# 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:

```py
import pandas as pd

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

data
```

Synchronized content works by mounting a custom [Emscripten Filesystem][fs] (FS) which
communicates with the JupyterLite content manager through the JupyterLite
[`ServiceWorker`](../configure/advanced/service-worker.md)-enabled.

```{note}
The `ServiceWorker` will not always be enabled, based on
[limitations](../configure/advanced/service-worker.md#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/`:

```py
import os

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

[fs]: https://emscripten.org/docs/api_reference/Filesystem-API.html
[caniuse-sw]: https://caniuse.com/serviceworkers

## 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:

```py
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:

```py
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
```