#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.

[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!

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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

[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

[aitoolswhitehattoolbox]

Python Lead (5 plus)

Job Description Good hands-on knowledge in Python with scripting, automation, data processing, API integration, testing Knowledge of AWS step functions using Serverless workflows, state machines, AWS service integration. Experience in designing & developing Microservices based applications, Unit testing using Pytest or similar, DevOps and Agile practices, Code Reviews. Ability to write code in an…

[pythonfullstackmasters]

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[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.

[appzlogic]

Overview

Our client runs a cloud-based platform that turns complex data from sources like firewalls and SIEMs into clear insights for better decision-making. It uses advanced ETL processes to gather and process large volumes of data, making it easy for users to access accurate and real-time information.

Why They Chose Us

As they launched a new app, they needed a testing partner to ensure high performance and reliability. They chose Appzlogic for our expertise in functional and automation testing. We built a custom automation framework tailored to their needs.

Our Testing Strategy

We started with manual testing (sanity, smoke, functional, regression) and later automated key UI and API workflows. Poor data quality and manual ETL testing are major reasons why BI projects fail. We addressed this by ensuring data accuracy and reducing manual work.

Manual Testing Process:

Requirement Analysis: Understood the product and its goals

Scope Definition: Identified what to test

Test Case Design: Created test cases for all scenarios

Execution & Defect Logging: Ran tests and reported issues in JIRA

Automation Testing Results:

We reduced manual effort by 60%. Automated tests were created for data validation across AWS and Azure services. Modular and end-to-end tests boosted efficiency and coverage.

Source Data Flow Overview

These events flowed through the volume controller and were distributed across multiple processing nodes, with one rule node actively handling 1 event. The transformation stage processed 1 event, which was then successfully delivered to the Raw-S3-1 destination. This streamlined flow highlights a well-structured and reliable data processing pipeline.

Centralized Data Operations Briefly

The Data Command Center showcases a well-orchestrated flow of data with 2,724 sources feeding into 3,520 pipelines, resulting in 98.4k events ingested and 21.3 MB of log data processed, all at an average rate of 1 EPS (event per second). Every connected destination received 100% of the expected data with zero loss. Additionally, 51 devices were newly discovered and connected, with no pending actions. This dashboard reflects a highly efficient and reliable data pipeline system in action.

Smooth and Reliable Data Flow

The source TC-DATAGENERATOR-SOURCE-STATUS-1745290102 is working well and is active. It collected 9.36k events and processed 933 KB of data. All events were successfully delivered to the Sandbox with no data loss. The graph shows a steady flow of data over time, proving the system is running smoothly and efficiently.

Tools & Frameworks Used:

Python + Pytest: For unit and functional tests

RequestLibrary: For API testing

Selenium: For UI automation

GitHub + GitHub Actions: For CI/CD

Boto3: To work with AWS

Paramiko: For remote server access

Conclusion

Our testing helped the client build a reliable and scalable platform. With a mix of manual and automated testing, we boosted test accuracy, saved time, and supported their continued growth.

We are The Best IT Service Provider across the globe.

Contact Us today.

[karatelabs]

api automated testing

Would you like to streamline api automated testing? Tools like Karate, REST-Assured, and PyTest are going to be your go-to guys for automation while making it robust for testing. Karate makes API test scripting quite easy by being user-friendly and fitting naturally into a host of CI/CD pipelines, with features such as API testing, performance testing, and mocks. REST-Assured offers an easy path for Java developers to write automated tests for REST APIs. PyTest provides flexibility and scalability for Python users.

[sunalimerchant123]

ETL Testing: How to Validate Your Python ETL Pipelines

In the world of data engineering, building a strong Extract Transform Load (ETL) process is only half the battle. Ensuring that your ETL pipelines are reliable, accurate, and efficient is just as crucial. When working with Extract Transform Load Python workflows, proper ETL testing is essential to maintain data quality, catch errors early, and guarantee trustworthy outputs for downstream applications. In this article, we'll explore why ETL testing matters and how to effectively validate your Python ETL pipelines.

Why ETL Testing Is Critical

ETL processes move and transform data between systems — often at massive scales. A small mistake during extraction, transformation, or loading can result in significant business consequences, from incorrect analytics to failed reporting. Especially when using Extract Transform Load Python pipelines, where flexibility is high and custom scripts are common, thorough testing helps to:

Detect data loss or corruption

Ensure transformations are applied correctly

Validate that data is loaded into the target system accurately

Confirm that performance meets expectations

Maintain data consistency across different stages

Without systematic ETL testing, you risk pushing flawed data into production, which could impact decision-making and operations.

Key Types of ETL Testing

When validating Extract Transform Load Python pipelines, several types of testing should be performed:

1. Data Completeness Testing

This ensures that all the expected data from the source system is extracted and made available for transformation and loading. You might use row counts, checksum comparisons, or aggregate validations to detect missing or incomplete data.

