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fsspec: Filesystem interfaces for Python

Filesystem Spec (fsspec) is a project to provide a unified pythonic interface to local, remote and embedded file systems and bytes storage.

Brief Overview

There are many places to store bytes, from in memory, to the local disk, cluster distributed storage, to the cloud. Many files also contain internal mappings of names to bytes, maybe in a hierarchical directory-oriented tree. Working with all these different storage media, and their associated libraries, is a pain. fsspec exists to provide a familiar API that will work the same whatever the storage backend. As much as possible, we iron out the quirks specific to each implementation, so you need do no more than provide credentials for each service you access (if needed) and thereafter not have to worry about the implementation again.

Why

fsspec provides two main concepts: a set of filesystem classes with uniform APIs (i.e., functions such as cp, rm, cat, mkdir, ...) supplying operations on a range of storage systems; and top-level convenience functions like :func:`fsspec.open`, to allow you to quickly get from a URL to a file-like object that you can use with a third-party library or your own code.

The section :doc:`intro` gives motivation and history of this project, but most users will want to skip straight to :doc:`usage` to find out how to use the package and :doc:`features` to see the long list of added functionality included along with the basic file-system interface.

Who uses fsspec?

You can use fsspec's file objects with any python function that accepts file objects, because of duck typing.

You may well be using fsspec already without knowing it. The following libraries use fsspec internally for path and file handling:

  1. Dask, the parallel, out-of-core and distributed programming platform
  2. Intake, the data source cataloguing and loading library and its plugins
  3. pandas, the tabular data analysis package
  4. xarray and zarr, multidimensional array storage and labelled operations
  5. DVC, version control system for machine learning projects
  6. Kedro, a Python framework for reproducible, maintainable and modular data science code
  7. Huggingface🤗 Datasets, a popular library to load&manipulate data for Deep Learning models

fsspec filesystems are also supported by:

  1. pyarrow, the in-memory data layout engine
  2. petl, a general purpose package for extracting, transforming and loading tables of data.

... plus many more that we don't know about.

Installation

fsspec can be installed from PyPI or conda and has no dependencies of its own

pip install fsspec
conda install -c conda-forge fsspec

Not all filesystem implementations are available without installing extra dependencies. For example to be able to access data in GCS, you can use the optional pip install syntax below, or install the specific package required

pip install fsspec[gcs]
conda install -c conda-forge gcsfs

fsspec attempts to provide the right message when you attempt to use a filesystem for which you need additional dependencies. The current list of known implementations can be found as follows

from fsspec.registry import known_implementations

known_implementations
.. toctree::
   :maxdepth: 1
   :caption: Contents:

   intro.rst
   usage.rst
   features.rst
   copying.rst
   developer.rst
   async.rst
   api.rst
   changelog.rst
   code-of-conduct.rst

These docs pages collect anonymous tracking data using goatcounter, and the dashboard is available to the public: https://fsspec.goatcounter.com/ .