pynguin
Pandas
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pynguin | Pandas | |
---|---|---|
11 | 393 | |
1,197 | 41,923 | |
1.3% | 1.4% | |
8.2 | 10.0 | |
2 days ago | 5 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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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.
pynguin
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There is framework for everything.
https://swagger.io/specification/ https://github.com/se2p/pynguin
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Supposed to create tests for a massive project, how should I go about it?
I would use black to reformat this, then, if you can't refactor/rewrite (which is a lot of work!) I would try automated test generation via something like pynguin or fuzzing. I mean … this is not going to be a reliable solution anyways if the codebase is like that. So I would go in a direction that I find interesting to learn about and that could be helpful for the project. That would be generating tests and doing fuzzing. In the end you should run some linters anyways so that you can justify your results and show that the task is not in the scope of an internship and needs extensive refactoring.
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Klara: Python automatic test generations and static analysis library
The main difference that Klara bring to the table, compared to similar tool like pynguin and Crosshair is that the analysis is entirely static, meaning that no user code will be executed, and you can easily extend the test generation strategy via plugin loading (e.g. the options arg to the Component object returned from function above is not needed for test coverage).
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Does anybody know a simple algorithm for generating unit tests given a function's code?
Automated White-box test generation software: * https://github.com/EMResearch/EvoMaster -- for integration tests. * https://github.com/se2p/pynguin, https://pynguin.readthedocs.io/en/latest/user/quickstart.html -- unit test generation for python
- se2p/pynguin Pynguin, the PYthoN General UnIt test geNerator, is a tool that allows developers to generate unit tests automatically.
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Hacker News top posts: Jun 1, 2021
Pynguin – Generate Python unit tests automatically\ (60 comments)
- Pynguin – Generate Python unit tests automatically
- Pynguin – Allow developers to generate Python unit tests automatically
Pandas
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
What are some alternatives?
CrossHair - An analysis tool for Python that blurs the line between testing and type systems.
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
EvoMaster - The first open-source AI-driven tool for automatically generating system-level test cases (also known as fuzzing) for web/enterprise applications. Currently targeting whitebox and blackbox testing of Web APIs, like REST, GraphQL and RPC (e.g., gRPC and Thrift).
tensorflow - An Open Source Machine Learning Framework for Everyone
klara - Automatic test case generation for python and static analysis library
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
icontract-hypothesis - Combine contracts and automatic testing.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
methods2test - methods2test is a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java software repositories
Keras - Deep Learning for humans
code - Example application code for the python architecture book
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration