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Build, Run & Share Python code online using online-python's IDE for free. It's one of the quick, robust, powerful online compilers for python language. Don't worry about setting up python environment in your local. Now Run the python code in your favorite browser instantly. Getting started with this Python editor is easy and fast.

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• Put Interactive Python Anywhere on the Web. Customize the code below and Share! Turtle Adding or removing images is disabled during broadcasting. You'll be able to update images again once the broadcast session is over. Images listed here are available to use in your code.

Run Python scripts,Jupyter notebooks, or even a graphical applicationin a full, remote Python environment.

CoCalc covers all the bases

• Data Science and Machine Learning:Uploadyour datafiles and analyze them usingTensorflow,scikit-learn,Keras, ... including anAnacondaenvironment.
• Mathematics:SymPy,SageMath, ...
• Statistics:pandas,statsmodels,rpy2 (R bridge), ...
• Visualization:matplotlib,plotly, seaborn, ...
• Teaching: learn Python online or teach a course.

Find more details in thelist of installed Python libraries.

Zero setup

• Immediately start working by creating oruploading, Jupyter Notebooksor Python scripts.
• No need to download and installPython,Anaconda, or other Python environments.
• CoCalc alreadyprovides many packagesfor you.
• The LaTeX editor is already integrated withPythonTeX andSageTeX.

As the name suggests, CoCalc's strength isonline code collaboration. Collaboration applies to editing plain Python files,Sage Worksheets,Jupyter Notebooks, and much more.

This enables you to work more effectively as a team to solve the challenges of data science, machine learning and statistics. Every collaborator is always looking at the most recent state of files, and they experience and inspect the same Python state.

You cancreate chatroomsand get help viaside chat by @mentioning collaborators.

CoCalc offers acomplete rewriteof the classicalJupyter notebook interface. It is tightly integrated into CoCalc and adds realtime collaboration,TimeTravel history and much more.

The user interface is very similar to Jupyter classic. It uses the same underlying Jupyter notebook file format, so you can download your *.ipynb file at any time and continue working locally.

There are severalPython environments available.

You can also easily runJupyter Classicaland JupyterLab in any CoCalc project.

• Manages the entire compilation pipeline for you: it automatically calls pyhontex3 orsage to pre-process the code,
• Supports forward and inverse search to help you navigating in your document,
• Captures and shows youwhere LaTeX or Python errors happen,
• and viaTimeTravelyou can go back in time to see your latest edits in order toeasily recover from a recent mistake.

1. Upload or fetch your datasets,
2. Use Jupyter Notebooks to explore the data, process it, and calculate your results,
3. Discuss andcollaborate with your research team,
4. Write your research paper in a LaTeX document,
5. Publish the datasets, your research code, and the PDF of your paper online, all hosted on CoCalc.

CoCalc has one-click code formatting for Jupyter notebooks and code files!

Your python code is formatted in a clean and consistent way usingyapf.

This reduces cognitive load reading source code, and ensures all code written by your team has a consistent and beautiful style.

Python code formatting works withpure .py filesand Jupyter Notebooks running a Python kernel.

Your existing Python scripts run on CoCalc. Either open aTerminal in the code editor, or click the 'Shell' button to open a Python command line.

Terminals also give you access togit andmany more utilities.

Regarding collaboration, terminals can be usedby multiple users at once. This means you can work with your coworkers in the same session at the same time. Everyone sees the same output, and coordinate viaside chat next to the terminal.

You can also simultaneously work with many terminal sessions.

For long-running programs, you can even close your browser and check on the result later.

Collaboration is a first class citizen on CoCalc. Useside chat for each file to discuss content with your colleagues or students.

Additionally, avatars give youpresence information about who is currently also working on a file.

Collaborators who are not online will be notified about new messages the next time they sign in.

Chat also supports markdown formatting and $\text{LaTeX}$LATE​X formulas.

CoCalc helps you share your work with the world. It offers its own hosting of shared documents, alongside with any associated data files.

You can configure if your published files should be listed publicly, or rather only be available via a confidential URL.

Snapshots are consistent read-only views of all your files in aCoCalc project. You can restore your files by copying back any that you accidentally deleted or corrupted.

Run your Python Unit Tests with GitHub Actions

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In this post we will give you information about Run your Python Unit Tests with GitHub Actions. Hear we will give you detail about Run your Python Unit Tests with GitHub ActionsAnd how to use it also give you demo for it if it is necessary.

In this tutorial, we’ll learn how to automatically run your Python unit tests using GitHub Actions.

We’ll see how to set up a GitHub Actions workflow that install Python 3.6 inside a Ubuntu system along with our project’s dependencies e.g. pytest and finnaly run the unit tests after pushing our code to a GitHub repository.

Let’s see how to automate running unit tests when making a commit and pushing your code to GitHub or when making a pull request.

Thanks to GitHub Actions it’s now easier than before without using any external services and they even provide a good free tier.

This will allow you to spot the right commit(s) that broke your code.

We’ll be using Python 3, and we will be working in a virtual environment. This is a good practice for Python to isolate system packages from our project’s package. Even if this is a small example but should be a practice that you need to always follow.

Let’s start by creating and activating a virtual environment for our project, by running the following commands:

This will create a virtual environment called .env in our project’s folder.

Next, we need to activate this virtual environment using the following command:

Next, let’s install pytest in our project’s virtual environment using the following command:

Setting up A Python Project with PyTest

We’ll be using pytest for testing.

It can be installed using the following command inside your virtual environment:

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Pytest expects our tests to be located in files whose names begin with test_ or end with _test.py.

Next, go ahead and add some tests:

Next, we’ll create a file named test_capitalize.py, next add the following Python code:

Next, we’ll need to write a test. We need prefix the test function name with test_, since this is what pytest expects:

You can run the test, by running the following command:

Finally, we need to create a requirements.txt file using the following command:

Now that we made sure that our example is running locally with this simple example, let’s set up a GitHub Actions workflow for automatically running the test(s) when our code is pushed to GitHub.

Setting up a GitHub Actions Workflow

You can create a workflow by creating a YAML file inside the .github/workflows/ci.yml folder.

Next, open the file and add the following content:

This workflow is named Run Python Tests. it will be started when pushing or pulling code from the master branch of our repository. It contains one job named build with four steps which will run inside a Ubuntu runner.

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We first give the workflow access to the code of the repository using the checkout@v2 action. Next, we add a step named Install Python 3 which makes use of the setup-python@v1 action to install Python 3.6. Next, we add a step that will install the dependencies of our project in Ubuntu. Finally we add a step for running our tests using pytest.

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Now you simply need to run the following commands to commit and push to GitHub repository and wait for your tests to automatically run:

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Conclusion

In this tutorial, we’ve seen how to use a GitHub Actions workflow to automate running your Python tests with Pytest.

Hope this code and post will helped you for implement Run your Python Unit Tests with GitHub Actions. if you need any help or any feedback give it in comment section or you have good idea about this post you can give it comment section. Your comment will help us for help you more and improve us. we will give you this type of more interesting post in featured also so, For more interesting post and code Keep reading our blogs

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