faker
faker
faker | faker | |
---|---|---|
9 | 45 | |
17,101 | 11,122 | |
- | 0.7% | |
9.5 | 8.7 | |
6 days ago | 3 days ago | |
Python | Ruby | |
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:
faker
- Faker – generate fake data such as names, addresses, and phone numbers
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Test Driving a Rails API - Part Two
While we’re at it, let's add a couple of other gems we’ll need for our test environment: factory_bot_rails is a fixtures replacement and generates test model instances. faker is handy for generating fake strings of data to be used in tests. Add those gems to the development and test group of your Gemfile:
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Full-Text Search for Ruby on Rails with Litesearch
Next up, we'll combine a few of the techniques we've reviewed to implement snappy typeahead searching. Before we do that, though, let's generate more sample data. I will use the popular faker gem to do that:
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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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How to Use Shoulda Matchers with RSpec for Ruby on Rails
Faker
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How to Setup RSpec on a Rails Project
rspec-rails factory_bot_rails faker
- Seeding the DB: Best approach?
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Users of the Ruby programming language now have FFXIV mock data
Ah, so the data is just a giant .yml file of potential options, that I compiled, and then faker grabs a random one based on that data. There's no "database" or anything akin to that.
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A proposal on how to deal with Monkey Patching
It's with the Faker library. I was able to reproduce it by spinning up a new rails up, specifying the faker gem , yours, and running the command gives the same error.
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Faker Gem
To begin using faker, you need to install the gem, which is simply done by running "gem install faker". After you do that, you should add "gem "faker"" to your gem file to ensure correct usage, otherwise you may get an error. Once thats done you can head over to the seeding file and add require "faker" the page. In the seeding file is where you would start to "fake" your data and we use the faker library to pick out random dummy data. In the seeding, your gonna start implementing the data which should look like this,
What are some alternatives?
Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
ffaker - Faker refactored.
FauxFactory - Generates random data for your tests.
factory_bot - A library for setting up Ruby objects as test data.
fake2db - create custom test databases that are populated with fake data
Forgery - Easy and customizable generation of forged data.
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.
Fabrication - This project has moved to GitLab! Please check there for the latest updates.
PyRestTest - Python Rest Testing
Fake Person - Create some fake personalities
radar
FactoryTrace - Simple tool to maintain factories and traits from FactoryBot