Micropip Install

[Jenette Bregantini]

PurePython packages are packages that do not have any compiled code.Most pure Python packages can be installed directly from PyPI with micropip.installif they have a pure Python wheel. Check if this is the case by trying micropip.install("package-name").

You can find a list of packages with pre-built wheels in thePyodide documentation.If your package is not in the list, you can build a wheel for it yourself.See the Building packages section of the Pyodide documentation for more information.

micropip does dependency resolution by default, but you can disable it,this is useful if you want to install a package that has a dependencywhich is not a pure Python package, but it is not mandatory for your use case:

micropip.install() (Python) for pure Python packages with wheels aswell as Pyodide packages (including Emscripten/wasm32 binary wheels). It caninstall packages from PyPI, the JsDelivr CDN or from other URLs.

pyodide.loadPackage() (Javascript) for packages built with Pyodide.This is a function with less overhead but also more limited functionality.micropip uses this function to load Pyodide packages. In most cases you shouldbe using micropip.

In some cases, and in particular in the REPL, packages are installed implicitlyfrom imports. The Pyodide REPL uses pyodide.loadPackagesFromImports()to automatically download all packages that the code snippet imports. This isuseful since users might import unexpected packages in REPL. At present,loadPackagesFromImports() will not download packages fromPyPI, it will only download packages included in the Pyodide distribution. SeePackages built in Pyodide to check the full list of packages included inPyodide.

While micropip.install() is written in Python andpyodide.loadPackage() in Javascript this has no incidence on when touse each of these functions. Indeed, you can easily switch languages using theType translations with,

I built a custom wheel of dlib package:ML-python-wheels/dlib-19.24.99-cp39-cp39-linux_x86_64.whl at main dnabanita7/ML-python-wheels GitHub

I want to install it using micropip but I am not sure why it is giving me errors.

After installing, how to use it as pyimport is deprecated?

Additionally - the Pyoddie 22.0 stable release just happened/is happening today, and it looks like some of the resources are still being deployed. Just for reference - are you using v0.21.3 or trying with the new Pyodide v0.22.0?

If you are trying to achieve this in node.js you need to install node-fetch (or any other library that provides fetch function) and make it available as global/globalThis element so that pyodide's js can also access it.

I am new to Python and trying to display some diagrams on a webpage using Pyscript. However, importing modules with Pyodide isn't working. I've tried every single example found in the documentation as well as this thread: How do I import modules in a project using pyodide without errors? but it's still not working.

I've tried "loadPackage", "py-env" and "micropip.install". Am I missing something?I am completely confused.I am getting the "ModuleNotFoundError: The module 'pandas' is included in the Pyodide distribution, but it is not installed." error again and again:

One thing I really appreciated about this approach (i.e. template repository) is that I was able to get JupyterLite deployed on my GitHub Pages without any previous knowledge of GitHub Pages nor GitHub Actions. So thanks a lot for creating this template repo.

My copy of the jupyterlite/demo template got me version JupyterLite version 0.1.0-beta.10 while a beta 12 has been released since. Looking at the commits on jupyterlite/demo, is it true that I only need to update my requirements.txt file? Should I do it manully or using some tool to check the consistency?

EDIT: I juste read more carefully Deploy your first JupyterLite website on GitHub Pages, and it clearly mentions that updating JupyterLite should be done by changing requirements.txt. However the question of the consistency of manual edits still holds.

However, when I run it for the first time, I get the ModuleNotFoundError: No module named 'ipywidgets' error. I only works if I re-run the cell (with %run -i my_setup.py) a second time (actually, this was the behavior in late July, but today it never works!).

In the mean time, I have updated my requirements.txt file to jupyterlite==0.1.0b12, taking jupyterlite/demo requirements as an example. However, I believe I must be missing something, because now even in a blank notebook (and after erasing website data from Firefox), I cannot install ipywidgets!

