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Bodo 2020.11 Release (Date: 11/19/2020)

This release includes many new features, bug fixes and performance improvements. Overall, 126 code patches were merged since the last release.

New Features and Improvements

  • Bodo is updated to use Apache Arrow 2.0 (latest)

  • Performance and memory optimizations

    • Significant memory usage optimizations for several operations involving string arrays
    • Up to 2x speedup for many string operations such as Series.str.replace/get/contains and groupby.sum()
  • User-defined functions (UDFs)

    • Support for returning datafarames from DataFrame.apply() and Series.apply()
    • Support for returning nested arrays
  • Caching: for Bodo functions that receive CSV and JSON file names as string arguments, the cache will now be reused when file name arguments differ but have the same dataset type (schema).

  • Support for distributed deep learning with Tensorflow and PyTorch:

  • Pandas coverage:

    • Support for tuple values in Series and DataFrame columns
    • Improvements to error checking and handling
    • Automatic unrolling of loops over dataframe columns when necessary for type stability
    • Support integer column names for Dataframes
    • Support for pd.Timedelta values
    • Support for pd.tseries.offsets.DateOffset and pd.tseries.offsets.Monthend
    • Support for Series.dt, Timestamp, and DateTimeIndex attributes (is_month_start, is_month_end, is_quarter_start, is_quarter_end, is_year_start, is_year_end, week, weekofyear, weekday)
    • Support for Series.dt and Timestamp normalize method
    • Support for Timestamp.components and Timestamp.strftime
    • Support for Series.dt.ceil and Series.dt.round
    • Support for pd.to_timedelta
    • Support Series.replace for categorical arrays where value and to_replace are scalars or lists
    • Support for comparison operators on Decimal types
    • Support for Series.add() with String, datetime, and timedelta
    • Support for Series.mul() with string and int literal
    • Support for setting values in categorical arrays
    • Initial support for pd.get_dummies()
    • Support for Series.groupby()
  • Scikit-learn: the following classes and functions are supported inside jit functions:

    • sklearn.linear_model.LinearRegression
    • sklearn.linear_model.LogisticRegression
    • sklearn.linear_model.Ridge
    • sklearn.linear_model.Lasso
    • sklearn.svm.LinearSVC
    • sklearn.naive_bayes.MultinomialNB
    • sklearn.metrics.accuracy_score
    • sklearn.metrics.mean_squared_error
    • sklearn.metrics.mean_absolute_error
  • XGBoost: Training XGBoost model (with Scitkit-learn like API) is now supported inside jit functions:

    • xgboost.XGBClassifier
    • xgboost.XGBRegressor

    Visit < more information about supported ML functions.

  • NumPy coverage:

    • Support for numpy.any and numpy.all for all array types
    • Support for numpy.cbrt
    • Support for numpy.linspace arguments endpoint, retstep, and dtype
    • np.argmin with axis=1
    • Support for np.float32(str)
  • Support for str.format, math.factorial, zlib.crc32

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