python 画图--饼图
 
这是python画图系列第三篇--饼图
 
画饼图用到的方法为：
 
matplotlib.pyplot.pie()
 
参数为：
 

 pie(x, explode=None, labels=None,
     colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'),
     autopct=None, pctdistance=0.6, shadow=False,
     labeldistance=1.1, startangle=None, radius=None,
     counterclock=True, wedgeprops=None, textprops=None,
     center = (0, 0), frame = False )
 
参数说明：
 
x       (每一块)的比例，如果sum(x) > 1会使用sum(x)归一化
 labels  (每一块)饼图外侧显示的说明文字
 explode (每一块)离开中心距离
 startangle  起始绘制角度,默认图是从x轴正方向逆时针画起,如设定=90则从y轴正方向画起
 shadow  是否阴影
 labeldistance label绘制位置,相对于半径的比例, 如<1则绘制在饼图内侧
 autopct 控制饼图内百分比设置,可以使用format字符串或者format function
         '%1.1f'指小数点前后位数(没有用空格补齐)
 pctdistance 类似于labeldistance,指定autopct的位置刻度
 radius  控制饼图半径
 
返回值:
 如果没有设置autopct,返回(patches, texts)
 如果设置autopct,返回(patches, texts, autotexts)
 patches -- list --matplotlib.patches.Wedge对象
 
texts autotexts -- matplotlib.text.Text对象
 

 下面是一个简单的示例：
 

 # -*- coding: utf-8 -*-
 import numpy as np  
 import matplotlib.mlab as mlab  
 import matplotlib.pyplot as plt  
 labels=['China','Swiss','USA','UK','Laos','Spain']
 X=[222,42,455,664,454,334]  
 
fig = plt.figure()
 plt.pie(X,labels=labels,autopct='%1.2f%%') #画饼图（数据，数据对应的标签，百分数保留两位小数点）
 plt.title("Pie chart")
   
 
plt.show()  
 plt.savefig("PieChart.jpg")
 
下面是结果：
 

 下面是另一个示例：
 

 # -*- coding: utf-8 -*-
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib as mpl
 
def draw_pie(labels,quants):
     # make a square figure
     plt.figure(1, figsize=(6,6))
     # For China, make the piece explode a bit
     expl = [0,0.1,0,0,0,0,0,0,0,0]   #第二块即China离开圆心0.1
     # Colors used. Recycle if not enough.
     colors  = ["blue","red","coral","green","yellow","orange"]  #设置颜色（循环显示）
     # Pie Plot
     # autopct: format of "percent" string;百分数格式
     plt.pie(quants, explode=expl, colors=colors, labels=labels, autopct='%1.1f%%',pctdistance=0.8, shadow=True)
     plt.title('Top 10 GDP Countries', bbox={'facecolor':'0.8', 'pad':5})
     plt.show()
     plt.savefig("pie.jpg")
     plt.close()
 
# quants: GDP
 
# labels: country name
 
labels   = ['USA', 'China', 'India', 'Japan', 'Germany', 'Russia', 'Brazil', 'UK', 'France', 'Italy']
 
quants   = [15094025.0, 11299967.0, 4457784.0, 4440376.0, 3099080.0, 2383402.0, 2293954.0, 2260803.0, 2217900.0, 1846950.0]
 
draw_pie(labels,quants)
 

 
但是，我在尝试应用重叠标签时遇到了一些问题。。有什么想法。。。我是Python的初学者。在
 
错误，如
 
AttributeError:“list”对象没有属性“theta2”
 
TypeError:“function”对象不可读取
 
属性错误：'数字.int64'对象没有属性'theta2'
 
这是我的代码：from collections import Counter
 
import numpy as np
 
import matplotlib
 
import matplotlib.pyplot as plt
 
import matplotlib.cm as cm, matplotlib.font_manager as fm
 
import pandas as pd
 
import csv
 
import itertools
 
from collections import OrderedDict
 
import operator
 
# Create database of duplicates - check if the mac and os pairs have duplicates
 
reader = csv.reader(open('Workbook1.csv', 'r'), delimiter=',')
 
writer = csv.writer(open('remacos.csv', 'w'), delimiter=',')
 
entries = set()
 
for row in reader:
 
key = (row[1], row[2])
 
if key not in entries:
 
writer.writerow(row)
 
entries.add(key)
 
del writer #Force the writer to clean up
 
# Create database of duplicates - check if the mac and browser pairs have duplicates
 
reader = csv.reader(open('Workbook1.csv', 'r'), delimiter=',')
 
writer = csv.writer(open('remacbrowser.csv', 'w'), delimiter=',')
 
entries = set()
 
for row in reader:
 
key = (row[1], row[3])
 
if key not in entries:
 
writer.writerow(row)
 
entries.add(key)
 
del writer #Force the writer to clean up
 
df = pd.read_csv('remacos.csv', index_col="mac")
 
counteros = Counter(df['os'])
 
os_names = counteros.keys()
 
os_counts = counteros.values()
 
