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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.

1. OnlineIDE - Online Python Compiler is a web-based tool powered by ACE code editor. This tool can be used to learn, build, run, test your programs. You can open the code from your local and continue to build using this IDE.
2. Python Compiler (Editor) With our online Python compiler, you can edit Python code, and view the result in your browser. Edit the code and click on the 'Run.
3. A lot of Python scripts don’t request the user to enter data a line at a time; they take a file as their input, process it, and produce a file as the output. Here’s a simple script that asks for an input filename and an output filename.

Run the experiment, or select the module and click Run selected to run just the Python script. All of the data and code is loaded into a virtual machine, and run using the specified Python environment. Online Python IDE is a web-based tool powered by ACE code editor. This tool can be used to learn, build, run, test your python script. You can open the script from your local and continue to build using this IDE.

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!

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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.

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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.

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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.

Running Python in the web browser has been getting a lot of attention lately. Shaun Taylor-Morgan knows what he’s talking about here - he works for Anvil, a full-featured application platform for writing full-stack web apps with nothing but Python. So I invited him to give us an overview and comparison of the open-source solutions for running Python code in your web browser.

In the past, if you wanted to build a web UI, your only choice was JavaScript.

That’s no longer true. There are quite a few ways to run Python in your web browser. This is a survey of what’s available.

SIX SYSTEMS

I’m looking at six systems that all take a different approach to the problem. Here’s a diagram that sums up their differences.

The x-axis answers the question: when does Python get compiled? At one extreme, you run a command-line script to compile Python yourself. At the other extreme, the compilation gets done in the user’s browser as they write Python code.

The y-axis answers the question: what does Python get compiled to? Three systems make a direct conversion between the Python you write and some equivalent JavaScript. The other three actually run a live Python interpreter in your browser, each in a slightly different way.

1. TRANSCRYPT

Transcrypt gives you a command-line tool you can run to compile a Python script into a JavaScript file.

You interact with the page structure (the DOM) using a toolbox of specialized Python objects and functions. For example, if youimport document, you can find any object on the page by using document like a dictionary. To get the element whose ID isname-box, you would use document['name-box']. Any readers familiar with JQuery will be feeling very at home.

Here’s a basic example. I wrote a Hello, World page with just an input box and a button:

To make it do something, I wrote some Python. When you click the button, an event handler fires that displays an alert with a greeting:

I wrote this in a file called hello.pyand compiled it using transcrypt hello.py. The compiler spat out a JavaScript version of my file, called hello.js.

Transcrypt makes the conversion to JavaScript at the earliest possible time - before the browser is even running. Next we’ll look at Brython, which makes the conversion on page load.

2. BRYTHON

Brython lets you write Python in script tags in exactly the same way you write JavaScript. Just as with Transcrypt, it has a document object for interacting with the DOM.

The same widget I wrote above can be written in a script tag like this:

Pretty cool, huh? A script tag whose type is text/python!

There’s a good explanation of how it works on the Brython GitHub page. In short, you run a function when your page loads:

that transpiles anything it finds in a Python script tag:

which results in some machine-generated JavaScript that it runs using JS’s eval() function.

3. SKULPT

Skulpt sits at the far end of our diagram - it compiles Python to JavaScript at runtime. This means the Python doesn’t have to be written untilafter the page has loaded.

The Skulpt website has a Python REPL that runs in your browser. It’s not making requests back to a Python interpreter on a server somewhere, it’s actually running on your machine.

Skulpt does not have a built-in way to interact with the DOM. This can be an advantage, because you can build your own DOM manipulation system depending on what you’re trying to achieve. More on this later.

Skulpt was originally created to produce educational tools that need a live Python session on a web page (example: Trinket.io). While Transcrypt and Brython are designed as direct replacements for JavaScript, Skulpt is more suited to building Python programming environments on the web (such as the full-stack app platform, Anvil).

We’ve reached the end of the x-axis in our diagram. Next we head in the vertical direction: our final three technologies don’t compile Python to JavaScript, they actually implement a Python runtime in the web browser.

4. PYPY.JS

PyPy.jsis a JavaScript implementation of a Python interpreter. The developers took a C-to-JavaScript compiler calledemscriptenand ran it on the source code ofPyPy. The result is PyPy, but running in your browser.

Advantages: It’s a very faithful implementation of Python, and code gets executed quickly.

Disadvantages: A web page that embeds PyPy.js contains an entire Python interpreter, so it’s pretty big as web pages go (think megabytes).

You import the interpreter using <script> tags, and you get an object called pypyjs in the global JS scope.

There are three main functions for interacting with the interpreter. To execute some Python, run pypyjs.exec(<python code>). To pass values between JavaScript and Python, use pypyjs.set(variable, value) and pypyjs.get(variable).

