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Bodo 2021.3 Release (Date: 3/25/2021)

This release includes many new features, bug fixes and usability improvements. Overall, 148 code patches were merged since the last release.

New Features and Improvements

  • Bodo is updated to use Numba 0.53 (latest) and support Python 3.9

  • Many improvements to error checking and reporting

  • Compilation time is reduced, especially for user-defined functions (UDFs)

  • Reduced initialization time when importing Bodo

  • Distributed diagnostics improvements:

    • Show distributed diagnostics when raising errors for distributed flag
    • Only show user defined variables in diagnostics level one

  • Performance optimizations:

    • Faster groupby nunique with improved scaling
    • Faster setitem for categorical arrays

  • Connectors:

    • Google Cloud Storage (GCS) support with Parquet
    • Support reading Delta Lake tables
    • Improved Snowflake support
    • Removed s3fs dependency (Bodo now fully relies on Apache Arrow for S3 connectivity)

  • Change default parallelism semantics of unique() to replicated output to match user expectations better

  • Support objmode in groupby apply UDFs

  • Pandas coverage:

    • Support pd.DataFrame.duplicated() with categorical data
    • Groupby support for min/max on categorical data
    • Support for categorical in pd.Series.dropna
    • Support nullable int array in pd.Categorical constructor
    • Support for pd.Series.where and pd.Series.mask with categorical data and a scalar value.
    • Support for pd.Series.diff()
    • Support for pd.DataFrame.diff()
    • Support for pd.Series.repeat()
    • Support list of functions in groupby.agg()
    • Support tuple of UDFs inside groupby.agg() dictionary case
    • Support single row and scalar UDF output in groupby.apply()
    • Support Categorical values in Groupby.shift
    • Support case=False in Series.str.contains
    • Support mapper with axis=1 for pd.DataFrame.rename.
    • Support Timedelta64 data in pd.Groupby
    • Support for datetime.date arrays in Series.max and Series.min
    • Support for pd.timedelta_range
    • Support equality between datetime64/pd.Timestamp and timedelta64/pd.Timedelta
    • Support for iterating across most index types
    • Support getting the name attribute of data inside df.apply
    • Support Series.reset_index(drop=False) for common cases
    • Support == and != on Dataframe and a scalar with a different type
    • Sequential support for `pd.Series.idxmax`, `pd.Series.idxmin`,

:   `pd.DataFrame.idxmax`, and `pd.DataFrame.idxmin` with
    Nullable and Categorical arrays.

  • Python coverage:

    • Support datetime.date.replace()
    • Improved support for datetime.date.strftime()
    • Support for calendar.month_abbr

  • SciPy:

    • Initial support for scipy.sparse.csr_matrix

  • Scikit-learn:

    • Support for sklearn.feature_extraction.text.HashingVectorizer
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