Plotting a 2D heatmap with Matplotlib

Solution 1:

The imshow() function with parameters interpolation='nearest' and cmap='hot' should do what you want.

import matplotlib.pyplot as plt
import numpy as np

a = np.random.random((16, 16))
plt.imshow(a, cmap='hot', interpolation='nearest')
plt.show()

Solution 2:

Seaborn takes care of a lot of the manual work and automatically plots a gradient at the side of the chart etc.

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()

Or, you can even plot upper / lower left / right triangles of square matrices, for example a correlation matrix which is square and is symmetric, so plotting all values would be redundant anyway.

corr = np.corrcoef(np.random.randn(10, 200))
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask)] = True
with sns.axes_style("white"):
    ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True,  cmap="YlGnBu")
    plt.show()