Heatmaps with various input formats


This post explains how to make heatmaps using seaborn. Three main types of input exist to plot a heatmap: wide format, correlation matrix, and long format.

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.

# library
import seaborn as sns
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"])
 
# Default heatmap: just a visualization of this square matrix
sns.heatmap(df)
<AxesSubplot:>

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.

# library
import seaborn as sns
import pandas as pd
import numpy as np
 
# Create a dataset
df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])
 
# Calculate correlation between each pair of variable
corr_matrix=df.corr()
 
# plot it
sns.heatmap(corr_matrix, cmap='PuOr')
<AxesSubplot:>

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:

# library
import seaborn as sns
import pandas as pd
import numpy as np
np.random.seed(0)
 
# Create a dataset
df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])

# 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)
<AxesSubplot:>

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:

# library
import seaborn as sns
import pandas as pd
import numpy as np
 
# Create long format
people = np.repeat(("A","B","C","D","E"),5)
feature = list(range(1,6))*5
value = np.random.random(25)
df = pd.DataFrame({'feature': feature, 'people': people, 'value': value })

# Turn long format into a wide format
df_wide = df.pivot_table( index='people', columns='feature', values='value')

# plot it
sns.heatmap(df_wide)
<AxesSubplot:xlabel='feature', ylabel='people'>

Scatterplot

Heatmap

Correlogram

Bubble

Connected Scatter

2D Density

Contact & Edit

👋 This document is a work by Yan Holtz. Any feedback is highly encouraged. You can fill an issue on Github, drop me a message onTwitter, or send an email pasting yan.holtz.data with gmail.com.

This page is just a jupyter notebook, you can edit it here. Please help me making this website better 🙏!

Violin

Density

Histogram

Boxplot

Ridgeline

Scatterplot

Heatmap

Correlogram

Bubble

Connected Scatter

2D Density

Barplot

Spider / Radar

Wordcloud

Parallel

Lollipop

Circular Barplot

Treemap

Venn Diagram

Donut

Pie Chart

Dendrogram

Circular Packing

Line chart

Area chart

Stacked Area

Streamgraph

Map

Choropleth

Hexbin

Cartogram

Connection

Bubble

Chord Diagram

Network

Sankey

Arc Diagram

Edge Bundling

Colors

Interactivity

Animation with python

Animation

Cheat sheets

Caveats

3D