Pandas vs. Polars: A Comparative Analysis of Python's Dataframe Libraries
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Pandas has long been the go-to library when dealing with data. However, I am pretty sure most of you might have already experienced the agony of sitting for hours while our Pandas try to deal with big DataFrames.

For those who have followed the recent developments in Python, it’s hard to miss the buzz around Polars, a robust dataframe library specifically developed to assess large datasets.

So today I will try to delve into the key technical distinctions between these two dataframe libraries, examining their respective strengths and limitations.



First things first, why all this obsession to compare Pandas and Polars libraries?

Distinct from other libraries tailored for large datasets, like Spark or Ray, Polars is uniquely crafted for single-machine use, leading to frequent comparisons with pandas. 

Yet, Polars and pandas diverge significantly in their approach to data handling and their ideal use cases. 

The secret behind Polars’ impressive performance relies on 4 main reasons:


1. Rust boosted efficiency


In stark contrast to Pandas, which is grounded in Python libraries like NumPy, Polars is built using Rust. This low-level language, renowned for its rapid performance, can be compiled into machine code without the use of an interpreter. 


Pandas vs. Polars: A Comparative Analysis of Python's Dataframe Libraries
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Such a foundation provides Polars with a substantial advantage, particularly in managing data types that are challenging for Python. 


2. Eager and lazy execution options


Pandas follows an eager execution model, processing operations as they are coded, while Polars provides both eager and lazy execution options. 

Polars uses a query optimizer in its lazy execution to efficiently plan and potentially reorganize the order of operations, eliminating any unnecessary steps. 

This is in contrast to Pandas, which might process an entire DataFrame before applying filters. 

For example, in calculating the mean of a column for certain categories, Polars would first apply the filter and then perform the group-by operation, optimizing the process for efficiency. 


3. Parallelization of the processes


According to the Polars User Guide, its main aim is: 


“To provide a lightning-fast DataFrame library that utilizes all available cores on your machine.”


Another benefit of Rust’s design is its support for safe concurrency, ensuring predictable and efficient parallelism. This feature enables Polars to fully utilize a machine’s multiple cores for complex. 


Pandas vs. Polars: A Comparative Analysis of Python's Dataframe Libraries
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Consequently, Polars significantly outperforms Pandas, which is limited to single-core operations. 


4. Expressive APIs


Polars boasts a highly versatile API, enabling virtually all desired tasks to be executed using its methods. In comparison, performing intricate tasks in pandas frequently requires using the apply method coupled with lambda expressions within its apply method.

This approach, however, has a downside: it iteratively processes each row of the DataFrame, performing the operation sequentially.

Conversely, Polars’ capability to utilize inherent methods facilitates operations at the column level, leveraging a distinct parallelism type known as SIMD (Single Instruction, Multiple Data).



Is Polars superior to Pandas? Could it potentially supplant Pandas in the future?

As always, it mainly depends on the use case. 

The main advantage that Polars has over Pandas lies in its speed, particularly with large datasets. For those handling extensive data processing tasks, exploring Polars is highly recommended.

While Polars excels in data transformation efficiency, it falls short in areas like data exploration and integration into machine learning pipelines, where Pandas remains superior. 

Polars’ incompatibility with most Python data visualization and machine learning libraries, such as scikit-learn and PyTorch, limits its applicability in these fields.

There’s an ongoing discussion about integrating the Python dataframe interchange protocol across these packages to support diverse dataframe libraries. 

This development could streamline data science and machine learning processes, currently reliant on Pandas, but it’s a relatively new concept and will require time for implementation.



Both Pandas and Polars have their unique strengths and limitations. Pandas continues to be the go-to library for data exploration and machine learning integration, while Polars stands out for its performance in large-scale data transformations. 

Understanding the capabilities and optimal applications of each library is key to navigating the evolving landscape of Python data frames effectively.

With all these insights, you’re likely keen to experiment with Polars yourself!

As data scientists and Python enthusiasts, embracing both tools can enhance our workflows, allowing us to leverage the best of both worlds in our data-driven endeavors. 

With the continued development of these libraries, we can expect even more refined and efficient ways of handling data in Python.


Josep Ferrer is an analytics engineer from Barcelona. He graduated in physics engineering and is currently working in the Data Science field applied to human mobility. He is a part-time content creator focused on data science and technology. You can contact him on LinkedIn, Twitter or Medium.

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