Heatmaps

Matplotlib Basics

1 min read

Published Oct 5 2025

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A heatmap is a graphical representation of a matrix or 2D dataset where colours represent the magnitude of values.

Rows and columns correspond to indices of the data matrix.
Colour intensity encodes numerical values.
Useful for visualising correlation matrices, grids, or spatial data.

Syntax:

plt.imshow(data, cmap=None, interpolation='nearest', origin='upper')

Parameters:

data = 2D array (NumPy array, list of lists, or Pandas DataFrame)
cmap = Colourmap (e.g., 'viridis', 'coolwarm')
interpolation = Pixel interpolation ('nearest' is common)
origin = 'upper' (default) or 'lower' for y-axis origin

To add a colour scale, use plt.colorbar().

Basic heatmap example

import matplotlib.pyplot as plt

import numpy as np

# 5x5 matrix of random values

data = np.random.rand(5, 5)

plt.imshow(data, cmap='viridis')

plt.colorbar()

plt.title("Basic Heatmap")

plt.show()

Each cell’s colour corresponds to its value.

Adjusting colourmaps

import matplotlib.pyplot as plt

import numpy as np

data = np.random.rand(5, 5)

plt.imshow(data, cmap='coolwarm')

plt.colorbar()

plt.title("Heatmap with 'coolwarm' Colourmap")

plt.show()

Popular colourmaps: 'viridis', 'plasma', 'inferno', 'magma', 'cividis', 'coolwarm', 'RdYlBu'.

matplotlib heatmap chart coolwarm example

Control colour scale (vmin/vmax)

You can fix the colour scale to compare multiple heatmaps:

import matplotlib.pyplot as plt

import numpy as np

data = np.random.rand(5, 5)

plt.imshow(data, cmap='viridis', vmin=0, vmax=1)

plt.colorbar()

plt.title("Fixed Colour Scale Heatmap")

plt.show()

matplotlib heatmap chart fixedscale example

Display values on each cell

import matplotlib.pyplot as plt

import numpy as np

data = np.random.rand(5, 5)

for i in range(data.shape[0]):

for j in range(data.shape[1]):

plt.text(j, i, f"{data[i, j]:.2f}",

ha='center', va='center', color='white')

plt.imshow(data, cmap='viridis')

plt.colorbar()

plt.title("Heatmap with Values")

plt.show()

Adjust color='white' or 'black' depending on background contrast.

Horizontal and vertical axis labels

import matplotlib.pyplot as plt

import numpy as np

data = np.random.rand(5, 5)

plt.imshow(data, cmap='viridis')

plt.colorbar()

plt.xticks(range(5), ['A','B','C','D','E'])

plt.yticks(range(5), ['W','X','Y','Z','V'])

plt.title("Heatmap with Axis Labels")

plt.show()

matplotlib heatmap chart axislabels example

Aspect ratio and grid

import matplotlib.pyplot as plt

import numpy as np

data = np.random.rand(5, 5)

plt.imshow(data, cmap='plasma', aspect='auto')

plt.colorbar()

plt.title("Heatmap with Custom Aspect")

plt.show()

aspect='equal' → square cells
aspect='auto' → fills plot area

Add gridlines using plt.grid() if desired (less common).

matplotlib heatmap chart custom aspect example

Using `pcolormesh` for heatmaps

pcolormesh allows non-uniform grids and finer control:

import matplotlib.pyplot as plt

import numpy as np

X, Y = np.meshgrid(np.arange(6), np.arange(6))

Z = np.random.rand(6, 6)

plt.pcolormesh(X, Y, Z, cmap='coolwarm', shading='auto')

plt.colorbar()

plt.title("Heatmap with pcolormesh")

plt.show()

matplotlib heatmap chart pcolormesh example

Using pandas DataFrame directly

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

df = pd.DataFrame(np.random.rand(4, 4), columns=list('ABCD'), index=list('WXYZ'))

plt.imshow(df, cmap='viridis')

plt.colorbar()

plt.xticks(range(df.shape[1]), df.columns)

plt.yticks(range(df.shape[0]), df.index)

plt.title("Heatmap from DataFrame")

plt.show()

matplotlib heatmap chart pandas dataframe example

Heatmaps

Matplotlib Basics

1 min read