chartify 5.0.0

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

     

[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

Use the [chartify tag on StackOverflow](https://stackoverflow.com/questions/tagged/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](https://github.com/spotify/chartify/blob/master/CONTRIBUTING.rst).

4.0.5 (2023-10-12)

• Relaxed scipy and pandas version requirements to allow verions 2.x

4.0.4 (2023-08-23)

• Documentation build fix

• Pin tornado requirement to reduce vulnerability

4.0.3 (2023-04-21)

• Require jupyter_bokeh to enable html output

4.0.2 (2023-03-30)

• Fix categorical_order_by check for scatter plot

• Fix categorical_order_by check for _construct_source

• Refactor category sorting in _construct_source

• Add tests for categorical_order_by

• Fix scatter plot tests that used line plots

4.0.1 (2023-03-24)

• Updated version requirement of pillow to avoid bug

4.0.0 (2023-03-23)

• Dropped support for python 3.6 and 3.7

3.1.0 (2023-03-22)

• Added Boxplot Chart including example in examples notebook

3.0.5 (2022-12-13)

• Fixed a few errors in example and tutorial notebooks

• Fixed a typo in requirements.txt

3.0.4 (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.