In [1]:

    

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline


    

In [6]:

    

df = pd.read_csv("07-hw-animals.csv")


    

In [8]:

    

df.columns


    

    
Out[8]:





Index(['animal', 'name', 'length'], dtype='object')

In [9]:

    

df.head(3)


    

    
Out[9]:






  

    

      

      
animal
      
name
      
length
    
  
  

    

      
0
      
cat
      
Anne
      
35
    
    

      
1
      
cat
      
Bob
      
45
    
    

      
2
      
dog
      
Egglesburg
      
65
    
  

In [11]:

    

df.sort_values(by='length', ascending=False).head(3)


    

    
Out[11]:






  

    

      

      
animal
      
name
      
length
    
  
  

    

      
2
      
dog
      
Egglesburg
      
65
    
    

      
3
      
dog
      
Devon
      
50
    
    

      
1
      
cat
      
Bob
      
45
    
  

In [23]:

    

df['animal'].value_counts()


    

    
Out[23]:





dog    3
cat    3
Name: animal, dtype: int64

In [25]:

    

dogs = df[df['animal']=='dog']
dogs


    

    
Out[25]:






  

    

      

      
animal
      
name
      
length
    
  
  

    

      
2
      
dog
      
Egglesburg
      
65
    
    

      
3
      
dog
      
Devon
      
50
    
    

      
5
      
dog
      
Fontaine
      
35
    
  

In [28]:

    

df[df['length'] > 40]


    

    
Out[28]:






  

    

      

      
animal
      
name
      
length
    
  
  

    

      
1
      
cat
      
Bob
      
45
    
    

      
2
      
dog
      
Egglesburg
      
65
    
    

      
3
      
dog
      
Devon
      
50
    
  

In [29]:

    

df['inches'] = .393701 * df['length']


    

In [30]:

    

df


    

    
Out[30]:






  

    

      

      
animal
      
name
      
length
      
inches
    
  
  

    

      
0
      
cat
      
Anne
      
35
      
13.779535
    
    

      
1
      
cat
      
Bob
      
45
      
17.716545
    
    

      
2
      
dog
      
Egglesburg
      
65
      
25.590565
    
    

      
3
      
dog
      
Devon
      
50
      
19.685050
    
    

      
4
      
cat
      
Charlie
      
32
      
12.598432
    
    

      
5
      
dog
      
Fontaine
      
35
      
13.779535
    
  

In [31]:

    

cats = df[df['animal']=='cat']
dogs = df[df['animal']=='dog']


    

In [37]:

    

# Display all of the animals that are cats and above 12 inches long.
# First do it using the "cats" variable, then do it using your normal dataframe.

cats[cats['inches'] > 12]
df[(df['animal'] == 'cat') & (df['inches'] > 12)]


    

    
Out[37]:






  

    

      

      
animal
      
name
      
length
      
inches
    
  
  

    

      
0
      
cat
      
Anne
      
35
      
13.779535
    
    

      
1
      
cat
      
Bob
      
45
      
17.716545
    
    

      
4
      
cat
      
Charlie
      
32
      
12.598432
    
  

In [47]:

    

cats['length'].describe()[['mean']]


    

    
Out[47]:





mean    37.333333
Name: length, dtype: float64

In [48]:

    

dogs['length'].describe()[['mean']]


    

    
Out[48]:





mean    50.0
Name: length, dtype: float64

In [58]:

    

animals = df.groupby( [ "animal"] )
animals['length'].mean()


    

    
Out[58]:





animal
cat    37.333333
dog    50.000000
Name: length, dtype: float64

In [103]:

    

plt.style.use('ggplot')
dogs['length'].hist()


    

    
Out[103]:





<matplotlib.axes._subplots.AxesSubplot at 0x104bb03c8>






    

In [104]:

    

labels = dogs['name']
sizes = dogs['length']
explode = (0.1, 0.2, 0.2) # fun

plt.pie(sizes, explode=explode, labels=labels,
        autopct='%1.2f%%', shadow=True, startangle=30)

#cf: recent.head().plot(kind='pie', y='networthusbillion', labels=recent['name'].head(), legend=False)


    

    
Out[104]:





