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1. Cats and dogs





In [74]:



    


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>

2. Millionaires

What country are most billionaires from? For the top ones, how many billionaires per billion people?





In [108]:



    


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



    











In [109]:



    


df2.keys()



    


















    
Out[109]:








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 [119]:



    


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



    


















    
Out[119]:








United States     903
Germany           160
China             153
Russia            119
Japan              96
Brazil             81
Hong Kong          77
France             72
United Kingdom     65
India              63
Name: citizenship, dtype: int64

















In [125]:



    


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



    











In [135]:



    


df_pop.keys()
#recent_pop = df_pop['2015']

#join: see http://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index
left = pd.DataFrame(df2, index=['countrycode'])
right = pd.DataFrame(df_pop, index=['Country Code'])

millionaires_and_pop = left.join(right)
result = pd.merge(left, right, left_index=True, right_index=True, how='outer')



    











In [136]:



    


millionaires_and_pop
result
#millionaires_and_pop['citizenship'].value_counts().head(10)



    


















    
Out[136]:










  
    

      
      year
      name
      rank
      citizenship
      countrycode
      networthusbillion
      selfmade
      typeofwealth
      gender
      age
      ...
      2007
      2008
      2009
      2010
      2011
      2012
      2013
      2014
      2015
      Unnamed: 60
    

  
  
    

      Country Code
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      countrycode
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

  



2 rows × 91 columns

What's the average wealth of a billionaire? Male? Female?





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

Who is the poorest billionaire? Who are the top 10 poorest billionaires?





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

What is 'relationship to company'? And what are the most common relationships?





In [222]:



    


#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:')

df2['relationshiptocompany'].value_counts().head(5)



    


















    






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

The five most common relationships are:










    
Out[222]:








founder     1214
relation     945
owner         94
chairman      76
investor      36
Name: relationshiptocompany, dtype: int64

Most common source of wealth? Male vs. female?





In [233]:



    


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

Given the richest person in a country, what % of the GDP is their wealth?





In [303]:



    


#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[303]:










  
    

      
      name
      networthusbillion
      percofgdp
    

  
  
    

      1011
      Issad Rebrab
      3.2
      NaN
    

    

      1005
      Isabel dos Santos
      3.7
      NaN
    

    

      338
      Carlos and Alejandro Bulgheroni
      5.5
      NaN
    

    

      819
      Gina Rinehart
      17.7
      NaN
    

    

      556
      Dietrich Mateschitz
      9.2
      NaN
    

    

      24
      Ahmed Ali Kanoo
      1.0
      16.393443
    

    

      50
      Albert Frere
      4.9
      NaN
    

    

      672
      Ernest Stempel
      2.1
      57.065217
    

    

      1223
      Jorge Paulo Lemann
      19.7
      NaN
    

    

      523
      David Thomson
      22.6
      NaN
    

    

      998
      Iris Fontbona
      15.5
      NaN
    

    

      2473
      Wang Jianlin
      15.1
      NaN
    

    

      1519
      Luis Carlos Sarmiento
      14.2
      NaN
    

    

      1168
      John Fredriksen
      13.6
      NaN
    

    

      1859
      Petr Kellner
      11.0
      NaN
    

    

      1333
      Kjeld Kirk Kristiansen
      10.2
      NaN
    

    

      120
      Alvaro Noboa
      1.2
      4.761905
    

    

      1747
      Nassef Sawiris
      6.7
      NaN
    

    

      199
      Antti Herlin
      3.3
      NaN
    

    

      1469
      Liliane Bettencourt
      34.5
      NaN
    

    

      280
      Bidzina Ivanishvili
      5.2
      NaN
    

    

      1275
      Karl Albrecht
      25.0
      NaN
    

    

      2226
      Spiro Latsis
      4.6
      3.382353
    

    

      2263
      Stephen Lansdown
      2.4
      NaN
    

    

      1454
      Li Ka-shing
      31.0
      NaN
    

    

      1728
      Mukesh Ambani
      18.6
      NaN
    

    

      1915
      R Budi Hartono
      7.6
      NaN
    

    

      1798
      Pallonji Mistry
      12.8
      NaN
    

    

