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How to draw 2D Heatmap using Matplotlib in python?

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  • Last Updated : 26 Nov, 2020
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A 2-D Heatmap is a data visualization tool that helps to represent the magnitude of the phenomenon in form of colors. In python, we can plot 2-D Heatmaps using Matplotlib package. There are different methods to plot 2-D Heatmaps, some of them are discussed below. 

Method 1: Using matplotlib.pyplot.imshow() Function

Syntax: matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None,
vmax=None, origin=None, extent=None, shape=<deprecated parameter>, filternorm=1, filterrad=4.0,
imlim=<deprecated parameter>, resample=None, url=None, \*, data=None, \*\*kwargs)
 

Python3




# Program to plot 2-D Heat map
# using matplotlib.pyplot.imshow() method
import numpy as np
import matplotlib.pyplot as plt
  
data = np.random.random(( 12 , 12 ))
plt.imshow( data , cmap = 'autumn' , interpolation = 'nearest' )
  
plt.title( "2-D Heat Map" )
plt.show()

Output:

Method 2: Using Seaborn Library

For this we use seaborn.heatmap() function

Syntax: seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False,annot=None,
fmt=’.2g’, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, cbar_kws=None, cbar_ax=None,
square=False, xticklabels=’auto’, yticklabels=’auto’, mask=None, ax=None, **kwargs)
 

Python3




# Program to plot 2-D Heat map
# using seaborn.heatmap() method
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
  
data_set = np.random.rand( 10 , 10 )
ax = sns.heatmap( data_set , linewidth = 0.5 , cmap = 'coolwarm' )
  
plt.title( "2-D Heat Map" )
plt.show()

Output:

Method 3: Using matplotlib.pyplot.pcolormesh() Function

Syntax: matplotlib.pyplot.pcolormesh(*args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, 
shading=’flat’, antialiased=False, data=None, **kwargs)
 

Python3




# Program to plot 2-D Heat map
# using matplotlib.pyplot.pcolormesh() method
import matplotlib.pyplot as plt
import numpy as np
  
Z = np.random.rand( 15 , 15 )
  
plt.pcolormesh( Z , cmap = 'summer' )
  
plt.title( '2-D Heat Map' )
plt.show()

Output:


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