chartpy

chartpy creates a simple easy to use API to plot in a number of great Python chart libraries like plotly (via cufflinks), bokeh and matplotlib, with a unified interface. You simply need to change a single keyword to change which chart engine to use (see below), rather than having to learn the low level details of each library. I've also created new stylesheets and formating to ensure that the default matplotlib styling looks more modern using Open Sans font.

Contributors for the project are very much welcome!

chart = Chart(df=df, chart_type='line', style=style)

# we now plot using multiple plotting libraries, with the same dataframe
chart.plot(engine='matplotlib')
chart.plot(engine='bokeh')
chart.plot(engine='plotly')

I had previously written the open source PyThalesians financial library. This new chartpy library has some similar functionality to the chart part of that library. However, I've totally rewritten the API to make it much cleaner and easier to use. It is also now a fully standalone package, so it'll be easier to use for both non-financial and financial applications. It also has many more features including subplots and much more.

At present chartpy supports several types of plots

• line (bokeh, plotly and matplotlib)
• bar (bokeh, plotly and matplotlib)
• scatter (bokeh, plotly and matplotlib)
• bar horizontal (matplotlib and plotly)
• heatmap (matplotlib and plotly)
• surface (plotly)
• map plots (plotly)
• looking to add more (any requests?)

Other points to note

• Please bear in mind at present chartpy is currently a highly experimental alpha project and isn't yet fully documented
• Uses Apache 2.0 licence

Gallery

Doing plots in multiple libraries by simply changing a keyword, from the Jupyter notebook.

Create subplots with minimal extra coding (see examples/subplot_examples.py)

Do surface plots (plotly only at present - see examples/surface_examples.py)

Do bar horizontal plots (matplotlib and plotly - see examples/chart_demo.py)

Create heatmaps (matplotlib and plotly - see examples/chart_demo.py)

Create HTML webpages (using Canvas class - see examples/canvas_demo.py) - using mixture of plots (plotly, bokeh & matplotlib), tables and text, with just a few lines of Python! The HTML file can then be uploaded to a webserver for display (or sent around by e-mail etc)

Requirements

Major requirements

• Required: Python 3.4, 3.5
• Required: pandas, matplotlib, plotly, cufflinks, bokeh, numpy etc.

To install Open Sans font

My chartpy stylesheet for matplotlib uses the free Open Sans font. For it to display properly you need to install Open Sans font on your computer

• First download font from https://www.fontsquirrel.com/fonts/open-sans
• Windows: Install font by dragging to Windows/fonts folder
• Windows: Reset matplotlib font cache (delete file eg. c:/users/username/.matplotlib/fontList.py3k.cache
• On Mac OS X/Linux procedure for installing fonts is different

Installation

You can install the library using the below. After installation:

• Make sure you edit the ChartConstants class for the correct Plotly API and Twitter API keys

pip install git+https://github.com/cuemacro/chartpy.git

Why did I write chartpy?

There are many good charting libraries. However, they all have different APIs. It took me a lot of time to learn a new API, for each new plotting library I wanted to use. I ended up writing chartpy to abstract away all this complexity, so I could concentrate on analysing data, rather than getting caught up in complex API visualisation calls. Rather than having to totally edit my code each time, a single keyword is enough to switch between for example plotly and matplotlib.

I've also tried to design the library so that adding a new plotting engine is fairly straightforward (extend EngineTemplate and edit the get_engine method in Chart), so it's basically future proof for whatever new chart libraries come along, with relatively small changes to your code base.

chartpy examples

In chartpy/examples you will find several demos

Release Notes

• No formal releases yet

Coding log

• 17 Oct 2016 - Added grid flags for bokeh, plotly and matplotlib, improved formating on printing dataframes
• 14 Oct 2016 - Fixed missing stylesheets in package setup
• 12 Oct 2016 - Fixed handling of auto generated filenames
• 11 Oct 2016 - Changed handling of auto generated filenames
• 10 Oct 2016 - Added xkcd style plots for matplotlib
• 07 Oct 2016 - Added .idea to .gitignore
• 06 Oct 2016 - Added support for secondary y-axis in Plotly
• 29 Sep 2016 - New webpage_examples notebook to illustrate creating chart webpages
• 26 Sep 2016 - Added thin margin setting for Plotly
• 25 Sep 2016 - Added Canvas method to easily create webpage reports with charts
• 23 Sep 2016 - Fixed deprecated method for uploading to Twitter
• 03 Sep 2016 - Added horizontal bars and heatmap for matplotlib and plotly
• 28 Aug 2016 - Added explanation of why chartpy?
• 20 Aug 2016 - Added Plotly default palette, surface examples
• 19 Aug 2016 - Added HTML examples for bokeh & plotly, subplotting for bokeh, plotly & matplotlib (with subplot_examples)
• 17 Aug 2016 - Uploaded first code

End of note