【转载】python画带方差的折线图（csdn上最简洁的代码之一附上）

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原文链接：https://blog.csdn.net/a1920993165/article/details/122277716

python画带方差的折线图

画好后效果图（直接一个图的）

实现代码如下

点击查看代码

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pyplot
plt.style.use('seaborn-whitegrid')
palette = pyplot.get_cmap('Set1')
font1 = {'family' : 'Times New Roman',
'weight' : 'normal',
'size'   : 32,
}

fig=plt.figure(figsize=(20,10))
iters=list(range(7))
#这里随机给了alldata1和alldata2数据用于测试
alldata1=[]#算法1所有纵坐标数据
data=np.array([2,4,5,8,11,13,15])#单个数据
alldata1.append(data)
data=np.array([2,3,6,12,13,13,15])
alldata1.append(data)
data=np.array([2,2,7,9,13,14,16])
alldata1.append(data)
alldata1=np.array(alldata1)
alldata2=[]#算法2所有纵坐标数据
data=np.array([2,4,5,8,10,10,11])#单个数据
alldata2.append(data)
data=np.array([3,3,3,6,7,8,10])
alldata2.append(data)
data=np.array([3,3,5,5,6,7,9])
alldata2.append(data)
alldata2=np.array(alldata2)

def draw_line(name_of_alg,color_index,datas):
    color=palette(color_index)
    avg=np.mean(datas,axis=0)
    std=np.std(datas,axis=0)
    r1 = list(map(lambda x: x[0]-x[1], zip(avg, std)))#上方差
    r2 = list(map(lambda x: x[0]+x[1], zip(avg, std)))#下方差
    plt.plot(iters, avg, color=color,label=name_of_alg,linewidth=3.5)
    plt.fill_between(iters, r1, r2, color=color, alpha=0.2)

draw_line("alg1",1,alldata1)
draw_line("alg2",2,alldata2)

plt.xticks(fontsize=22)
plt.yticks(fontsize=22)
plt.xlabel('Time(s)',fontsize=32)
plt.ylabel('metric',fontsize=32)
plt.legend(loc='upper left',prop=font1)
plt.title("instance",fontsize=34)

画好后效果图（在ax子图里面的）

点击查看代码

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pyplot
plt.style.use('seaborn-whitegrid')
palette = pyplot.get_cmap('Set1')
font1 = {'family' : 'Times New Roman',
'weight' : 'normal',
'size'   : 18,
}

fig=plt.figure(figsize=(20,10))
iters=list(range(7))
#这里随机给了alldata1和alldata2数据用于测试
alldata1=[]#算法1所有纵坐标数据
data=np.array([2,4,5,8,11,13,15])#单个数据
alldata1.append(data)
data=np.array([2,3,6,12,13,13,15])
alldata1.append(data)
data=np.array([2,2,7,9,13,14,16])
alldata1.append(data)
alldata1=np.array(alldata1)
alldata2=[]#算法2所有纵坐标数据
data=np.array([2,4,5,8,10,10,11])#单个数据
alldata2.append(data)
data=np.array([3,3,3,6,7,8,10])
alldata2.append(data)
data=np.array([3,3,5,5,6,7,9])
alldata2.append(data)
alldata2=np.array(alldata2)

for i in range(2):
    color=palette(0)#算法1颜色
    ax=fig.add_subplot(1,2,i+1)   
    avg=np.mean(alldata1,axis=0)
    std=np.std(alldata1,axis=0)
    r1 = list(map(lambda x: x[0]-x[1], zip(avg, std)))#上方差
    r2 = list(map(lambda x: x[0]+x[1], zip(avg, std)))#下方差
    ax.plot(iters, avg, color=color,label="algo1",linewidth=3.0)
    ax.fill_between(iters, r1, r2, color=color, alpha=0.2)
    
    color=palette(1)
    avg=np.mean(alldata2,axis=0)
    std=np.std(alldata2,axis=0)
    r1 = list(map(lambda x: x[0]-x[1], zip(avg, std)))
    r2 = list(map(lambda x: x[0]+x[1], zip(avg, std)))
    ax.plot(iters, avg, color=color,label="algo2",linewidth=3.0)
    ax.fill_between(iters, r1, r2, color=color, alpha=0.2)
    
    ax.legend(loc='lower right',prop=font1)
    ax.set_xlabel('Outer loop iterations',fontsize=22)
    ax.set_ylabel('Objectives',fontsize=22)