Handling large datasets in Python can be slow and resource-intensive with traditional tools like Pandas. Recently, Polars has gained attention for its speed and efficiency. This article compares how both libraries handle real-world data problems, highlighting the performance advantages of Polars.

Why Switch from Pandas to Polars

Pandas has been the go-to library for data manipulation in Python for years. It’s easy to use and works well with small to medium datasets. But as data grows into millions of rows, Pandas can slow down significantly. Operations like grouping, ranking, and window functions can take several seconds or more, mainly because Pandas executes tasks sequentially and relies heavily on Python loops.

Polars, on the other hand, is built in Rust and designed for speed. It uses Apache Arrow for data storage and supports parallel processing and lazy evaluation. This means Polars can prepare a query plan and execute multiple tasks concurrently across all CPU cores, making large data operations much faster. The library also simplifies some tasks, like ranking, by reducing the need for complex functions that slow things down.

Real-World Data Challenges: Ranking Users

A common task is ranking users based on their email activity. With Pandas, you would group data by user, count emails sent, and then assign a rank. However, to ensure each user has a unique rank, you need to be careful with how ties are handled. Using Pandas’ rank method with ‘dense’ can assign the same rank to users with equal email counts, which isn’t always desired. Instead, using ‘first’ as the method breaks ties based on the user’s position after sorting alphabetically, ensuring unique ranks.

Polars offers a more straightforward approach. It sorts the data by email count and user ID, then assigns a sequential row number as a rank. This avoids the overhead of the rank function altogether. When dealing with millions of records, this method can be 5 to 10 times faster than Pandas because it leverages parallel processing and reduces the number of data passes needed.

Finding Returning Customers with Cumulative Counts

Another common problem involves identifying users who made a second purchase within a specific time frame. In Pandas, this might involve using the cumcount() function combined with pivoting and window functions. These operations can become complex and slow with large datasets.

Polars simplifies this task by using its lazy evaluation model. It groups data by user, calculates cumulative counts of purchases, and then filters based on the time difference between first and second transactions. Since Polars processes data in a single, optimized chain, it can handle millions of records efficiently. This results in faster computation and easier code, making real-time analysis more feasible.

Overall, Polars is designed to handle big data tasks more efficiently than Pandas. Its ability to perform multiple operations in parallel and optimize query execution makes it a strong choice for data engineers and scientists working with large datasets. Switching to Polars can significantly reduce processing time and resource usage, opening new possibilities for data analysis and modeling.

Inspired by

• https://www.kdnuggets.com/using-polars-instead-of-pandas-performance-deep-dive

Sources

• addtoany.com
• pydata.org
• cprogramming.com
• rust-lang.org
• pola.rs