([<matplotlib.patches.Wedge at 0x104988b00>,
  <matplotlib.patches.Wedge at 0x104d02d68>,
  <matplotlib.patches.Wedge at 0x104ecbba8>],
 [<matplotlib.text.Text at 0x104a5aef0>,
  <matplotlib.text.Text at 0x1049a3e80>,
  <matplotlib.text.Text at 0x104f40438>],
 [<matplotlib.text.Text at 0x1049ab0f0>,
  <matplotlib.text.Text at 0x104ecb8d0>,
  <matplotlib.text.Text at 0x104f40b38>])






    

In [105]:

    

#Make a horizontal bar graph of the length of the animals, with their name as the label
df.plot(kind='barh', x='name', y='length', legend=False)


    

    
Out[105]:





<matplotlib.axes._subplots.AxesSubplot at 0x1045fa470>






    

In [106]:

    

#Make a sorted horizontal bar graph of the cats, with the larger cats on top.
cats.sort_values(by='length').plot(kind='barh', x='name', y='length', legend=False)


    

    
Out[106]:





<matplotlib.axes._subplots.AxesSubplot at 0x10567d710>






    

In [2]:

    

df2 = pd.read_excel("richpeople.xlsx")


    

In [5]:

    

df2.keys()


    

    
Out[5]:





Index(['year', 'name', 'rank', 'citizenship', 'countrycode',
       'networthusbillion', 'selfmade', 'typeofwealth', 'gender', 'age',
       'industry', 'IndustryAggregates', 'region', 'north',
       'politicalconnection', 'founder', 'generationofinheritance', 'sector',
       'company', 'companytype', 'relationshiptocompany', 'foundingdate',
       'gdpcurrentus', 'sourceofwealth', 'notes', 'notes2', 'source',
       'source_2', 'source_3', 'source_4'],
      dtype='object')

In [14]:

    

top_countries = df2['citizenship'].value_counts().head(10)

df_top_countries = pd.DataFrame.from_dict(top_countries)
df_top_countries['Country Name'] = df_top_countries.index
df_top_countries


    

    
Out[14]:






  

    

      

      
citizenship
      
Country Name
    
  
  

    

      
United States
      
903
      
United States
    
    

      
Germany
      
160
      
Germany
    
    

      
China
      
153
      
China
    
    

      
Russia
      
119
      
Russia
    
    

      
Japan
      
96
      
Japan
    
    

      
Brazil
      
81
      
Brazil
    
    

      
Hong Kong
      
77
      
Hong Kong
    
    

      
France
      
72
      
France
    
    

      
United Kingdom
      
65
      
United Kingdom
    
    

      
India
      
63
      
India
    
  

In [3]:

    

# population: data from http://data.worldbank.org/indicator/SP.POP.TOTL
df_pop = pd.read_csv("world_pop.csv", header=2)


    

In [37]:

    

df_pop_mini = pd.DataFrame.from_records(df_pop[['Country Name','2014']])

dfx = df_top_countries.merge(df_pop_mini, on='Country Name', how='left')

pd.set_option('display.float_format', lambda x: '%.2f' % x) # to get rid of Pandas automatic scientific notation
dfx['Billionaires per million of habitants'] = (dfx['citizenship']*1000000) / dfx['2014']

dfx


    

    
Out[37]:






  

    

      

      
citizenship
      
Country Name
      
2014
      
Billionaires per million of habitants
    
  
  

    

      
0
      
903
      
United States
      
318857056.00
      
2.83
    
    

      
1
      
160
      
Germany
      
80970732.00
      
1.98
    
    

      
2
      
153
      
China
      
1364270000.00
      
0.11
    
    

      
3
      
119
      
Russia
      
nan
      
nan
    
    

      
4
      
96
      
Japan
      
127131800.00
      
0.76
    
    

      
5
      
81
      
Brazil
      
206077898.00
      
0.39
    
    

      
6
      
77
      
Hong Kong
      
nan
      
nan
    
    

      
7
      
72
      
France
      
66217509.00
      
1.09
    
    

      
8
      
65
      
United Kingdom
      
64559135.00
      
1.01
    
    

      
9
      
63
      
India
      
1295291543.00
      
0.05
    
  

In [145]:

