#pytest

[sweetswesf]

I can’t remember when I wrote these down, but I think it was last year sometime:

Interview Prep Goals To Accomplish

Complete React tutorial

Get to a place where the AlgoExpert Hard questions are easy for me

Notice & understand common algo solving patterns

Clearly describe how the internet works

Complete Advent of Code 2022

Complete 100 Days of Code

Complete AlgoExpert from AlgoExpert

Complete FrontendExpert from AlgoExpert

Complete MLExpert from AlgoExpert

Complete SystemsExpert from AlgoExpert

Building a plan before solving problems and speaking through them as I work

Understand latency, availability, load balancer, long polling, web socket

Understand sync/async flow

Understand pytests better

Understand protobufs better

Passing practice interviews

Passing real interviews

Get multiple offers

Here’s what I’ve actually been able to accomplish:

Got pretty far in React tutorial, learned a good amount, interviewed with it, & dropped it after realizing there’s so much I need to do to get hired as a full stack and solidified my place as a Backend SWE :) for now at least. I know enough React to do projects as I need to, but not enough to pass an interview.

SOME AlgoExpert Hard questions are feasible for me, nowhere near EASY yet, and I don’t HAVE to get there…for any reason

Notice & understand common algo solving patterns

Somewhat understand and can articulate how the internet works

Completed some questions on AlgoExpert from AlgoExpert

Did some FrontendExpert from AlgoExpert & took some of their quizzes

Started SystemsExpert from AlgoExpert & took some of their quizzes

Building a plan before solving problems and speaking through them as I work

Understand latency, availability, load balancers

Understand sync/async flow somewhat

Understand pytests better

Passing practice interviews

Passing real interviews, no offers yet though

Completed 5-week interview prep course

Learned more about APIs

Understand how to implement pagination & searching

Understand Postman, SQLAlchemy, & FastAPI

Can call APIs in a coding interview environment like Coderpad

Here are some things in my life I have accomplished also:

Improved my relationship with my family.

I’m strong as heck physically and have been losing fat and gaining muscle.

I can sit and work 12 hour days. You couldn’t get me to side for more than 3 previously.

I can get through the day without a nap.

I’m more disciplined in every area of my life.

I release people who don’t want to be in my life anymore.

Got admitted to an improv theater after passing their multi-day auditions.

Made a rude guy who disrespected me apologize to my face.

All glory to God.

[alok401]

✨ Maximize Your Python Dev Game: The IDE That Works With You 🐍💻

Picking the right Python IDE isn't just a preference—it’s a productivity multiplier. Whether you're deep in data science, scaling web apps, or debugging tests, the right environment makes all the difference.

🔍 Here’s what a modern Python IDE should do for you:

🧠 AI-powered autocomplete that actually understands your code

🐞 Built-in debuggers + test runners for pytest, unittest, etc.

🌐 Git integration, cloud sync, and real-time collaboration

⚙️ Plugin support to customize for Flask, Django, Pandas, etc.

🚀 Remote dev on cloud without leaving your local workspace

🧹 Code quality checks, linting, and even security scanning

🛠️ An IDE should feel like your co-pilot, not a clunky toolbox.

🧪 Want to boost testing in any IDE? Pair it with Keploy — a powerful testing automation tool that integrates seamlessly with your dev environment.

🔗 Read the full guide here → Best IDEs for Python

[webappinsights]

A Comprehensive Guide to the Top 7 Python Testing Frameworks

In today’s fast-paced development landscape, delivering high-quality, bug-free software is a non-negotiable requirement. Whether you're developing a web app, data pipeline, or AI solution, one thing remains constant—testing is essential. And when it comes to testing in Python, developers are spoiled for choice.

Python has long been celebrated for its simplicity and versatility, making it the backbone of many industries—from web development to AI. If you're serious about reliability and continuous delivery, it’s time to explore the top Python testing frameworks dominating 2025’s development practices.

