print the heat map python
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data, linewidth=0.5)
plt.show()print the heat map python
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data, linewidth=0.5)
plt.show()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()Copyright © 2021 Codeinu
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