Libraries & Dataset
First, we need to load a few libraries:
- seaborn for the heatmap
- matplotlib for chart customization
- pandas for data manipulation
numpyfor data generation
# library
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Create a dataset
df = pd.DataFrame(
np.random.random((5,5)),
columns=["a","b","c","d","e"]
)Wide Format (Untidy)
The wide format (or the untidy format) is a matrix where each row is an individual, and each column is an observation. In this case, the heatmap makes a visual representation of the matrix: each square of the heatmap represents a cell. The color of the cell changes according to its value.
In order to draw a heatmap with a wide format dataset, you can use the heatmap() function of seaborn.
# Default heatmap: just a visualization of this square matrix
sns.heatmap(df)
plt.show()
Correlation Matrix (Square)
Suppose you measured several variables for n individuals. A common task is to check if some variables are correlated. You can easily calculate the correlation between each pair of variable, and plot this as a heatmap. This lets you discover which variable is related to the other.
As a difference from the previous example, you will give a correlation matrix as an input instead of a wide format data.
# Calculate correlation between each pair of variable
corr_matrix=df.corr()
# plot it
sns.heatmap(corr_matrix, cmap='PuOr')
plt.show()
Note that in this case, both correlations (i.e. from a to b and from b to a) will appear in the heatmap. You might want to plot a half of the heatmap using mask argument like this example:
# Calculate correlation between each pair of variable
corr_matrix=df.corr()
# Can be great to plot only a half matrix
# Generate a mask for the upper triangle
mask = np.zeros_like(corr_matrix)
mask[np.triu_indices_from(mask)] = True
# Draw the heatmap with the mask
sns.heatmap(corr_matrix, mask=mask, square=True)
plt.show()
Long Format (Tidy)
In the tidy or long format, each line represents an observation. You have 3 columns: individual, variable name, and value (x, y and z). You can plot a heatmap from this kind of data as follow:
# Create long format
df = pd.DataFrame({
'feature': list(range(1,6))*5,
'people': np.repeat(("A","B","C","D","E"),5),
'value': np.random.random(25)
})
# Turn long format into a wide format
df_wide = df.pivot_table(
index='people',
columns='feature',
values='value'
)
# plot it
sns.heatmap(df_wide)
plt.show()
Going further
This post explains how to create a heatmap with matplotlib and seaborn.
You might be interested by:
- how to customize heatmaps
- how to control colors in heatmap
- use heatmap with clustering and dendograms
