Answers for "heatmap using seaborn"

seaborn heatmap parameters

# seaborn heatmap best parameter
plt.figure(figsize=(20,8))
sns.heatmap(corr, vmax=1, vmin=-1, center=0,
			linewidth=.5,square=True, annot = True,
            annot_kws = {'size':8},fmt='.1f', cmap='BrBG_r', ax=ax1,  # ax: use this when using subplot
            cbar_kws = dict(use_gridspec=False,location="top", shrink=0.9)) # cbar_kws: for positioning cbar and "shrink" for reducing cbar size
plt.title('Correlation')
plt.show()

heatmap of pandas dataframe with seaborn

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# create some random data; replace that by your actual dataset
data = pd.DataFrame(np.random.rand(11, 5), columns=['A', 'B', 'C', 'D', 'E'], index = range(2000, 2011, 1))

# plot heatmap
ax = sns.heatmap(data.T)

# turn the axis label
for item in ax.get_yticklabels():
    item.set_rotation(0)

for item in ax.get_xticklabels():
    item.set_rotation(90)

# save figure
plt.savefig('seabornPandas.png', dpi=100)
plt.show()