PyWaffle

PyPI page: https://pypi.python.org/pypi/pywaffle

Documentation: http://pywaffle.readthedocs.io

Introduction

PyWaffle is a Python package to make waffle chart, bases on Matplotlib.

It provides a Figure constructor class Waffle, which could be passed to matplotlib.pyplot.figure and generates a matplotlib Figure object.

Installation

pip install pywaffle

Requirements

• Python 3
• Matplotlib

Examples

1. Basic Example

import matplotlib.pyplot as plt
from pywaffle import Waffle

# The values are rounded to 10 * 5 blocks
fig = plt.figure(
    FigureClass=Waffle, 
    rows=5, 
    columns=10, 
    values=[48, 46, 3]
)
plt.show()

2. Values in dict & Auto-columns

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()

If parameter columns is empty, PyWaffle uses absolute number in values as block number.

If values is a dict, its keys are used as labels.

3. Title, Legend, Colors, Background Color, Block Color and Direction

data = {'Democratic': 48, 'Republican': 46, 'Libertarian': 3}
fig = plt.figure(
    FigureClass=Waffle, 
    rows=5, 
    values=data, 
    colors=("#983D3D", "#232066", "#DCB732"),
    title={'label': 'Vote Percentage in 2016 US Presidential Election', 'loc': 'left'},
    labels=["{0} ({1}%)".format(k, v) for k, v in data.items()],
    legend={'loc': 'lower left', 'bbox_to_anchor': (0, -0.4), 'ncol': len(data), 'framealpha': 0},
    plot_direction='NW'
)
fig.gca().set_facecolor('#EEEEEE')
fig.set_facecolor('#EEEEEE')
plt.show()

It is now clear to see that there are 3% votes to other parties/candidates.

4. Icons

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', icon_size=12, 
    icon_legend=True
)

PyWaffle supports Font Awesome icons in the chart.

5. Multiple Plots

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': ["{0} ({1})".format(n, v) for n, 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': ["{0} ({1})".format(n, v) for n, 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': ["{0} ({1})".format(n, v) for n, 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,
    colors=("#2196f3", "#ff5252", "#999999"),  # Default argument values for subplots
    figsize=(9, 5)  # figsize is a parameter of plt.figure
)

In this chart, 1 block = 30000 votes.

_{Data source https://en.wikipedia.org/wiki/United_States_presidential_election,_2016.}

License

• PyWaffle is under MIT license, see LICENSE file for the details.
• The Font Awesome font is licensed under the SIL OFL 1.1: http://scripts.sil.org/OFL