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python3-apache-airflow-yandex-2.8.0-2.lbn36.noarch
Apache/Airflow yandex provider
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python3-apache-airflow-zendesk-2.8.0-2.lbn36.noarch
Apache/Airflow zendesk provider
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python3-jupyter-c-kernel-1.2.2-14.fc36.noarch
Minimalistic C kernel for Jupyter
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python3-jupyter-cache-0.5.0-1.lbn36.noarch
jupyter-cache[![Github-CI][github-ci]][github-link] [![Coverage
Status][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd-
link] [![Code style: black][black-badge]][black-link] [![PyPI][pypi-
badge]][pypi-link]A defined interface for working with a cache of jupyter
notebooks. Why use jupyter-cache?If you have a number of notebooks whose
execution outputs you want to...
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python3-jupyter-client-8.4.0-1.lbn36.noarch
Jupyter Client
jupyter_client contains the reference implementation of the Jupyter protocol.
It also provides client and kernel management APIs for working with kernels.
It also provides the jupyter kernelspec entrypoint
for installing kernelspecs for use with Jupyter frontends.
Development Setup
The Jupyter Contributor Guides provide extensive information on contributing code or documentation to Jupyter projects. The limited instructions below for setting up a development environment are for your convenience.
Coding
You'll need Python and pip on the search path. Clone the Jupyter Client git repository to your computer, for example in /my/project/jupyter_client
cd /my/projects/
git clone git@github.com:jupyter/jupyter_client.git
Now create an editable install
and download the dependencies of code and test suite by executing:
cd /my/projects/jupyter_client/
pip install -e ".[test]"
pytest
The last command runs the test suite to verify the setup. During development, you can pass file
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python3-jupyter-console-6.6.3-1.lbn36.noarch
Jupyter Console
A terminal-based console frontend for Jupyter kernels.
This code is based on the single-process IPython terminal.
Install with pip:
pip install jupyter-console
Install with conda:
conda install -c conda-forge jupyter_console
Start:
jupyter console
Help:
jupyter console -h
Jupyter Console allows for console-based interaction with non-python
Jupyter kernels such as IJulia, IRKernel.
To start the console with a particular kernel, ask for it by name::
jupyter console --kernel=julia-0.4
A list of available kernels can be seen with::
jupyter kernelspec list
Release build:
$ pip install pep517
$ python -m pep517.build .
Resources
Project Jupyter website
Documentation for Jupyter Console [PDF]
Documentation for Project Jupyter [PDF]
Issues
Technical support - Jupyter Google Group
About the Jupyter Development Team
The Jupyter Development Team is the set of all contributors to the Jupyter project.
This includes all of the Jupyter subprojects.
The core team that coordi
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python3-jupyter-core-5.7.2-1.lbn36.noarch
There is no reason to install this package on its own.
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python3-jupyter-kernel-singular-0.9.9-8.fc36.noarch
This package contains a Jupyter kernel for Singular, to enable using
Jupyter as the front end for Singular.
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python3-jupyter-kernel-test-0.6.0-1.lbn36.noarch
jupyter_kernel_test is a tool for testing Jupyter kernels. It tests kernels
for successful code execution and conformance with the Jupyter Messaging Protocol
(currently 5.0).
Install
Install it with pip (python3.4 or greater required):
pip3 install jupyter_kernel_test
Usage
To use it, you need to write a (python) unittest file containing code
samples in the relevant language which test various parts of the messaging protocol.
A short example is given below, and you can also refer to the
test_ipykernel.py and test_irkernel.py files for complete examples.
Some parts of the messaging protocol are relevant only to the browser-based
notebook (rich display) or console interfaces (code completeness,
history searching). Only parts of the spec for which you provide code samples
are tested.
Run this file directly using python, or use nosetests or py.test to find
and run it.
Example
import unittest
import jupyter_kernel_test
class MyKernelTests(jupyter_kernel_test.KernelTests):
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python3-jupyter-packaging-0.12.3-1.lbn36.noarch
Jupyter Packaging
Tools to help build and install Jupyter Python packages that require a pre-build step that may include JavaScript build steps.
Install
pip install jupyter-packaging
Usage
There are three ways to use jupyter-packaging in another package.
In general, you should not depend on jupyter_packaging as a runtime dependency, only as a build dependency.
As a Build Requirement
Use a pyproject.toml file as outlined in pep-518.
An example:
[build-system]
requires = ["jupyter_packaging>=0.10,<2"]
build-backend = "setuptools.build_meta"
Below is an example setup.py using the above config.
It assumes the rest of your metadata is in setup.cfg.
We wrap the import in a try/catch to allow the file to be run without jupyter_packaging
so that python setup.py can be run directly when not building.
from setuptools import setup
try:
from jupyter_packaging import wrap_installers, npm_builder
builder = npm_builder()
cmdclass = wrap_installers(pre_develop=builder, pre_dist=builder)
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