User Guide#
Contents#
Frequently-Asked Questions#
How is JupyterLite different than JupyterLab?#
If you’re using a JupyterLite site, there isn’t much to know. It works like a regular, server-backed JupyterLab site, except:
The list of kernels, usually visible from the Launcher as different Notebook flavors, will be different
Your data is written to in-browser storage
though you may be able to copy
None of your data leaves your browser unless…
Extensions are installed and enabled
Your Notebooks include code that uses the browser’s
fetch
mechanism
How can I put my own content in JupyterLite?#
See the developer guide: it can get pretty involved!
How can I read content from Python?#
Accessing contents like other files and notebooks from a kernel might be tricky.
Currently the content visible by the end user is a mix of server provided files, and
files saved locally in the web browser. This means that trying to access a server
provided files from Python with paradigms like open('data.csv')
will most likely fail.
A common workaround is be to manually fetch a file from a remote URL with the fetch
method from the browser before manipulating its content:
import pandas as pd
from js import fetch
URL = "https://yourdomain.com/path/to/file.csv"
res = await fetch(URL)
text = await res.text()
filename = 'data.csv'
with open(filename, 'w') as f:
f.write(text)
data = pd.read_csv(filename, sep=';')
data
Note
See the following issues and discussions for more information:
It is also possible to manipulate the data stored in IndexedDB
from Python, but it can be pretty involved.
See the example notebook for more details.