    

print("The average wealth of a billionaire (in billions) is:", df2['networthusbillion'].describe()['mean'])

print("The average wealth of a male billionaire is:", df2[df2['gender'] == 'male']['networthusbillion'].describe()['mean'])

print("The average wealth of a female billionaire is:", df2[df2['gender'] == 'female']['networthusbillion'].describe()['mean'])


    

    




The average wealth of a billionaire (in billions) is: 3.53194338179
The average wealth of a male billionaire is: 3.5168814433
The average wealth of a female billionaire is: 3.81927710843

In [188]:

    

print('The poorest billionaire is:', df2.get_value(df2.sort_values('networthusbillion', ascending=True).index[0],'name'))

df2.sort_values('networthusbillion', ascending=True).head(10)


    

    




The poorest billionaire is: T.S. Kalyanaraman






    
Out[188]:






  

    

      

      
year
      
name
      
rank
      
citizenship
      
countrycode
      
networthusbillion
      
selfmade
      
typeofwealth
      
gender
      
age
      
...
      
relationshiptocompany
      
foundingdate
      
gdpcurrentus
      
sourceofwealth
      
notes
      
notes2
      
source
      
source_2
      
source_3
      
source_4
    
  
  

    

      
2316
      
2014
      
T.S. Kalyanaraman
      
1565
      
India
      
IND
      
1.0
      
self-made
      
founder non-finance
      
male
      
66.0
      
...
      
founder
      
1993.0
      
NaN
      
jewelry
      
NaN
      
NaN
      
http://en.wikipedia.org/wiki/T.S._Kalyanaraman
      
http://www.bloomberg.com/news/articles/2013-01...
      
NaN
      
NaN
    
    

      
1202
      
2001
      
John Todd
      
490
      
New Zealand
      
NZL
      
1.0
      
inherited
      
inherited
      
male
      
NaN
      
...
      
relation
      
1929.0
      
5.387259e+10
      
NaN
      
3rd generation
      
NaN
      
http://en.wikipedia.org/wiki/Todd_Corporation#...
      
http://en.wikipedia.org/wiki/Bryan_Todd_(New_Z...
      
Inside the Todd empire The New Zealand Herald ...
      
NaN
    
    

      
482
      
2014
      
Dariusz Milek
      
1565
      
Poland
      
POL
      
1.0
      
self-made
      
self-made finance
      
male
      
46.0
      
...
      
founder
      
1989.0
      
NaN
      
real estate, retail
      
NaN
      
NaN
      
http://pl.wikipedia.org/wiki/Dariusz_Mi%C5%82ek
      
http://www.forbes.com/profile/dariusz-milek/
      
NaN
      
NaN
    
    

      
1204
      
2014
      
John Van Lieshout
      
1565
      
Australia
      
AUS
      
1.0
      
self-made
      
self-made finance
      
male
      
68.0
      
...
      
founder
      
NaN
      
NaN
      
real estate
      
NaN
      
NaN
      
http://www.forbes.com/profile/john-van-lieshout/
      
The Courier Mail (Australia) July 25, 2015 Sat...
      
NaN
      
NaN
    
    

      
2174
      
2014
      
Serhiy Tihipko
      
1565
      
Ukraine
      
UKR
      
1.0
      
self-made
      
self-made finance
      
male
      
54.0
      
...
      
founder
      
1992.0
      
NaN
      
banking, agriculture
      
NaN
      
NaN
      
http://www.forbes.com/profile/serhiy-tihipko/
      
http://en.wikipedia.org/wiki/PrivatBank
      
http://www.kyivpost.com/content/business/priva...
      
NaN
    
    

      
2547
      
2014
      
Wu Chung-Yi
      
1565
      
Taiwan
      
Taiwan
      
1.0
      
self-made
      
executive
      
male
      
55.0
      
...
      
investor
      
1991.0
      
NaN
      
manufacturing
      
NaN
      
NaN
      
http://www.forbes.com/profile/wu-chung-yi/
      
http://en.wikipedia.org/wiki/Tingyi_(Cayman_Is...
      
Tingyi-Campbell sale talks stay on track South...
      
NaN
    
    

      
122
      
2001
      
Amalia Lacroze de Fortabat
      
490
      
Argentina
      
ARG
      
1.0
      
inherited
      
inherited
      
female
      
NaN
      
...
      
relation
      
1926.0
      
2.690000e+11
      
NaN
      
widow
      
NaN
      
http://en.wikipedia.org/wiki/Mar%C3%ADa_Amalia...
      
