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Pandas has long been the go-to library when it comes to data. However, I'm pretty sure most of you will have already experienced the agony of sitting for hours while our Pandas try to deal with large DataFrames.
For those who have followed recent developments in Python, it's hard to miss the rumors around Polars, a robust data framework library developed specifically for evaluating large data sets.
So today I'll try to dive deeper into the key technical distinctions between these two data frame libraries, examining their respective strengths and limitations.
First things first, why all this obsession with comparing Pandas and Polars libraries?
Unlike other libraries designed for large data sets, such as Spark or Ray, Polars is designed exclusively for use on a single machine, leading to frequent comparisons with pandas.
However, Polars and Pandas differ significantly in their approach to data management and their ideal use cases.
The secret behind the impressive performance of Polar is based on 4 main reasons:
1. Oxidation increased efficiency
In stark contrast to Pandas, which is based on Python libraries like NumPy, Polars is built with Rust. This low-level language, known for its fast performance, can be compiled into machine code without the use of an interpreter.
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This foundation gives Polars a substantial advantage, particularly in handling data types that are challenging for Python.
2. Eager and lazy execution options
Pandas follows a keen 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 schedule and potentially rearrange the order of operations, eliminating any unnecessary steps.
This is in contrast to Pandas, which can process an entire DataFrame before applying filters.
For example, when calculating the mean of a column for certain categories, Polars would first apply the filter and then perform the grouping operation, optimizing the process for efficiency.
3. Parallelization of processes
According to the Polars User Guide, its main purpose is to:
“Provide a lightning-fast DataFrame library that uses all the available cores on your machine.”
Another benefit of Rust's design is its support for safe concurrency, ensuring predictable and efficient parallelism. This feature allows Polars to fully utilize the multiple cores of a machine for complex applications.
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Consequently, Polars significantly outperforms Pandas, which is limited to single-core operations.
4. Expressive APIs
Polars has a very versatile API that allows you to execute practically all the desired tasks using its methods. In comparison, performing complex tasks in Pandas often requires using the app method along with lambda expressions within your app method.
This approach, however, has a disadvantage: it iteratively processes each row of the DataFrame and performs the operation sequentially.
In contrast, Polars' ability to use inherent methods facilitates column-level operations, taking advantage of a different type of parallelism known as SIMD (Single Instruction, Multiple Data).
Are polar bears 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 have over Pandas lies in their speed, especially with large data sets. For those handling extensive data processing tasks, exploring the polar zones 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 machine learning and data visualization libraries, such as scikit-learn and PyTorch, limits its applicability in these fields.
There is an ongoing discussion about integrating Python's data frame exchange protocol into these packages to support various data frame libraries.
This development could streamline data science and machine learning processes, which currently rely on Pandas, but it is a relatively new concept and will take time to implement.
Both pandas and polar bears have their unique strengths and limitations. Pandas remains the go-to library for data exploration and machine learning integration, while Polars excels at its performance in large-scale data transformations.
Understanding the capabilities and optimal applications of each library is key to effectively navigating the changing landscape of Python data frames.
With all this knowledge, you are probably interested in experimenting with Polares.
As data scientists and Python enthusiasts, adopting both tools can improve our workflows, allowing us to leverage the best of both worlds in our data-driven efforts.
With the continued development of these libraries, we can expect even more refined and efficient ways of handling data in Python.
Joseph Ferrer He is an analytical engineer from Barcelona. He graduated in physical engineering and currently works in the field of Data Science applied to human mobility. He is a part-time content creator focused on data science and technology. You can contact him at LinkedIn, Twitter either Half.
