Metadata-Version: 2.1
Name: gptables
Version: 1.1.0
Summary: Simplifying good practice in statistical tables.
Home-page: https://gptables.readthedocs.io/en/latest/
Author: David Foster, Rowan Hemsi
Author-email: david.foster@ons.gov.uk, rowan.hemsi@ons.gov.uk
Maintainer: Rowan Hemsi
Maintainer-email: rowan.hemsi@ons.gov.uk
License: MIT license
Description: Good Practice Tables (gptables)
        ===============================
        
        .. image:: https://github.com/best-practice-and-impact/gptables/workflows/continuous-integration/badge.svg
            :target: https://github.com/best-practice-and-impact/gptables/actions
            :alt: Actions build status
            
        .. image:: https://readthedocs.org/projects/gptables/badge/?version=latest
            :target: https://gptables.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        
        .. image:: https://badge.fury.io/py/gptables.svg
            :target: https://badge.fury.io/py/gptables
            :alt: PyPI release
        
        
        ``gptables`` is an opinionated python package for spreadsheet production.
        It produces ``.xlsx`` files from your ``pandas`` dataframes or using
        ``reticulate`` in R. You define the mapping from your data to elements of the
        table. It does the rest.
        
        ``gptables`` uses the official `guidance on good practice spreadsheets`_.
        It advocates a strong adherence to the guidance by restricting the range of operations possible.
        The default theme ``gptheme`` should accommodate most use cases.
        However, the ``Theme`` object allows development of custom themes, where other formatting is required.
        
        ``gptables`` is developed and maintained by the `Analysis Function`_. It can be
        installed from `PyPI`_ or `GitHub`_. The source code is maintained on GitHub.
        Users may also be interested in `a11ytables`_, an R native equivalent to
        ``gptables``, and `csvcubed`_, a package for turning data and metadata into
        machine-readable CSV-W files.
        
        .. _`guidance on good practice spreadsheets`: https://analysisfunction.civilservice.gov.uk/policy-store/releasing-statistics-in-spreadsheets/
        .. _`Analysis Function`: https://analysisfunction.civilservice.gov.uk/
        .. _`PyPI`: https://pypi.org/project/gptables/
        .. _`GitHub`: https://github.com/best-practice-and-impact/gptables
        .. _`a11ytables`: https://co-analysis.github.io/a11ytables/index.html
        .. _`csvcubed`: https://gss-cogs.github.io/csvcubed-docs/external/
        
        
        5 Simple Steps
        --------------
        
        1. You map your data to the elements of a ``GPTable``.
        
        2. You can define the format of each element with a custom ``Theme``, or simply use the default - gptheme.
        
        3. Optionally design a ``Cover`` page to provide information that relates to all of the tables in your Workbook.
        
        4. Optionally upload a ``notes_table`` with information about any notes.
        
        5. You ``write_workbook`` to win.
        
Keywords: reproducible tables excel xlsxwriter reproducible-analytical-pipelines
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: docs
Provides-Extra: testing
