How to Create a Calendar Plot in Python?

A richer alternative to line plot.

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

You must have seen them on GitHub:

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.

Here's an earlier issue on ​8 Classic Alternatives to Traditional Plots →​

I will give many ideas on replacing 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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P.S. For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?

• Drive revenue?

• Can you scale ML models?

• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1.

• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here: Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring – Part 1.

• Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware.

• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.

• Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1

• Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.

• Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

• Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.

All these resources will help you cultivate key skills that businesses and companies care about the most.

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