Metadata-Version: 2.1
Name: pyautodata
Version: 0.1.3
Summary: Python library designed to minimize the setup/arrange phase of your unit tests
Home-page: https://github.com/christianhelle/pyautodata
Author: Christian Helle
Author-email: christian.helle@outlook.com
License: MIT License
Description: # PyAutodata
        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.
        
        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, simply to satisfy the compiler/interpreter.
        
        PyAutodata can help by creating such anonymous variables for you. Here's a simple example:
        
        ```python
        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](https://pypi.org/project/pyautodata/) is available from PyPI and should be installed using `pip`
        
        ```
        pip install pyautodata
        ```
        
        Next you need to import the `Autodata` class
        
        ```python
        from pyautodata import Autodata
        ```
        
        Create anonymous built-in types like `int`, `float`, `str` and datetime types like `datetime` and `date`
        
        ```python
        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
        
        ```python
        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)}')
        ```
        
        Creates an anonymous class
        
        ```python
        
        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
        
        ```python
        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
        
        ```python
        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
        
        ```python
        
        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
        
        ```python
        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
        
        ```python
        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
        
        ```python
        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|
        +----------+--------------------+----------+
        ```
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
