Principle:Eventual Inc Daft Pandas Export
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Interoperability |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Technique for converting a Daft DataFrame to a pandas DataFrame for interoperability.
Description
Pandas export materializes a Daft DataFrame and converts it to a pandas DataFrame, enabling integration with the broader pandas ecosystem including visualization libraries (matplotlib, seaborn), ML frameworks (scikit-learn), and any other tool that expects pandas input. The conversion supports an optional flag to coerce temporal columns to nanosecond precision for compatibility with older pandas behavior.
Usage
Use pandas export when you need to pass data to libraries or workflows that expect pandas DataFrames. Common scenarios include visualization, ML model training with scikit-learn, and interoperating with legacy pandas-based code.
Theoretical Basis
Columnar-to-columnar conversion through Apache Arrow as intermediate representation:
Daft DataFrame
-> collect() (materialize all partitions)
-> Convert internal representation to Arrow format
-> pyarrow.Table.to_pandas()
-> pandas.DataFrame
The Arrow intermediate step enables efficient zero-copy conversion where possible, minimizing memory overhead during the export process. The coerce_temporal_nanoseconds flag controls whether temporal types are coerced to nanosecond precision, which is necessary for compatibility with pandas versions prior to 2.0.