Learning the programming language Python is easy but becoming a proficient developer can be quite cumbersome:

Python has a complex ecosystem of package / dependency management, some finicky language details and of course you should learn some tricks of the trade as well.

So here is the place to start your journey into the world full of Python

Before you start

When you start a new project it’s good to think about the structure before you start to code

How to structure your Python project

Autarke Skripte in Python

Language Tips

Basic Features

Python datetime and format

Python variable length arguments

Running external commands from Python

Python Type Checking

How to work with the Python os module

How to use the sys module in Python

How to use defaultdict

match case in Python 3.10

So funktionieren structs in Python

Program Input

Argparse

Python module unicodecsv

How to use the configparser module

File handling & manipulation

How to work with the Python os module

Getting the file extension from a file path in Python

How to use glob in Python

How to use Python shutil

More about structure

How to write your __init__.py

How to implement Python Decorators

How to implement Python Decorators – Part 2

Classes & Data Structures

Python – data classes

What’s the difference between classmethod and staticmethod in Python?

What are Python properties?

Python Protocols

Python3: ChainMap

Useful 3rd party packages

How to use google translate with python

How to manipulate nested dictionaries with dotty

How to store sensible data in a .env file

Overcoming PyInstaller Pitfalls

Intro to OpenCV with Python

Pillow how to convert images to webp

How to create youtube thumbnails

numpy

Linear Algebra with numpy – Part 1

numpy random choice

Numpy linspace function

matplotlib

Introduction to matplotlib

Introduction to matplotlib – Part 2

Introduction to matplotlib – Part 3

Scatterplot with matplotlib

pandas

Introduction to Pandas

Pandas Cheat Sheet

JuPyteR Notebooks

Introduction to Jupyter Notebook

How to visualize geo coordinates with ipyleaflet

Testing & Quality

Software Testing Concepts

pytest Tutorial – Part 1

pytest Tutorial – Part 2

pytest Tutorial – Part 3

How to let test cases run in a predefined order

Python Package parameterized

Why you should use PyHamcrest in testing

How to use tox to test your code on multiple platforms

How to add Travis CI to your github project

How to use the timeit module to check performance of your code

Logging

Logging in Python – Cheat Sheet

Packaging & Dependencies

Python Pipfile and pipenv

pip optional dependencies

Distributing your own package on PyPi

Distributing your own package on PyPi – Part 2

What is python pbr?

Visualizing Python dependencies with pipdeptree

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Writing Tests

If You want to work with pytest you can install it via:

pip install pytest

When you know basic python unittest fixtures, good news ahead:

pytest is compatible and will run your old unittest classes as well, but pytest doesn’t need to have a testclass derived from some subclass.

It is sufficient to have a single python file which name starts with test_ containing a function which name also starts with test_

def test_stuff():
    x = 5.0
    assert x == 5

That’s all You need!

Autodiscovery

If you run pytest from the command line it will automatically discover tests which cohere to this naming convention like unittest -discover

pytest

Select test cases

You can specify the tests to be run by module::test_function or by module::test_class::test_method e.g.

pytest test_sort_algortihms.py::TestSortAlgorithms::test_python_sorted

As an alternative you can use the option -k “test_case_name” to glob testcases.

Execution time

If you pass --durations=0 execution time for all tests will be reported.

Useful command line arguments

When learning pytest, it is good to learn some command line flags as well

• -h : help
• -k “<name_of_test>” : glob test name
• -x : quit on first error
• – -pdb : start debugger on error
• -v : increase verbosity
• -s : show print output
• -rf : reason for failure
• -rs : reason for skipping
• -m <marker_name> : run only marked tests
• –fixtures : show fixtures in modules

Configuration via file

You can save your preferences for running pytest in a file called pytest.ini

[pytest]
addopts = -x --durations=0 -vv

Understanding Fixtures

Fixtures are pytest’s equivalent to setUp methods from unittest with the difference that you can define when they should be used.

import pytest 

@pytest.fixture 
def some_value(): 
    return 42 

def test_function(some_value): 
    assert some_value == 42

After declaring a fixture with decorator @pytest.fixture you can use this special function as a parameter to your test functions.

Fixture scope

Without any further definition a fixture function is called before every function it is used in. When You do some heavy lifting in a fixture and you want to reuse that you can specify the module scope so that the fixture function just runs once per module

@pytest.fixture(scope="module")

If you even want to instantiate a fixture once per session this is also possible with

@pytest.fixture(scope="session")

Next part pytest Tutorial –  Part 2

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