Testing Your Python Code Base: Unit vs. Integration

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Jan 31, 2025

Christopher Trudeau, a regular contributor at PyCoder's Weekly, dives into the intricacies of automated testing for Python code. He highlights the critical difference between unit tests and integration tests, sharing valuable insights from his own experiences. Christopher discusses practical strategies for integrating tests into legacy codebases and emphasizes the importance of consistent testing principles. The conversation also touches on innovative tools for log analysis and mocking time, giving listeners a broader perspective on enhancing their testing capabilities.

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INSIGHT

Test Automation Benefits

Automated tests improve code quality, speed up delivery, and act as a safety net for developers.
This encourages experimentation and refactoring, ultimately boosting long-term productivity.

INSIGHT

Test Types

Unit tests target the smallest testable parts, like functions.
Integration tests connect these parts at a higher level, checking interactions.

ADVICE

FIRST Principles

Follow the FIRST principles (Fast, Independent, Repeatable, Self-Validating, Timely) when creating unit tests.
This ensures tests are quick, isolated, consistent, clear, and written promptly.

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What goes into creating automated tests for your Python code? Should you focus on testing the individual code sections or on how the entire system runs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article from Semaphore about unit testing vs. integration testing. Christopher shares his experiences setting up automated tests for his own smaller projects. He also answers questions about building tests in an existing codebase and integrating tests across systems.

We also share several other articles and projects from the Python community, including a news roundup, improving default line charts to journal-quality infographics, why hash(-1) == hash(-2) in Python, data cleaning in data science, ways to work with large files in Python, a lightweight CLI viewer for log files, and a tool for mocking the datetime module for testing.

This episode is sponsored by Postman.

Course Spotlight: Testing Your Code With pytest

In this video course, you’ll learn how to take your testing to the next level with pytest. You’ll cover intermediate and advanced pytest features such as fixtures, marks, parameters, and plugins. With pytest, you can make your test suites fast, effective, and less painful to maintain.

Topics:

00:00:00 – Introduction
00:02:28 – Python news and releases
00:04:02 – From Default Line Charts to Journal-Quality Infographics
00:07:25 – PyViz: Python Tools for Data Visualization
00:09:25 – Why Is hash(-1) == hash(-2) in Python?
00:12:40 – Sponsor: Postman
00:13:32 – Data Cleaning in Data Science
00:19:29 – 10 Ways to Work With Large Files in Python
00:23:40 – Unit Testing vs. Integration Testing
00:29:17 – Does university curriculum cover this?
00:31:22 – Building tests into smaller projects
00:36:04 – Video Course Spotlight
00:37:30 – How does the approach differ with clients or larger-scale projects?
00:40:45 – How do tests act as documentation?
00:42:02 – Difficulties in building integration tests
00:45:24 – How do you limit the results of tests?
00:47:52 – klp: Lightweight CLI Viewer for Log Files
00:50:54 – freezegun: Mocks the datetime Module for Testing
00:53:11 – Thanks and goodbye

News:

Topics:

From Default Line Charts to Journal-Quality Infographics – “Everyone who has used Matplotlib knows how ugly the default charts look like.” In this series of posts, Vladimir shares some tricks to make your visualizations stand out and reflect your individual style.
PyViz: Python Tools for Data Visualization – This site contains an overview of all the different visualization libraries in the Python ecosystem. If you’re trying to pick a tool, this is a great place to better understand the pros and cons of each.
Why Is hash(-1) == hash(-2) in Python? – Somewhat surprisingly, hash(-1) == hash(-2) in CPython. This post examines how and discovers why this is the case.
Data Cleaning in Data Science – “Real-world data needs cleaning before it can give us useful insights. Learn how you can perform data cleaning in data science on your dataset.”
10 Ways to Work With Large Files in Python – “Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program.” This article covers different ways of dealing with this challenge.

Discussion:

Unit Testing vs. Integration Testing – Discover the key differences between unit testing vs. integration testing and learn how to automate both with Python.

Project:

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