Metadata-Version: 2.1
Name: chartify
Version: 2.3.2
Summary: Python library to make plotting simpler for data scientists
Home-page: https://github.com/spotify/chartify
Author: 
Author-email: chalpert@spotify.com
License: UNKNOWN
Keywords: chartify
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5,<4
Requires-Dist: pandas (<1.0.0,>=0.21.0)
Requires-Dist: jupyter (<2.0.0,>=1.0.0)
Requires-Dist: Pillow (<5.0.0,>=4.3.0)
Requires-Dist: selenium (<=3.8.0,>=3.7.0)
Requires-Dist: bokeh (<1.0.0,>=0.12.15)
Requires-Dist: scipy (<2.0.0,>=1.0.0)
Requires-Dist: colour (<1.0.0,>=0.1.5)

Chartify
========

|status|  |release|  |python|

.. |status| image:: https://img.shields.io/badge/Status-Beta-blue.svg
.. |release| image:: https://img.shields.io/badge/Release-2.3.2-blue.svg
.. |python| image:: https://img.shields.io/badge/Python-3.6-blue.svg

Chartify is a Python library that aims to make it as easy as possible for data scientists to create charts.

Why use Chartify?
-----------------

- Consistent input data format: Spend less time transforming data to get your charts to work. All plotting functions use a consistent tidy input data format.
- Smart default styles: Create pretty charts with very little customization required.
- Simple API: We've attempted to make to the API as intuitive and easy to learn as possible.
- Flexibility: Chartify is built on top of `Bokeh <http://bokeh.pydata.org/en/latest/>`_, so if you do need more control you can always fall back on Bokeh's API.

Examples
--------

.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify1.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify2.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify3.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify4.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify5.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify6.png

`See this notebook for more examples! </examples/Examples.ipynb>`_.

Installation
------------

1. Chartify can be installed via pip:

``pip3 install chartify``

2. Install chromedriver requirement (Optional. Needed for PNG output):
    - Install google chrome.
    - Download the appropriate version of chromedriver for your OS `here <https://sites.google.com/a/chromium.org/chromedriver/downloads>`_.
    - Copy the executable file to a directory within your PATH.
	- View directorys in your PATH variable: ``echo $PATH``
	- Copy chromedriver to the appropriate directory, e.g.: ``cp chromedriver /usr/local/bin``

Getting started
---------------

This `tutorial notebook </examples/Chartify%20Tutorial.ipynb>`_ is the best place to get started with a guided tour of the core concepts of Chartify.

From there, check out the `example notebook </examples/Examples.ipynb>`_ for a list of all the available plots.

Code of Conduct
---------------

This project adheres to the `Open Code of Conduct <https://github.com/spotify/code-of-conduct/blob/master/code-of-conduct.md>`_. By participating, you are expected to honor this code.

Contributing
------------

`See the contributing docs <CONTRIBUTING.rst>`_.


=======
History
=======

2.3.2 (2018-10-18)
------------------

* Stacked bar and area order now matches default vertical legend order.
* Added method for shifting color palettes.
* Added scatter plots with a single categorical axis.
* Fixed bug with text_stacked that occurred with multiple categorical levels.

2.3.1 (2018-09-27)
------------------

* Fix scatter plot bug that can occur due to nested data types.

2.3.0 (2018-09-26)
------------------

* Added hexbin plot type.
* More control over grouped axis label orientation.
* Added alpha control to scatter, line, and parallel plots.
* Added control over marker style to scatter plot.
* Added ability to create custom color palettes.
* Changed default accent color.
* Visual tweaks to lollipop plot.
* Bar plots with a few number of series will have better widths.


2.2.0 (2018-09-17)
------------------

* First release on PyPI.


