Polars: Lightning-fast DataFrame library for Rust and Python

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • db-benchmark

    reproducible benchmark of database-like ops

    Hmmm .. in the linked benchmarks [1], DataFrames.jl (Julia library) appears to be fairly competitive.

    [1] https://h2oai.github.io/db-benchmark/

  • dataframe-api

    RFC document, tooling and other content related to the dataframe API standard

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  • dtplyr

    Data table backend for dplyr

  • explorer

    Series (one-dimensional) and dataframes (two-dimensional) for fast data exploration in Elixir (by elixir-nx)

    I've been working on a dataframe library for Elixir that's built on top of Polars and that's heavily influenced by dplyr if you're interested in checking it out: https://github.com/elixir-nx/explorer

  • polars

    Fast multi-threaded DataFrame library in Rust | Python | Node.js

  • db-benchmark

    reproducible benchmark of database-like ops (by oscardssmith)

    Can you make a PR to https://github.com/oscardssmith/db-benchmark? I don't know DuckDB, so I don't know what the change would be.

  • vaex

    Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀

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  • Datamancer

    A dataframe library with a dplyr like API

  • dataiter

    Python classes for data manipulation

    Agreed, dplyr is great.

    I built my own data frame implementation on top of NumPy specifically trying to accomplish a better API, similar to dplyr. It's not exactly the same naming or operations, but should feel familiar and much simpler and consistent than Pandas. And no indexes or axes.

    Having done this, a couple notes on what will unavoidably differ in Python

    * It probably makes more sense in Python to use classes, so method chaining instead of function piping. I wish one could syntactically skip enclosing parantheses in Python though, method chains look a bit verbose.

    * Python doesn't have R's "non-standard evaluation", so you end up needing lambda functions for arguments in method chains and group-wise aggregation etc. I'd be interested if someone has a better solution.

    * NumPy (and Pandas) is still missing a proper missing value (NA). It's a big pain to try to work around that.

    https://github.com/otsaloma/dataiter

  • minimal-pandas-api-for-polars

    pip install minimal-pandas-api-for-polars

    https://github.com/austospumanto/minimal-pandas-api-for-pola...

    pip install minimal-pandas-api-for-polars

    I wrote a library that wraps polars DataFrame and Series objects to allow you to use them with the same syntax as with pandas DataFrame and Series objects. The goal is not to be a replacement for polars' objects and syntax, but rather to (1) Allow you to provide (wrapped) polars objects as arguments to existing functions in your codebase that expect pandas objects and (2) Allow you to continue writing code (especially EDA in notebooks) using the pandas syntax you know and (maybe) love while you're still learning the polars syntax, but with the underlying objects being all-polars. All methods of polars' objects are still available, allowing you to interweave pandas syntax and polars syntax when working with MppFrame and MppSeries objects.

    Furthermore, the goal should always be to transition away from this library over time, as the LazyFrame optimizations offered by polars can never be fully taken advantage of when using pandas-based syntax (as far as I can tell). In the meantime, the code in this library has allowed me to transition my company's pandas-centric code to polars-centric code more quickly, which has led to significant speedups and memory savings even without being able to take full advantage of polars' lazy evaluation. To be clear, these gains have been observed both when working in notebooks in development and when deployed in production API backends / data pipelines.

    I'm personally just adding methods to the MppFrame and MppSeries objects whenever I try to use pandas syntax and get AttributeErrors.

  • https://github.com/austospumanto/minimal-pandas-api-for-pola...

    pip install minimal-pandas-api-for-polars

    I wrote a library that wraps polars DataFrame and Series objects to allow you to use them with the same syntax as with pandas DataFrame and Series objects. The goal is not to be a replacement for polars' objects and syntax, but rather to (1) Allow you to provide (wrapped) polars objects as arguments to existing functions in your codebase that expect pandas objects and (2) Allow you to continue writing code (especially EDA in notebooks) using the pandas syntax you know and (maybe) love while you're still learning the polars syntax, but with the underlying objects being all-polars. All methods of polars' objects are still available, allowing you to interweave pandas syntax and polars syntax when working with MppFrame and MppSeries objects.

    Furthermore, the goal should always be to transition away from this library over time, as the LazyFrame optimizations offered by polars can never be fully taken advantage of when using pandas-based syntax (as far as I can tell). In the meantime, the code in this library has allowed me to transition my company's pandas-centric code to polars-centric code more quickly, which has led to significant speedups and memory savings even without being able to take full advantage of polars' lazy evaluation. To be clear, these gains have been observed both when working in notebooks in development and when deployed in production API backends / data pipelines.

    I'm personally just adding methods to the MppFrame and MppSeries objects whenever I try to use pandas syntax and get AttributeErrors.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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