图和子图刻度标签重叠

如何解决图和子图刻度标签重叠

我试图在一个图形上放置四个子图。 我想要的东西是：

1-该图引入了自己的x和y标签，但我不希望这样。

2-我想知道在所有子图中所有的y轴标签是否可能具有相似的值

3-我想要的实际图形可能包含高达3x3（最多9个子图）的子图。有没有一种方法可以使某种功能从每个子图的数据框中提取数据并绘制图形？

这是我使用的代码和输出图。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
  
fig,(df_256,df_128,df_64,df_32) = plt.subplots(4,2,sharex='col',sharey='row')
file_locn = ''r'C:\Users\me\Desktop\output.xlsx'''
df = pd.read_excel(file_locn,sheet_name='1',header=[0,1])
   
#print(df)

df_256 = df.xs(256,axis=1,level=0)
df_128 = df.xs(128,level=0)
df_64 = df.xs(64,level=0)
df_32 = df.xs(32,level=0)

ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)

ax1.set_xscale('symlog',base=2)
ax2.set_xscale('symlog',base=2)
ax3.set_xscale('symlog',base=2)
ax4.set_xscale('symlog',base=2)

ax1.set_yscale('log')
ax2.set_yscale('log')
ax3.set_yscale('log')
ax4.set_yscale('log')
    
'''print(df_256)
print(df_128)
print(df_64)
print(df_32)'''

color = ['blue','limegreen','#bc15b0','indigo']
linestyle = ["-",":","--","-."]
plot_lines = ["A","B","C","D"]
df_256.set_index('X').plot( style=linestyle,ax=ax1)
df_128.set_index('X').plot(style=linestyle,ax=ax2)
df_64.set_index('X').plot( style=linestyle,ax=ax3)
df_32.set_index('X').plot( style=linestyle,ax=ax4)
 
plt.show()

输出：

解决方法

我做了一些阅读，并按如下方法解决了。

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

linestyle = ["-s","-x","-+","o-"]
plot_lines = ["A","B","C","D"]
X=[4,8,16,32,64,128,256,512,1024]
plot_title=['256MB','128MB','64MB','16MB','8MB','4MB']

file_locn = ''r'C:\Users\me\Desktop\output.xlsx'''
df = pd.read_excel(file_locn,sheet_name='1',header=[0,1])
df_256 = df.xs(256,axis=1,level=0)
df_128 = df.xs(128,level=0)
df_64 = df.xs(64,level=0)
df_32 = df.xs(32,level=0)
df_16 = df.xs(64,level=0)
df_8 = df.xs(32,level=0)
df_4 = df.xs(4,level=0)

nrow=2
ncol=3
df_list = [df_256,df_128,df_64,df_16,df_8,df_4]    
fig,axes = plt.subplots(nrow,ncol,sharex=True,sharey=True)
# plot counter
count=0
for c in range(ncol):
    df_list[count].set_axis('X')

plt.xscale('symlog',base=2)

count=0
axes[0,0].set_ylabel('Y-Axis label')
axes[1,0].set_xlabel('X-Axis label')
axes[1,1].set_xlabel('X-Axis label')

for r in range(nrow):
    for c in range(ncol):
        df_list[count].set_index('X').plot(style=linestyle,ax=axes[r,c],legend=False)
        axes[r,c].set_title(plot_title[count])
        axes[r,c].set_xlim(4,1024)
        count+=1

lines,labels = fig.axes[-1].get_legend_handles_labels()    
fig.legend(lines,labels,loc='upper center',ncol=4)

plt.show()