Let’s dive into the top 7 Python testing frameworks and see how they stack up in real-world development environments.

1. Pytest – The Developer Favorite

Pytest is arguably the most popular testing framework in the Python ecosystem. It’s simple, powerful, and incredibly flexible.

Key Features:

Supports unit testing, functional testing, and API testing

Fixtures for complex setup

Plugins like pytest-django, pytest-cov, and more

Ideal for both beginners and seasoned developers, Pytest is often the top choice when you hire Python developers to build robust web or software applications.

2. Unittest (Built-in) – Python’s Native Test Framework

Inspired by Java’s JUnit, Unittest is Python’s standard testing library. While it's not as flashy or feature-rich as Pytest, it's perfect for developers who prefer sticking to built-in modules.

Key Features:

Test discovery

Test fixtures (setUp, tearDown)

Supports test automation in CI/CD environments

For teams new to testing, this is often the starting point before moving to more advanced frameworks.

3. Behave – Behavior-Driven Development (BDD)

Behave enables Behavior-Driven Development, allowing teams to write human-readable tests in the "Given-When-Then" format.

Key Features:

Great for cross-functional collaboration

Gherkin syntax support

Ideal for user journey or acceptance testing

Startups and enterprises alike choose Behave when they hire dedicated Python developers to build user-centric applications with business logic validation at every step.

4. Nose2 – Successor to Nose

While the original Nose is no longer actively maintained, Nose2 is here to pick up the torch. It's compatible with unittest and offers more plugins and improved extensibility.

Key Features:

Automatic test discovery

Plugins for test coverage, parallel testing, and more

Supports legacy Nose tests

Nose2 is perfect for teams transitioning from older testing ecosystems or managing large-scale test suites.

5. Robot Framework – For Acceptance Testing

Robot Framework is a keyword-driven testing tool perfect for acceptance testing and robotic process automation.

Key Features:

Supports Selenium, API testing, database testing

Human-readable syntax

Integrates with Python libraries

It's widely used in enterprise environments and often seen in projects managed by a mature Python development company.

6. Testify – Scalable Testing for Large Codebases

Testify is a modern, feature-rich alternative to unittest and Nose, designed with scalability and readability in mind.

Key Features:

Class-based test organization

Built-in assertion methods

Clean API for large-scale development

For companies scaling their operations, Testify offers a neat balance of power and readability. It’s a good option for teams using Python for modern software development.

7. Tox – Testing Across Environments

Tox isn’t a test runner in itself but a tool that automates testing in different Python environments. It’s indispensable for Python library authors or those managing multiple versions.

Key Features:

Test automation for different Python versions

Dependency management

Seamless CI/CD integration

Tox is especially useful when paired with other frameworks like Pytest or Unittest, ensuring your code is compatible across all Python environments.

How to Choose the Right Framework?

Choosing the right Python testing framework depends on:

Project size and complexity

Team skill level

Framework support and community

Integration with CI/CD tools and third-party services

If your business is investing in Python, the smart move is to hire Python developers who are proficient in one or more of these frameworks and can align with your development goals.

Why Testing Frameworks Matter in Modern Development

With the growing demand for faster delivery and fewer bugs, adopting structured testing processes has become standard practice. Testing ensures stability, increases confidence in releases, and accelerates development cycles.

Modern frameworks also enable:

Continuous Integration/Delivery (CI/CD) pipelines

Test-driven development (TDD)

Behavior-driven development (BDD)

Cross-platform compatibility checks

The developers you choose must align with these practices—an experienced Python development company will already have these workflows baked into their development culture.

Closing Thoughts

In 2025, the role of Python in shaping digital products continues to grow—from web platforms and enterprise solutions to AI-driven software. To keep up with this momentum, testing must be at the heart of every project.

Whether you're enhancing your development pipeline, scaling your startup, or modernizing enterprise systems, these frameworks will guide your way. But tools are only as good as the hands that wield them.

