华夫饼图(Waffle Chart),有的人也会叫它“Square Pie Chart”,是饼图的一种变形,擅长展示部分在整体中的占比关系。一般来说,华夫饼图是由100个格子组成,一个格子代表“1%”。用不同颜色的格子区分不同的分类数据,以展示各部分在整体中的占比。华夫饼图(Waffle Chart),或称为直角饼图,可以直观的描绘百分比完成比例情况。与传统的饼图相比较,华夫饼图表达的百分比更清晰和准确,它的每一个格子代表 1%。
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 15 20:33:45 2020
@author: czh
"""
%reset -f
%clear
# In[*]
import matplotlib.pyplot as plt
from pywaffle import Waffle
import os
os.chdir('D:\\data\\feiyan\\2.15')
# In[*]
fig = plt.figure(
FigureClass=Waffle,
rows=5,
columns=10,
values=[48, 46, 6],
figsize=(5, 3) # figsize is a parameter of matplotlib.pyplot.figure
)
plt.show()
# In[*]
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[*]
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},
starting_location='NW'
)
fig.set_facecolor('#EEEEEE')
plt.show()
# In[*]
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
)
# In[*]
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, # shared parameter among subplots
colors=("#2196f3", "#ff5252", "#999999"), # shared parameter among subplots
figsize=(9, 5) # figsize is a parameter of plt.figure
)
# In[*]