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Bodo 2020.08 Release (Date: 08/21/2020)

This release includes many new features, bug fixes and performance improvements. Overall, 112 code patches were merged since the last release.

New Features and Improvements

  • Bodo is updated to use the latest versions of Numba, pandas and Arrow:

    • Numba 0.51.0
    • pandas 1.1.0
    • Arrow 1.0

  • Support reading and writing Parquet files with columns where values are arrays or structs, which can contain other arrays/structs with arbitrary nesting.

  • S3 I/O: automatically determine the region of the S3 bucket when reading and writing.

  • Initial support for scikit-learn RandomForestClassifier (fit, predict and score methods)

  • Support sklearn.metrics.precision_score, sklearn.metrics.recall_score and sklearn.metrics.f1_score.

  • Improved caching support (caching @bodo.jit functions with cache=True)

  • Initial support for arrays of map data structures

  • Support count and offset arguments of np.fromfile

  • New bodo.rebalance() function for load balancing dataframes manually if desired

  • Support setting dataframe column as attribute, for example: df.B = "AA"

  • Support DataFrame min/max/sum/prod/mean/median functions with axis=1

  • Support df.loc[:,columns] indexing

  • pd.concat support for mix of Numpy and nullable integer/bool arrays

  • Support parallel append to dataframes (concatenation reduction)

  • Support GroupBy.idxmin and GroupBy.idxmax

  • Improvements and optimizations in user-defined function (UDF) handling

  • Basic support for Series.where()

  • Support calling bodo.jit functions inside prange loops

  • Support DataFrame.select_dtypes with constant strings

  • Support DataFrame.sample

  • Support Series.replace() and df.replace() (scalars and lists)

  • Support for Series.dt methods: total_seconds() and to_pytimedelta()

  • Improved support for Categorical data types

  • Support for pandas.Timestamp.isocalendar()

  • Support np.digitize()

  • Improved error handling during I/O when input CSV or Parquet file does not exist

  • Support pd.concat(axis=1) for dataframes

  • Significant improvements in compilation time for dataframes with large number of columns

  • bodo.is_jit_execution() can be used to know if a function is running with Bodo.

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