From: hu-po

Polars is a data frame library for Python that is designed to be lightning fast for tabular data operations [00:00:08]. Traditionally, the most popular framework for dealing with tabular data in Python has been Pandas, widely used in data science and machine learning workflows [00:00:19]. Polars aims to be a faster alternative to Pandas [00:00:31].

What is Polars?

Polars is written in Rust, a systems programming language known for its focus on safety, performance, and concurrency [00:00:32][00:01:10]. Rust aims to provide the speed and control of low-level languages while preventing common programming errors [00:01:12].

Key characteristics and goals of Polars include:

  • Performance: It is designed to be memory-efficient and fast, making it a good choice for data processing tasks [00:01:30]. It leverages all available CPU cores, optimizing queries to reduce unneeded work and memory allocation [00:02:11].
  • Rust Backend: Being written in Rust gives Polars C/C++-like performance [00:02:47]. It works to reduce random copies, traverse memory cache efficiently, minimize contention, and process data in chunks [00:02:51].
  • Apache Arrow: Polars uses Apache Arrow, an in-memory columnar data format, to transmit Rust creates [00:01:47][00:01:56].
  • Lazy and Eager Execution: Polars supports both eager and lazy (or semi-lazy) execution [00:03:10]. Eager execution allows for line-by-line code execution, similar to a Python script [00:03:22]. Lazy execution builds a query plan that is optimized and reordered before execution, providing the entire context of the query for Polars to choose the fastest algorithm [00:03:56][00:07:08]. This is similar to how modern deep learning frameworks (like Jax, PyTorch, and TensorFlow) optimize computational graphs over time [00:03:36].

Polars vs. Pandas: A Direct Comparison

Installation and Setup

Installation of Polars is straightforward, typically requiring only pip install polars [00:05:24]. It generally does not install a large number of additional dependencies [00:05:29].

API Similarity

Polars offers a data frame API very similar to Pandas [00:01:25]. This similarity makes transitioning from Pandas to Polars relatively easy for users familiar with Pandas [00:07:00]. Many common Pandas functions like .head(), .tail(), .sample(), and .describe() have direct equivalents in Polars [00:19:35]. Even complex operations like filtering and grouping often use very similar syntax, though minor differences exist (e.g., .is_between vs. .between for date filtering) [00:15:07][00:25:57]. ChatGPT can often assist in rewriting Pandas code for Polars [00:14:49][00:36:43].

Performance Benchmarks

Polars is marketed as one of the best-performing data frame solutions available [00:04:10]. Benchmarks often show Polars significantly outperforming Pandas, especially with larger datasets.

CSV Reading

For reading CSV files, Polars is generally faster than Pandas [00:10:51]. While the speed difference can be marginal for small files, Polars consistently shows an edge [00:15:40].

Data Filtering and Aggregation

When performing filtering and grouping operations (e.g., filtering by column value and then grouping by a categorical variable), Polars demonstrates a significant speed improvement over Pandas [00:15:47].

  • CPU Utilization: A key factor in Polars’ performance advantage is its ability to utilize multiple CPU cores [00:29:32]. Pandas is largely single-threaded, meaning it often only uses one CPU at a time, leading to lower overall CPU utilization during computations [00:29:08][00:35:09]. Polars, by contrast, actively utilizes multiple CPU cores, leading to substantial speed-ups [00:29:32][00:35:21].
  • Lazy Execution Impact: Using Polars’ lazy API (by adding .lazy() to queries and ending with .collect()) can provide further performance boosts, sometimes doubling the speed, especially for larger datasets [00:16:04][00:17:10][00:18:27].

Data Joining

Joining two data frames also highlights Polars’ performance advantage [00:30:35]. Similar to filtering, Polars’ multi-threaded execution allows it to process joins much faster than Pandas [00:35:17].

Conclusion

Polars generally offers a significant speed boost compared to Pandas, particularly for complex data manipulations like filtering, grouping, and joining, and when dealing with larger datasets [00:15:58]. This performance advantage is largely due to its Rust backend and intelligent utilization of all available CPU cores [00:29:57][00:36:41]. While the APIs are very similar, making the transition easier, Polars’ lazy execution model provides additional optimization capabilities [00:17:02].