great-assertions 0.0.40

Lightweight assertions inspired by the great-expectations library

This library is inspired by the Great Expectations library. The library has made the various expectations found in Great Expectations available when using the inbuilt python unittest assertions.

For example if you wanted to use expect_column_values_to_be_between then you can access assertExpectColumnValuesToBeBetween.

Install

pip install great-assertions

Code example Pandas

from great_assertions import GreatAssertions
import pandas as pd

class GreatAssertionTests(GreatAssertions):
    def test_expect_table_row_count_to_equal(self):
        df = pd.DataFrame({"col_1": [100, 200, 300], "col_2": [10, 20, 30]})
        self.assertExpectTableRowCountToEqual(df, 3)

Code example PySpark

from great_assertions import GreatAssertions
from pyspark.sql import SparkSession

class GreatAssertionTests(GreatAssertions):

    def setUp(self):
        self.spark = SparkSession.builder.getOrCreate()

    def test_expect_table_row_count_to_equal(self):
        df = self.spark.createDataFrame(
            [
                {"col_1": 100, "col_2": 10},
                {"col_1": 200, "col_2": 20},
                {"col_1": 300, "col_2": 30},
            ]
        )
        self.assertExpectTableRowCountToEqual(df, 3)

List of available assertions

	Pandas	PySpark
assertExpectTableRowCountToEqual	white_check_mark:	white_check_mark:
assertExpectColumnValuesToBeBetween	white_check_mark:	white_check_mark:
assertExpectColumnValuesToMatchRegex	white_check_mark:	white_check_mark:
assertExpectColumnValuesToBeInSet	white_check_mark:	white_check_mark:
assertExpectColumnValuesToBeOfType	white_check_mark:	white_check_mark:
assertExpectTableColumnsToMatchOrderedList	white_check_mark:	white_check_mark:
assertExpectTableColumnsToMatchSet	white_check_mark:	white_check_mark:
assertExpectDateRangeToBeMoreThan	white_check_mark:	white_check_mark:
assertExpectDateRangeToBeLessThan	white_check_mark:	white_check_mark:
assertExpectDateRangeToBeBetween	white_check_mark:	white_check_mark:
assertExpectColumnMeanToBeBetween	white_check_mark:	white_check_mark:
assertExpectColumnValueCountsPercentToBeBetween	white_check_mark:	white_check_mark:

Assertion Descriptions

For a description of the assertions see Assertion Definitions

Running the tests

Executing the tests still require unittest, the following options have been tested with the examples provided.

Option 1

import unittest
suite = unittest.TestLoader().loadTestsFromTestCase(GreatAssertionTests)
runner = unittest.TextTestRunner(verbosity=2)
runner.run(suite)

Options 2

if __name__ == '__main__':
    unittest.main()

Notes

If you get an arrows function warning when running in Databricks, this will happen becuase a toPandas() method is called. The plan is to remove pandas conversion for Spark at a later date as use native PySpark code. For make sure the datasets are not too big, to cause the driver to crash.

Development

To create a development environment, create a virtualenv and make a development installation:

::

$ virtualenv ve $ source ve/bin/activation

To run tests, just use pytest

(ve) $ pytest