In [23]:

    

import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go

df = pd.read_csv("https://raw.githubusercontent.com/democratia/political_science/master/Interkantonale-Korrelation/alle-volksabstimmungen-resultate.csv", sep=";")

data = df[list(df.columns[2:-1])] 
kantonsnamen = list(df)[2:-1]
print(kantonsnamen)
data_mat = data.as_matrix()
kantone = data_mat[:, 1:]


    

    




['Schweiz', 'Zürich', 'Bern / Berne', 'Luzern', 'Uri', 'Schwyz', 'Obwalden', 'Nidwalden', 'Glarus', 'Zug', 'Fribourg / Freiburg', 'Solothurn', 'Basel-Stadt', 'Basel-Landschaft', 'Schaffhausen', 'Appenzell Ausserrhoden', 'Appenzell Innerrhoden', 'St. Gallen', 'Graubünden / Grigioni / Grischun', 'Aargau', 'Thurgau', 'Ticino', 'Vaud', 'Valais / Wallis', 'Neuchâtel', 'Genève']

In [24]:

    

covariance = np.corrcoef(kantone.T)
trace = go.Heatmap(
    z=covariance,
    x=kantonsnamen,
    y=kantonsnamen)
layout = go.Layout(
    title='Interkantonale Korrelationen',
    )
data=[trace]
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='interkantonale-korrelationen')


    

    
Out[24]:

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