pyautodata 0.1.4

Python library designed to minimize the setup/arrange phase of your unit tests

A Python library designed to minimize the setup/arrange phase of your unit tests by removing the need to manually write code to create anonymous variables as part of a test cases setup/arrange phase.

This library was initially created for simplifying how to write unit tests for ETL (Extract-Transform-Load) code running from a python library on an Apache Spark cluster in Big Data solutions.

When writing unit tests you normally start with creating objects that represent the initial state of the test. This phase is called the arrange or setup phase of the test. In most cases, the system you want to test will force you to specify much more information than you really care about, so you frequently end up creating objects with no influence on the test itself just simply to satisfy the compiler/interpreter

PyAutodata can help by creating such anonymous variables for you. Here's a simple example:

import unittest
from pyautodata import Autodata

class Calculator:
  def add(self, number1: int, number2: int):
    return number1 + number2

class CalculatorTests(unittest.TestCase):

    def test_can_add_two_numbers(self):      
        # arrange
        numbers = Autodata.create_many(int, 2)
        sut = Autodata.create(Calculator)

        # act
        result = sut.add(numbers[0], numbers[1])

        # assert
        self.assertEqual(numbers[0] + numbers[1], result)

Supported data types

Currently PyAutodata supports creating anonymous variables for the following data types:

Built-in types:

• int
• float
• str

Datetime types:

• datetime
• date

Classes:

• Simple classes
• @dataclass
• Nested classes (and recursion)
• Classes containing lists of other types

Dataframes:

• Pandas dataframe
• Spark dataframe

Getting Started

PyAutodata is available from PyPI and should be installed using pip

pip install pyautodata

Next you need to import the Autodata class

from pyautodata import Autodata

Create anonymous built-in types like int, float, str and datetime types like datetime and date

print(f'anonymous string:    {Autodata.create(str)}')
print(f'anonymous int:       {Autodata.create(int)}')
print(f'anonymous float:     {Autodata.create(float)}')
print(f'anonymous datetime:  {Autodata.create(datetime)}')
print(f'anonymous date:      {Autodata.create(datetime.date)}')

The code above might output the following

anonymous string:    f91954f1-96df-463f-a427-665c99213395
anonymous int:       2066712686
anonymous float:     725758222.8712853
anonymous datetime:  2017-06-19 02:40:41.000084
anonymous date:      2019-11-10 00:00:00

Create collections containing anonymous variables of built-in types and dates

print(f'anonymous strings:    {Autodata.create_many(str)}')
print(f'anonymous ints:       {Autodata.create_many(int, 10)}')
print(f'anonymous floats:     {Autodata.create_many(float, 5)}')
print(f'anonymous datetime:   {Autodata.create_many(datetime)}')
print(f'anonymous date:       {Autodata.create_many(datetime.date)}')

The code above might output the following

anonymous strings:    ['f7ebaf30-468b-4282-9189-1c9f5a98c6e4', '6736ccdd-fb06-4fd0-89d7-0979a8493d79', 'f15736eb-2860-4f42-91c1-e48c8d25ea24']
anonymous ints:       [706865421, 1670005830, 1966806327, 434303989, 454137879, 1940686334, 307384148, 1090980019, 936257994, 9154693]
anonymous floats:     [684196408.4474499, 1188750352.5678303, 963320036.0875875, 1702383000.8534415, 1865911801.1348956]
anonymous datetime:   [datetime.datetime(2028, 12, 2, 13, 0, 52, 140), datetime.datetime(2021, 9, 7, 22, 39, 58, 919), datetime.datetime(2027, 12, 25, 13, 15, 28, 694)]
anonymous date:       [datetime.datetime(2019, 3, 3, 0, 0), datetime.datetime(2026, 4, 13, 0, 0), datetime.datetime(2016, 2, 24, 0, 0)]

Creates an anonymous class

class SimpleClass:
    id = 123
    text = 'test'

cls = Autodata.create(SimpleClass)
print(f'id = {cls.id}')
print(f'text = {cls.text}')