Make the right choice—hire dedicated Python developers who understand the importance of quality and know how to integrate these tools effectively.

For those beginning their journey, here’s a solid starting point: our Guide to Python for Web Development and Python: The Top Frameworks & Best Practices blog series cover everything you need to build stable, scalable applications.

Need help with your next project? Tuvoc Technologies offers expert Python development services tailored for today’s software landscape. Let’s build something exceptional—together.

[billloguidice]

Learn to program in Python with this bundle of more than 20 unique courses!

Learn to program in Python with this bundle of more than 20 unique courses! #python #sale #programming #coding #education #pytest #pandas #software

Use this link to check out the three bundle options with up to 29 items! Learn a cornerstone of programming language with the Next Level Python Bundle. This comprehensive curriculum will help entry level coders and veterans of the trade with more than 20 unique courses on Python. Get the expert advice and knowledge you seek, and help support the charity of your choice with your purchase! Next…

[minhphong306]

[Hướng dẫn] Debug PyTest qua venv trên PyCharm

Bước 1: Chọn ở góc 5h vào intepreter hiện tại, click add new interpreter, add local interpreter Continue reading Untitled

[mentalisttraceur-software]

I figured out we can do this with Markdown if we just

add an empty line at the end of every fenced doctest code block (this prevents the closing fence from being interpreted as an expected output line), and

use the `--doctest-glob` option to force pytest to not ignore our file despite the extension:

pytest --doctest-glob='*.md' README.md

TIL that `pytest` can run ReStructuredText (.rst) files and automatically execute any doctests it finds in any of the code blocks in them.

This works out of the box, just about in every way how you'd want it to:

pytest README.rst

The only quirk is that it counts all doctest examples in the whole file as one test. But you can at least add `-‍-doctest-continue-on-failure` to still see all failures at once, which is similar to the default `pytest` experience of all tests being run and all failures getting reported.

[foundationsofdecay]

I love looking at job apps because I am constantly seeing things i have never heard of in my entire life. Experience with pants is a plus

[risingwinter]

The top result on duckduckgo for when I was looking for comparisons between Selenium and Pytest (Python programming testing tools). This is a website for comparing medication. Selenium is also a real medication, Pytest is not. (You can see it's trying to compare the tech versions in the body.)

It looks to me like someone had AI crawl through, scrape, and format the analyzed results for anything that looks like medication and put it into this format. The information in the body of the page doesn't look too crazy, the biggest consternation face from me comes from how it determined Pytest is an oral medication? (What is 14c urea?) And neither of them are addictive? I think? Pytest being less "safe," what does that mean? How did it determine any of this?

If anyone asks me why I don't trust the results of stuff like Chat GPT, this is why. "Confidently wrong" is my best description.

[kitscodingblog]

Coding Update 6

I think its been a while since I've updated. I fell behind a little on my learning cause life has been really difficult lately.

Hope y'all had a good Thanksgiving and having a good start into the holiday season!! Yadda yadda more under the cut.

So I just finished Part 1 of my book. This mostly contained the introduction to Python, obviously, while learning a lot of the major functions of the program. I think it took me a bit to get into the swing of coding, especially cause it felt like I've had to rewire my own brain doing this haha.

The good news is I feel a LOT more comfortable with Python now. Not like "i can do anything!" yet but enough that it's actually super fun and I'm excited to work on projects!

The last part of the chapter taught me to use the "pytest" ability. I.E: writing test code so that I can make sure my programs are working properly and as intended. That part was really interesting, mostly because it was super duper busted at first for me.

That ended up being because where my "default folder" is set is like my main python hub, so i have to use the uh. What's it called? True access link? Where I write the entire string to the code's location.

Which also taught me that in the Terminal I have to use quotes for the location cause before I learned proper coding practices, I used spaces in some of the initial folders.

We're good now though.

The next part, Part II, is all about learning to build fully functional projects!! I'm so HYPED. There's four projects, of which it was like choose whatever you want! But I'm gonna start with an Alien Invasion remake. You know, the game where you're the little ship at the bottom shooting at aliens as they slowly decend on the screen. I should learn a lot from this one.