# Read Removed Duplicated entries Database and Count Values for Browsers.
 
df = pd.read_csv('remacbrowser.csv', index_col="mac")
 
counterbrowsers = Counter(df['browser'])
 
browser_names = counterbrowsers.keys()
 
browser_counts = counterbrowsers.values()
 
#New Bar Chart with Fonts
 
countdata = df['browser'].value_counts()
 
ax = countdata.plot(kind='bar',
 
figsize=[9, 6],
 
width=0.9,
 
alpha=0.6,
 
color='g',
 
edgecolor='w',
 
grid=False,
 
ylim=[0, 400])
 
ax.set_xticks(range(len(countdata)))
 
ax.set_xticklabels(countdata.index, rotation=45, rotation_mode='anchor', ha='right', fontproperties=ticks_font)
 
ax.yaxis.grid(True)
 
for label in ax.get_yticklabels():
 
label.set_fontproperties(ticks_font)
 
ax.set_title('Most Popular Browsers', fontproperties=title_font)
 
ax.set_xlabel('', fontproperties=label_font)
 
ax.set_ylabel('Number of counts', fontproperties=label_font)
 
plt.show()
 
# Make Pie Chart for Browsers
 
plt.figure(figsize=plt.figaspect(1))
 
#values = sorted(browser_counts)
 
#labels = browser_names
 
labels, values = zip(*sorted(counterbrowsers.items(), key=lambda x: x[1])) #Counteros is the Dictionary-like object we are trying to sort. This 'zip' solves the problem of sorting each of them independently. This will sort as pairs, by value.
 
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral', '#008DB8','#00AAAA','#001CF0','#00FF80', 'c','m','r','b', '#191970','#0038E2','#0055D4','#0071C6','#00E28E', '#00C69C']
 
#explode = (0, 0, 0, 0.1, 0.1, 0.2, 0.3, 0.4, 0.6)
 
explode = list()
 
for k in labels:
 
explode.append(0.1)
 
#sizes = counts.values.tolist()
 
def make_autopct(values):
 
def my_autopct(pct):
 
total = sum(values)
 
val=int(round(pct*total/100))
 
return '{p:.2f}% ({v:d})'.format(p=pct,v=val)
 
return my_autopct
 
pie = plt.pie(values, labels=labels, explode=explode, colors=colors, shadow=True, startangle=90, autopct=make_autopct(values))
 
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
 
arrowprops=dict(arrowstyle="-",connectionstyle="angle,angleA=0,angleB=90")
 
kw = dict(xycoords='data',textcoords='data',arrowprops=arrowprops,
 
bbox=bbox_props, zorder=0, va="center")
 
for i, p in enumerate(pie):
 
ang = (p.theta2 - p.theta1)/2.+p.theta1
 
y = np.sin(ang/180.*np.pi)
 
x = 1.35*np.sign(np.cos(ang/180.*np.pi))
 
plt.gca().annotate(str(1+i), xy=(0, 0), xytext=( x, y), **kw )
 
plt.legend(labels, loc="lower right")
 
plt.axis('equal') # Set aspect ratio to be equal so that pie is drawn as a circle, # View the plot drop above
 
plt.title('Browsers Analytics', bbox={'facecolor':'0.8', 'pad':5})
 
plt.show()
 
CSV文件显示在如下表中：
 
一个CSV比较Mac地址和浏览器
 
另一个CSV链接Mac和OS以获取计数数据。在这个饼图中，我只是想弄一个来显示我可以复制结果(希望如此)。在
 
^{pr2}$
 
条形图只是显示数据的一种方式，但目标是在带有标注的饼图中显示数据。我很难实现重叠/调用标签的解决方案。这里用于枚举(pie)中i，p的值是我得到错误的地方，比如AttributeError:“list”对象没有属性“theta2”Traceback (most recent call last):
 
File "csv-test.py", line 176, in 
 
ang = (p.theta2 - p.theta1)/2.+p.theta1
 
AttributeError: 'list' object has no attribute 'theta2'
 
如果将(饼图)值更改为(counterbrowser)，则得到：Traceback (most recent call last):
 
File "csv-test.py", line 176, in 
 
ang = (p.theta2 - p.theta1)/2.+p.theta1
 
AttributeError: 'str' object has no attribute 'theta2'
 
