SeabornHeatmap.py

# heatmap with seaborn

import numpy as np  
import pandas as pd  
import seaborn as sns  
import matplotlib.pyplot as plt  
sns.set(style="white")  
import os  
pd.set_option('precision', 10)  
os.chdir("C:/Users/Dome/Desktop/nu/Wahl- und Strukturdaten/Tabellen/")  
ma = pd.read_csv("MASTERENG.csv",float_precision='round_trip')  
  
# subsetting of master table required for desired factors  
collist=[ "INSERT ALL COLUMNS FOR CORRELATION" # ma.columns.values  
# AND add following columns for correlation  
,'Area','selfindex17', 'deltaindex', 'linke17', 'gruenen17','spd17',  
 'fdp17','cducsu17', 'afd17']  
  
df = ma[collist].copy() # new df with columns above  
  
# loc for subsetted East or West datasets  
west = df.loc[df['Area'] == "West"]  
east = df.loc[df['Area'] == "East"]  
# Alternative: Berlin only with AreaB == Berlin, or for each county   
  
# Compute the correlation matrix  
  
corr = East.corr()  
# or   West.corr()  
# or   df.corr()  
  
# mask for the  triangle  
mask = np.zeros_like(corr, dtype=np.bool)  
mask[np.triu_indices_from(mask)] = True  
  
# matplotlib figure  
f, ax = plt.subplots(figsize=(11, 9))  
  
# custom diverging colormap  
cmap = sns.diverging_palette(220, 10, as_cmap=True)  
  
# heatmap with the mask and correct aspect ratio  
sns.heatmap(corr, mask=mask, cmap=cmap, center=0, square=True, linewidths=.5,  
            cbar_kws={"shrink": .5})