spark-frame 0.1.1

A library containing various utility functions for playing with PySpark DataFrames

Spark-frame

What is it ?

Spark-frame is a library that super-charges your Spark DataFrames!

It brings several utility methods and transformation functions for PySpark DataFrames. These methods were initially part of the karadoc project used at Younited, but they were fully independent from karadoc, so it made more sense to keep them as a standalone library.

Several of these methods were my initial inspiration to make the cousin project bigquery-frame, which was first made to illustrate this blog article. This is why you will find similar methods in both spark_frame and bigquery_frame, except the former runs on PySpark while the latter runs on BigQuery (obviously). I try to keep both projects consistent together, and new eventually port new developments made on one project to the other one.

Getting Started

Visit the official Spark-frame website documentation for use cases examples and reference.

Installation

spark-frame is available on PyPi.

pip install spark-frame

Compatibilities and requirements

This library does not depend on any other library. Pyspark must be installed separately to use it. It is compatible with the following versions:

• Python: requires 3.8.1 or higher (tested against Python 3.9, 3.10 and 3.11)
• pyspark: requires 3.3.0 or higher

This library is tested against Mac and Linux.

Release notes

v0.1.1

• Added a new transformation spark_frame.transformations.flatten_all_arrays.
• Added support for multi-arg transformation to nested.select and nested.with_fields With this feature, we can now access parent fields from higher levels when applying a transformation. Example:

>>> nested.print_schema(df)
"""
root
 |-- id: integer (nullable = false)
 |-- s1!.average: integer (nullable = false)
 |-- s1!.values!: integer (nullable = false)
"""
>>> df.show(truncate=False)
+---+--------------------------------------+
|id |s1                                    |
+---+--------------------------------------+
|1  |[{2, [1, 2, 3]}, {3, [1, 2, 3, 4, 5]}]|
+---+--------------------------------------+
>>> new_df = df.transform(nested.with_fields, {
>>>     "s1!.values!": lambda s1, value: value - s1["average"]  # This transformation takes 2 arguments
>>> })
+---+-----------------------------------------+
|id |s1                                       |
+---+-----------------------------------------+
|1  |[{2, [-1, 0, 1]}, {3, [-2, -1, 0, 1, 2]}]|
+---+-----------------------------------------+

v0.1.0

• Added a new amazing module called spark_frame.nested, which makes manipulation of nested data structure much easier! Make sure to check out the reference and the use-cases.

• Also added a new module called spark_frame.nested_functions, which contains aggregation methods for nested data structures (See Reference).

• New transformations:

  • spark_frame.transformations.transform_all_field_names
  • spark_frame.transformations.transform_all_fields
  • spark_frame.transformations.unnest_field
  • spark_frame.transformations.unnest_all_fields
  • spark_frame.transformations.union_dataframes

v0.0.3

• New transformation: spark_frame.transformations.convert_all_maps_to_arrays.
• New transformation: spark_frame.transformations.sort_all_arrays.
• New transformation: spark_frame.transformations.harmonize_dataframes.