I did try to use a different TMP_DIR as suggested by some StackOverflow answers like this one but that did not fix the problem in my case. My system drive seems to be a bit full but it looks like there should be enough space to install the package. I also tried to install the package locally (e.g. pip3 install --user) but that did not help either.

The traditional way of deploying Shiny involves in a separate server and client: the server runs Python and Shiny, and clients connect via the web browser. Each client keeps an open websocket connection as long as they are using the application.

These URLs have a hash that includes #code=.... The code for the entire application is encoded in that hash. Notably, web browsers do not send the hash to the web server, so the server actually never sees the content of the Shiny application.

The sharing dialog shows how long the URL is, in bytes. If you want to share a link on Twitter, the maximum length of a URL is about 4000 bytes, and it will be shortened using their t.co service. If you use bit.ly, the maximum length is about 2000 bytes. These link shorteners redirect the user to the longer URL.

Sharing via gists has some important differences from sharing via encoded-app URL. If you use a gist, you can modify the gist, and the sharing URL will stay the same. If you are sharing an encoded-app URL, the URL itself contains the application code, so if you want modify the code, you will have to generate a new URL and share that.

Sharing via GitHub gist may not be appropriate for all use cases, because the GitHub API has rate limits: for a given IP address, the GitHub API allows 60 requests per hour. So an end user would only be able to load Shinylive applications 60 times in an hour. And if there are many users behind a single IP address with network address translation, they collectively would have a limit of 60 requests per hour.

The easiest way to deploy Shinylive applications is using the quarto-shinylive extension. This extension allows you to embed Shiny apps into a quarto html document, and deploy those applications anywhere that can host quarto websites. Once you have the extension installed, you can insert shinylive-python code blocks into the document.

To run a Shinylive application, the files must be served with a web server; simply pointing your browser to the files on disk will not work. This is because security restrictions in web browsers require some assets to be retrieved from a web server instead of from disk.

The site/shinylive/pyodide/ directory will contain a Pyodide distribution containing just the Python packages needed to run the exported application(s). There are some cases where you may want to include other packages. For example, if you want users who visit the edit/ URL to be able to load more packages. In order to include extra packages, you have two options:

The Shinylive distribution is under rapid development, and the files in the distribution will change. The shinylive export command automatically downloads and caches a a copy of the Shinylive distribution on your computer. To make sure you are up to date, run:

After creating the directory with the application and Shinylive bundle, you can deploy it to many different of static web hosting services. Posit Connect is one of those options, and allows you to control over who can access the application.

Each time someone runs your Shiny application, their web browser will fetch those packages from PyPI. It will then install the packages to a virtual file system (VFS); when the user closes the page or navigates away from it, the VFS is discarded. If the user goes back and runs the application again, those files can be fetched from the browser cache instead of from PyPI.

What ultimately worked was declaring the pip version when installing. So pip3.10 install openai did it for me. There was nothing online when I first had this problem, so I wrote it out here: Python Error When Istalling OpenAI.

To keep that clean, or to use different sets or versions of libraries for different purposes,you may want to look into the venv documentation,which explains both the concept of virtual environmentsand how they are used on different platforms.

If you need to install the latest development version of aiocoap but do notplan on editing (eg. because you were asked in the course of a bug report totest something against the latest aiocoap version), you can install it directlyfrom the web:

As a workaround, it can be helpful to not install with all extras, but replace theall with the extras you actually want from the list below. For example, ifyou see errors from DTLSSocket, rather than installing with [all,docs], youcan leave out the tinydtls extra and install with[linkheader,oscore,prettyprint,docs].

linkheader: Originally needed for generating and parsing files inRFC6690 link format, eg. .well-known/core files. This extra does notcontain any external dependencies, but was left in place for compatibility.

This proved to be more challenging than I expected, this post follows the process of tinkering around with Pyodide and Pyscript in order to get the client running and ultimately build a semi-interactive webpage that can query InfluxDB using Python in the browser.

This only works for packages that are either pure Python or for packages with C extensions that are built in Pyodide. If a pure Python package is not found in the Pyodide repository it will be loaded from PyPI.

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