How to Create the Elegant Calendar Plot in Python?

Calendar maps are a pretty cool way to depict the day-by-day variation in a dataset over a longer period, usually a year.

GitHub is possibly one place where I am sure you must have seen them.

I always wondered how one can create them in Python.

Turns out, there’s a pretty simple way to do it just two lines of Python using Plotly Calplot.

To create a calendar map, we can use the calplot() method from this library.

The input should be a Pandas DataFrame. Each row should represent a date and a corresponding value we wish to depict in the calendar plot, as depicted below:

Done!

Next, we can create a calendar plot as follows:

It’s pretty clear that this plot reveals many insights that otherwise would have been difficult to extract with traditional plots or aggregation methods.

• This team took a one-month break in April and December. Or, if they are a remote team, they may have met in person during these two months.

• They were pretty consistent on a 5-day work policy.

• August is when they worked the most.

One of the best use cases of calendar plots is to understand weekly/monthly seasonality in data.

I prepared this Jupyter Notebook for you to get started: Calendar Plot Notebook.

Also, do check out an earlier issue on “8 Classic Alternatives to Traditional Plots”

I will give many ideas on how to replace traditional plots with better plots, and under which situations.

👉 Over to you: What other usages of calendar plots have I missed?

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