FireDucks with Seaborn

Speed + Seamless third-party integration.

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FireDucks with Seaborn

We have talked about FireDucks a few times before.

For starters, while Pandas is the most popular DataFrame library, it is terribly slow.

• It only uses a single CPU core.

• It has bulky DataFrames.

• It eagerly executes code, which prevents any possible optimization.

FireDucks is a highly optimized, drop-in replacement for Pandas with the same API.

You just need to change one line of code → 𝐢𝐦𝐩𝐨𝐫𝐭 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝐩𝐚𝐧𝐝𝐚𝐬 𝐚𝐬 𝐩𝐝

Done!

One thing we haven’t covered yet is its seamless integration with third-party libraries like Seaborn.

Let’s look at a quick demo below (here’s the Colab notebook with code).

We start by installing it:

Next, we download a sample dataset to work with.

Moving on, we load this data with Pandas and FireDucks:

Creating a correlation heatmap on the Pandas DataFrame takes 4.36 seconds:

Doing the same on FireDucks DataFrame takes over 60% less time:

Technically, Seaborn doesn't recognize a FireDucks DataFrame.

However, because of the import-hook (via %load_ext fireducks.pandas), FireDucks can be integrated seamlessly with a third-party library like Seaborn that expects a Pandas DataFrame.

And everything while accelerating the overall computation.

We have several benchmarking before and in all tests, FireDucks comes out to be the fastest:

• Here's Pandas and FireDucks head-to-head on several common tabular operations under eager evaluation.

• Here's Pandas vs. cuDF (a GPU DataFrame library):

• And here's FireDucks vs. Pandas vs. DuckDB vs. Polars:

In all cases, FireDucks is the fastest.

You can find the Colab notebook for Seaborn integration here →

Thanks for reading!

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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) 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.