python-patterns
hypothesis
python-patterns | hypothesis | |
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31 | 20 | |
39,439 | 7,289 | |
- | 0.7% | |
0.0 | 9.9 | |
24 days ago | about 16 hours ago | |
Python | Python | |
- | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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python-patterns
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Any good resource on design patterns with examples in Python?
GitHub: Collection of design patterns and idioms
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Top GitHub Resources to Level Up Your Python game
🎇 Repository Link: Python Patterns
- How to improve design
- How to design Python scripts for sensitivity analysis of portfolios?
- They still scare me
- Out of curiosity: what is the python project structure you usually go gor?
- For those of you in industry, are there any resources that discuss best practices and whatnot?
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100+ Must Know Github Repositories For Any Programmer
4. Python Patterns
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Python toolkits
Your post has so many good elements that I've saved them for study and prompted feedback about the applications in Natural Language Processing and ML in Finance/BioTech. Most of my work lately has been NLP analysis research so devops and other GOF software concepts in your repo https://github.com/faif/python-patterns has been challenging.
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[Free Resource] Python Design Patterns
GitHub: Collection of design patterns and idioms
hypothesis
- Hypothesis
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Hypothesis for Property-Based Testing: Hypothesis is a Python library facilitating property-based testing. It offers a distinct advantage by generating a wide array of input data based on specified properties or invariants within the code. The perks of Hypothesis include:
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Pix2tex: Using a ViT to convert images of equations into LaTeX code
But then add tests! Tests for LaTeX equations that had never been executable as code.
https://github.com/HypothesisWorks/hypothesis :
> Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.
> This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck, but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.
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pgregory.net/rapid v1.0.0, modern Go property-based testing library
pgregory.net/rapid is a modern Go property-based testing library initially inspired by the power and convenience of Python's Hypothesis.
- Was muss man als nicht-technischer Quereinsteiger in Data Science *wirklich* können?
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Python toolkits
Hypothesis to generate dummy data for test.
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Best way to test GraphQL API using Python?
To create your own test cases, I recommend you use hypothesis-graphql in combination with hypothesis. hypothesis is a property-based testing library. Property-based testing is an approach to testing in which you make assertions about the result of a test given certain conditions and parameters. For example, if you have a mutation that requires a boolean parameter, you can assert that the client will receive an error if it sends a different type. hypothesis-graphql is a GraphQL testing library that knows how to use hypothesis strategies to generate query documents.
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Fuzzcheck (a structure-aware Rust fuzzer)
The Hypothesis stateful testing code is somewhat self-contained, since it mostly builds on top of internal APIs that already existed.
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Running C unit tests with pytest
We've had a lot of success combining that approach with property-based testing (https://github.com/HypothesisWorks/hypothesis) for the query engine at backtrace: https://engineering.backtrace.io/2020-03-11-how-hard-is-it-t... .
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Machine Readable Specifications at Scale
Systems I've used for this include https://agda.readthedocs.io/en/v2.6.0.1/getting-started/what... https://coq.inria.fr https://www.idris-lang.org and https://isabelle.in.tum.de
An easier alternative is to try disproving the statement, by executing it on thousands of examples and seeing if any fail. That gives us less confidence than a full proof, but can still be better than traditional "there exists" tests. This is called property checking or property-based testing. Systems I've used for this include https://hypothesis.works https://hackage.haskell.org/package/QuickCheck https://scalacheck.org and https://jsverify.github.io
What are some alternatives?
PyPattyrn - A simple library for implementing common design patterns.
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
TheAlgorithms - All Algorithms implemented in Python
Robot Framework - Generic automation framework for acceptance testing and RPA
sortedcontainers - Python Sorted Container Types: Sorted List, Sorted Dict, and Sorted Set
Behave - BDD, Python style.
algorithms
nose2 - The successor to nose, based on unittest2
more-itertools - More routines for operating on iterables, beyond itertools
nose - nose is nicer testing for python
python-ds - No non-sense and no BS repo for how data structure code should be in Python - simple and elegant.
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time