Here’s a script that uses PyPy.js to calculate the first ten square numbers:

PyPy.js has a few features that make it feel like a native Python environment - there’s even an in-memory filesystem so you can read and write files. There’s also a document object that gives you access to the DOM from Python.

The project has a great readme if you’re interested in learning more.

5. BATAVIA

Batavia is a bit like PyPy.js, but it runs bytecode rather than Python. Here’s a Hello, World script written in Batavia:

Bytecode is the ‘assembly language’ of the Python virtual machine - if you’ve ever looked at the .pyc files Python generates, that’s what they contain (Yasoob dug into some bytecode in a recent post on this blog). This example doesn’t look like assembly language because it’s base64-encoded.

Batavia is potentially faster than PyPy.js, since it doesn’t have to compile your Python to bytecode. It also makes the download smaller -around 400kB. The disadvantage is that your code needs to be written and compiled in a native (non-browser) environment, as was the case with Transcrypt.

Again, Batavia lets you manipulate the DOM using a Python module it provides (in this case it’s called dom).

The Batavia project is quite promising because it fills an otherwise unfilled niche - ahead-of-time compiled Python in the browser that runs in a full Python VM. Unfortunately, the GitHub repo’s commit rate seems to have slowed in the past year or so. If you’re interested in helping out, here’s their developer guide.

6. PYODIDE

Mozilla’s Pyodidewas announced in April 2019. It solves a difficult problem: interactive data visualisation in Python, in the browser.

Python has become a favourite language for data science thanks to libraries such as NumPy, SciPy, Matplotlib and Pandas. We already have Jupyter Notebooks, which are a great way to present a data pipeline online, but they must be hosted on a server somewhere.

If you can put the data processing on the user’s machine, they avoid the round-trip to your server so real-time visualisation is more powerful. And you can scale to so many more users if their own machines are providing the compute.

It’s easier said than done. Fortunately, the Mozilla team came across a version of the reference Python implementation (CPython) that was compiled into WebAssembly. WebAssembly is a low-level compliment to JavaScript that performs closer to native speeds, which opens the browser up for performance-critical applications like this.

Mozilla took charge of the WebAssembly CPython project and recompiled NumPy, SciPy, Matplotlib and Pandas into WebAssembly too. The result is a lot like Jupyter Notebooks in the browser -here’s an introductory notebook.

It’s an even bigger download than PyPy.js (that example is around 50MB), but as Mozilla point out, a good browser will cache that for you. And for a data processing notebook, waiting a few seconds for the page to load is not a problem.

You can write HTML, MarkDown and JavaScript in Pyodide Notebooks too. And yes, there’s adocumentobject to access the DOM. It’s a really promising project!

MAKING A CHOICE

I’ve given you six different ways to write Python in the browser, and you might be able to find more. Which one to choose? This summary table may help you decide.

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There’s a more general point here too: the fact that there _is_ a choice.

As a web developer, it often feels like you_have_to write JavaScript, you_have_to build an HTTP API, you_have_to write SQL and HTML and CSS. The six systems we’ve looked at make JavaScript seem more like a language that getscompiled to, and you choose what to compile to it (And WebAssembly is actually_designed_to be used this way).

Why not treat the whole web stack this way? The future of web development is to move beyond the technologies that we’ve always ‘had’ to use. The future is to build abstractions on top of those technologies, to reduce the unnecessary complexity and optimise developer efficiency. That’s why Python itself is so popular - it’s a language that puts developer efficiency first.

ONE UNIFIED SYSTEM

There should be one way to represent data, from the database all the way to the UI. Since we’re Pythonistas, we’d like everything to be a Python object, not an SQL SELECT statement followed by a Python object followed by JSON followed by a JavaScript object followed by a DOM element.

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That’s what Anvil does - it’s a full-stack Python environment that abstracts away the complexity of the web. Here’s a 7-minute video that covers how it works.

Remember I said that it can be an advantage that Skulpt doesn’t have a built-in way to interact with the DOM? This is why. If you want to go beyond ‘Python in the browser’ and build a fully-integrated Python environment, your abstraction of the User Interface needs to fit in with your overall abstraction of the web system.

So Python in the browser is just the start of something bigger. I like to live dangerously, so I’m going to make a prediction. In 5 years’ time, more than 50% of web apps will be built with tools that sit one abstraction level higher than JavaScript frameworks such as React and Angular. It has already happened for static sites: most people who want a static site will use WordPress or Wix rather than firing up a text editor and writing HTML. As systems mature, they become unified and the amount of incidental complexity gradually minimises.

If you’re reading this in 2024, why not get in touch and tell me whether I was right?

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