import seaborn as sns
def heatMap(df, mirror):

   # Create Correlation df
   corr = df.corr()
   # Plot figsize
   fig, ax = plt.subplots(figsize=(20, 20))
   # Generate Color Map
   colormap = sns.diverging_palette(220, 10, as_cmap=True)

   if mirror == True:
      #Generate Heat Map, allow annotations and place floats in map
      sns.heatmap(corr, cmap=colormap, annot=True, fmt=".2f")
      #Apply xticks
      plt.xticks(range(len(corr.columns)), corr.columns);
      #Apply yticks
      plt.yticks(range(len(corr.columns)), corr.columns)
      #show plot

   else:
      # Drop self-correlations
      dropSelf = np.zeros_like(corr)
      dropSelf[np.triu_indices_from(dropSelf)] = True
      # Generate Color Map
      colormap = sns.diverging_palette(220, 10, as_cmap=True)
      # Generate Heat Map, allow annotations and place floats in map
      sns.heatmap(corr, cmap=colormap, annot=True, fmt=".2f", mask=dropSelf)
      # Apply xticks
      plt.xticks(range(len(corr.columns)), corr.columns);
      # Apply yticks
      plt.yticks(range(len(corr.columns)), corr.columns)
   # show plot
   plt.show()


heatMap(df,mirror=True)