libs/lib.c:16264)()
pandas/_libs/src/util.pxd in util.set_value_at_unsafe (pandas/_libs/lib.c:73298)()
ValueError: invalid literal for int() with base 10: 'data not included in P3'
In [24]:
def invalid(elem):
try:
elem
return int(elem)
except ValueError:
del elem
In [41]:
df["Approved Amount"].apply(invalid)
Out[41]:
0 11619.0
1 41022.0
2 79732.0
3 52627.0
4 120042.0
5 53009.0
6 25403.0
7 47100.0
8 25814.0
9 360000.0
10 153886.0
11 862200.0
12 116991.0
13 112664.0
14 5000.0
15 204018.0
16 149485.0
17 83983.0
18 38152.0
19 14138.0
20 164602.0
21 147795.0
22 24552.0
23 44802.0
24 56000.0
25 152535.0
26 225000.0
27 179124.0
28 20000.0
29 445198.0
...
67132 4500.0
67133 8900.0
67134 21500.0
67135 12150.0
67136 7300.0
67137 5050.0
67138 10000.0
67139 4400.0
67140 12000.0
67141 180000.0
67142 180000.0
67143 8432.0
67144 5600.0
67145 8900.0
67146 180000.0
67147 180000.0
67148 13130.0
67149 10080.0
67150 3000.0
67151 9960.0
67152 2725.0
67153 4500.0
67154 11050.0
67155 15700.0
67156 180000.0
67157 180000.0
67158 25000.0
67159 1544165.0
67160 3360.0
67161 7575.0
Name: Approved Amount, Length: 67162, dtype: float64
In [26]:
df["Approved Amount"] = df["Approved Amount"].apply(invalid)
In [33]:
df["Approved Amount"].sum()/1000000 #Aufgabe 4
Out[33]:
14719.815766
In [56]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 67162 entries, 0 to 67161
Data columns (total 19 columns):
Project Number 67162 non-null int64
Project Number String 67162 non-null object
Project Title 67161 non-null object
Project Title English 26872 non-null object
Responsible Applicant 67162 non-null object
Funding Instrument 67162 non-null object
Funding Instrument Hierarchy 65362 non-null object
Institution 61766 non-null object
Institution Country 61701 non-null object
University 66893 non-null object
Discipline Number 67162 non-null int64
Discipline Name 67162 non-null object
Discipline Name Hierarchy 66671 non-null object
All disciplines 67161 non-null object
Start Date 67160 non-null object
End Date 67160 non-null object
Approved Amount 55637 non-null float64
Keywords 42110 non-null object
Approved Amount Total 67162 non-null float64
dtypes: float64(2), int64(2), object(15)
memory usage: 9.7+ MB
In [55]:
df.groupby('University')["Approved Amount"].sum().sort_values(ascending=False).head(10) #Aufgabe 5
Out[55]:
University
University of Geneva – GE 2.090010e+09
University of Zurich – ZH 2.089479e+09
ETH Zurich – ETHZ 1.865874e+09
University of Berne – BE 1.741320e+09
University of Basel – BS 1.512640e+09
EPF Lausanne – EPFL 1.381486e+09
University of Lausanne – LA 1.294302e+09
University of Fribourg – FR 5.031677e+08
University of Neuchatel – NE 4.238921e+08
Non-profit organisations (libraries, museums, academies, foundations) and administration – NPO 3.408352e+08
Name: Approved Amount, dtype: float64
In [13]:
df['Responsible Applicant'].value_counts().tail() #Aufgabe 3
Out[13]:
Mirzaaghaei Mehdi 1
Brühl Annette 1
Panizzon Renato G. 1
Sabbatini Marco 1
Denes Carpentier Ildiko 1
Name: Responsible Applicant, dtype: int64
In [16]:
df_new = pd.DataFrame(df['Responsible Applicant'].value_counts())
In [18]:
df_new[df_new['Responsible Applicant']< 30]
Out[18]:
Responsible Applicant
Schmid Stefan
29
Dietler Giovanni
29
Kreis Georg
29
Zenobi Renato
29
Clark Allan Geoffrey
28
Girault Hubert
28
Vogel Pierre
28
Oeschger Hans
28
Schneider Olivier
27
Lang Jürg
27
Burg Jean-Pierre
27
Martinoli Piero
27
Commission nationale pour la publication des DDS
27
Blondel Alain
27
Smith Ian F.C.
