Styleguide/utilities for plotting data in the style of apolitical.co
This module defines the Apolitical (https://apolitical.co) 'house style' for data visualisation and provides various tools for plotting data. It is intended to be compatible with Google Colab.
To install from the command line via pip, do:
pip install apolitical-data-viz
To upgrade to the latest version via
pip install apolitical-data-viz --upgrade
To use via pipenv put the following in your Pipfile:
[packages] apolitical-data-viz = ">=1.0.0"
If you've cloned the repository, the best way to make it work is using
If you don't yet have
pipenv, you can use
pip to install it from the command line:
pip install pipenv --upgrade
Then, in the top level directory of this repository,
pipenv install --dev
This will create the virtual environment and install the requirements (viewable in the Pipfile). The
--dev flag will install packages needed for testing etc.
To import the module, do the following:
import apol_dataviz as adv
This provides a variety of resources (they can also be imported separately, as demonstrated in the next example).
Applying the house style
The simplest usage is to simply enforce the house style. In order to do this, do the following:
from apol_dataviz import style style.use_apol_style()
This will create a number of new matplotlib colour aliases as well as some new named colourmaps. Note that if you are using Google Colab, we anticipate that the Lato font will be missing and so we download the relevant
.ttf files. This functionality relies on a check that
google-colab is in the
sys.modules list. If it is not, nothing is downloaded (you should ensure the Lato font is installed in your local
<PATH>/matplotlib/mpl-data/fonts/ttf/ directory in this case, else matplotlib will fall back on the default font).
Accessing the colour definitions
If you don't want to enforce the full style, but do want to access our colour definitions, do the following:
from apol_dataviz.colours import ColourDefinitions cd = ColourDefinitions() cd.apply_definitions() # this command sets up the matplotlib aliases
ColourDefinitions class contains the definitions of a wide variety of colours used in our visualisations. Its
apply_definitions() method sets up the aliases for these in matplotlib.
Using the custom plot utilities
We provide a number of different functions for plotting custom charts. These are accessible via the
plots resource. These are intended to be compatible with pandas Series and DataFrames
from apol_dataviz.plots import doughnut, hbarplot axis = doughnut(pandas_series) axis = hbarplot(pandas_series)
The above commands would make nicely formatted doughnut (donut) plots and horizontal bar plots respectively.
In many cases we may want to format a plot in a particular way, and for this purpose we provide formatters that either alter the formatting of a particular plot or act as a utility for doing so.
As an example:
from apol_dataviz import formatters new_tick_labels = formatters.get_ts_tick_labels(pandas_timeseries)
This creates well-formatted tick labels for a datetime-indexed pandas time series.
Using the palette generators
We make available a number of functions for generating palettes of specific types.
To give a couple of examples:
from apol_dataviz import palette_generators as palgen easy_sequential = palgen.apol_teal_pal() categorical_via_HuSL = palgen.spaced_hue_palette()
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