semvec/pypi-packages huggingface dataset:
pulp-cookbook 0.1.0b9
Cookbook plugin for the Pulp Project

.. image:: https://github.com/pulp/pulp_cookbook/actions/workflows/nightly.yml/badge.svg
   :target: https://github.com/pulp/pulp_cookbook/actions/workflows/nightly.yml
   :alt: Cookbook Nightly CI/CD
.. image:: https://img.shields.io/pypi/v/pulp-cookbook.svg
   :target: https://pypi.python.org/pypi/pulp-cookbook
   :alt: PyPI
.. image:: https://img.shields.io/pypi/pyversions/pulp-cookbook.svg
   :target: https://pypi.python.org/pypi/pulp-cookbook
   :alt: PyPI - Python Version
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/ambv/black
   :alt: Code style: black


pulp-cookbook Plugin
====================

The ``cookbook`` plugin extends `pulpcore
<https://pypi.python.org/pypi/pulpcore/>`_ to support hosting Chef cookbooks.
This plugin assumes some familiarity with the `pulpcore documentation
<https://docs.pulpproject.org/en/3.0/nightly/>`_.

For more information, please see the `documentation <https://pulp-cookbook.readthedocs.io/>`_
or the `Pulp project page <https://pulpproject.org>`_.
data-profile-viewer 0.2.7
Data Profile Viewer tool. Enables the exploration of data profile in Jupyter Notebooks
Requires Python: >=3.6

# DataProfileViewer

Data profile viewer is compatible with Jupyter Notebooks. Supports the metadata format generated by [datamart-profiler](https://docs.auctus.vida-nyu.org/python/datamart-profiler.html#) library.


![Data summary viewer](https://github.com/soniacq/DataProfileVis/blob/master/imgs/data_summary_plot.png)

![Refine data profiler results viewer](https://github.com/soniacq/DataProfileVis/blob/master/imgs/edit_profiler_plot.png)

## Install via pip

~~~~
pip install data-profile-viewer
pip install datamart-profiler
~~~~

## Demo

In Jupyter Notebook:
~~~~
import DataProfileViewer
data = DataProfileViewer.get_lifeexpectancy_data()
DataProfileViewer.plot_data_summary(data)
~~~~

## Data Profile Exploration

~~~~
import DataProfileViewer
import datamart_profiler
~~~~

In a jupyter notebook, load the data

~~~~
data_path = 'lifeexpectancydata.csv'
metadata = datamart_profiler.process_dataset(data_path, include_sample=True, plots=True)
~~~~

and then plot it using:

~~~~
DataProfileViewer.plot_data_summary(metadata)
~~~~

## Refine Data Profiler Results

You might want to correct/refine the type information, or provide additional annotations for the columns. To do so, use the code:

~~~~
DataProfileViewer.plot_edit_profiler(metadata)
~~~~

To retrieve the updated metadata, use the code:

~~~~
updatedMetadata = DataProfileViewer.get_exported_metadata(data_path)
~~~~
aiida-environ 1.0.0
AiiDA plugin for Environ
Keywords: aiida,workflows,environ

# `aiida-environ`

This plugin builds on top of [aiida-quantumespresso](https://github.com/aiidateam/aiida-quantumespresso).

## Compatibility

Currently tested with aiida-core~1.6 and aiida-quantumespresso~3.5

## Installation

To install from PyPI,

    pip install aiida-environ

To install from source

    git clone https://github.com/environ-developers/aiida-environ
    pip install aiida-environ

## License

See the `LICENSE.txt` file for more details.