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Python繪制餅圖、圓環(huán)圖的實(shí)例

更新時(shí)間：2024年02月19日 10:02:50 作者：螞蟻*漫步

這篇文章主要介紹了Python繪制餅圖、圓環(huán)圖的實(shí)例,具有很好的參考價(jià)值,希望對(duì)大家有所幫助,如有錯(cuò)誤或未考慮完全的地方,望不吝賜教

Python繪制餅圖、圓環(huán)圖

下面是我們作圖需要使用到的數(shù)據(jù)(數(shù)據(jù)是個(gè)人虛構(gòu)的、不代表各品牌真實(shí)銷售數(shù)據(jù))

品牌	子品牌	銷量	總銷量/臺(tái)
比亞迪	唐	10000	29000
比亞迪	宋	9000	29000
比亞迪	元	6000	29000
比亞迪	海豚	4000	29000
特斯拉	Model3	8000	17500
特斯拉	ModelS	6000	17500
特斯拉	ModelY	3500	17500
大眾	朗逸	3000	12000
大眾	速騰	3000	12000
大眾	高爾夫	6000	12000
豐田	卡羅拉	6000	12000
豐田	雷凌	4000	12000
豐田	凱美瑞	2000	12000
奇瑞	艾瑞澤5	1000	2000
奇瑞	艾瑞澤8	1000	2000
領(lǐng)克	領(lǐng)克01	1000	1000

1.各品牌的銷售數(shù)量餅圖

import  pandas as pd
from matplotlib import pyplot as plt
 
#解決中文亂碼
plt.rcParams['font.sans-serif'] = ['SimHei']
 
data=pd.read_excel(r'汽車銷量數(shù)據(jù)數(shù)據(jù).xlsx',sheet_name='Sheet1')
 
#根據(jù)各品牌去重
 
data_total_sale=data.loc[:,["品牌","總銷量"]].drop_duplicates()
print(data_total_sale)
out:
 
    品牌    總銷量
0   比亞迪  29000
4   特斯拉  17500
7    大眾  12000
10   豐田  12000
13   奇瑞   2000
15   領(lǐng)克   1000
 
fig, ax = plt.subplots(figsize=(10, 7))
ax.pie(data_total_sale['總銷量'],labels=data_total_sale['品牌'],autopct='%1.1f%%')
plt.show()

2.各品牌銷售數(shù)據(jù)圓環(huán)圖

import  pandas as pd
from matplotlib import pyplot as plt
 
#解決中文亂碼
plt.rcParams['font.sans-serif'] = ['SimHei']
 
data=pd.read_excel(r'汽車銷量數(shù)據(jù)數(shù)據(jù).xlsx',sheet_name='Sheet1')
 
#根據(jù)各品牌去重
 
data_total_sale=data.loc[:,["品牌","總銷量"]].drop_duplicates()
 
total_sale=data_total_sale['總銷量'].sum()
 
fig, ax = plt.subplots(figsize=(10, 7))
 
ax.pie(data_total_sale['總銷量'], radius=1.5, wedgeprops={'width': 0.7}, labels = data_total_sale['品牌'], autopct='%3.2f%%',  pctdistance=0.75)  #保留2位小數(shù)
plt.text(0, 0, total_sale, ha='center', va='center', fontsize=28)
plt.show()

3.將數(shù)據(jù)少的合并為其它

import  pandas as pd
from matplotlib import pyplot as plt
 
#解決中文亂碼
plt.rcParams['font.sans-serif'] = ['SimHei']
 
data=pd.read_excel(r'汽車銷量數(shù)據(jù)數(shù)據(jù).xlsx',sheet_name='Sheet1')
 
#根據(jù)各品牌去重
 
data_total_sale=data.loc[:,["品牌","總銷量"]].drop_duplicates()
 
others=["奇瑞","領(lǐng)克"]
data_new=data_total_sale.loc[~data['品牌'].isin(others)]
other_sum=data_total_sale['總銷量'].loc[data['品牌'].isin(others)].sum()
data_new=data_new.append({"品牌":'其它',"總銷量":other_sum},ignore_index=True)
fig, ax = plt.subplots(figsize=(10, 7))
ax.pie(data_new['總銷量'],labels=data_new['品牌'],autopct='%1.1f%%')
plt.show()

4.其它類中展開(kāi)

import  pandas as pd
from matplotlib import pyplot as plt
from matplotlib.patches import ConnectionPatch
from matplotlib import cm
 
#解決中文亂碼
plt.rcParams['font.sans-serif'] = ['SimHei']
 
data=pd.read_excel(r'汽車銷量數(shù)據(jù)數(shù)據(jù).xlsx',sheet_name='Sheet1')
 
#根據(jù)各品牌去重
 
data_total_sale=data.loc[:,["品牌","總銷量"]].drop_duplicates()
 
others=["奇瑞","領(lǐng)克"]
data_new=data_total_sale.loc[~data['品牌'].isin(others)]
other_sum=data_total_sale['總銷量'].loc[data['品牌'].isin(others)].sum()
data_new=data_new.append({"品牌":'其它',"總銷量":other_sum},ignore_index=True)
data_other=data_total_sale.loc[data['品牌'].isin(others)]
fig = plt.figure(figsize=(10,4))
ax1 = fig.add_subplot(1,2,1)
ax1.pie(data_new['總銷量'],labels=data_new['品牌'],autopct='%1.1f%%')
ax2 = fig.add_subplot(1,2,2)
ax2.pie(data_other['總銷量'],labels=data_other['品牌'],autopct='%1.1f%%',radius=0.5,wedgeprops=dict(width=0.3, edgecolor='w'))
 
theta1, theta2 = ax1.patches[-1].theta1+15, ax1.patches[-1].theta2-15
center, r = ax1.patches[-1].center,ax1.patches[-1].r
x = r*np.cos(np.pi/180*theta1)+center[0]
y = np.sin(np.pi/180*theta1)+center[1]
con1 = ConnectionPatch(xyA=(0, 0.5),xyB=(x,y),
                     coordsA=ax2.transData, coordsB=ax1.transData,axesA=ax2,axesB=ax1)
 
x = r * np.cos(np.pi / 180 * theta2) + center[0]
y = np.sin(np.pi / 180 * theta2) + center[1]
con2 = ConnectionPatch(xyA=(-0.1, -0.49),
                       xyB=(x, y),
                       coordsA=ax2.transData,
                       coordsB=ax1.transData,
                       axesA=ax2, axesB=ax1)
for con in [con1, con2]:
    con.set_color('gray')
    ax2.add_artist(con)
    con.set_linewidth(1)
fig.subplots_adjust(wspace=0)
plt.show()