def test_get_total_by_products_when_product_is_blank_should_ignore(spark_session):
sales_data = [(1, 'mango', 1, 50, 50), (1, '', 1, 10, 10), (1, 'mango', 2, 40, 80)]
sales_df = spark_session.createDataFrame(sales_data, get_schema())
expected = spark_session.createDataFrame([('mango', 130)], ['producto', 'ventas'])
actual = get_total_by_products(sales_df)
assert expected.collect() == actual.collect()
def test_get_total_by_products_when_only_one_product(spark_session):
sales_data = [(1, 'mango', 1, 50, 50), (1, 'mango', 3, 50, 150), (1, 'mango', 3, 50, 150)]
sales_df = spark_session.createDataFrame(sales_data, get_schema())
expected = spark_session.createDataFrame([('mango', 350)], ['producto', 'ventas'])
actual = get_total_by_products(sales_df)
assert expected.collect() == actual.collect()
def test_get_total_by_products_happy_path(spark_session):
sales_data = [(1, 'mango', 1, 50, 50), (1, 'manzana', 2, 10, 10), (1, 'mango', 2, 40, 80)]
sales_df = spark_session.createDataFrame(sales_data, get_schema())
expected = spark_session.createDataFrame([('mango', 130),('manzana', 10)], ['producto', 'ventas'])
actual = get_total_by_products(sales_df)
assert expected.collect() == actual.collect()
def test_get_total_by_products_when_data_set_is_null():
sales_df = None
actual = get_total_by_products(sales_df)
assert actual is None
