Jupyter Storage Explorer
StorageExplorer is a file explorer for cloud storage and data lakes, right inside your Jupyter notebook, in the
spirit of Azure Storage Explorer.
Browse Amazon S3, Azure Data Lake / Blob Storage, Google Cloud Storage, local disks and more; preview Parquet files, Delta Lake tables, CSV, Excel, JSON and other formats; page through, sort and filter tables; get ready-to-run code that loads the data into your notebook; and upload, download, move and edit files without leaving JupyterLab.
This package also provides the DataExplorer widget, which lets you browse tabular data in your notebook: any
dataframe of pandas, polars, Spark, DuckDB and more. Page through, sort and filter it in the widget, then get the
result back in code.
Highlights
StorageExplorer: any fsspec storage, several in one explorer; copy and paste between storages and notebooks.DataExplorer: page, sort and filter any dataframe; get the result back in code.- Previews of Delta Lake, Parquet, CSV, JSON, Excel, text and images, with code to load them.
- Background file operations with progress and Cancel; folder operations only in Unsafe mode.
- Bookmarks, remembered queries, transfer rules, and a JupyterLab launcher item.
Installation
pip install jupyter-storage-explorer
Install the package for your storage too:
| Storage | Package | Address |
|---|---|---|
| Amazon S3 | s3fs |
s3://bucket/path |
| Azure Data Lake / Blob Storage | adlfs |
abfs://container@account.dfs.core.windows.net/path |
| Google Cloud Storage | gcsfs |
gs://bucket/path |
| Local disk, in-memory | (included) | /path, memory:// |
Previewing and querying data files needs a data library, e.g. pip install polars deltalake; the preview tells you
which.
Quick start
From the JupyterLab launcher
jupyter-storage-explorer-launcher
adds Storage Explorer to JupyterLab's launcher, opening the explorer in its own tab:
pip install jupyter-storage-explorer-launcher
From Python code
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.
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 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.
Requirements
- Python 3.11 or later
- JupyterLab 4 (other Jupyter frontends show the widgets, without drag and drop from JupyterLab's file browser)
- For querying data: polars (tested with 2.0)
1 maintainer
Metadata
1 maintainer
Release files for jupyter-storage-explorer 0.8.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| jupyter_storage_explorer-0.8.0.tar.gz | 977.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jupyter_storage_explorer-0.8.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / jupyter_storage_explorer-0.8.0.tar.gz
| Download URL | jupyter_storage_explorer-0.8.0.tar.gz |
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| Size | 977.1 kB |
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| Size | 207.5 kB |
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