PyWaffle is an open source, MIT-licensed Python package for plotting waffle charts.
It provides a Figure constructor class Waffle, which could be passed to matplotlib.pyplot.figure and generates a matplotlib Figure object.
PyPI Page: https://pypi.org/project/pywaffle/
Documentation: http://pywaffle.readthedocs.io/
pip install pywaffle- Python 3.5+
- Matplotlib
import matplotlib.pyplot as plt
from pywaffle import Wafflefig = plt.figure(
FigureClass=Waffle,
rows=5,
columns=10,
values=[48, 46, 6],
figsize=(5, 3)
)
plt.show()
The values are automatically scaled to 24, 23 and 3 to fit 5 * 10 chart size.
data = {'Democratic': 48, 'Republican': 46, 'Libertarian': 3}
fig = plt.figure(
FigureClass=Waffle,
rows=5,
values=data,
legend={'loc': 'upper left', 'bbox_to_anchor': (1.1, 1)}
)
plt.show()
In this example, since only rows is specified and columns is empty, absolute values in values are used as block numbers. Similarly, rows could also be optional if columns is specified.
If values is a dict, its keys are used as labels.
data = {'Democratic': 48, 'Republican': 46, 'Libertarian': 3}
fig = plt.figure(
FigureClass=Waffle,
rows=5,
values=data,
colors=["#232066", "#983D3D", "#DCB732"],
title={'label': 'Vote Percentage in 2016 US Presidential Election', 'loc': 'left'},
labels=[f"{k} ({v}%)" for k, v in data.items()],
legend={'loc': 'lower left', 'bbox_to_anchor': (0, -0.4), 'ncol': len(data), 'framealpha': 0},
starting_location='NW',
block_arranging_style='snake'
)
fig.set_facecolor('#EEEEEE')
plt.show()
Many parameters, like title and legend, accept the same parameters as in Matplotlib.
data = {'Democratic': 48, 'Republican': 46, 'Libertarian': 3}
fig = plt.figure(
FigureClass=Waffle,
rows=5,
values=data,
colors=["#232066", "#983D3D", "#DCB732"],
legend={'loc': 'upper left', 'bbox_to_anchor': (1, 1)},
icons='child',
font_size=12,
icon_legend=True
)
plt.show()
PyWaffle supports Font Awesome icons in the chart.
import pandas as pd
data = pd.DataFrame(
{
'labels': ['Hillary Clinton', 'Donald Trump', 'Others'],
'Virginia': [1981473, 1769443, 233715],
'Maryland': [1677928, 943169, 160349],
'West Virginia': [188794, 489371, 36258],
},
).set_index('labels')
# A glance of the data:
# Maryland Virginia West Virginia
# labels
# Hillary Clinton 1677928 1981473 188794
# Donald Trump 943169 1769443 489371
# Others 160349 233715 36258
fig = plt.figure(
FigureClass=Waffle,
plots={
311: {
'values': data['Virginia'] / 30000,
'labels': [f"{k} ({v})" for k, v in data['Virginia'].items()],
'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 8},
'title': {'label': '2016 Virginia Presidential Election Results', 'loc': 'left'}
},
312: {
'values': data['Maryland'] / 30000,
'labels': [f"{k} ({v})" for k, v in data['Maryland'].items()],
'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.2, 1), 'fontsize': 8},
'title': {'label': '2016 Maryland Presidential Election Results', 'loc': 'left'}
},
313: {
'values': data['West Virginia'] / 30000,
'labels': [f"{k} ({v})" for k, v in data['West Virginia'].items()],
'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.3, 1), 'fontsize': 8},
'title': {'label': '2016 West Virginia Presidential Election Results', 'loc': 'left'}
},
},
rows=5, # outside parameter applied to all subplots
colors=["#2196f3", "#ff5252", "#999999"], # outside parameter applied to all subplots
figsize=(9, 5)
)
plt.show()
In this chart, 1 block = 30000 votes.
Data source https://en.wikipedia.org/wiki/United_States_presidential_election,_2016.
Wanna try it yourself? There is Online Demo!
See CHANGELOG
- PyWaffle is under MIT license, see
LICENSEfile for the details. - The Font Awesome font is licensed under the SIL OFL 1.1: http://scripts.sil.org/OFL
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