Accessing files and notebooks from a kernel

Starting with JupyterLite 0.1.0b9, the contents of the user’s File Browser and some kernels, including the Pyodide kernel, are automatically synchronized.

You can, for example, drag and drop a file file.csv into the JupyterLite UI, then load it in Python:

import pandas as pd

data = pd.read_csv('file.csv')

data

Synchronized content works by mounting a custom Emscripten Filesystem (FS) which communicates with the JupyterLite content manager through the JupyterLite ServiceWorker-enabled.

Note

The ServiceWorker will not always be enabled, based on limitations of both the user’s browser and the HTTP server.

Verifying the Filesystem

To check if the filesystem syncing is enabled, see whether cwd starts with /drive/:

import os

os.getcwd()  # If successful:  "/drive/path/to/notebook"
             # ... otherwise:  "/home/pyodide"

Fetching remote content

It is also possible to fetch content from remote URL. For example if you are using the Pyodide kernel, you can use the fetch function to do so:

import pandas as pd
from js import fetch

URL = "https://raw.githubusercontent.com/jupyterlite/jupyterlite/main/examples/data/iris.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

As an alternative, you can import the pyodide-http package (distributed with Pyodide by default) to use familiar methods more seamlessly:

import pyodide_http
import pandas as pd

pyodide_http.patch_all()

data = pd.read_csv("https://raw.githubusercontent.com/jupyterlite/jupyterlite/main/examples/data/iris.csv")
data