Heatmaps

Matplotlib Basics

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Published Oct 5 2025, updated Aug 17 2026

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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 pltimport numpy as np​# 5x5 matrix of random valuesdata = 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.


matplotlib heatmap chart basic example




Adjusting colourmaps

import matplotlib.pyplot as pltimport 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 pltimport 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 pltimport 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.


matplotlib heatmap chart values example




Horizontal and vertical axis labels

import matplotlib.pyplot as pltimport 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 pltimport 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 pltimport 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 pltimport numpy as npimport 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
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