Australian Financial Review February 28, 2014 ...
      
NaN
      
NaN
    
    

      
1019
      
1996
      
Jack Taylor
      
421
      
United States
      
USA
      
1.0
      
self-made
      
founder non-finance
      
male
      
0.0
      
...
      
founder
      
1957.0
      
8.100200e+12
      
NaN
      
NaN
      
NaN
      
http://en.wikipedia.org/wiki/Jack_C._Taylor
      
http://www.forbes.com/profile/jack-taylor/
      
FATHER AND SON WHO BUILT ENTERPRISE ACCEPT CIT...
      
NaN
    
    

      
475
      
1996
      
Daniel Ziff
      
419
      
United States
      
USA
      
1.0
      
inherited
      
inherited
      
male
      
24.0
      
...
      
relation
      
1927.0
      
8.100200e+12
      
NaN
      
3rd generation
      
NaN
      
http://en.wikipedia.org/wiki/Ziff_Davis
      
http://en.wikipedia.org/wiki/William_Bernard_Z...
      
http://www.wsj.com/articles/ziffs-shut-down-he...
      
NaN
    
    

      
2123
      
1996
      
Saleh Kamel
      
403
      
Saudi Arabia
      
SAU
      
1.0
      
self-made
      
self-made finance
      
male
      
54.0
      
...
      
founder
      
1969.0
      
1.577430e+11
      
NaN
      
NaN
      
NaN
      
http://en.wikipedia.org/wiki/Saleh_Abdullah_Kamel
      
ISLAMIC BANKS TURN ATTENTION TO US MARKET The ...
      
NaN
      
NaN
    
  

10 rows × 30 columns

In [42]:

    

#relationship_values = set
relationship_list = df2['relationshiptocompany'].tolist()
relationship_set = set(relationship_list)
relationship_set = [s.strip() for s in relationship_set if s == s] # to remove a naughty NaN and get rid of dumb whitespaces

print("The relationships are: “" + str.join('”, “', relationship_set) + "”.")

print('\nThe five most common relationships are:')

# DataFrame from dict: to make it pretty
pd.DataFrame.from_dict(df2['relationshiptocompany'].value_counts().head(5))


    

    




The relationships are: “founder and CEO”, “founder and chairwoman”, “lawer”, “co-chairman”, “ceo”, “president”, “Honorary President for Life”, “director”, “founder and executive chairman”, “Chairman and Chief Executive Officer”, “Chairman and Chief Executive Officer”, “president and ceo”, “founder CEO owner”, “employee”, “Vice President”, “inherited”, “co-director of zinc, copper and lead”, “chairwomen”, “founder, chairwoman, ceo”, “supervisory board or directors”, “lawyer”, “executive chairman”, “founder, chairman, ceo”, “vice chairman”, “CEO”, “chariman”, “founder/president”, “leadership”, “owner and former CEO”, “deputy chairman”, “general director”, “relation and chairman”, “former chairman and CEO”, “Vice President of Infrastructure Software”, “head of high-yield bond trading dept”, “founder and ceo”, “Exectuitve Director”, “owner and vice chair”, “chairman of the board”, “founder/relation”, “chairman and ceo”, “Chairman, CEO”, “relation”, “founder and executive vice chairman”, “inventor”, “Chairman”, “investor”, “Vice Chairman”, “founder and chairman”, “investor and  CEO”, “chairman of management committee”, “Relation”, “founder/CEO”, “founder, chairman”, “investor”, “partner”, “former CEO”, “chairman”, “founder/vice chairman”, “relative”, “Chairman/shareholder”, “relation and ceo”, “relation/vice chairman”, “Chairman/founder”, “Head of Board of Directors”, “founder”, “Chief Executive”, “vice-chairman”, “founder/chairman”, “COO”, “investor/founder”, “head of Microsoft's application software group”, “shareholder”, “Global Head of Real Estate”, “owner”.

