faker
pytest
faker | pytest | |
---|---|---|
9 | 30 | |
17,101 | 11,371 | |
- | 1.0% | |
9.5 | 9.8 | |
7 days ago | 5 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
faker
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Leveling up your custom fake data with Faker.js
Faker was originally written in Perl and is also available as a library for Ruby, Java, and Python.
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The Uncreative Software Engineer's Compendium to Testing
Faker: a library that generates fake data that can be useful when you need data to test for various components.
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Exploring LLMs for Data Synthesizing & Anonymization: looking for Insights on Current & Future Solutions
Don't get me wrong, LLMs are awesome but totally unsuited for what you are describing. Classic data science tools like faker will be better for the task in pretty much every aspect. They can generate synthetic datasets and anonymize existing ones faster and far more reliable than any LLM.
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Undercover work
The Python package, Faker, is just what you're looking for!
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Is there a way to automate testing in python? In my case :
for datatypes like string/date and other stuff, there is a Python library called faker, which you can use. It can generate fake names, fake phone numbers, dates, and addresses. here is the link to the documentation. https://faker.readthedocs.io/ here is a link to a blog post explaining Faker. https://levelup.gitconnected.com/pythons-faker-library-your-go-to-solution-for-test-data-generation-3a070065cc04
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Testing files in Python like a pro
Then test cases became more complex. Primary data sources were often files. We needed to test pipelines. Faker still helped a lot, but it was not convenient to copy your last-best-approach for files and reinvent the wheel over and over with each project.
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Database automation challenges and how to solve them
For a cloud-based solution, one can write their own Terraform or CloudFormation for installation as soon as their RDS instance boots up with appropriate security and authentication details. For a local dev environment, one can rely on Faker to create mock database data for your database.
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How to create a 1M record table with a single query
Creating realistic fake data is useful in lower environments and for load testing. Outside of SQL I like faker: https://github.com/joke2k/faker
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DuckDB: an embedded DB for data wrangling
To test a database, first you need some data. So I created a python script and used Faker to create the following CSV files:
pytest
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Integrating Lab Equipment into pytest-Based Tests
In this blog post I want to demonstrate how my lab equipment such as a lab power supply or a digital multimeter (DMM) have been integrated into some pytest-based tests. Would love to get your feedback and thoughts! 🚀
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The Uncreative Software Engineer's Compendium to Testing
Pytest: is a third-party testing framework that supports fixtures, parameterized testing, and easy test discovery while having room to add plugins to extend its functionality.
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pytest VS vedro - a user suggested alternative
2 projects | 16 Jul 2023
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TDD vs BDD - A Detailed Guide
Next, you need to install a testing framework that will be used for performing unit testing in your project. Several testing frameworks are available depending on the programming language used to create an application. For example, JUnit is commonly used for Java apps, pytest for Python apps, NUnit for .NET apps, Jest for JavaScript apps, and so on. We’ll use the Jest framework for this tutorial since we are using JavaScript.
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Is there a way to automate testing in python? In my case :
Yea, read through the pytest docs.
- Testing an automation framework
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Pytest Tips and Tricks
I absolutely agree about fixtures-as-arguments thing. Ward does this a lot better, using default values for the fixture factory. There's a long issue on ideas to implement something like that as a pytest plugin (https://github.com/pytest-dev/pytest/issues/3834), but it seems the resulting plugin relies on something of a hack.
- 2023 Development Tool Map
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Is my merge sort right?
I recommend writing a few tests. py.test makes that quite simple:
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How to raise the quality of scientific Jupyter notebooks
Since ITK's inception in 1999, there has been a focus on engineering practices that result in high-quality software. High-quality scientific software is driven by regression testing. The ITK project supported the development of CTest and CDash unit testing and software quality dashboard tools for use with the CMake build system. In the Python programming language, the pytest test driver helps developers write small, readable scripts that ensure their software will continue to work as expected. However, pytest can only test Python scripts by default, and errors in untested computational notebooks are more common than well-tested Python code.
What are some alternatives?
Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
nose2 - The successor to nose, based on unittest2
FauxFactory - Generates random data for your tests.
Robot Framework - Generic automation framework for acceptance testing and RPA
fake2db - create custom test databases that are populated with fake data
Behave - BDD, Python style.
picka - pip install picka - Picka is a python based data generation and randomization module which aims to increase coverage by increasing the amount of tests you _dont_ have to write by hand.
Slash - The Slash testing infrastructure
PyRestTest - Python Rest Testing
hypothesis - Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
radar
nose - nose is nicer testing for python