⏱ Quick start
If you're in a rush and want to make a heatmap with
Python as quick as possible, have a look to this code snippet that uses the
heatmap() function of
# 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 p1 = sns.heatmap(df)
Seaborn is a python library allowing to make better charts easily thanks to its
heatmap() function. This section starts with a post describing the basic usage of the function based on any kind of data input. Next it will guide you through the different ways to customize the chart, like controling color and data normalization.
⚠️ Python heatmap and normalization
Consider the left heatmap below. The second column from the left (
variable 1) has very high values compared to others. As a result, the variation existing in other variables is hidden.
variable 1 can be the main message of your chart. But if you're interested in other variable variations as well, you probably want to apply some normalization as shown on the right heatmap.
❄ Python, Heatmap and Clustering
It is very common to apply some
clustering techniques on a heatmap. The idea is to group items that have the same kind of pattern for their numeric variables. 💡
Usually, it is recommended to display a
dendrogram on top of the heatmap to explain how the clusterization has been performed. Last but not least, it can be useful to compare the grouping we got with an expected structure, shown as an additional color.
Heatmap for timeseries
A heatmap can be used to display some temporal data. Here is an example using matplotlib where the evolution of a temperature is displayed over the hour of the day (Y axis) and the day of the year (X axis) organized by month.