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Jupyter Storage Explorer

PyPI - Version codecov

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.

StorageExplorer with a JSON Lines file open in the data browser

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. If one is missing, the preview tells you what to install.

Quick start

From the JupyterLab launcher

The separate package jupyter-storage-explorer-launcher adds Storage Explorer to JupyterLab's launcher and command palette, opening the explorer in its own tab:

pip install jupyter-storage-explorer-launcher

The Storage Explorer item in JupyterLab's launcher

From Python code

from jupyter_storage_explorer import StorageExplorer

StorageExplorer("s3://my-bucket/data")

The explorer appears as the cell's output. Options after the address are passed to the storage package, e.g. StorageExplorer("s3://my-bucket/data", profile="analytics"). Double-click a file to preview it; right-click for everything else.

To 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 (storage_explorer.ipynb, data_explorer.ipynb) on sample data made by make_samples.py.

Documentation

  • StorageExplorer: storages, using the explorer, unsafe operations, transfer policy, remembered states, supported formats, format options and all the options.
  • DataExplorer: the data browser, result(), on_change(), QuerySettings and the options.
  • The launcher: installing it and configuring its tab with launcher.py files.
  • Development: building from source, the JupyterLab extension, releasing, and customizing the explorer's icons with CSS.

Requirements

  • Python 3.11 or later
  • JupyterLab 4. The widgets also show in other Jupyter frontends, but dragging files from JupyterLab's file browser works only in JupyterLab.
  • For querying data: a recent polars (tested with polars 2.0). DataExplorer works with any dataframe narwhals supports; with polars installed, data in memory is queried with polars, which is faster.

Metadata

Release files for jupyter-storage-explorer 0.7.2

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Source distribution for jupyter-storage-explorer 0.7.2
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Table of built distributions (wheels) for jupyter-storage-explorer 0.7.2
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jupyter_storage_explorer-0.7.2-py3-none-any.whl Python 3 none any Details

Total release size: 591.7 kB

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