# python – Plotting a 2D heatmap with Matplotlib

## python – Plotting a 2D heatmap with Matplotlib

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()

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))
with sns.axes_style(white):
plt.show()

#### python – Plotting a 2D heatmap with Matplotlib

I would use matplotlibs pcolor/pcolormesh function since it allows nonuniform spacing of the data.

Example taken from matplotlib:

import matplotlib.pyplot as plt
import numpy as np

# generate 2 2d grids for the x & y bounds
y, x = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))

z = (1 - x / 2. + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
# x and y are bounds, so z should be the value *inside* those bounds.
# Therefore, remove the last value from the z array.
z = z[:-1, :-1]
z_min, z_max = -np.abs(z).max(), np.abs(z).max()

fig, ax = plt.subplots()

c = ax.pcolormesh(x, y, z, cmap=RdBu, vmin=z_min, vmax=z_max)
ax.set_title(pcolormesh)
# set the limits of the plot to the limits of the data
ax.axis([x.min(), x.max(), y.min(), y.max()])
fig.colorbar(c, ax=ax)

plt.show()