Skip to main content

Dataflow Python Kernel for Jupyter

Project description

Dataflow Kernel for Jupyter/Python

License PyPI version

This package is part of the Dataflow Notebooks project and provides the Dataflow Python kernel for Jupyter, and is intended to be used with JupyerLab in concert with the dfnotebook-extension. This kernel seeks to elevate outputs as memorable waypoints during exploratory computation. To that end,

  • Cell identifiers are persistent across sessions and are random UUIDs to signal they do not depend on top-down order.
  • As with standard IPython, outputs are designated by being written as expressions or assignments on the last line of a cell.
  • Each output is identified by its variable name if one is specified (e.g. a, c,d = 4,5), and the cell identifier if not (e.g. 4 + c)
  • Variable names can be reused across cells.
  • Cells are executed as closures so only the outputs are accessible from other cells.
  • An output can then be referenced in three ways:
    1. unscoped: foo refers to the most recent execution output named foo
    2. persistent: foo$ba012345 refers to output foo from cell ba012345
    3. tagged: foo$bar refers to output foo from the cell tagged as bar
  • All output references are transformed to persistent names upon execution.
  • Output references implicitly define a dataflow in a directed acyclic graph, and the kernel automatically executes dependencies.

Example Notebook

Dataflow Notebook Example

Installation

These instructions only install the kernel. Please see the dfnotebook-extension instructions for full instructions.

PyPI

pip install dfkernel

From source

  1. git clone https://github.com/dataflownb/dfkernel
  2. cd dfkernel
  3. pip install -e .
  4. python -m dfkernel install [--user|--sys-prefix]

Note that --sys-prefix works best for conda environments.

Dependencies

  • IPython >= 7.0
  • JupyterLab >= 2.0
  • ipykernel >= 4.8.2

Previous Versions

dfkernel 1.0 worked with Jupyter Notebook, but we have decided to support JupyterLab in the future. Documentation and tutorials for v1.0 are below, but still need to be updated for v2.0.

v1.0 Documentation

General

Advanced Usage

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dfkernel-4.0.0a2.tar.gz (1.2 MB view details)

Uploaded Source

Built Distribution

dfkernel-4.0.0a2-py3-none-any.whl (1.7 MB view details)

Uploaded Python 3

File details

Details for the file dfkernel-4.0.0a2.tar.gz.

File metadata

  • Download URL: dfkernel-4.0.0a2.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.1

File hashes

Hashes for dfkernel-4.0.0a2.tar.gz
Algorithm Hash digest
SHA256 94966215310c151b0ff247bb923ed16c673196e7fcc3f49c35ecfbaf2224255a
MD5 f9a665a817776c715af492aedba1f438
BLAKE2b-256 7680121c8da8043bb8ebc2d164efa219d72d8202647704b31c20c3034736e84d

See more details on using hashes here.

File details

Details for the file dfkernel-4.0.0a2-py3-none-any.whl.

File metadata

  • Download URL: dfkernel-4.0.0a2-py3-none-any.whl
  • Upload date:
  • Size: 1.7 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.1

File hashes

Hashes for dfkernel-4.0.0a2-py3-none-any.whl
Algorithm Hash digest
SHA256 814f33a8c68c34051ee711c173f1d0d0e1cd1ed678b96357dbeb1d8e72ffdccb
MD5 47fe07bfa0e91913e0067c25fdb8bf7c
BLAKE2b-256 56463a4d520e8a38e8bb55daa34efb4a9810da7c22f7b3fc57d18274e5dde1a2

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page