The other project I'm looking forward to is a simple online blog database. It'll have users create accounts, be accessible online, and you can make little journal posts! That should actually teach me a lot of stuff that I want to do.

There's another for data visualization, which I think I'll send to my cousin. He works in a lab at MIT and I know they use python for their programs. Maybe I can work my way into his work by doing that lmao.

Anyway, I'm really excited for all of this. It should teach me a ton of usable skills, and then i can add these projects to my portfolio to show off. Also I can spin off and make my own stuff.

Also also, if anyone wants to help me test my projects, feel free to let me know! I already know a few who are more than willing, but I'd appreciate any and all feedback as I go.

Oh! It also recommended learning version control, which I know almost nothing about. So I'll learn how to use GitHUB to store projects and recall old ones as I go if things break horribly. Which will be fun! Cause I know that for sure is going to be an important skill to have.

For a last fun fact, did you know places are like "requirements: typing 30 words per second." Do you know how fast I can type? At my peak I'm like over 110. I baseline at like 95. I don't know if that's actually fast but it makes me feel like the specialist little guy.

I hope you all have a good holiday season. Sorry no code in this post, I'm writing it so I can give you all an update, and I'm dog tired today. But but I promise to snip actual code for you as I go forward. And It'll be fun, especially cause this alien project will teach me about making VISUAL things!

Seasons Greasons Tumblr! -Kit

[cciehub]

Join our "Automation Testing with Python" training program in Delhi NCR, India, and unlock the skills needed to excel in software testing. This comprehensive course covers essential Python Automation and advanced automation testing concepts, focusing on tools like Selenium and PyTest for hands-on learning. Ideal for beginners and professionals, the training provides real-world projects, expert mentorship, and a deep dive into Python's robust testing frameworks. Elevate your career with in-demand automation testing skills right here in Delhi NCR.

[irregular-developments]

More Scorem feedback

Bunch of good feedback came in from Joe B today. Much of it is already in the backlog (markdown, sanity test, init, pytest) but definitely some items I hadn't yet considered (tox, black, isort, move to python v3.10). Everything's in the backlog (30 items for SCOREM now, after this week). Still at v0.0.5 in both test and prod pypi.

[kennak]

parametrize使うと、ほぼ例外なく後で見直すと何してるかわからなくなる。使うときは便利に見えるんだがな。。

[B! python] Python(pytest)でテスト書くならfixture,conftest,parametrizeを理解すると世界が一気に変わる

[pythonjobsupport]

PyTest in PySpark using CI-CD pipelines | GitHub Action |

Delta Lake using Databricks Course Link: … source

[karatelabs]

API automation testing

Looking for an efficient API automation testing solution? Embrace code-based open-source tools like Karate, REST-Assured, and PyTest for faster, more reliable testing. These tools offer significant benefits, including faster execution, greater scalability, and seamless integration with your CI/CD pipelines, enabling continuous testing. By automating your API tests, you eliminate manual errors, ensure comprehensive test coverage, and significantly reduce costs in the long run. Karate is a standout option, offering an intuitive DSL for simplified test scripting, built-in features like parallel execution, and excellent integration with existing tools.

[kontakrecruitmentagency]

Python/Django QA Tester (JB5455) Bellville, Cape Town (Hybrid - 3 days in office) Salary: Negotiable Permanent This role is ideal for a hands-on QA tester with strong Python skills and a deep understanding of how code, APIs, and system architecture work together. You’ll work across insurance and payments systems, focusing on testing, code quality, and performance. Minimum Requirements: Completed IT or Applied Maths-related degree preferred 3 or more years of QA automation or SDET experience 2 or more years experience mentoring junior team members Python and Django testing tools Strong API testing (Postman, Requests) Pytest, Selenium, Playwright, or Locust SQL, Docker, Bitbucket, Jira CI/CD, agile, and performance-aware insurance/payments background For full JD & to apply online https://bit.ly/PythonDjangoTesterJB5455 @kontakrecruitmentagency

