Modified items

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python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-inline-0.1.7-2.lbn36.noarch: Inline Matplotlib backend for Jupyter

python3-matplotlib-data-fonts-3.10.0-3.lbn36.noarch: Fonts used by python-matplotlib

python3-matplotlib-data-fonts-3.10.0-3.lbn36.noarch: Fonts used by python-matplotlib

python3-matplotlib-data-fonts-3.10.0-3.lbn36.noarch: Fonts used by python-matplotlib

python3-matplotlib-data-3.10.0-3.lbn36.noarch: Data used by python-matplotlib

python3-matplotlib-data-3.10.0-3.lbn36.noarch: Data used by python-matplotlib

python3-matplotlib-data-3.10.0-3.lbn36.noarch: Data used by python-matplotlib

python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-marshmallow-sqlalchemy-1.0.0-1.lbn36.noarch: ********************** marshmallow-sqlalchemy **********************|pypi- package| |build-status| |docs| |marshmallow3| |black|Homepage: < integration with the marshmallow < (de)serialization library.Declare your models .. code- block:: python import sqlalchemy as sa from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import scoped_session, sessionmaker, relationship,...

python3-jupyterlab_pygments-0.1.2-5.fc36.noarch: This package contains a syntax coloring theme for pygments making use of the JupyterLab CSS variables.

python3-jupyter-sphinx-0.3.2-3.fc36.noarch: Jupyter-Sphinx enables running code embedded in Sphinx documentation and embedding output of that code into the resulting document. It has support for rich output such as images and even Jupyter interactive widgets.

python3-jupyter-polymake-0.16-18.20180129.7049940.fc36.noarch: This package contains a Jupyter kernel for polymake.

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)

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)

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):

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):

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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