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Bodo 2022.1 Release (Date: 1/31/2022)

This release includes many new features and usability improvements. Overall, 71 code patches were merged since the last release.

New Features and Improvements

  • Bodo is now available with pip on both Linux and Windows

  • Bodo is upgraded to use Numba 0.55.0 (the latest release)

  • Bodo can now evaluate JIT functions at compilation time if possible to extract constant values. This improves user experience by simplifying type stability requirements. For example, the function below can be refactored to be type stable easily:

    @bodo.jit
def f(df):
    df.columns = [f"m_{c}" if c not in ["B", "C"] else c for c in df.columns]
    return df

    @bodo.jit
def g(df_cols):
    return [f"m_{c}" if c not in ["B", "C"] else c for c in df_cols]
>
@bodo.jit
def f(df):
    df.columns = g(df.columns)
    return df

  • Connectors:

    • read_csv now skips hidden files when reading from a directory.
    • read_parquet now supports reading a list of files.
    • Improved error handling for both read_csv and read_sql

  • Improved null value handling in user-defined-functions that aren't inlined.

  • Truncated error messages with DataFrames with large numbers of columns to improve readability.

  • Improved support for the logging standard library:

    • Support regular logging.Logger in addition to the logging.RootLogger.
    • Supports passing a logger as a constant global.
    • Supports the attributes: level, name, propagate, disabled, and parent.
    • Supports the methods: debug, warning, warn, error, exception, critical, log, and setLevel.

  • Improvments to global value handling of the compiler to avoid memory leaks in corner cases.

  • Pandas:

    • Support for DataFrame.pivot() and DataFrame.pivot_table() without requiring a constant list of output columns. Bodo currently only supports limited operations on output DataFrames of pivot, so users are recommended to immediately return these DataFrames to Python before doing any further processing in Bodo.
    • Support for Index.rename
    • Support for Index.is_monotonic, Index.is_montonic_increasing, and Index.is_monotonic_decreasing
    • Support for Index.notna and Index.notnull
    • Support for Index.drop_duplicates
    • Support for groupby.min, groupby.max, groupby.first, and groupby.last on DataFrames with Categorical columns
    • Support for column slice assignment with df.iloc (e.g. df.iloc[0,:] = 0)
    • Support for Series.first, Series.last, DataFrame.first, and DataFrame.last