The five most common relationships are:






    
Out[42]:






  

    

      

      
relationshiptocompany
    
  
  

    

      
founder
      
1214
    
    

      
relation
      
945
    
    

      
owner
      
94
    
    

      
chairman
      
76
    
    

      
investor
      
36
    
  

In [49]:

    

print("The three most common sources of wealth are:\n" + str(df2['typeofwealth'].value_counts().head(3)))

print("\nFor men, they are:\n" + str(df2[df2['gender'] == 'male']['typeofwealth'].value_counts().head(3)))

print("\nFor women, they are:\n" + str(df2[df2['gender'] == 'female']['typeofwealth'].value_counts().head(3)))


    

    




The three most common sources of wealth are:
inherited              953
founder non-finance    713
self-made finance      500
Name: typeofwealth, dtype: int64

For men, they are:
inherited              724
founder non-finance    687
self-made finance      493
Name: typeofwealth, dtype: int64

For women, they are:
inherited              205
founder non-finance     25
self-made finance        7
Name: typeofwealth, dtype: int64

In [63]:

    

per_country = df2.groupby(['citizenship'])
#per_country['networthusbillion'].max()
#per_country['networthusbillion'].idxmax() # DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
# per_country['gdpcurrentus']

df2['percofgdp'] = (100*1000000000*df2['networthusbillion']) / (df2['gdpcurrentus'])


#pd.Series(["{0:.2f}%".format(percofgdp)])

print("NB: most countries doesn't have their GDP in the 'gdpcurrentus' column.")

df2.loc[per_country['networthusbillion'].idxmax()][['name', 'networthusbillion', 'percofgdp']]


    

    




NB: most countries doesn't have their GDP in the 'gdpcurrentus' column.






    
Out[63]:






  

    

      

      
name
      
networthusbillion
      
percofgdp
    
  
  

    

      
1011
      
Issad Rebrab
      
3.20
      
nan
    
    

      
1005
      
Isabel dos Santos
      
3.70
      
nan
    
    

      
338
      
Carlos and Alejandro Bulgheroni
      
5.50
      
nan
    
    

      
819
      
Gina Rinehart
      
17.70
      
nan
    
    

      
556
      
Dietrich Mateschitz
      
9.20
      
nan
    
    

      
24
      
Ahmed Ali Kanoo
      
1.00
      
16.39
    
    

      
50
      
Albert Frere
      
4.90
      
nan
    
    

      
672
      
Ernest Stempel
      
2.10
      
57.07
    
    

      
1223
      
Jorge Paulo Lemann
      
19.70
      
nan
    
    

      
523
      
David Thomson
      
22.60
      
nan
    
    

      
998
      
Iris Fontbona
      
15.50
      
nan
    
    

      
2473
      
Wang Jianlin
      
15.10
      
nan
    
    

      
1519
      
Luis Carlos Sarmiento
      
14.20
      
nan
    
    

      
1168
      
John Fredriksen
      
13.60
      
nan
    
    

      
1859
      
Petr Kellner
      
11.00
      
nan
    
    

      
1333
      
Kjeld Kirk Kristiansen
      
10.20
      
nan
    
    

      
120
      
Alvaro Noboa
      
1.20
      
4.76
    
    

      
1747
      
Nassef Sawiris
      
6.70
      
nan
    
    

      
199
      
Antti Herlin
      
3.30
      
nan
    
    

      
1469
      
Liliane Bettencourt
      
34.50
      
nan
    
    

      
280
      
Bidzina Ivanishvili
      
5.20
      
nan
    
    

      
1275
      
Karl Albrecht
      
25.00
      
nan
    
    

      
2226
      
Spiro Latsis
      
4.60
      
3.38
    
    

      
2263
      
Stephen Lansdown
      
2.40
      
nan
    
    

      
1454
      
Li Ka-shing
      
31.00
      
nan
    
    

      
1728
      
Mukesh Ambani
      
18.60
      
nan
    
    

      
1915
      
R Budi Hartono
      
7.60
      
nan
    
    

      
1798
      
Pallonji Mistry
      
12.80
      
nan
    
    

      
690
      
Eyal Ofer
      
7.00
      
nan
    
    

      
1680
      
Michele Ferrero
      
26.50
      
nan
    
    

      
...
      
...
      
...
      
...
    
