Metadata-Version: 2.1
Name: pyspark-testframework
Version: 2.3.0
Summary: Testframework for PySpark DataFrames
Author-email: Woonstad Rotterdam <info@woonstadrotterdam.nl>, Tomer Gabay <tomer.gabay@woonstadrotterdam.nl>, Vincent van der Meij <vincent.van.der.meij@woonstadrotterdam.nl>, Tiddo Loos <tiddo.loos@woonstadrotterdam.nl>, Ben Verhees <ben.verhees@woonstadrotterdam.nl>
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# pyspark-testframework

The goal of the `pyspark-testframework` is to provide a simple way to create tests for PySpark DataFrames. The test results are returned in DataFrame format as well.

# Tutorial

**Let's first create an example pyspark DataFrame**

The data will contain the primary keys, street names and house numbers of some addresses.

```python
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
from pyspark.sql import functions as F
```

```python
# Initialize Spark session
spark = SparkSession.builder.appName("PySparkTestFrameworkTutorial").getOrCreate()

# Define the schema
schema = StructType(
    [
        StructField("id", IntegerType(), True),
        StructField("street", StringType(), True),
        StructField("house_number", IntegerType(), True),
    ]
)

# Define the data
data = [
    (1, "Rochussenstraat", 27),
    (2, "Coolsingel", 31),
    (3, "%Witte de Withstraat", 27),
    (4, "Lijnbaan", -3),
    (5, None, 13),
]

df = spark.createDataFrame(data, schema)

df.show(truncate=False)
```

    +---+--------------------+------------+
    |id |street              |house_number|
    +---+--------------------+------------+
    |1  |Rochussenstraat     |27          |
    |2  |Coolsingel          |31          |
    |3  |%Witte de Withstraat|27          |
    |4  |Lijnbaan            |-3          |
    |5  |null                |13          |
    +---+--------------------+------------+

**Import and initialize the `DataFrameTester`**

```python
from testframework.dataquality import DataFrameTester
```

```python
df_tester = DataFrameTester(
    df=df,
    primary_key="id",
    spark=spark,
)
```

**Import configurable tests**

```python
from testframework.dataquality.tests import ValidNumericRange, RegexTest
```

**Initialize the `RegexTest` to test for valid street names**

```python
valid_street_format = RegexTest(
    name="ValidStreetFormat",
    pattern=r"^[A-Z][a-zéèáàëï]*([ -][A-Z]?[a-zéèáàëï]*)*$",
)
```

**Run `valid_street_format` on the _street_ column using the `.test()` method of `DataFrameTester`.**

```python
df_tester.test(
    col="street",
    test=valid_street_format,
    nullable=False,  # nullable is False, hence null values are converted to False
    description="Street is in valid Dutch street format.",
).show(truncate=False)
```

    +---+--------------------+-------------------------+
    |id |street              |street__ValidStreetFormat|
    +---+--------------------+-------------------------+
    |1  |Rochussenstraat     |true                     |
    |2  |Coolsingel          |true                     |
    |3  |%Witte de Withstraat|false                    |
    |4  |Lijnbaan            |true                     |
    |5  |null                |false                    |
    +---+--------------------+-------------------------+

**Run the `IntegerString` test on the _number_ column**

By setting the `return_failed_rows` parameter to `True`, we can get only the rows that failed the test.

```python
df_tester.test(
    col="house_number",
    test=ValidNumericRange(
        min_value=1,
    ),
    nullable=False,
    # description="House number is in a valid format" # optional, let's not define it for illustration purposes
    return_failed_rows=True,  # only return the failed rows
).show()
```

    +---+------------+-------------------------------+
    | id|house_number|house_number__ValidNumericRange|
    +---+------------+-------------------------------+
    |  4|          -3|                          false|
    +---+------------+-------------------------------+

**Let's take a look at the test results of the DataFrame using the `.results` attribute.**

```python
df_tester.results.show(truncate=False)
```

    +---+-------------------------+-------------------------------+
    |id |street__ValidStreetFormat|house_number__ValidNumericRange|
    +---+-------------------------+-------------------------------+
    |1  |true                     |true                           |
    |2  |true                     |true                           |
    |3  |false                    |true                           |
    |4  |true                     |false                          |
    |5  |false                    |true                           |
    +---+-------------------------+-------------------------------+

**We can use `.descriptions` or `.descriptions_df` to get the descriptions of the tests.**

<br>
This can be useful for reporting purposes.   
For example to create reports for the business with more detailed information than just the column name and the test name.

```python
df_tester.descriptions
```

    {'street__ValidStreetFormat': 'Street is in valid Dutch street format.',
     'house_number__ValidNumericRange': 'house_number__ValidNumericRange(min_value=1.0, max_value=inf)'}

```python
df_tester.description_df.show(truncate=False)
```

    +-------------------------------+-------------------------------------------------------------+
    |test                           |description                                                  |
    +-------------------------------+-------------------------------------------------------------+
    |street__ValidStreetFormat      |Street is in valid Dutch street format.                      |
    |house_number__ValidNumericRange|house_number__ValidNumericRange(min_value=1.0, max_value=inf)|
    +-------------------------------+-------------------------------------------------------------+

### Custom tests

Sometimes tests are too specific or complex to be covered by the configurable tests. That's why we can create custom tests and add them to the `DataFrameTester` object.

