2021)¶

This release includes many new features and usability improvements. Overall, 67 code patches were merged since the last release.

New Features and Improvements¶

Significantly upgrades to the Bodo documentation to improve the developer experience
Improvements to documentation and unsupported attribute handling for Pandas APIs
Significant enhancements to objmode user experience and robustness, such as automatic output data type checking and automatic conversion if possible
Improved support for re package, such as support for re flags, better support for returning None when necessary, and better catching of unsupported corner cases
Support caching functions that take a string as input and create a file path using concatenation. For example:
```
@bodo.jit(cache=True)
def f(folder):
  return pd.read_parquet(folder + "/example.pq")
```
Connectors:
- Improved read_parquet runtime performance when reading from S3
- Decreased compilation time for read_csv on DataFrames with large number of columns (100)
Improved compilation time for dataframes with large number of columns (>10,000)
Improved NA handling in User Defined Functions with df.apply when functions are not inlined
Support for using logging.RootLogger.info when passing the logger as an argument to a JIT function
Support for datetime.datetime.today
Simpler bodo.scatterv usage from regular Python. Other ranks are ignored but not required to have None as their data
Improved support for map arrays in various operations
Support feature_importances_ of XGBoost
Support predict_proba and predict_log_proba in Scikit-learn classifier algorithms
Pandas:
- Support for Bodo specific argument _bodo_upcast_to_float64 in pd.read_csv. This can be used when all data is numeric but schema inference cannot accurate predict data types.
- Support for using DataFrame.to_parquet with "wide" DataFrames with large number of columns
- Support for storing a DateTimeIndex with DataFrame.to_parquet
- Support for the 'method' argument in DataFrame.fillna and Series.fillna
- Support for Series.bfill, Series.ffill, Series.pad, and Series.backfill
- Support for Series.keys
- Support for Series.infer_objects and DataFrame.infer_objects
- Decreased runtime when calling .astype("categorical") on Series with large numbers of categories