      690
      Eyal Ofer
      7.0
      NaN
    

    

      1680
      Michele Ferrero
      26.5
      NaN
    

    

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

    

      842
      Graeme Hart
      7.0
      NaN
    

    

      111
      Aliko Dangote
      25.0
      NaN
    

    

      2251
      Stein Erik Hagen
      5.0
      NaN
    

    

      1712
      Mohammed Al Barwani
      1.2
      NaN
    

    

      615
      Eduardo Belmont Anderson
      2.5
      NaN
    

    

      933
      Henry Sy
      11.4
      NaN
    

    

      1070
      Jan Kulczyk
      3.6
      NaN
    

    

      125
      Americo Amorim
      5.3
      NaN
    

    

      996
      Ioan Niculae
      1.2
      NaN
    

    

      112
      Alisher Usmanov
      18.6
      NaN
    

    

      1902
      Prince Alwaleed Bin Talal Alsaud
      20.4
      NaN
    

    

      2008
      Robert & Philip Ng
      11.0
      NaN
    

    

      1144
      Johann Rupert
      7.6
      NaN
    

    

      1405
      Lee Kun-Hee
      11.1
      NaN
    

    

      124
      Amancio Ortega
      64.0
      NaN
    

    

      1023
      Jacky Xu
      1.2
      NaN
    

    

      1749
      Nathan Kirsh
      3.7
      NaN
    

    

      2242
      Stefan Persson
      34.4
      NaN
    

    

      1785
      Oeri Hoffman and Sacher
      13.1
      3.969697
    

    

      2407
      Tsai Wan-lin
      12.2
      NaN
    

    

      2092
      Rostam Azizi
      1.0
      NaN
    

    

      543
      Dhanin Chearavanont
      11.4
      NaN
    

    

      1734
      Mustafa Rahmi Koc
      4.0
      2.204148
    

    

      2288
      Sudhir Ruparelia
      1.1
      NaN
    

    

      2005
      Rinat Akhmetov
      12.5
      NaN
    

    

      6
      Abdulla bin Ahmad Al Ghurair
      4.8
      NaN
    

    

      797
      Gerald Cavendish Grosvenor
      13.0
      NaN
    

    

      284
      Bill Gates
      76.0
      NaN
    

    

      857
      Gustavo Cisneros
      5.3
      4.312098
    

    

      1863
      Pham Nhat Vuong
      1.6
      NaN
    

  



73 rows × 3 columns

Trains stations





In [313]:



    


df_trains = pd.read_csv("stations.csv", delimiter=';')
df_trains



    


















    
Out[313]:










  
    

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

  
  
    

      0
      10
      Basel SBB
      1.0
      1.0
      1.0
      1.0
      1.0
      10 Min. Übergangszeit für Züge von und nach Fr...
      0.0
      19.0
      47.5474041527, 7.58955146721
      1
      Haltestelle
    

    

      1
      2213
      Wohlen
      1.0
      1.0
      1.0
      1.0
      1.0
      NaN
      0.0
      2.0
      47.3484618374, 8.26978121872
      1
      Haltestelle_und_Bedienpunkt
    

    

      2
      2116
      Schinznach Bad
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      1.0
      47.4517752695, 8.16692832223
      1
      Haltestelle
    

    

      3
      2211
      Hendschiken
      1.0
      0.0
      1.0
      0.0
      0.0
      NaN
      0.0
      1.0
      47.3894242237, 8.20737399629
      1
      Haltestelle
    

    

      4
      2226
      Knonau
      0.0
      0.0
      0.0
      1.0
      1.0
      NaN
      0.0
      1.0
      47.2202591199, 8.4667085831
      1
      Haltestelle
    

    

      5
      3105
      Uetikon
      0.0
      0.0
      1.0
      1.0
      1.0
      NaN
      0.0
      1.0
      47.2590294057, 8.678774746
      1
      Haltestelle_und_Bedienpunkt
    

    

      6
      3015
      Zürich Wipkingen
      0.0
      0.0
      1.0
      0.0
      0.0
      NaN
      0.0
      1.0
      47.3930313233, 8.52935905447
      1
      Haltestelle
    

    