The code above might output the following

id = 2020177162
text = ac54a65d-b4a3-4eda-a840-eb948ad10d5f

Create a collection of an anonymous class

class SimpleClass:
    id = 123
    text = 'test'

classes = Autodata.create_many(SimpleClass)
for cls in classes:
  print(f'id = {cls.id}')
  print(f'text = {cls.text}')
  print()

The code above might output the following

id = 242996515
text = 5bb60504-ccca-4104-9b7f-b978e52a6518

id = 836984239
text = 079df61e-a87e-4f26-8196-3f44157aabd6

id = 570703150
text = a3b86f08-c73a-4730-bde7-4bdff5360ef4

Creates an anonymous dataclass

from dataclasses import dataclass

@dataclass
class DataClass:
    id: int
    text: str

cls = Autodata.create(DataClass)
print(f'id = {cls.id}')
print(f'text = {cls.text}')

The code above might output the following

id = 314075507
text = 4a3b3cae-f4cf-4502-a7f3-61115a1e0d2a

Create an anonymous class with nested types

class NestedClass:
    id = 123
    text = 'test'
    inner = SimpleClass()

cls = Autodata.create(NestedClass)
print(f'id = {cls.id}')
print(f'text = {cls.text}')
print(f'inner.id = {cls.inner.id}')
print(f'inner.text = {cls.inner.text}')

The code above might output the following

id = 1565737216
text = e66ecd5c-c17a-4426-b755-36dfd2082672
inner.id = 390282329
inner.text = eef94b5c-aa95-427a-a9e6-d99e2cc1ffb2

Create a collection of an anonymous class with nested types

class NestedClass:
    id = 123
    text = 'test'
    inner = SimpleClass()

classes = Autodata.create_many(NestedClass)
for cls in classes:
  print(f'id = {cls.id}')
  print(f'text = {cls.text}')
  print(f'inner.id = {cls.inner.id}')
  print(f'inner.text = {cls.inner.text}')
  print()

The code above might output the following

id = 1116454042
text = ceeecf0c-7375-4f3a-8d4b-6d7a4f2b20fd
inner.id = 1067027444
inner.text = 079573ce-1ef4-408d-8984-1dbc7b0d0b80

id = 730390288
text = ff3ca474-a69d-4ff6-95b4-fbdb1bea7cdb
inner.id = 1632771208
inner.text = 9423e824-dc8f-4145-ba47-7301351a91f8

id = 187364960
text = b31ca191-5031-43a2-870a-7bc7c99e4110
inner.id = 1705149100
inner.text = e703a117-ba4f-4201-a31b-10ab8e54a673

Create a Pandas DataFrame using anonymous data generated from a specified type

class DataClass:
    id = 0
    type = '' 
    value = 0

pdf = Autodata.create_pandas_dataframe(DataClass)
print(pdf)

The code above might output the following

          id                                  type       value
0  778090854  13537c5a-62e7-488b-836e-a4b17f2f3ae9  1049015695
1  602015506  c043ca8d-e280-466a-8bba-ec1e0539fe28  1016359353
2  387753717  986b3b1c-abf4-4bc1-95cf-0e979390e4f3   766159839

Create a Spark DataFrame using anonymous data generated from a specified type

class DataClass:
    id = 0
    type = '' 
    value = 0

df = Autodata.create_spark_dataframe(DataClass)
df.printSchema()
df.show()

The code above might output the following

root
 |-- id: long (nullable = true)
 |-- type: string (nullable = true)
 |-- value: long (nullable = true)

+----------+--------------------+----------+
|        id|                type|     value|
+----------+--------------------+----------+
| 938634666|630040b1-0703-437...|1417827879|
| 239684437|69ca65d5-81a6-418...|1932787106|
|1978525110|dfdc19df-ba47-43d...| 366058214|
+----------+--------------------+----------+