Data Analysis with Polars in Python

Over the last decade Python has become more utilized in Data Pipelines. However, most pipelines faced performance issues when processing large datasets in Python. This limitation hindered Python’s ability to manage “Big Data”.

But in recent years, Polars unlocked the door to processing large datasets with its high performance data structures. It uses parallel processing to quickly read data into DataFrames and Series.

And its performance doesn’t stop there! Not only can Polars read and write data quickly, it can also manipulate vast amounts data faster than Pandas.

FAQs

Q: Is the switch from Pandas difficult?

A: No. The basic concepts are the same. There are definitely differences between the two libraries, but functionality between the two are very similar. If you can do it in Pandas, you can do it in Polars!

Q: I’m already learning Pandas, would you say I’m wasting my time?

A: No. My first exposure to DataFrames was using Pandas. Many of the concepts I learned in Pandas helped me understand Polars. They are definitely different in terms of performance. Pandas may at some point release a faster version, but as for now Polars is much faster when working with large datasets.

Q: Pandas has integrations with many more libraries than Polars. Won’t I be missing out on these if I make the switch?

A: Absolutely not. Its true that Polars does not have as many integrations with other python libraries, but switching from a polars DataFrame to a Pandas DataFrame is easy. Polars has a function that allows you to convert to and from a Pandas DataFrame. This allows you to get the performance of Polars while also getting the integrations of Pandas. Other libraries have also begun to build integrations with Polars so that may change altogether.

Q: What kind of bear is best? 

A: There are basically two schools of thought… Pandas and Polars are indeed competing DataFrame libraries. Its probably for you to decide the answer to this question!