Know someone perfect for this role? Tag them below! 👇

[hawskstack]

Test Applications: Foundations and Implementation of Unit, Integration, and Functional Testing

In today’s fast-paced development environments, testing is no longer a luxury — it’s a non-negotiable necessity. With increasing complexity in microservices, APIs, and user expectations, organizations must ensure that applications function as intended across every layer. Comprehensive application testing provides the confidence to release faster, scale safely, and reduce technical debt.

In this blog, we’ll walk through the foundational principles of application testing and explore the different levels — unit, integration, and functional — each serving a unique purpose in the software delivery lifecycle.

🔍 Why Application Testing Matters

Application testing ensures software reliability, performance, and quality before it reaches the end users. A robust testing strategy leads to:

Early bug detection

Reduced production incidents

Faster feedback cycles

Higher developer confidence

Improved user experience

In essence, testing allows teams to fail fast and fix early, which is vital in agile and DevOps workflows.

🧱 Core Principles of Effective Application Testing

Before diving into types of testing, it’s essential to understand the guiding principles behind a solid testing framework:

1. Test Early and Often

Integrate testing as early as possible (shift-left approach) to catch issues before they become expensive to fix.

2. Automation is Key

Automated tests increase speed and consistency, especially across CI/CD pipelines.

3. Clear Separation of Test Layers

Structure tests by scope: small, fast unit tests at the bottom, and more complex functional tests at the top.

4. Repeatability and Independence

Tests should run reliably in any environment and not depend on each other to pass.

5. Continuous Feedback

Testing should provide immediate insights into what’s broken and where, ideally integrated with build systems.

🧪 Levels of Application Testing

🔹 1. Unit Testing – The Building Block

Unit testing focuses on testing the smallest pieces of code (e.g., functions, methods) in isolation.

Purpose: To validate individual components without relying on external dependencies.

Example Use Case: Testing a function that calculates tax percentage based on income.

Benefits:

Fast and lightweight

Easy to maintain

Pinpoints issues quickly

Tools: JUnit (Java), pytest (Python), Jest (JavaScript), xUnit (.NET)

🔹 2. Integration Testing – Ensuring Components Work Together

Integration testing verifies how different modules or services interact with each other.

Purpose: To ensure multiple parts of the application work cohesively, especially when APIs, databases, or third-party services are involved.

Example Use Case: Testing the interaction between the frontend and backend API for a login module.

Benefits:

Identifies issues in communication or data flow

Detects misconfigurations between components

Tools: Postman, Spring Test, Mocha, TestContainers

🔹 3. Functional Testing – Validating Business Requirements

Functional testing evaluates the application against user requirements and ensures that it performs expected tasks correctly.

Purpose: To confirm the system meets functional expectations from an end-user perspective.

Example Use Case: Testing a user’s ability to place an order through an e-commerce cart and payment system.

Benefits:

Aligns testing with real-world use cases

Detects user-facing defects

Often automated using scripts for regression checks

Tools: Selenium, Cypress, Cucumber, Robot Framework

🧰 Putting It All Together – A Layered Testing Strategy

A well-rounded testing strategy typically includes:

70% unit tests – quick feedback and bug detection

20% integration tests – ensure modules talk to each other

10% functional/UI tests – mimic user behavior

This approach, often referred to as the Testing Pyramid, ensures balanced test coverage without bloating the test suite.

🚀 Conclusion

Comprehensive application testing isn’t just about writing test cases — it’s about building a culture of quality and reliability. From unit testing code logic to validating real-world user journeys, every layer of testing contributes to software that’s stable, scalable, and user-friendly.

As you continue to build and ship applications, remember: “Test not because you expect bugs, but because you know where they hide.”

For more info, Kindly follow: Hawkstack Technologies