2. Data Transformation Testing

In this step, you verify that transformation rules (like calculations, data type changes, or standardizations) have been correctly applied. Writing unit tests for transformation functions is a best practice when coding ETL logic in Python.

3. Data Accuracy Testing

Data must be correctly inserted into the target system without errors. Validation includes checking field mappings, constraints (like foreign keys), and ensuring values match expectations after loading.

4. Performance Testing

An efficient Extract Transform Load Python pipeline should process data within acceptable timeframes. Performance testing identifies slow stages and bottlenecks in your ETL workflow.

5. Regression Testing

Whenever changes are made to the ETL code, regression testing ensures that new updates don't break existing functionality.

How to Perform ETL Testing in Python

Python provides a wide range of tools and libraries that make ETL testing approachable and powerful. Here’s a practical roadmap:

1. Write Unit Tests for Each Stage

Use Python’s built-in unittest framework or popular libraries like pytest to create test cases for extraction, transformation, and loading functions individually. This modular approach ensures early detection of bugs.

2. Validate Data with Pandas

Pandas is excellent for comparing datasets. For example, after extracting data, you can create Pandas DataFrames and use assertions like:

python

CopyEdit

import pandas as pd

3. Create Test Data Sets

Set up controlled test databases or files containing predictable datasets. Using mock data ensures that your Extract Transform Load Python process can be tested repeatedly under consistent conditions.

4. Automate ETL Test Workflows

Incorporate your ETL testing into automated CI/CD pipelines. Tools like GitHub Actions, Jenkins, or GitLab CI can trigger tests automatically whenever new code is pushed.

5. Use Data Validation Libraries

Libraries like great_expectations can make ETL testing even more robust. They allow you to define "expectations" for your data — such as field types, allowed ranges, and value uniqueness — and automatically validate your data against them.

Common ETL Testing Best Practices

Always test with real-world data samples when possible.

Track and log all test results to maintain visibility into pipeline health.

Isolate failures to specific ETL stages to debug faster.

Version-control both your ETL code and your test cases.

Keep test cases updated as your data models evolve.

Final Thoughts

Validating your Extract Transform Load Python pipelines with thorough ETL testing is vital for delivering trustworthy data solutions. From unit tests to full-scale validation workflows, investing time in testing ensures your ETL processes are accurate, reliable, and scalable. In the fast-paced world of data-driven decision-making, solid ETL testing isn't optional — it’s essential.

[fromdevcom]

In the ever-evolving world of software development, adopting effective coding practices is crucial for ensuring code quality, maintainability, and efficiency. Here are ten essential coding practices every developer should follow to excel in their craft. 1. Write Clean and Readable Code Clean code is easy to read, understand, and maintain. Use meaningful variable names, consistent formatting, and avoid complex logic. Comment your code where necessary, but aim for self-explanatory code that minimizes the need for comments. 2. Follow Consistent Coding Standards Adhering to a consistent coding standard helps maintain uniformity across the codebase, making it easier for team members to collaborate. Popular standards include PEP 8 for Python and the Google Java Style Guide. 3. Use Version Control Systems Version control systems (VCS) like Git allow developers to track changes, collaborate efficiently, and revert to previous versions if needed. Familiarize yourself with basic Git commands and workflows to manage your code effectively. 4. Write Unit Tests Unit tests verify that individual components of your code work as expected. Writing tests ensures that new changes do not break existing functionality and helps maintain a high level of code quality. Use testing frameworks like JUnit for Java or pytest for Python. 5. Practice Code Reviews Code reviews provide an opportunity to catch errors, improve code quality, and share knowledge within the team. Review code changes thoroughly and provide constructive feedback. As a reviewer, focus on readability, logic, and adherence to coding standards. 6. Refactor Regularly Refactoring involves improving the internal structure of the code without changing its external behavior. Regular refactoring helps eliminate technical debt, improve code readability, and make the codebase more maintainable. Focus on simplifying complex functions and removing redundancies. 7. Optimize for Performance Write code that performs efficiently and scales well with increasing data or user load. Profile your code to identify bottlenecks and optimize critical sections. Avoid premature optimization; focus on writing clear and correct code first, then optimize as needed. 8. Keep Learning and Adapting The tech industry evolves rapidly, and staying up-to-date with the latest trends, tools, and practices is essential. Follow industry blogs, participate in forums, and take online courses to continuously improve your skills. 9. Document Your Code Good documentation helps others understand your code and its intended use. Write clear and concise documentation for your functions, classes, and modules. Use tools like JSDoc for JavaScript or Sphinx for Python to generate documentation automatically. 10. Practice Continuous Integration and Deployment (CI/CD) CI/CD automates the process of integrating code changes, running tests, and deploying applications. This practice ensures that code changes are tested and deployed quickly and reliably. Use CI/CD tools like Jenkins, Travis CI, or GitHub Actions to streamline your workflow. Conclusion By following these ten essential coding practices, developers can create high-quality, maintainable, and efficient code. These practices not only improve individual productivity but also foster better collaboration and code management within teams. Embrace these habits to elevate your development skills and contribute to building robust software solutions.