      7
      3112
      Kempraten
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      1.0
      47.2383929337, 8.81429184846
      1
      Haltestelle
    

    

      8
      3100
      Zollikon
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.3373317337, 8.569717612
      1
      Haltestelle
    

    

      9
      3502
      Siggenthal-Würenlingen
      0.0
      0.0
      0.0
      0.0
      0.0
      Postauto fahren ab Bahnhofplatz
      0.0
      1.0
      47.5179218559, 8.24015363326
      1
      Haltestelle_und_Bedienpunkt
    

    

      10
      3505
      Wettingen
      0.0
      0.0
      1.0
      1.0
      1.0
      Gleise 1, 3, 4 und 5 stufenlos zugänglich.
      0.0
      0.0
      47.4596367944, 8.3159863528
      1
      Haltestelle_und_Bedienpunkt
    

    

      11
      3427
      Schlatt
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.6795011585, 8.68749675648
      1
      Haltestelle_und_Bedienpunkt
    

    

      12
      3527
      Buchs-Dällikon
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.4533973239, 8.43557852512
      1
      Haltestelle
    

    

      13
      3429
      Schlattingen
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.6666263051, 8.77041438581
      1
      Haltestelle
    

    

      14
      4007
      Moreillon
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      1.0
      46.5083872786, 6.78564812794
      1
      Haltestelle
    

    

      15
      2237
      Lupfig
      0.0
      0.0
      0.0
      1.0
      1.0
      Postautohaltestelle Lupfig Bahnhof = 3 Minuten...
      0.0
      0.0
      47.4451758366, 8.21496733295
      1
      Haltestelle_und_Bedienpunkt
    

    

      16
      3007
      Zürich Seebach
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.4187469105, 8.54463614173
      1
      Haltestelle_und_Bedienpunkt
    

    

      17
      3001
      Zürich Altstetten
      1.0
      1.0
      1.0
      1.0
      1.0
      NaN
      0.0
      2.0
      47.3914808361, 8.4889402654
      1
      Haltestelle
    

    

      18
      2229
      Urdorf Weihermatt
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.3809708849, 8.43033031194
      1
      Haltestelle
    

    

      19
      3011
      Zürich Wiedikon
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      2.0
      47.3714716204, 8.52346258297
      1
      Haltestelle
    

    

      20
      2300
      Zug Postplatz
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.168273486, 8.51691604796
      1
      Haltestelle
    

    

      21
      2231
      Zug Oberwil
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.147675872, 8.50992153777
      1
      Haltestelle
    

    

      22
      3313
      Niederglatt
      0.0
      0.0
      0.0
      1.0
      1.0
      NaN
      0.0
      1.0
      47.4874689477, 8.50329612568
      1
      Haltestelle_und_Bedienpunkt
    

    

      23
      3316
      Steinmaur
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      1.0
      47.4899801946, 8.4468407224
      1
      Haltestelle_und_Bedienpunkt
    

    

      24
      3304
      Kemptthal
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      0.0
      47.452833265, 8.70557069755
      1
      Haltestelle_und_Bedienpunkt
    

    

      25
      3420
      Lottstetten
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.6257356296, 8.56680664761
      1
      Haltestelle
    

    

      26
      3409
      Bad Zurzach
      0.0
      0.0
      1.0
      1.0
      1.0
      NaN
      0.0
      1.0
      47.5883101087, 8.29556465398
      1
      Haltestelle
    

    

      27
      3425
      Feuerthalen
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.6921636376, 8.64595208918
      1
      Haltestelle
    

    

      28
      3421
      Jestetten
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.6544006039, 8.57327389271
      1
      Haltestelle
    

    

      29
      3414
      Mellikon
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.5684839744, 8.35249723256
      1
      Haltestelle
    

    

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

    

      701
      8005
      Burgdorf
      1.0
      1.0
      1.0
      1.0
      1.0
      NaN
      0.0
      4.0
      47.0606957305, 7.62168894507
      1
      Haltestelle_und_Bedienpunkt
    

    

      702
      6308
      Arbon
      0.0
      0.0
      1.0
      1.0
      1.0
      NaN
      0.0
      2.0
      47.5105854479, 9.43333858591
      1
      Haltestelle_und_Bedienpunkt
    