    

      
842
      
Graeme Hart
      
7.00
      
nan
    
    

      
111
      
Aliko Dangote
      
25.00
      
nan
    
    

      
2251
      
Stein Erik Hagen
      
5.00
      
nan
    
    

      
1712
      
Mohammed Al Barwani
      
1.20
      
nan
    
    

      
615
      
Eduardo Belmont Anderson
      
2.50
      
nan
    
    

      
933
      
Henry Sy
      
11.40
      
nan
    
    

      
1070
      
Jan Kulczyk
      
3.60
      
nan
    
    

      
125
      
Americo Amorim
      
5.30
      
nan
    
    

      
996
      
Ioan Niculae
      
1.20
      
nan
    
    

      
112
      
Alisher Usmanov
      
18.60
      
nan
    
    

      
1902
      
Prince Alwaleed Bin Talal Alsaud
      
20.40
      
nan
    
    

      
2008
      
Robert & Philip Ng
      
11.00
      
nan
    
    

      
1144
      
Johann Rupert
      
7.60
      
nan
    
    

      
1405
      
Lee Kun-Hee
      
11.10
      
nan
    
    

      
124
      
Amancio Ortega
      
64.00
      
nan
    
    

      
1023
      
Jacky Xu
      
1.20
      
nan
    
    

      
1749
      
Nathan Kirsh
      
3.70
      
nan
    
    

      
2242
      
Stefan Persson
      
34.40
      
nan
    
    

      
1785
      
Oeri Hoffman and Sacher
      
13.10
      
3.97
    
    

      
2407
      
Tsai Wan-lin
      
12.20
      
nan
    
    

      
2092
      
Rostam Azizi
      
1.00
      
nan
    
    

      
543
      
Dhanin Chearavanont
      
11.40
      
nan
    
    

      
1734
      
Mustafa Rahmi Koc
      
4.00
      
2.20
    
    

      
2288
      
Sudhir Ruparelia
      
1.10
      
nan
    
    

      
2005
      
Rinat Akhmetov
      
12.50
      
nan
    
    

      
6
      
Abdulla bin Ahmad Al Ghurair
      
4.80
      
nan
    
    

      
797
      
Gerald Cavendish Grosvenor
      
13.00
      
nan
    
    

      
284
      
Bill Gates
      
76.00
      
nan
    
    

      
857
      
Gustavo Cisneros
      
5.30
      
4.31
    
    

      
1863
      
Pham Nhat Vuong
      
1.60
      
nan
    
  

73 rows × 3 columns

In [67]:

    

df_trains = pd.read_csv("stations.csv", delimiter=';')
print('A dataset about train stations and their accessibility in Switzerland:')
df_trains


    

    




A dataset about train stations and their accessibility in Switzerland:






    
Out[67]:






  

    

      

      
didok
      
station_name
      
accessible_ticket
      
accessible_wc
      
rollstuhl_verlad
      
mobilift
      
stepless_perron
      
bats
      
autelca
      
automat_ktu
      
geopos
      
TUNummer
      
Betriebspunkttyp
    
  
  

    

      
0
      
10
      
Basel SBB
      
1.00
      
1.00
      
1.00
      
1.00
      
1.00
      
10 Min. Übergangszeit für Züge von und nach Fr...
      
0.00
      
19.00
      
47.5474041527, 7.58955146721
      
1
      
Haltestelle
    
    

      
1
      
2213
      
Wohlen
      
1.00
      
1.00
      
1.00
      
1.00
      
1.00
      
NaN
      
0.00
      
2.00
      
47.3484618374, 8.26978121872
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
2
      
2116
      
Schinznach Bad
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.4517752695, 8.16692832223
      
1
      
Haltestelle
    
    

      
3
      
2211
      
Hendschiken
      
1.00
      
0.00
      
1.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.3894242237, 8.20737399629
      
1
      
Haltestelle
    
    

      
4
      
2226
      
Knonau
      
0.00
      
0.00
      
0.00
      
1.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.2202591199, 8.4667085831
      
1
      
Haltestelle
    
    

      
5
      
3105
      
Uetikon
      
0.00
      
0.00
      
1.00
      
1.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.2590294057, 8.678774746
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
6
      
3015
      
Zürich Wipkingen
      
0.00
      
0.00
      
1.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.3930313233, 8.52935905447
      
1
      
Haltestelle
    
    

      
7
      
3112
      
Kempraten
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.2383929337, 8.81429184846
      
1
      
Haltestelle
    
    

      
8
      
3100
      
Zollikon
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.3373317337, 8.569717612
      
1
      
Haltestelle
    
    

      
9
      
3502
      
Siggenthal-Würenlingen
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
Postauto fahren ab Bahnhofplatz
      
0.00
      
1.00
      
47.5179218559, 8.24015363326
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
10
      
3505
      
Wettingen
      
0.00
      
0.00
      
1.00
      
1.00
      
1.00
      
Gleise 1, 3, 4 und 5 stufenlos zugänglich.
      