Let's do this using a custom test which should tests that every house has a bath room. We'll start by creating a new DataFrame with rooms rather than houses.

```python
rooms = [
    (1,1, "living room"),
    (2,1, "bathroom"),
    (3,1, "kitchen"),
    (4,1, "bed room"),
    (5,2, "living room"),
    (6,2, "bed room"),
    (7,2, "kitchen"),
]

schema_rooms = StructType(
    [   StructField("id", IntegerType(), True),
        StructField("house_id", IntegerType(), True),
        StructField("room", StringType(), True),
    ]
)

room_df = spark.createDataFrame(rooms, schema=schema_rooms)

room_df.show(truncate=False)
```

    +---+--------+-----------+
    |id |house_id|room       |
    +---+--------+-----------+
    |1  |1       |living room|
    |2  |1       |bathroom   |
    |3  |1       |kitchen    |
    |4  |1       |bed room   |
    |5  |2       |living room|
    |6  |2       |bed room   |
    |7  |2       |kitchen    |
    +---+--------+-----------+

To create a custom test, we should create a pyspark DataFrame which contains the same primary_key column as the DataFrame to be tested using the `DataFrameTester`.

Let's create a boolean column that indicates whether the house has a bath room or not.

```python
house_has_bathroom = room_df.groupBy("house_id").agg(
    F.max(F.when(F.col("room") == "bathroom", True).otherwise(False)).alias(
        "has_bathroom"
    )
)

house_has_bathroom.show(truncate=False)
```

    +--------+------------+
    |house_id|has_bathroom|
    +--------+------------+
    |1       |true        |
    |2       |false       |
    +--------+------------+

**We can add this 'custom test' to the `DataFrameTester` using `add_custom_test_result`.**

In the background, all kinds of data validation checks are done by `DataFrameTester` to make sure that it fits the requirements to be added to the other test results.

```python
df_tester.add_custom_test_result(
    result=house_has_bathroom.withColumnRenamed("house_id", "id"),
    name="has_bathroom",
    description="House has a bathroom",
    # fillna_value=0, # optional; by default null.
).show(truncate=False)
```

    +---+------------+
    |id |has_bathroom|
    +---+------------+
    |1  |true        |
    |2  |false       |
    |3  |null        |
    |4  |null        |
    |5  |null        |
    +---+------------+

**Despite that the data whether a house has a bath room is not available in the house DataFrame; we can still add the custom test to the `DataFrameTester` object.**

```python
df_tester.results.show(truncate=False)
```

    +---+-------------------------+-------------------------------+------------+
    |id |street__ValidStreetFormat|house_number__ValidNumericRange|has_bathroom|
    +---+-------------------------+-------------------------------+------------+
    |1  |true                     |true                           |true        |
    |2  |true                     |true                           |false       |
    |3  |false                    |true                           |null        |
    |4  |true                     |false                          |null        |
    |5  |false                    |true                           |null        |
    +---+-------------------------+-------------------------------+------------+

```python
df_tester.descriptions
```

    {'street__ValidStreetFormat': 'Street is in valid Dutch street format.',
     'house_number__ValidNumericRange': 'house_number__ValidNumericRange(min_value=1.0, max_value=inf)',
     'has_bathroom': 'House has a bathroom'}

**We can also get a summary of the test results using the `.summary` attribute.**

```python
df_tester.summary.show(truncate=False)
```

    +-------------------------------+-------------------------------------------------------------+-------+--------+-----------------+--------+-----------------+
    |test                           |description                                                  |n_tests|n_passed|percentage_passed|n_failed|percentage_failed|
    +-------------------------------+-------------------------------------------------------------+-------+--------+-----------------+--------+-----------------+
    |street__ValidStreetFormat      |Street is in valid Dutch street format.                      |5      |3       |60.0             |2       |40.0             |
    |house_number__ValidNumericRange|house_number__ValidNumericRange(min_value=1.0, max_value=inf)|5      |4       |80.0             |1       |20.0             |
    |has_bathroom                   |House has a bathroom                                         |2      |1       |50.0             |1       |50.0             |
    +-------------------------------+-------------------------------------------------------------+-------+--------+-----------------+--------+-----------------+

**If you want to see all rows that failed any of the tests, you can use the `.failed_tests` attribute.**

```python
df_tester.failed_tests.show(truncate=False)
```

    +---+-------------------------+-------------------------------+------------+
    |id |street__ValidStreetFormat|house_number__ValidNumericRange|has_bathroom|
    +---+-------------------------+-------------------------------+------------+
    |2  |true                     |true                           |false       |
    |3  |false                    |true                           |null        |
    |4  |true                     |false                          |null        |
    |5  |false                    |true                           |null        |
    +---+-------------------------+-------------------------------+------------+

**Of course, you can also see all rows that passed all tests using the `.passed_tests` attribute.**

```python
df_tester.passed_tests.show(truncate=False)
```

    +---+-------------------------+-------------------------------+------------+
    |id |street__ValidStreetFormat|house_number__ValidNumericRange|has_bathroom|
    +---+-------------------------+-------------------------------+------------+
    |1  |true                     |true                           |true        |
    +---+-------------------------+-------------------------------+------------+