    

      703
      6217
      Sulgen
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      1.0
      47.5387993681, 9.18367857671
      1
      Haltestelle_und_Bedienpunkt
    

    

      704
      6316
      Au SG
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.4361134139, 9.64124454757
      1
      Haltestelle_und_Bedienpunkt
    

    

      705
      6304
      Mörschwil
      0.0
      0.0
      1.0
      1.0
      0.0
      NaN
      0.0
      1.0
      47.4747707737, 9.41437284119
      1
      Haltestelle
    

    

      706
      6309
      Egnach
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.5439914495, 9.38300007743
      1
      Haltestelle
    

    

      707
      9000
      Chur
      1.0
      1.0
      1.0
      1.0
      1.0
      Postautostation per Rolltreppe und Lift erreic...
      0.0
      6.0
      46.8530797552, 9.52892561961
      1
      Haltestelle_und_Bedienpunkt
    

    

      708
      8217
      Schachen LU
      0.0
      0.0
      0.0
      0.0
      1.0
      keine schienenfreien Zugänge
      0.0
      0.0
      47.0379660567, 8.1432466759
      1
      Haltestelle_und_Bedienpunkt
    

    

      709
      8100
      Langenthal
      1.0
      1.0
      1.0
      0.0
      1.0
      NaN
      0.0
      3.0
      47.2173003777, 7.78470885276
      1
      Haltestelle_und_Bedienpunkt
    

    

      710
      8200
      Worb SBB
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      0.0
      46.9152218276, 7.56483999982
      1
      Haltestelle_und_Bedienpunkt
    

    

      711
      8208
      Trubschachen
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      46.9216924283, 7.84613113265
      1
      Haltestelle
    

    

      712
      8213
      Entlebuch
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      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
      0.0
      1.0
      1.0
      1.0
      1.0
      Postautos fahren ab Bahnhofplatz
      0.0
      1.0
      47.0103066077, 9.50521579767
      1
      Haltestelle
    

    

      715
      9413
      Flums
      0.0
      1.0
      1.0
      0.0
      0.0
      NaN
      0.0
      1.0
      47.0966128468, 9.34791662857
      1
      Haltestelle_und_Bedienpunkt
    

    

      716
      16218
      Winterthur Hegi
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.5015644748, 8.76930571536
      1
      Haltestelle
    

    

      717
      9405
      Räfis-Burgerau
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.1499462602, 9.48516673222
      1
      Haltestelle
    

    

      718
      15992
      Baar Neufeld
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.1884876743, 8.51775897551
      1
      Haltestelle
    

    

      719
      9415
      Mols
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      1.0
      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
      0.0
      0.0
      1.0
      0.0
      1.0
      Gleis 3 Unterführung, steile Rampe
      0.0
      2.0
      47.656108874, 8.85500693112
      1
      Haltestelle
    

    

      722
      6209
      Flawil
      1.0
      1.0
      1.0
      1.0
      1.0
      NaN
      0.0
      2.0
      47.4151898192, 9.1897661689
      1
      Haltestelle_und_Bedienpunkt
    

    

      723
      6046
      Henggart
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.5642523105, 8.68505196166
      1
      Haltestelle_und_Bedienpunkt
    

    

      724
      6044
      Winterthur Töss
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.4898075094, 8.70929159585
      1
      Haltestelle
    

    

      725
      6009
      Saland
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.3942126094, 8.85420315671
      1
      Haltestelle_und_Bedienpunkt
    

    

      726
      6017
      Wiesendangen
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.5255401974, 8.77601496118
      1
      Haltestelle
    

    

      727
      6021
      Dinhard
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      0.0
      47.5533468989, 8.75295793686
      1
      Haltestelle
    

    

      728
      5204
      Faido
      0.0
      0.0
      0.0
      0.0
      0.0
      NaN
      0.0
      1.0
      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
      0.0
      0.0
      0.0
      0.0
      1.0
      NaN
      0.0
      1.0
      47.5008611902, 9.44212099652
      1
      Haltestelle
    

  



731 rows × 13 columns

Content source: palrogg/foundations-homework

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