0.00
      
0.00
      
47.4596367944, 8.3159863528
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
11
      
3427
      
Schlatt
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.6795011585, 8.68749675648
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
12
      
3527
      
Buchs-Dällikon
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.4533973239, 8.43557852512
      
1
      
Haltestelle
    
    

      
13
      
3429
      
Schlattingen
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.6666263051, 8.77041438581
      
1
      
Haltestelle
    
    

      
14
      
4007
      
Moreillon
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
46.5083872786, 6.78564812794
      
1
      
Haltestelle
    
    

      
15
      
2237
      
Lupfig
      
0.00
      
0.00
      
0.00
      
1.00
      
1.00
      
Postautohaltestelle Lupfig Bahnhof = 3 Minuten...
      
0.00
      
0.00
      
47.4451758366, 8.21496733295
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
16
      
3007
      
Zürich Seebach
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.4187469105, 8.54463614173
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
17
      
3001
      
Zürich Altstetten
      
1.00
      
1.00
      
1.00
      
1.00
      
1.00
      
NaN
      
0.00
      
2.00
      
47.3914808361, 8.4889402654
      
1
      
Haltestelle
    
    

      
18
      
2229
      
Urdorf Weihermatt
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.3809708849, 8.43033031194
      
1
      
Haltestelle
    
    

      
19
      
3011
      
Zürich Wiedikon
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
2.00
      
47.3714716204, 8.52346258297
      
1
      
Haltestelle
    
    

      
20
      
2300
      
Zug Postplatz
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.168273486, 8.51691604796
      
1
      
Haltestelle
    
    

      
21
      
2231
      
Zug Oberwil
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.147675872, 8.50992153777
      
1
      
Haltestelle
    
    

      
22
      
3313
      
Niederglatt
      
0.00
      
0.00
      
0.00
      
1.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.4874689477, 8.50329612568
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
23
      
3316
      
Steinmaur
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.4899801946, 8.4468407224
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
24
      
3304
      
Kemptthal
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
0.00
      
47.452833265, 8.70557069755
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
25
      
3420
      
Lottstetten
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.6257356296, 8.56680664761
      
1
      
Haltestelle
    
    

      
26
      
3409
      
Bad Zurzach
      
0.00
      
0.00
      
1.00
      
1.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.5883101087, 8.29556465398
      
1
      
Haltestelle
    
    

      
27
      
3425
      
Feuerthalen
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.6921636376, 8.64595208918
      
1
      
Haltestelle
    
    

      
28
      
3421
      
Jestetten
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.6544006039, 8.57327389271
      
1
      
Haltestelle
    
    

      
29
      
3414
      
Mellikon
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.5684839744, 8.35249723256
      
1
      
Haltestelle
    
    

      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
701
      
8005
      
Burgdorf
      
1.00
      
1.00
      
1.00
      
1.00
      
1.00
      
NaN
      
0.00
      
4.00
      
47.0606957305, 7.62168894507
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
702
      
6308
      
Arbon
      
0.00
      
0.00
      
1.00
      
1.00
      
1.00
      
NaN
      
0.00
      
2.00
      
47.5105854479, 9.43333858591
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
703
      
6217
      
Sulgen
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.5387993681, 9.18367857671
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
704
      
6316
      
Au SG
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.4361134139, 9.64124454757
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
705
      
6304
      
Mörschwil
      
0.00
      
0.00
      
1.00
      
1.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.4747707737, 9.41437284119
      
1
      
Haltestelle
    
    

      
706
      
6309
      
Egnach
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.5439914495, 9.38300007743
      
1
      
Haltestelle
    
    

      
707
      
9000
      
Chur
      
1.00
      
1.00
      
1.00
      
1.00
      
1.00
      
Postautostation per Rolltreppe und Lift erreic...
      
