nan_to_null=True stops converting nans to nulls after the dataframe grows to a certain size. This did not happen in polars==0.20.10 Expected behavior nan_to_null=True should behave exactly as pl.from_pandas().with_columns(cs.float().fill_nan(None)) Installed versions ---Version info...
The library also enables easy writing to parquet files. Why might you want to convert models to schemas? One scenario is for a data processing pipeline: Import / extract the data from its source Validate the data using pydantic Process the data in pyarrow / pandas / polars Store the raw ...
pip install polars (if you want to run the samples / tests). At the lowest level you can interact with the results using pyArrow or any tools which can work with the Apache Arrow ecosystem e.g. Pandas, Polars, Duckdb, Vaex etc.. If you are using poetry this will be taken care of ...
mdpd is a simpler tool for convert markdown table to pandas. This tool is a lightweight tool for testing a code, so note that we are not validating the user's input. install pip install mdpd usage import mdpd df = mdpd.from_md(""" +---+---+ | id | score | +---+---+ |...
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations