提问人:hope 提问时间:9/28/2020 最后编辑:hope 更新时间:9/29/2020 访问量:1075
图和子图刻度标签重叠
figure and subplots tick labels overlapping
问:
我试图在一个数字上放置四个子图。 我想要的东西是:
1- 该图引入了自己的 x 和 y 标签,我不希望这样。
2- 我想知道是否有可能在子图的所有标签中具有相似的 y 轴标签值
3- 我想要的实际数字可以包含大到 3x3(最多 9 个子数字)的子图。有没有办法制作某种函数,可以从每个子图的数据帧中提取数据并绘制图形?
这是我使用的代码和输出数字。
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, axis=1, level=0)
df_64 = df.xs(64, axis=1, level=0)
df_32 = df.xs(32, axis=1, 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()
答:
0赞
hope
9/29/2020
#1
我做了一些阅读并解决了如下问题。
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, axis=1, level=0)
df_64 = df.xs(64, axis=1, level=0)
df_32 = df.xs(32, axis=1, level=0)
df_16 = df.xs(64, axis=1, level=0)
df_8 = df.xs(32, axis=1, level=0)
df_4 = df.xs(4, axis=1, 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_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()
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plt.subplots()
ax1=fig.add_subplot()...
subplots()
fig=plt.figure()
plt.subplots()
df_128
ax1, ax2, ...
add_subplot