Matplotlib添加子图后无法保存图像问题求助

原代码 #

import matplotlib.pyplot as plt
import pandas as pd


#750k_mesh
df1=pd.read_excel("Filelocation/tcdata_750K_mesh.xlsx", "tcdata_750K_mesh", usecols='B:E',skiprows=5 )
df1_x1 = pd.DataFrame(df1, columns= ['Unnamed: 1'])
df1_y1 = pd.DataFrame(df1, columns= ['Unnamed: 2'])
df1_y2 = pd.DataFrame(df1, columns= ['Unnamed: 3'])
df1_y3 = pd.DataFrame(df1, columns= ['Unnamed: 4'])

#1.5M_mesh
df2=pd.read_excel("Filelocation//tcdata_1.5M_mesh.xlsx", "tcdata_1.5M_mesh", usecols='B:E',skiprows=5 )
df2_x1 = pd.DataFrame(df2, columns= ['Unnamed: 1'])
df2_y1 = pd.DataFrame(df2, columns= ['Unnamed: 2'])
df2_y2 = pd.DataFrame(df2, columns= ['Unnamed: 3'])
df2_y3 = pd.DataFrame(df2, columns= ['Unnamed: 4'])

#3M_mesh
df3=pd.read_excel("Filelocation//tcdata_3M_mesh.xlsx", "tcdata_3M_mesh", usecols='B:E',skiprows=5 )
df3_x1 = pd.DataFrame(df3, columns= ['Unnamed: 1'])
df3_y1 = pd.DataFrame(df3, columns= ['Unnamed: 2'])
df3_y2 = pd.DataFrame(df3, columns= ['Unnamed: 3'])
df3_y3 = pd.DataFrame(df3, columns= ['Unnamed: 4'])

#9M_mesh
df4=pd.read_excel("Filelocation//tcdata_9M_mesh.xlsx", "tcdata_9M_mesh", usecols='B:E',skiprows=5 )
df4_x1 = pd.DataFrame(df4, columns= ['Unnamed: 1'])
df4_y1 = pd.DataFrame(df4, columns= ['Unnamed: 2'])
df4_y2 = pd.DataFrame(df4, columns= ['Unnamed: 3'])
df4_y3 = pd.DataFrame(df4, columns= ['Unnamed: 4'])

linewidth=1
markersize=0.25
markernumber=10

ax1 = plt.plot(df1_x1,df1_y1,'b-', linewidth=linewidth,markevery=markernumber,label='750K'),
ax1 = plt.plot(df2_x1,df2_y1,'g-', linewidth=linewidth,markevery=markernumber,label='1.5M'),
ax1 = plt.plot(df3_x1,df3_y1,'r-',linewidth=linewidth,markevery=markernumber,label='3M'),
ax1 = plt.plot(df4_x1,df4_y1,'c-', linewidth=linewidth,markevery=markernumber,label='9M'),


ax1 = plt.ylabel('Temperature (''$^\circ$''C)'),
ax1 = plt.xlabel('Time (s)'),

ax1 = plt.axis([-1,17, 1200,1550])
ax1 = plt.xticks(fontsize=10)
ax1 = plt.yticks(fontsize=10)

ax1 = leg = plt.legend(loc=1,fontsize=10)
ax1 = leg.get_frame().set_edgecolor('k') 
ax1 = leg.get_frame().set_facecolor('w')



# this is an inset axes over the main axes
ax2 = plt.axes([.22, .22, .28, .28], facecolor='w') #x1,y1, x2, y2 location of the subplot in the current figure
ax2 = plt.plot(df1_x1,df1_y1,'b-', linewidth=linewidth,markevery=markernumber,label='750K'),
ax2 = plt.plot(df2_x1,df2_y1,'g-', linewidth=linewidth,markevery=markernumber,label='1.5M'),
ax2 = plt.plot(df3_x1,df3_y1,'r-',linewidth=linewidth,markevery=markernumber,label='3M'),
ax2 = plt.plot(df4_x1,df4_y1,'c-', linewidth=linewidth,markevery=markernumber,label='9M'),
ax2 = ax3=plt.axis([12,14, 1280,1320])
ax2 = plt.xticks(fontsize=7)
ax2 = plt.yticks(fontsize=7)


fig_1 = plt.figure(1)
fig_2 = fig_1.add_subplot(111)
fig_2.grid(color='#929591', linestyle='--', linewidth=0.5)

plt.show()
plt.draw()
fig_1.savefig('grid_independency_test.png', dpi=1000)

问题现象 #

• 可正常绘制主图+内嵌子图，但无法保存图像到指定目录
• 收到Matplotlib弃用警告：

MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.

