ai-helpers-pyspark-utils 0.1.0a4

Common pyspark utils

AI Helpers - PySpark utils

pyspark-utils is a Python module that provides a collection of utilities to simplify and enhance the use of PySpark. These utilities are designed to make working with PySpark more efficient and to reduce boilerplate code.

Table of Contents

• AI Helpers - PySpark utils
  • Table of Contents
  • Installation
  • Getting Started
  • Utilities & Examples
  • Contributing

Installation

You can install the pyspark-utils module via pip:

pip install ai-helpers-pyspark-utils

Getting Started

First, import the module in your Python script:

import pyspark_utils as psu

Now you can use the utilities provided by pyspark-utils.

Utilities & Examples

• get_spark_session: Recover appropriate SparkSession.

  Create a spark dataframe:

  >>> import pyspark_utils as psu
  
  >>> spark = psu.get_spark_session("example")
  >>> sdf = spark.createDataFrame(
        [
            [None, "a", 1, 1.0],
            ["b", "b", 1, 2.0],
            ["b", "b", None, 3.0],
            ["c", "c", None, 2.0],
            ["c", "c", 3, 4.0],
            ["d", None, 4, 2.0],
            ["d", None, 5, 6.0],
        ],
        ["col0", "col1", "col2", "col3"],
    )
  >>> sdf.show()
  +----+----+----+----+
  |col0|col1|col2|col3|
  +----+----+----+----+
  |NULL|   a|   1| 1.0|
  |   b|   b|   1| 2.0|
  |   b|   b|NULL| 3.0|
  |   c|   c|NULL| 2.0|
  |   c|   c|   3| 4.0|
  |   d|NULL|   4| 2.0|
  |   d|NULL|   5| 6.0|
  +----+----+----+----+ 
  

• with_columns: Use multiple 'withColumn' calls on a dataframe in a single command.

  >>> import pyspark_utils as psu
  >>> import pyspark.sql.functions as F
  
  >>> col4 = F.col("col3") + 2
  >>> col5 = F.lit(True)
  
  >>> transformed_sdf = psu.with_columns(
    sdf, 
    col_func_mapping={"col4": col4, "col5": col5}
    )
  >>> transformed_sdf.show()
  +----+----+----+----+----+----+
  |col0|col1|col2|col3|col4|col5|
  +----+----+----+----+----+----+
  |NULL|   a|   1| 1.0| 3.0|true|
  |   b|   b|   1| 2.0| 4.0|true|
  |   b|   b|NULL| 3.0| 5.0|true|
  |   c|   c|NULL| 2.0| 4.0|true|
  |   c|   c|   3| 4.0| 6.0|true|
  |   d|NULL|   4| 2.0| 4.0|true|
  |   d|NULL|   5| 6.0| 8.0|true|
  +----+----+----+----+----+----+
  

• keep_first_rows: Keep the first row of each group defined by partition_cols and order_cols.

  >>> transformed_sdf = psu.utils.keep_first_rows(sdf, [F.col("col0")], [F.col("col3")])
  >>> transformed_sdf.show()
  +----+----+----+----+
  |col0|col1|col2|col3|
  +----+----+----+----+
  |NULL|   a|   1| 1.0|
  |   b|   b|   1| 2.0|
  |   c|   c|NULL| 2.0|
  |   d|NULL|   4| 2.0|
  +----+----+----+----+
  

• assert_cols_in_df: Assserts that all specified columns are present in specified dataframe.

• assert_df_close: Asserts that two dataframes are (almost) equal, even if the order of the columns is different.

Contributing

We welcome contributions to pyspark-utils. To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -am 'Add some feature').
5. Push to the branch (git push origin feature-branch).
6. Create a new Pull Request.

Please ensure your code follows the project's coding standards and includes appropriate tests.