Patches=pie显示以下错误(将(pie)替换为Patches)Traceback (most recent call last):
 
File "csv-test.py", line 176, in 
 
ang = (p.theta2 - p.theta1)/2.+p.theta1
 
AttributeError: 'list' object has no attribute 'theta2'

推荐两个非常好的博客内容：
 
Python实现可视化（二）--label字体大小调整、图例设置https://blog.csdn.net/weixin_39602776/article/details/111314454Python绘制饼图调节字体大小、防止标签重叠解决方法 https://www.cnblogs.com/mengxiaoleng/p/12804117.html然后是我自己的饼图代码，里面用到了字体大小调整这部分的功能：
 
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['backend'] = 'SVG'
A=[15.8,0.6,0.0296]
labels='','',''
sizes=A[0],A[1],A[2]
explode=0.05,0.1,0.15
colors='skyblue','gold','green'
fig,ax = plt.subplots(figsize=(13,7))  #创建大小为（14，7）的画布，同时创建一个axes，把axes赋值给ax
patches,l_text,p_text=ax.pie(sizes,explode=explode,labels=labels,
      colors=colors,autopct='%1.1f%%',shadow=False,startangle=50)
# ax.set_title('FEMM Loss percentage',fontsize=18)
ax.legend(labels = ['conductor 1','conductor 2','others'],loc=2,fontsize=14)
ax.set_aspect('equal')
for t in p_text:
    t.set_size(15)
 

复合饼图
 
fig = plt.figure(figsize=(10,4))
ax1 = fig.add_subplot(121)
a = kcf.groupby('一级分类').总金额.sum().sort_values(ascending=False)
ax1.pie(a, labels=a.index, autopct='%.2f%%',startangle=-110, explode=[0,0.1],
        colors=['#508cb4','#d7e4ef'])
plt.title('一级分类销售额分类汇总')
ax2 = fig.add_subplot(122)
a = kcf[kcf.一级分类=='饮料'].groupby('二级分类').总金额.sum().sort_values(ascending=False)
ax2.pie(a, radius=0.9, labels=a.index, autopct='%.2f%%', colors=['#265f95', '#508cb4', '#89b5ce', '#c5d8e7', '#e7eff7'])
plt.title('饮料品类下的销售额分类汇总')

#使用ConnectionPatch画出两个饼图的间连线
#先得到饼图边缘的数据
theta1, theta2 = ax1.patches[0].theta1+20, ax1.patches[0].theta2-20
center, r = ax1.patches[0].center,ax1.patches[0].r
x = r*np.cos(np.pi/180*theta2)+center[0]
y = np.sin(np.pi/180*theta2)+center[1]
con = ConnectionPatch(xyA=(0, 0.9),xyB=(x,y),
                     coordsA='data', coordsB='data',axesA=ax2,axesB=ax1)
con.set_linewidth(2)
con.set_color=([0,0,0])
ax2.add_artist(con)

#画出下边缘的连线
x = r*np.cos(np.pi/180*theta1)+center[0]
y = np.sin(np.pi/180*theta1)+center[1]
con = ConnectionPatch(xyA=(0,-0.9),xyB=(x,y),
                     coordsA='data', coordsB='data',axesA=ax2,axesB=ax1)
con.set_linewidth(2)
con.set_color=([0,0,0])
ax2.add_artist(con)
 
参考:python生成复合饼图
 
环形图
 
加入参数wedgeprops=dict(width=0.3, edgecolor='w')
 
a = pe[pe.PID.isin(ksf.PID.drop_duplicates())].性别.value_counts()
plt.pie(a, labels=a.index, autopct='%.1f%%', colors=['#8ebad9','#f1a4bd'],
       wedgeprops=dict(width=0.3, edgecolor='w'))
 
 
标签重叠、显示部分标签
 
 
方法一：
 
参数 explode 部分环块分开
参数 autopct 只显示百分比大于1%的数据标签
 
def my_autopct(pct):
    return ('%.1f%%' % pct) if pct > 1 else ''
 
plt.pie(a, labels=a.index, explode=[0,0,0.1,0.2,0,0,0,0], colors=colorSeries[a.index], autopct=my_autopct, wedgeprops=dict(width=0.3, edgecolor='w'),pctdistance=0.58)
plt.title('统一', fontsize=15)
 
 
方法二：
 
调整饼图图表位置
 
参数 pctdistance 是数据标签离圆心的距离
 
ax2.pie(a, labels=a.index, autopct='%.1f%%',textprops=dict(fontsize=11),
       wedgeprops=dict(width=0.3, edgecolor='w'),pctdistance=0.55)
 
#### 饼图图标重叠怎么办
 更改位置
 更改前：
 
 
更改后：