Scintilla is a small Python library that makes use of Faker to generate multiple DataFrames that can then be used for property based testing.
If you're looking for a library to test DataFrame assertions I recommend using the chispa library as it contains several methods to help in comparing DataFrames.
This module lets you create DataFrames, according to a schema, with fake synthetic data (using Faker) so that you can either use it in your tests or make use of property based testing (explained further down).
The starting point is the DataFrameGenerator data class, and it can be created by doing the following:
df_gen: DataFrameGenerator = DataFrameGenerator(schema=schema)Where the schema is a StructType object like the following:
schema: StructType = StructType([
StructField("expected_name", StringType(), True),
StructField("int", IntegerType(), True),
StructField("long", LongType(), True),
StructField("byte", ByteType(), True),
StructField("short", ShortType(), True),
StructField("double", DoubleType(), True),
StructField("float", FloatType(), True),
StructField("decimal", DecimalType(), True),
StructField("bool", BooleanType(), True),
StructField("binary", BinaryType(), True),
StructField("date", DateType(), True),
StructField("timestamp", TimestampType(), True)
])Those are the supported types right now, each of those types has a matching Faker provider in the DEFAULT_CONFIG dictionary:
DEFAULT_CONFIG: dict = {
"StringType": {"provider": "pystr"},
"ByteType": {"provider": "pyint", "kwargs": {"min_value": -128, "max_value": 127}},
"ShortType": {"provider": "pyint", "kwargs": {"min_value": -32768, "max_value": 32767}},
"IntegerType": {"provider": "pyint", "kwargs": {"min_value": -2147483648, "max_value": 2147483647}},
"LongType": {"provider": "pyint", "kwargs": {"min_value": -9223372036854775808, "max_value": 9223372036854775807}},
"DoubleType": {"provider": "pyfloat"},
"FloatType": {"provider": "pyfloat"},
"DecimalType(10,0)": {"provider": "pydecimal", "kwargs": {"left_digits": 10, "right_digits": 0}},
"DateType": {"provider": "date_object"},
"TimestampType": {"provider": "date_time"},
"BooleanType": {"provider": "pybool"},
"BinaryType": {"provider": "binary", "kwargs": {"length": 64}}
}Calling the method arbitrary_dataframes from the DataFrameGenerator will give you a Python generator of
DataFrames (default is 10 DataFrames) with rows filled with fake data (default is 10 rows).
For instance, the following code:
schema: StructType = StructType([
StructField("string", StringType(), True),
StructField("int", IntegerType(), True),
StructField("date", DateType(), True)
])
df_gen: DataFrameGenerator = DataFrameGenerator(schema=schema)
for df in df_gen.arbitrary_dataframes():
df.show()will result in:
+--------------------+-----------+----------+
| string| int| date|
+--------------------+-----------+----------+
|athKDRmDyDoOFTtMyEpS| 208977570|1998-10-05|
|KsiKWhuWhrxwjIfZObWE| 36536111|2009-02-07|
|yBnbHFvtUaMITurvzgGa|-1150452234|1975-06-04|
|hJgwshOrnGuOVYSHiQvT| -394148922|1996-09-03|
|RRwPfXMSXfwPTpEbCCYd|-2126030849|2020-11-01|
|NxjZLvalBmUxlHCCdvRS| -868167137|2017-01-09|
|OCKxJFjEWnXFLTnmxlAL| 378510418|2004-09-08|
|FcPQoSKsaWtkVAtsHtmE|-1778979182|1976-06-08|
|gGLrGwLlHQUJgqLoHscd|-1707952693|1975-07-04|
|OQfOJfUAqYMdoDKyIODt| 2042919219|1974-08-18|
+--------------------+-----------+----------+
+--------------------+-----------+----------+
| string| int| date|
+--------------------+-----------+----------+
|YdBiqoUTPchuiVVCToYb| 837577396|2011-10-20|
|ENUTmYcJbIlAHXdXrlcK| 1965683018|1999-06-11|
|qkYABZaLxKSTSKULvJUn|-1534538904|2007-07-21|
|IBekMJxdILbHrseyELjI| -778855686|1995-02-21|
|eOeDuqcyQrmMKyHsdIqi| 2062228449|2021-04-24|
|gvdnhvZEWHxjdVOCNVNO| 634606029|1988-07-28|
|XPooEkKLCsdDBBDPBxdw| 1147520365|2010-10-26|
|QuyvBSnhmNDFViNtZloD| -615531044|1988-11-11|
|wfNVuyjNwLOlIMILwEyY| -438993|1998-05-08|
|vGqmOojchnEBiFUrIyEF|-1961143065|1995-10-13|
+--------------------+-----------+----------+
.
