acmetric-plotting 1.3.1

A package to easily build ACMetric branded plots

Introducing ACMetric package!

Current version: 1.3.1

This package is created to help you use ACMetric's brand colors and build plots without hours of tuning. Enjoy!

Installing on Google Colab

Setting up in Google Colab is described here.

Importing

We recommend importing it along with matplotlib and seaborn.

%matplotlib inline # display plots in the notebook right away
%config InlineBackend.figure_format='retina' # high resolution
import matplotlib.pyplot as plt
import seaborn as sns
import acmetric_plotting as ac

And it is ready to go!

You can find code examples here: Jupyter | Google Colab

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Some things you need to know

ac.display_colors() will show you a table with all the colors available and their names.

ac.colors module contains ACMetric colors, you can access them by writing ac.colors.coral, ac.colors.sky_60, etc.

ac.palette is a matplotlib color palette. You can call it and choose a color you like by index, e.g. ac.palette[3].

ac.cmap is a gradient colormap that can be used in seaborn heatmap and other plots.

Run ac.params.layout_color('black') to make axes and text black. Run ac.params.layout_color('default') to make them grey again.

Now 4 kinds of plots are available in the package: bar chart, pie chart, scatter plot and box plot. You can make them using ac.bar, ac.pie, ac.scatter and ac.box. All the possible parameters can be found in the docstring.

Note: it doesn't mean you can't build other kinds of plots. Just import matplotlib or seaborn, and all the plots you create will also be ACMetric branded!