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\"grizzlys\")\n\n\u003chr\u003e\n\n\u003cdiv style=\"text-align: center;\"\u003e\n\n[![Code style: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json\u0026label=Formatter)](https://github.com/charliermarsh/ruff)\n[![Linting: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json\u0026label=Linter)](https://github.com/charliermarsh/ruff)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)\n\n\u003c/div\u003e\n\n# grizzlys: User-friendly Python DataFrames powered by Julia\n\n**grizzlys** is a Python package that provides a native interface on top of Julia's popular\n[___DataFrames.jl___](https://github.com/JuliaData/DataFrames.jl) package.\n\nAs a user-friendly alternative to existing Python packages such as __pandas__ and __polars__, it is designed to be a\nconvenient \u0026 easy to use DataFrames tool for data analysts, data engineers and data scientists alike, while still\nproviding high performance and abstractions, thanks to Julia's high-performance computing capabilities.\n\n## Why you might consider using grizzlys\n\n✅ You are transitioning into Python from a **Julia** or **R** programming background\n\n✅ You are accustomed to working with **Jupyter notebooks** (or a REPL) and performing exploratory data\nanalysis **(EDA)** on-the-fly\n\n✅ You need a quick-and-dirty data wrangling tool that provides readymade **macros** and **convenience\nfunctions** out of the box\n\n✅ You work with **statistics** or **linear algebra** often and require a wide range of\nstatistical/algebraic functions to be well-integrated with your DataFrames\n\n## What is grizzlys (currently) NOT well-suited for\n\n❌ __Larger-than-memory datasets__ - grizzlys' current implementation relies on data being stored in-memory, and therefore\nit is not a good choice if you work with datasets that don't fit in your machine's RAM.\n\nFor such cases, using [__Polars__](https://github.com/pola-rs/polars) or\n[__Dask DataFrames__](https://docs.dask.org/en/stable/dataframe.html) would be a much better choice as of now.\n\n❌ __Lazy Evaluation__ - Similar to the above, grizzlys is currently designed to be fully eager, which means it always\nimmediately executes your code, as opposed to building a task/computation graph or thereabout and delaying execution\nuntil it's needed.\n\n❌ __Backwards compatibility__ - grizzlys is based on a relatively new programming language in Julia, and is developed\nusing an advanced version of Python, with little regard to end-of-life versions or any compatibility with Python 2.7,\nfor example.\n\nYou should therefore not rely on grizzlys for integrations with very old code or any other legacy/deprecated tools and\nimplementations.\n\n❌ __Best-in-class Performance__ - Though Julia is widely considered a very high-performance language (it is actually a\nmajor reason why it's used under the hood here), grizzlys is still a work-in-progress (WIP) and therefore does not\ncurrently aim to compete with, or outperform, other high-performance DataFrame libraries, such as\n[__Polars__](https://github.com/pola-rs/polars) (written in Rust) or\n[__Modin__](https://github.com/modin-project/modin) (Multi-threaded pandas).\n\nThis, of course, might no longer be a limitation in the future, as __grizzlys__ will have undergone optimizations and\nmaturation.\n\n\u003chr\u003e\n\n[Go to Top](#grizzlys-user-friendly-python-dataframes-powered-by-julia)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhadarsharon%2Fgrizzlys","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhadarsharon%2Fgrizzlys","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhadarsharon%2Fgrizzlys/lists"}