Jupyter Storage Explorer
Explore your data lake without leaving Jupyter: built for data professionals
StorageExplorer is a file explorer for cloud storage and data lakes, right inside Jupyter, in the spirit of Azure
Storage Explorer. See
Using the explorer.
DataExplorer browses any dataframe in your notebook: pandas, polars, Spark, DuckDB and more. See
Using the data browser.
Highlights
StorageExplorer: one explorer to browse them all. S3, Azure, Google Cloud Storage, local disks and anything else fsspec reaches, side by side.DataExplorer: a table widget for your dataframes, more than meets the eye. Page, sort and filter large tables in your data lake, and get the result back in code.- Launcher: right at home in your Jupyter. Open the explorer in a tab or in the side panel, edit storage files, and copy and paste or drag and drop files between explorers, and more.
- Previews: look before you load. Delta Lake, Parquet, CSV, JSON, Excel, text and images open at a click, with ready-to-run code that loads them into your notebook.
- Plugin support: data professionals, start your engines. Plug your favourite data library, such as Polars, pandas or PySpark, into the explorer, and browse data with it on the fly. Not supported yet? You, or your AI assistant, can write a plugin for it: see Developing plugins.
- Safe file operations: keep calm and copy on. Copy, move and delete large folders in the background, with progress and Cancel. Folder operations wait for Unsafe mode, and transfer rules keep data where it may go.
- Bookmarks: there and back again. Bookmarks and remembered queries bring you back where you left off.
Quick start
Try it without installing anything: open the demo on Binder, a made-up data lake in a notebook and in the launcher's tab (it may take a minute to start).
From the JupyterLab launcher
jupyter-storage-explorer-launcher
adds Storage Explorer to JupyterLab's launcher, opening the explorer in its own tab, and to the left side bar:
pip install "jupyter-storage-explorer-launcher[polars]"
From Python code
pip install "jupyter-storage-explorer[polars]"
[polars] adds the Polars plugin with Polars itself; other engines and storages are extras too, e.g.
[polars-support] for the plugin alone, using the libraries you have (recommended in carefully set up kernels). See
Plugins for all of them, and
Installation
for the storage packages.
from jupyter_storage_explorer import StorageExplorer
StorageExplorer("s3://my-bucket/data")
Options after the address go to the storage package, e.g. profile="analytics". Double-click a file to preview it;
right-click for everything else. With the launcher installed, files that JupyterLab can show open in its editors
instead.
Browse a dataframe and get the rows you filtered back:
import polars as pl
from jupyter_storage_explorer import DataExplorer
explorer = DataExplorer(pl.scan_parquet("s3://my-bucket/lake/sales/*.parquet"))
explorer
explorer.result().collect() # after filtering and sorting in the widget
The notebooks folder has a demo of each widget on sample data.
Documentation
- StorageExplorer: storages, using the explorer, transfer policy, formats, working with it from Python and options.
- DataExplorer: the data
browser,
result(),on_change(),QuerySettingsand options. - The launcher: configuring
its tab with
launcher.pyfiles. - Development: building, releasing, and custom icons.
- Developing plugins: formats, engines, file operations and storages of your own, packaged as plugins.
Requirements
- Python 3.11 or later
- JupyterLab 4.0 or later (other Jupyter frontends show the widgets, without drag and drop from JupyterLab's file browser)
- For querying data: an engine plugin, e.g.
jupyter-storage-explorer[polars](Polars, tested with 2.0)
Why Storage Explorer?
A data-aware file explorer for large data lakes, in JupyterLab's main area (a notebook or a tab), with room for what working with data needs; it complements JupyterLab's file browser rather than replacing it.
- Large folders: paged, sorted and filtered as you type; tens of thousands of items listed by the kernel; file operations in the background, with progress and Cancel.
- Tables, not just files: Delta Lake tables and partitioned Parquet datasets open in the data browser, queried where they live with the engine you choose.
- From browsing to code: ready-to-run code and URLs for what you select; your code can follow what you browse (from Python).
- At home in JupyterLab: drag files to and from its file browser and between storages; edit files in its editors; keep an explorer in its side bar, beside your notebooks (the launcher's side panel).
- Safe by default: folders are moved, copied, renamed or deleted only in Unsafe mode, for one operation.
- Runs in your kernel: with your credentials and your libraries, and no server setup.
1 maintainer
Metadata
1 maintainer
Release files for jupyter-storage-explorer 0.21.0
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