27
Ansermet Jean-Philippe
27
Oser Fritz
26
Ohmura Atsumu
26
Axhausen Kay W.
26
Tissot Laurent
26
Magnenat-Thalmann Nadia
26
Weis Antoine
25
Kuratorium der Helvetia Sacra c/o Staatsarchiv Basel
25
Ilegems Marc
25
Maier John Paul
25
Körner Christian
25
Keller Ursula
25
Pretzl Klaus
25
Bünzli Jean-Claude
24
Chopard Bastien
24
...
...
Volken Henri
1
Murr Rabih
1
Mortillaro Marcello
1
Baumann Philipp
1
Marti-Schindler Anna Regina
1
Meyer Sauteur Patrick M.
1
Schofield Emma
1
Conte Joël Pascal
1
Boillat Thomas
1
Ligier Yves
1
Müller-Wiesner Sandra
1
Joho Rolf
1
Alvarez Angel
1
Preisig Matthias
1
Weber Benedikt
1
Poltier Hugues
1
Dellsberger Rudolf
1
Gaillard François
1
Villemure Jean-Guy
1
Hegoburu Chloé
1
Wujastyk Dagmar
1
Gisler Othmar
1
Hostettler Maya
1
Gallo Fernanda
1
Fischer Ole W.
1
Mirzaaghaei Mehdi
1
Brühl Annette
1
Panizzon Renato G.
1
Sabbatini Marco
1
Denes Carpentier Ildiko
1
25414 rows × 1 columns
In [39]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 67162 entries, 0 to 67161
Data columns (total 19 columns):
Project Number 67162 non-null int64
Project Number String 67162 non-null object
Project Title 67161 non-null object
Project Title English 26872 non-null object
Responsible Applicant 67162 non-null object
Funding Instrument 67162 non-null object
Funding Instrument Hierarchy 65362 non-null object
Institution 61766 non-null object
Institution Country 61701 non-null object
University 66893 non-null object
Discipline Number 67162 non-null int64
Discipline Name 67162 non-null object
Discipline Name Hierarchy 66671 non-null object
All disciplines 67161 non-null object
Start Date 67160 non-null object
End Date 67160 non-null object
Approved Amount 67162 non-null object
Keywords 42110 non-null object
Approved Amount 2 67162 non-null int64
dtypes: int64(3), object(16)
memory usage: 9.7+ MB
In [45]:
df_uni = pd.DataFrame(df.groupby('University')['Approved Amount 2'].mean())
In [47]:
df_uni['in mio'] = df_uni['Approved Amount 2'] / 1000000
In [50]:
df_uni.sort_values(by='in mio', ascending=False)
Out[50]:
Approved Amount 2
in mio
University
Swiss Centre of Expertise in the Social Sciences – FORS
1.969076e+06
1.969075
Swiss Center for Electronics and Microtech. – CSEM
6.296209e+05
0.629621
Swiss Institute of Allergy and Asthma Research – SIAF
4.356810e+05
0.435681
Idiap Research Institute – IDIAP
4.252486e+05
0.425249
Research Institute of Organic Agriculture – FiBL
4.154635e+05
0.415464
Institute Friedrich Miescher – FMI
4.051773e+05
0.405177
Biotechnology Institute Thurgau – BITG
3.558535e+05
0.355853
Swiss Institute of Bioinformatics – SIB
3.449561e+05
0.344956
Cantonal hospital of St.Gallen – KSPSG
3.141994e+05
0.314199
University of Geneva – GE
3.049343e+05
0.304934
Kantonsspital Baden – KSPB
2.963544e+05
0.296354
University of Basel – BS
2.956693e+05
0.295669
University of Berne – BE
2.943443e+05
0.294344
EPF Lausanne – EPFL
2.916988e+05
0.291699
University of Lausanne – LA
2.879467e+05
0.287947
University of Zurich – ZH
2.820952e+05
0.282095
ETH Zurich – ETHZ
