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test_prep_valuation_data

Tests for prep_valuation_data function in local_material module.

test_prep_valuation_data_duplicates(spark)

Test prep_valuation_data with duplicate records.

Source code in code\tests\test_local_material\test_prep_valuation_data.py 
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def test_prep_valuation_data_duplicates(spark: SparkSession):
    """Test prep_valuation_data with duplicate records."""
    data = [
        ("100", "VAL1", "COMP1"),
        ("100", "VAL1", "COMP1"),
    ]
    schema = "MANDT STRING, BWKEY STRING, BUKRS STRING"
    input_df = spark.createDataFrame(data, schema=schema)

    expected_data = [
        ("100", "VAL1", "COMP1"),
    ]
    expected_schema = "MANDT STRING, BWKEY STRING, BUKRS STRING"
    expected_df = spark.createDataFrame(expected_data, schema=expected_schema)

    result_df = prep_valuation_data(input_df)

    assertDataFrameEqual(result_df, expected_df), "DataFrames are not equal"

test_prep_valuation_data_invalid_input()

Test prep_valuation_data with invalid input type.

Source code in code\tests\test_local_material\test_prep_valuation_data.py 
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def test_prep_valuation_data_invalid_input():
    """Test prep_valuation_data with invalid input type."""
    with pytest.raises(TypeError, match="df must be a DataFrame"):
        prep_valuation_data(None)

test_prep_valuation_data_missing_columns(spark)

Test prep_valuation_data with missing columns in input DataFrame.

Source code in code\tests\test_local_material\test_prep_valuation_data.py 
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def test_prep_valuation_data_missing_columns(spark: SparkSession):
    """Test prep_valuation_data with missing columns in input DataFrame."""
    data = [
        ("100", "VAL1"),
        ("100", "VAL2"),
    ]
    schema = "MANDT STRING, BWKEY STRING"
    input_df = spark.createDataFrame(data, schema=schema)

    with pytest.raises(Exception):
        prep_valuation_data(input_df)

test_prep_valuation_data_valid_input(spark)

Test prep_valuation_data with valid input data.

Source code in code\tests\test_local_material\test_prep_valuation_data.py 
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def test_prep_valuation_data_valid_input(spark: SparkSession):
    """Test prep_valuation_data with valid input data."""
    data = [
        ("100", "VAL1", "COMP1"),
        ("100", "VAL2", "COMP2"),
    ]
    schema = "MANDT STRING, BWKEY STRING, BUKRS STRING"
    input_df = spark.createDataFrame(data, schema=schema)

    expected_data = [
        ("100", "VAL1", "COMP1"),
        ("100", "VAL2", "COMP2"),
    ]
    expected_schema = "MANDT STRING, BWKEY STRING, BUKRS STRING"
    expected_df = spark.createDataFrame(expected_data, schema=expected_schema)

    result_df = prep_valuation_data(input_df)

    assertDataFrameEqual(result_df, expected_df), "DataFrames are not equal"