"Narwhals is intended for Python library developers who need to analyze DataFrames in a range of standard formats, including Polars, pandas, DuckDB, and others. It does this by providing a compatibility layer of code that handles any differences between the various formats. In this tutorial, you'll learn how to use the same Narwhals code to analyze data produced by the latest versions of two very common data libraries. You'll also discover how Narwhals utilizes the efficiencies of your source data's underlying library when analyzing your data."
"Furthermore, because Narwhals uses syntax that is a subset of Polars, you can reuse your existing Polars knowledge to quickly gain proficiency with Narwhals. The table below will allow you to quickly decide whether or not Narwhals is for you: Whether you're wondering how to develop a Python library to cope with DataFrames from a range of common formats, or just curious to find out if this is even possible, this tutorial is for you."
Narwhals targets Python library developers needing to analyze DataFrames across formats such as Polars, pandas, and DuckDB. Narwhals provides a compatibility layer that abstracts differences between DataFrame implementations and leverages the efficiencies of the source library during analysis. Narwhals uses syntax that is a subset of Polars, enabling reuse of existing Polars expressions and contexts knowledge without replacing Polars. Installation requires Narwhals and the DataFrame libraries to be used, along with sample data such as a presidents Parquet file containing six fields describing U.S. presidents. An interactive quiz assesses knowledge and provides a completion score to track learning progress.
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