"Polars expressions and contexts let you build clear, optimized query pipelines without mutating your original data. LazyFrames with query optimization in Polars can outperform pandas for grouped and aggregated workloads. Streaming in Polars enables processing datasets that don't fit in memory, which pandas can't handle natively. .to_pandas() and from_pandas() let you convert between DataFrame formats, and Narwhals offers a library-agnostic API. Built-in plotting uses Altair for Polars and Matplotlib for pandas, allowing quick visualization directly from DataFrames."
"To get the most out of this tutorial, it's recommended that you already have a basic understanding of how to work with both pandas and Polars DataFrames, as well as Polars LazyFrames. To complete the examples in this tutorial, you'll use various tools and the Python REPL. You'll use the command line to run some scripts that time your code and reveal how pandas and Polars compare. You'll also take advantage of the plotting capabilities of Jupyter Notebook."
Polars uses expression-based APIs and contexts to build clear, optimized query pipelines that avoid mutating original data. LazyFrame query optimization accelerates grouped and aggregated workloads, often outperforming pandas. Streaming in Polars enables processing datasets that exceed available memory, a capability pandas lacks natively. Data conversion is supported via .to_pandas() and from_pandas(), and Narwhals provides a library-agnostic API for interoperability. Built-in plotting leverages Altair for Polars and Matplotlib for pandas, enabling quick visualization from DataFrames. Example workflows use Parquet-formatted retail data, the Python REPL, command-line timing scripts, and Jupyter Notebook for plotting.
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