2.786209e+05
0.278621
Haute école pédagogique du Valais/Pädagogische Hochschule Wallis – HEP-VS
2.783287e+05
0.278329
University of Neuchatel – NE
2.521666e+05
0.252167
Pädagogische Hochschule Thurgau – PHTG
2.489510e+05
0.248951
Institute for Research in Ophtalmology – IRO
2.484621e+05
0.248462
Physikal.-Meteorolog. Observatorium Davos – PMOD
2.471672e+05
0.247167
Università della Svizzera italiana – USI
2.439095e+05
0.243910
Ente Ospedaliero Cantonale – EOC
2.342570e+05
0.234257
Swiss Federal Laboratories for Materials Science and Technology – EMPA
2.324473e+05
0.232447
Research Institutes Agroscope – AGS
2.248560e+05
0.224856
Swiss Federal Institute of Aquatic Science and Technology – EAWAG
2.239121e+05
0.223912
Haute école pédagogique du canton de Fribourg/Pädagogische Hochschule Freiburg – HEP-FR
2.229490e+05
0.222949
Companies/ Private Industry – FP
2.226352e+05
0.222635
Kalaidos University of Applied Sciences – FHKD
2.209860e+05
0.220986
...
...
...
Interkantonale Hochschule für Heilpädagogik – HfH
1.641355e+05
0.164136
University of Applied Sciences Ostschweiz – FHO
1.640408e+05
0.164041
University of St.Gallen – SG
1.627570e+05
0.162757
Ostschweizer Kinderspital – OSKS
1.496490e+05
0.149649
Cardiocentro Ticino – CT
1.483015e+05
0.148302
University of Applied Sciences and Arts Western Switzerland – HES-SO
1.308101e+05
0.130810
Dipartimento formazione e apprendimento, Scuola universitaria professionale della Svizzera italiana – SUPSI-DFA
1.270429e+05
0.127043
Other Hospitals – ASPIT
1.238017e+05
0.123802
Pädagogische Hochschule Luzern – PHLU
1.227102e+05
0.122710
Institutes belonging to several higher education institutions – IMHS
1.023000e+05
0.102300
Institut für Kulturforschung Graubünden – IKG
1.016401e+05
0.101640
Kantonsspital Aarau – KSPA
9.659500e+04
0.096595
Haute école pédagogique du canton de Vaud – HEPL
9.578453e+04
0.095785
Robert Walser Foundation Bern – RWS
9.492983e+04
0.094930
Pädagogische Hochschule Schaffhausen – PHSH
9.218550e+04
0.092186
University Institute Kurt Bösch – IUKB
9.059159e+04
0.090592
Haute Ecole Pédagogique des cantons de Berne, du Jura et de Neuchâtel – HEP-BEJUNE
8.962571e+04
0.089626
Luzerner Kantonsspital – LUKS
8.937611e+04
0.089376
Pädagogische Hochschule Graubünden – PHGR
8.780186e+04
0.087802
Facoltà di Teologia di Lugano – FTL
7.947367e+04
0.079474
Zürcher Fachhochschule – ZFH
7.417360e+04
0.074174
Pädagogische Hochschule Zug – PHZG
6.479400e+04
0.064794
Unassignable – NA
5.399060e+04
0.053991
Istituto Svizzero di Roma – ISR
2.057143e+04
0.020571
Fernfachhochschule Schweiz (member of SUPSI) – FFHS
1.200000e+04
0.012000
Franklin University Switzerland – FUS
1.080800e+04
0.010808
Institut de recherche en réadaptation – réinsertion – IRR
1.050000e+04
0.010500
Staatsunabhängige Theologische Hochschule Basel – STH
5.766667e+03
0.005767
Institution abroad – IACH
1.273422e+03
0.001273
Institute for Research in Biomedicine – IRB
1.000000e+02
0.000100
87 rows × 2 columns
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