How to choose between Pandas and Polars for python data projects.
Pandas and NumPy have long been the open source dynamic duo of python data science.
They provide flexible data structures that can handle a wide range of data types including tabular data, time series data, and more.
They also provide a wide range of data analysis, data cleaning, data transformation, data aggregation, and data visualization tools.
And while they generally provide a performance boost over pure python implementations, they are still single threaded and so cannot take advantage of even moderately contemporary hardware.
This is being addressed by the Polars project which is a rust-based python library that leverages Apache Arrow and parallel procesing on multicore CPUs.
Polars is a library built in Rust and is designed to provide a fast and efficient data manipulation tool for large datasets.
It provides a user-friendly API that allows you to easily manipulate data, including data transformations, filtering, grouping, and aggregating.
Polars also provides a wide range of data visualization tools that can be used to create charts, graphs, and other types of visualizations.
Beyond Polars speed, its design also encourages a cleaner style.
Its focus on chaining/piping rather than the pandas default which is to create a bunch of intermediate variables in the code, leads to cleaner code and fewer side effects when working own notebook environments.
Polars has its performance increases baked in while pandas normally pushes you to find optimizable NumPy methods. This focus on optimization early in the process can leaded to unneeded complexity and other associated evils of pre-mature optimization.
With polars, the default approach is already optimized.
That said, there are still cases where you might need to push your Polars data into a Pandas data frame for some specific operation that Polars does not yet have.
Pandas and NumPy are still the king and queen, but Polars is steadily improving.
In conclusion, both Polars and Pandas are powerful data science libraries that are widely used in the field.
The choice between the two will depend on the specific needs of the data scientists and engineers, taking into consideration the size of the datasets, the complexity of the data, and the desired level of efficiency.
Both libraries have their advantages and disadvantages.
Polars is faster and more efficient than Pandas, but it not as battle tested and has a smaller community.
Pandas is more familiar and has a much larger community, but it is slower.
If you are working with large datasets and need a fast and efficient data manipulation tool, Polars may be the right choice for you.
If you are looking for a library that is easy to use and has a large community, Pandas may be a better choice.
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