Metadata-Version: 2.1
Name: chartify
Version: 3.0.4
Summary: Python library to make plotting simpler for data scientists
Home-page: https://github.com/spotify/chartify
Author: 
Author-email: chalpert@spotify.com
License: Apache 2
Keywords: chartify
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
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.5,<4
License-File: LICENSE
License-File: AUTHORS.rst

Chartify
========

|status|  |release|  |python|  |CI|

.. |status| image:: https://img.shields.io/badge/Status-Beta-blue.svg
.. |release| image:: https://img.shields.io/badge/Release-3.0.0-blue.svg
.. |python| image:: https://img.shields.io/badge/Python-3.6-blue.svg
.. |CI| image:: https://github.com/spotify/chartify/workflows/Tox/badge.svg
        :target: https://github.com/spotify/chartify/actions

Chartify is a Python library that makes it easy 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 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/chromium.org/driver/>`_.
    - 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 <https://github.com/spotify/chartify/blob/master/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 <https://github.com/spotify/chartify/blob/master/examples/Examples.ipynb>`_ for a list of all the available plots.

Docs
---------------

Documentation available on `chartify.readthedocs.io <https://chartify.readthedocs.io/en/latest/>`_

Getting support
---------------

Join #chartify on spotify-foss.slack.com (`Get an invite <https://slackin.spotify.com/>`_)

Use the `chartify tag on StackOverflow <https://stackoverflow.com/questions/tagged/chartify>`_.

Resources
---------------

- Data Visualization with `Chartify <https://www.section.io/engineering-education/data-viz-chartify/>`_

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
=======

3.0.3 (2022-10-18)
------------------

* Updated package requirements
* Got rid of future deprecation warnings
* Bugfix related to legend for graphs with multiple groups and colors

3.0.2 (2020-10-21)
------------------

* Support pyyaml 5.2+

3.0.1 (2020-06-02)
------------------

* Reduce dependencies by switching from Jupyter to IPython.

3.0.0 (2020-05-29)
------------------

* Updated Python to 3.6+ and Pandas to 1.0+ (Thanks @tomasaschan!)
* Updated Bokeh to 2.0+
* Removed colour dependency to fix setup errors.

2.7.0 (2019-11-27)
------------------

Bugfixes:

* Updated default yaml loader to move off of
  deprecated method (Thanks @vh920!)
* Updated legend handling to adjust for deprecated methods
  in recent versions of Bokeh (Thanks for reporting @jpkoc)
* Updated license in setup.py (Thanks for reporting @jsignell)
* Bump base Pillow dependency to avoid insecure version.
* Update MANIFEST to include missing files (Thanks @toddrme2178!)

2.6.1 (2019-08-15)
------------------

Bugfixes:

* Moved package requirements and fixed bug that occured with
  latest version of Bokeh (Thanks @emschuch & @mollymzhu!)
* Fixed bug in README while generating docs (Thanks @Bharat123rox!)

2.6.0 (2019-03-08)
------------------

Improvements:

* Allows users to plot colors on bar charts that aren't contained in the
  categorical axis.


Bugfixes:

* Fixed bug that caused float types to break when plotted with categorical
  text plots (Thanks for finding @danela!)
* Fixed broken readme links.

2.5.0 (2019-02-17)
------------------

Improvements:

* Added Radar Chart

2.4.0 (2019-02-16)
------------------

Improvements:

* Added second Y axis plotting.
* Removed Bokeh loading notification on import (Thanks @canavandl!)
* Added support for custom Bokeh resource loading (Thanks @canavandl!)
* Added example for Chart.save() method (Thanks @david30907d!)

Bugfixes:

* Updated documentation for saving and showing svgs.
* Fixed bug that broke plots with no difference between min and max
  points. (Thanks for finding @fabioconcina!)

2.3.5 (2018-11-21)
------------------

Improvements:

* Updated docstrings (Thanks @gregorybchris @ItsPugle!)
* Added SVG output options to Chart.show() and Chart.save()
  (Thanks for the suggestion @jdmendoza!)

Bugfixes:

* Fixed bug that caused source label to overlap with xaxis labels.
* Fixed bug that prevented x axis orientation changes
  with datetime axes (Thanks for finding @simonwongwong!)
* Fixed bug that caused subtitle to disappear
  with `outside_top` legend location (Thanks for finding @simonwongwong!)
* Line segment callout properties will work
  correctly. (Thanks @gregorybchris!)

2.3.4 (2018-11-13)
------------------

* Updated Bokeh version requirements to support 1.0

2.3.3 (2018-10-24)
------------------

* Removed upper bound of Pillow dependency.

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.
