🧇 Make Waffle Charts in Python.

Overview

PyWaffle

PyPI version ReadTheDocs Binder

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/

Installation

pip install pywaffle

Requirements

  • Python 3.5+
  • Matplotlib

Examples

1. Value Scaling

import matplotlib.pyplot as plt
from pywaffle import Waffle
fig = plt.figure(
    FigureClass=Waffle, 
    rows=5, 
    columns=10, 
    values=[48, 46, 6],
    figsize=(5, 3)
)
plt.show()

basic

The values are automatically scaled to 24, 23 and 3 to fit 5 * 10 chart size.

2. Values in dict & Auto-sizing

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()

Use values in dictionary; use absolute value as block number, without defining columns

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.

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

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()

Add title, legend and background color; customize the block color

Many parameters, like title and legend, accept the same parameters as in Matplotlib.

4. Plot with Icons - Pictogram Chart

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()

Use Font Awesome icons

PyWaffle supports Font Awesome icons in the chart.

5. Multiple Plots in One 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()

Multiple plots

In this chart, 1 block = 30000 votes.

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

Demo

Wanna try it yourself? There is Online Demo!

What's New

See CHANGELOG

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
Owner
Guangyang Li
Guangyang Li
With Holoviews, your data visualizes itself.

HoloViews Stop plotting your data - annotate your data and let it visualize itself. HoloViews is an open-source Python library designed to make data a

HoloViz 2.3k Jan 02, 2023
Implementation of SOMs (Self-Organizing Maps) with neighborhood-based map topologies.

py-self-organizing-maps Simple implementation of self-organizing maps (SOMs) A SOM is an unsupervised method for learning a mapping from a discrete ne

Jonas Grebe 6 Nov 22, 2022
Plotting data from the landroid and a raspberry pi zero to a influx-db

landroid-pi-influx Plotting data from the landroid and a raspberry pi zero to a influx-db Dependancies Hardware: Landroid WR130E Raspberry Pi Zero Wif

2 Oct 22, 2021
Custom Plotly Dash components based on Mantine React Components library

Dash Mantine Components Dash Mantine Components is a Dash component library based on Mantine React Components Library. It makes it easier to create go

Snehil Vijay 239 Jan 08, 2023
Python toolkit for defining+simulating+visualizing+analyzing attractors, dynamical systems, iterated function systems, roulette curves, and more

Attractors A small module that provides functions and classes for very efficient simulation and rendering of iterated function systems; dynamical syst

1 Aug 04, 2021
This is a Web scraping project using BeautifulSoup and Python to scrape basic information of all the Test matches played till Jan 2022.

Scraping-test-matches-data This is a Web scraping project using BeautifulSoup and Python to scrape basic information of all the Test matches played ti

Souradeep Banerjee 4 Oct 10, 2022
Create a visualization for Trump's Tweeted Words Using Python

Data Trump's Tweeted Words This plot illustrates twitter word occurences. We already did the coding I needed for this plot, so I was very inspired to

7 Mar 27, 2022
An application that allows you to design and test your own stock trading algorithms in an attempt to beat the market.

StockBot is a Python application for designing and testing your own daily stock trading algorithms. Installation Use the

Ryan Cullen 280 Dec 19, 2022
Visualization of numerical optimization algorithms

Visualization of numerical optimization algorithms

Zhengxia Zou 46 Dec 01, 2022
A python wrapper for creating and viewing effects for Matt Parker's christmas tree.

Christmas Tree Visualizer A python wrapper for creating and viewing effects for Matt Parker's christmas tree. Displays py or csv effect files and allo

4 Nov 22, 2022
By default, networkx has problems with drawing self-loops in graphs.

By default, networkx has problems with drawing self-loops in graphs. It makes it hard to draw a graph with self-loops or to make a nicely looking chord diagram. This repository provides some code to

Vladimir Shitov 5 Jan 06, 2022
Backend app for visualizing CANedge log files in Grafana (directly from local disk or S3)

CANedge Grafana Backend - Visualize CAN/LIN Data in Dashboards This project enables easy dashboard visualization of log files from the CANedge CAN/LIN

13 Dec 15, 2022
Statistical data visualization using matplotlib

seaborn: statistical data visualization Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing

Michael Waskom 10.2k Dec 30, 2022
CPG represent!

CoolPandasGroup CPG represent! Arianna Brandon Enne Luan Tracie Project requirements: use Pandas to clean and format datasets use Jupyter Notebook to

Enne 3 Feb 07, 2022
Data Visualizer Web-Application

Viz-It Data Visualizer Web-Application If I ask you where most of the data wrangler looses their time ? It is Data Overview and EDA. Presenting "Viz-I

Sagnik Roy 17 Nov 20, 2022
在原神中使用围栏绘图

yuanshen_draw 在原神中使用围栏绘图 文件说明 toLines.py 将一张图片转换为对应的线条集合,视频可以按帧转换。 draw.py 在原神家园里绘制一张线条图。 draw_video.py 在原神家园里绘制视频(自动按帧摆放,截图(win)并回收) cat_to_video.py

14 Oct 08, 2022
3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

PyVista Deployment Build Status Metrics Citation License Community 3D plotting and mesh analysis through a streamlined interface for the Visualization

PyVista 1.6k Jan 08, 2023
Visualize the training curve from the *.csv file (tensorboard format).

Training-Curve-Vis Visualize the training curve from the *.csv file (tensorboard format). Feature Custom labels Curve smoothing Support for multiple c

Luckky 7 Feb 23, 2022
flask extension for integration with the awesome pydantic package

Flask-Pydantic Flask extension for integration of the awesome pydantic package with Flask. Installation python3 -m pip install Flask-Pydantic Basics v

249 Jan 06, 2023
Typical: Fast, simple, & correct data-validation using Python 3 typing.

typical: Python's Typing Toolkit Introduction Typical is a library devoted to runtime analysis, inference, validation, and enforcement of Python types

Sean 171 Jan 02, 2023