Why Polars Destroy Pandas in All Possible Ways for Data Scientists?
Source: tai_blog

Pandas needs no introduction  but this article will dive deep into answering the question of why Polars is better than Pandas (even the Author of Pandas agrees).   You might be aware of some basics like memory and speed improvements  but why? How does Polars do their magic to achieve such high speeds and less memory usage?   This article will provide all the reasons why Polars has an advantage over Pandas as well as what it is lacking in comparison (for now).   Lets jump right into it!   Clean APIThere are so many tricks and hacks you can do with Pandas that probably developers themselves are not aware. Daily usage is no different because If I gave you a piece of code in Pandas like this: data.iloc[:  2:] >= 4 and assuming you dont have hyperthymesia  you would not know what this code does. It is known that developers use Google and AI bots to produce code and do not know everything off the top of their heads  but the point here is different.   The functions that the library provides should be straightforward  clear and dedicated to one use.   That is what Polars provides with their excellent documentation  function names  and overall feel of the library stability. Their expressive API is one of the best parts of the library. It provides such a different insight into working with data that going from one framework to another takes a toll on brainpower and shifts the mindset completely.   Speed and memory optimizationThere are multiple reasons for this  and two main ones are Apache Arrow and Rust. Arrow is a language-independent columnar memory format for flat and hierarchical data  organized for efficient analytic operations. Pandas struggles to utilize this efficiently because of the legacy code and data type extensions internal to the library. Polars out of the box works with the Arrow format and hence achieves much higher speeds.   Polars underlying code is implemented in Rust  and since it is a compiled language  unlike Python  which is interpreted  it has a speed advantage again. That is not the only reason  besides that there is memory safety and concurrency  which is better handled in Rust.   Production codeGreat API brings us back to the point of whether some should be using either library in production  which is another advantage for Polars. Pandas is not stable enough to be used in production  as it has been shown for years and discussed in the community. Many changes and underlying legacy code give so many pain points that it is not worth going with Pandas.   DependenciesI want to point out some of the advantages of Pandas as well  and those are dependencies  which are  in this case  a sword with two edges. Although this provides us with a lot of integration with libraries like Seaborn and matplotlib to achieve even better results  we are stuck with Pandas and sometimes cant move away from the library.   As mentioned  Polars primarily depends on the Arrow data format  which provides a high-performance  in-memory columnar data structure. This reduced dependency chain contributes to Polars overall performance and flexibility  as it avoids potential compatibility issues and overhead associated with managing multiple external libraries.   CommunityThe dependency problem will be solved as the community grows over time in this direction of clean code and efficiency  but it takes time. That is another advantage for Pandas because it has existed for so long.   With an increasing number of developers and data scientists adopting Polars for their projects  the ecosystem is expanding at an accelerated pace. While Pandas has a significant head start  the momentum behind Polars suggests that it will quickly close the gap in community size  resources  and available tools  positioning itself as a strong competitor in the data manipulation landscape. Still  this time  we are going in the right direction.   Switching from Pandas to PolarsTransitioning from Pandas to Polars can be a smooth process for many users due to the similar DataFrame structure and familiar Python syntax. While there are differences in API and functionality  Polars performance benefits  especially for large datasets  often outweigh the initial learning curve. Many everyday Pandas operations have direct equivalents in Polars  and the growing community provides ample resources and support to aid in the migration. However  for complex workflows heavily reliant on Pandas-specific features  a gradual adoption approach or hybrid use of both libraries might be necessary.   ConclusionStarting your Data Science journey with Polars can be good  but you will discover that many Stackoverflow questions and discussion forums are still focused on Pandas. Getting the right mindset from the get-go is vital so that Polars can be very beneficial later on as the starting point.   Switching from Pandas to Polars is also great  so going with Polars right now would benefit the project and developers working on the code.   That is all for today! If you have any questions  please send them my way!