.
.You can also make use of the seed parameter to always get the same results, could be beneficial in some test cases.
And, more importantly, you can make use of the config parameter to make sure the fake data in the DataFrames is as close
to the actual data you use.
For instance, the following:
schema: StructType = StructType([
StructField("bank_account_number", StringType(), True),
StructField("string", StringType(), True)
])
config: dict = {
"string": {
"data_type": StringType(),
"provider": "random_element",
"kwargs": {
"elements": ('x', 'y')
},
},
"bank_account_number": {
"data_type": StringType(),
"provider": "iban",
}
}
df_gen: DataFrameGenerator = DataFrameGenerator(schema=schema, config=config)
for df in df_gen.arbitrary_dataframes():
df.show(truncate=False)will result in:
+----------------------+------+
|bank_account_number |string|
+----------------------+------+
|GB05ZPOQ53062662223126|x |
|GB30FBEP02205427369768|y |
|GB77ZVZD72401097292467|y |
|GB37ZAUF42921111575037|x |
|GB94YOYW99106454150303|x |
|GB45AHZD58341571053644|y |
|GB49NIWO23000421077097|y |
|GB57KKMR90126850543238|x |
|GB36MJSE75788716032200|y |
|GB09WQRG06056962875254|x |
+----------------------+------+
+----------------------+------+
|bank_account_number |string|
+----------------------+------+
|GB05DOHQ75315263055315|x |
|GB97WKAZ86167865050998|x |
|GB74FNSZ74818713531062|y |
|GB09RNUK49954795362800|x |
|GB52EKEI43974684705487|y |
|GB71CMIO65098526908411|y |
|GB21FRNR40256327200553|y |
|GB78TPWY70848987416423|x |
|GB72FOTB13893525853918|x |
|GB88ZPWG41923933222632|y |
+----------------------+------+
.
.
.The other useful functionality of the DataFrameGenerator is that it can have a transformer function applied to the DataFrames,
perhaps you have a certain transformation that you would like to run over your DataFrame, if so you can pass this function to
the DataFrameGenerator, and it will then run it over the DataFrames and the iterator returned from the arbitrary_dataframes
method will now be DataFrames with that transformation applied.
For instance, the following:
schema: StructType = StructType([
StructField("string", StringType(), True),
StructField("number1", IntegerType(), True),
StructField("number2", IntegerType(), True),
])
def transformation(df: DataFrame) -> DataFrame:
return df.withColumn("number_sum", col("number1") + col("number2")).limit(2)
df_gen: DataFrameGenerator = DataFrameGenerator(schema=schema, transformer=transformation)
for df in df_gen.arbitrary_dataframes():
df.show(truncate=False)of course, a simple transformation can be in lambda form as well:
df_gen: DataFrameGenerator = DataFrameGenerator(schema=schema, transformer=lambda df: df.withColumn("number_sum", col("number1") + col("number2")).limit(2))will result in:
+--------------------+-----------+----------+----------+
|string |number1 |number2 |number_sum|
+--------------------+-----------+----------+----------+
|hfvXIxpHYWmeozQCgDdb|-404644089 |1391745093|987101004 |
|JMeuXcnlUBMabyYkckdL|-1019120536|1893116782|873996246 |
+--------------------+-----------+----------+----------+
+--------------------+-----------+-----------+----------+
|string |number1 |number2 |number_sum|
+--------------------+-----------+-----------+----------+
|IuGgFufcEWilkamohglP|-1867212230|-1935661407|492093659 |
|rhsttgaKKcWcMVRCSGIk|1412079017 |16007381 |1428086398|
+--------------------+-----------+-----------+----------+
+--------------------+-----------+-----------+-----------+
|string |number1 |number2 |number_sum |
+--------------------+-----------+-----------+-----------+
|RmihqKGAtoNxmofMNLms|-1729443426|377528902 |-1351914524|
|ZFScHJstlfOpJlvFFKmT|-2142767718|-1554653988|597545590 |
+--------------------+-----------+-----------+-----------+
.