0.00
      
6.00
      
46.8530797552, 9.52892561961
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
708
      
8217
      
Schachen LU
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
keine schienenfreien Zugänge
      
0.00
      
0.00
      
47.0379660567, 8.1432466759
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
709
      
8100
      
Langenthal
      
1.00
      
1.00
      
1.00
      
0.00
      
1.00
      
NaN
      
0.00
      
3.00
      
47.2173003777, 7.78470885276
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
710
      
8200
      
Worb SBB
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
0.00
      
46.9152218276, 7.56483999982
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
711
      
8208
      
Trubschachen
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
46.9216924283, 7.84613113265
      
1
      
Haltestelle
    
    

      
712
      
8213
      
Entlebuch
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
46.9936910245, 8.06235072562
      
1
      
Haltestelle
    
    

      
713
      
8695
      
Grasswil, Post
      
nan
      
nan
      
nan
      
nan
      
nan
      
NaN
      
nan
      
nan
      
47.1460754455, 7.66716135321
      
1
      
Haltestelle
    
    

      
714
      
9004
      
Bad Ragaz
      
0.00
      
1.00
      
1.00
      
1.00
      
1.00
      
Postautos fahren ab Bahnhofplatz
      
0.00
      
1.00
      
47.0103066077, 9.50521579767
      
1
      
Haltestelle
    
    

      
715
      
9413
      
Flums
      
0.00
      
1.00
      
1.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.0966128468, 9.34791662857
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
716
      
16218
      
Winterthur Hegi
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.5015644748, 8.76930571536
      
1
      
Haltestelle
    
    

      
717
      
9405
      
Räfis-Burgerau
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.1499462602, 9.48516673222
      
1
      
Haltestelle
    
    

      
718
      
15992
      
Baar Neufeld
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.1884876743, 8.51775897551
      
1
      
Haltestelle
    
    

      
719
      
9415
      
Mols
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
47.1129874927, 9.27680343664
      
1
      
Haltestelle
    
    

      
720
      
9852
      
Obstalden, Post
      
nan
      
nan
      
nan
      
nan
      
nan
      
NaN
      
nan
      
nan
      
47.1175812114, 9.14946758658
      
1
      
Haltestelle
    
    

      
721
      
6139
      
Stein am Rhein
      
0.00
      
0.00
      
1.00
      
0.00
      
1.00
      
Gleis 3 Unterführung, steile Rampe
      
0.00
      
2.00
      
47.656108874, 8.85500693112
      
1
      
Haltestelle
    
    

      
722
      
6209
      
Flawil
      
1.00
      
1.00
      
1.00
      
1.00
      
1.00
      
NaN
      
0.00
      
2.00
      
47.4151898192, 9.1897661689
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
723
      
6046
      
Henggart
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.5642523105, 8.68505196166
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
724
      
6044
      
Winterthur Töss
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.4898075094, 8.70929159585
      
1
      
Haltestelle
    
    

      
725
      
6009
      
Saland
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.3942126094, 8.85420315671
      
1
      
Haltestelle_und_Bedienpunkt
    
    

      
726
      
6017
      
Wiesendangen
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.5255401974, 8.77601496118
      
1
      
Haltestelle
    
    

      
727
      
6021
      
Dinhard
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
0.00
      
47.5533468989, 8.75295793686
      
1
      
Haltestelle
    
    

      
728
      
5204
      
Faido
      
0.00
      
0.00
      
0.00
      
0.00
      
0.00
      
NaN
      
0.00
      
1.00
      
46.48307671, 8.79070214577
      
1
      
Haltestelle
    
    

      
729
      
6027
      
Ramsen
      
nan
      
nan
      
nan
      
nan
      
nan
      
NaN
      
nan
      
nan
      
47.707435847, 8.8211461179
      
1
      
Haltestelle
    
    

      
730
      
16271
      
Steinach
      
0.00
      
0.00
      
0.00
      
0.00
      
1.00
      
NaN
      
0.00
      
1.00
      
47.5008611902, 9.44212099652
      
1
      
Haltestelle
    
  

731 rows × 13 columns