• 控制台输出：

fig_2 = fig_1.add_subplot(111)

return (<Figure size 432x288 with 0 Axes>
)

问题原因及解决方案 #

1. 图像保存失败的原因 #

代码中先调用plt.show()，这个函数会弹出图形窗口并清空当前Figure对象的内容，后续调用fig_1.savefig()时，fig_1已经是空白状态，因此无法保存正确图像。

解决办法：调整保存和显示的顺序，把savefig移到show之前：

fig_1.savefig('grid_independency_test.png', dpi=1000)
plt.show()
plt.draw()

2. 弃用警告的原因 #

代码混用了Matplotlib的面向对象API和pyplot状态机API：

• 前面用plt.plot()等函数时，Matplotlib会自动创建默认的Figure和Axes实例
• 后续又执行fig_1 = plt.figure(1)和fig_2 = fig_1.add_subplot(111)，试图给已存在的Figure(1)重复添加子图，触发了Axes实例复用的警告。

解决办法：统一使用面向对象API，重构后的完整代码：

import matplotlib.pyplot as plt
import pandas as pd

# 读取数据
df1=pd.read_excel("Filelocation/tcdata_750K_mesh.xlsx", "tcdata_750K_mesh", usecols='B:E',skiprows=5 )
df1_x1 = df1['Unnamed: 1']
df1_y1 = df1['Unnamed: 2']

df2=pd.read_excel("Filelocation//tcdata_1.5M_mesh.xlsx", "tcdata_1.5M_mesh", usecols='B:E',skiprows=5 )
df2_x1 = df2['Unnamed: 1']
df2_y1 = df2['Unnamed: 2']

df3=pd.read_excel("Filelocation//tcdata_3M_mesh.xlsx", "tcdata_3M_mesh", usecols='B:E',skiprows=5 )
df3_x1 = df3['Unnamed: 1']
df3_y1 = df3['Unnamed: 2']

df4=pd.read_excel("Filelocation//tcdata_9M_mesh.xlsx", "tcdata_9M_mesh", usecols='B:E',skiprows=5 )
df4_x1 = df4['Unnamed: 1']
df4_y1 = df4['Unnamed: 2']

linewidth=1
markersize=0.25
markernumber=10

# 创建主图的Figure和Axes
fig_1, ax1 = plt.subplots(figsize=(6,4))

# 主图绘图
ax1.plot(df1_x1,df1_y1,'b-', linewidth=linewidth,markevery=markernumber,label='750K')
ax1.plot(df2_x1,df2_y1,'g-', linewidth=linewidth,markevery=markernumber,label='1.5M')
ax1.plot(df3_x1,df3_y1,'r-',linewidth=linewidth,markevery=markernumber,label='3M')
ax1.plot(df4_x1,df4_y1,'c-', linewidth=linewidth,markevery=markernumber,label='9M')

# 设置主图属性
ax1.set_ylabel('Temperature ($^\circ$C)')
ax1.set_xlabel('Time (s)')
ax1.set_xlim(-1,17)
ax1.set_ylim(1200,1550)
ax1.tick_params(axis='both', labelsize=10)

# 配置图例
leg = ax1.legend(loc=1,fontsize=10)
leg.get_frame().set_edgecolor('k') 
leg.get_frame().set_facecolor('w')

# 添加网格
ax1.grid(color='#929591', linestyle='--', linewidth=0.5)

# 创建内嵌子图，添加唯一label消除警告
ax2 = fig_1.add_axes([.22, .22, .28, .28], facecolor='w', label='inset_axes')
ax2.plot(df1_x1,df1_y1,'b-', linewidth=linewidth,markevery=markernumber)
ax2.plot(df2_x1,df2_y1,'g-', linewidth=linewidth,markevery=markernumber)
ax2.plot(df3_x1,df3_y1,'r-',linewidth=linewidth,markevery=markernumber)
ax2.plot(df4_x1,df4_y1,'c-', linewidth=linewidth,markevery=markernumber)

# 设置子图属性
ax2.set_xlim(12,14)
ax2.set_ylim(1280,1320)
ax2.tick_params(axis='both', labelsize=7)

# 先保存再显示
fig_1.savefig('grid_independency_test.png', dpi=1000)
plt.show()

额外优化点 #

• 无需将单列DataFrame重复转换，直接用df['列名']获取Series即可，代码更简洁
• 用set_xlim()/set_ylim()替代plt.axis()，更符合面向对象编程风格
• 给内嵌子图添加label='inset_axes'参数，彻底消除弃用警告

内容的提问来源于stack exchange，提问作者User646972