Developer Shares Practice of Using Polars for Fast A-Share K-Line Data Processing

On 2026-11-11 a developer posted a hands-on walkthrough on Juejin describing how to use Polars instead of pandas to process A-share K-line data quickly. The data comes from the AlphaFeed market data library: with to_dataframe=True it returns a pandas DataFrame, which can then be converted with pl.from_pandas(). Polars runs on a Rust kernel, stores data column-wise, and supports multithreading plus lazy execution, so the author reports that aggregations, rolling windows, and grouping across multiple instruments run faster, with the gains most visible in full-market batch jobs.

The post includes a real, working example: on 120 daily bars, a Polars aggregation produced avg=9827.46, and rolling_mean(5) yielded MA5. For integration, from_pandas requires pyarrow, which is also needed when converting object-typed columns, and the analysis baseline uses post-adjusted prices (后复权). Lazy evaluation can be adopted by chaining operations through pl.LazyFrame or .lazy() and letting collect() optimize and execute the whole pipeline, which suits larger processing pipelines.

On how much faster Polars is than pandas, the author is explicit that it depends on the scenario: on small datasets the two are roughly comparable, while for aggregations, grouping, and window computations across several thousand instruments covering the whole market, Polars is typically several times faster and uses less memory. The exact gain depends on data size and operation types, so the author advises benchmarking on your own data. Limits are spelled out as well: Polars is a third-party library and its API follows the official documentation; AlphaFeed only supplies market data; and raw lists can be used to build a Polars DataFrame directly, so going through pandas is not mandatory.