.
.Although far from mature, the code here is a good starting point and hopefully can only be made better, this approach takes inspiration from the work done by Holden Karau in the spark-testing-base repository. You can check the wiki for that DataFrameGenerator and see how the scala solution is done there. Here the approach to property based testing is similar.
There are two methods in the dataframe_generator module, for_all and check_property that you can make use of to do property checks.
For instance, the following simple test:
def test_that_passes(self):
schema: StructType = StructType([
StructField("string", StringType(), True),
StructField("number1", IntegerType(), True),
StructField("number2", IntegerType(), True),
])
def transformation(df: DataFrame) -> DataFrame:
return df.withColumn("number_sum", col("number1") + col("number2")).limit(2)
df_gen: DataFrameGenerator = DataFrameGenerator(schema=schema, transformer=transformation)
new_data_schema: StructType = StructType([
StructField("string", StringType(), True),
StructField("number1", IntegerType(), True),
StructField("number2", IntegerType(), True),
StructField("number_sum", IntegerType(), True),
])
property_results: Iterator[PropertyResult] = for_all(
dfs=df_gen.arbitrary_dataframes(),
property_to_check=lambda df: df.schema == new_data_schema and df.count() == 2
)
self.assertTrue(check_property(property_results=property_results))will check the property defined in the property_to_check parameter in all the DataFrames and generate a report that is
then passed to the check_property that will either return True if all DataFrames conform to the property or it will raise
a PropertyCheckException and show the failed DataFrames in a pretty table.
Changing the property to check to:
lambda df: df.schema == new_data_schema and df.count() == 3Will cause the test to fail, and it will print out the DataFrames that failed the property check:
E chispa.dataframe_generator.PropertyCheckException: Property Check failed:
E +----------------------+------------+-------------+------------+
E | string | number1 | number2 | number_sum |
E +----------------------+------------+-------------+------------+
E | eRNTppUYCUECmgCEDLUu | 1035096828 | -1427731638 | -392634810 |
E | rQPPNXQuSGVuidEnWCxS | 774843839 | -1050333669 | -275489830 |
E +----------------------+------------+-------------+------------+
E +----------------------+-------------+-------------+-------------+
E | string | number1 | number2 | number_sum |
E +----------------------+-------------+-------------+-------------+
E | DYldTLJOXDsoLmpaSAUQ | -2040281200 | -106962224 | -2147243424 |
E | AjSnLZSGoGAjPcufpUgc | 709943750 | -1092598909 | -382655159 |
E +----------------------+-------------+-------------+-------------+
E +----------------------+------------+------------+-------------+
E | string | number1 | number2 | number_sum |
E +----------------------+------------+------------+-------------+
E | BStGdFtsgZEyNSdAkLPr | 1526463691 | 1333610860 | -1434892745 |
E | ddIYTJHNDWSXglhaTrnn | 482503049 | 1506843170 | 1989346219 |
E +----------------------+------------+------------+-------------+
.
.
.MIT License
Copyright (c) [2022] [Gonçalo André Carneiro de Castro]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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