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()show integer seabron heatmap values
sns.heatmap(table2,annot=True,cmap='Blues', fmt='g')python add labels to seaborn heatmap
# Basic syntax:
sns.heatmap(df, xticklabels=x_labels, yticklabels=y_labels)
# Example usage:
import seaborn as sns
flight = sns.load_dataset('flights') # Load flights datset from GitHub
# seaborn repository
# Reshape flights dataeset to create seaborn heatmap
flights_df = flight.pivot('month', 'year', 'passengers')
x_labels = [1,2,3,4,5,6,7,8,9,10,11,12] # Labels for x-axis
y_labels = [11,22,33,44,55,66,77,88,99,101,111,121] # Labels for y-axis
# Create seaborn heatmap with required labels
sns.heatmap(flights_df, xticklabels=x_labels, yticklabels=y_labels)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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