matplotlib 画双轴子图无法显示x轴的解决方法
(编辑：jimmy 日期: 2024/9/22 浏览：2)
主要问题
1.介绍
如题，画双轴子图不能显示 x-轴坐标轴标签，似乎 “双轴”与“子图”存在冲突有关，当前版本是 anaconda 3.7.4。比较奇葩的是 我家里的电脑，3.7.3 却没这个问题。但我把公司电脑换成 3.7.3 问题依旧，崩溃。
import pandas as pd
import matplotlib.pyplot as plt
a = pd.date_range('2020-07-01','2020-07-20')
b = [2,3,4,5,7,9,20,20,11,13,1,2,3,9,23,2,6,7,7,7]
c = [0.20,0.1,0.13,0.1,0.2,0.3,0.9,0.23,0.2,0.6,0.7,0.7,0.7,0.2,0.3,0.4,0.5,0.7,0.9,0.2]
data = pd.DataFrame({'a':a,'b':b,'c':c})
data = data.groupby(['a'])['b','c'].sum()
nrow = 2
ncol = 1
fig = plt.figure(figsize=(ncol*10,nrow*4))
ax_1 = plt.subplot2grid((nrow, ncol), (0, 0), colspan=1, rowspan=1,facecolor = 'black')
ax_2 = plt.subplot2grid((nrow, ncol), (1, 0), colspan=1, rowspan=1,facecolor = 'black')
data['b'].plot(ax = ax_1,color = 'r')
data['c'].plot(ax = ax_1.twinx(),color = 'y')
2.只画双轴，正常显示
import pandas as pd
import matplotlib.pyplot as plt
a = pd.date_range('2020-07-01','2020-07-20')
b = [2,3,4,5,7,9,20,20,11,13,1,2,3,9,23,2,6,7,7,7]
c = [0.20,0.1,0.13,0.1,0.2,0.3,0.9,0.23,0.2,0.6,0.7,0.7,0.7,0.2,0.3,0.4,0.5,0.7,0.9,0.2]
data = pd.DataFrame({'a':a,'b':b,'c':c})
data = data.groupby(['a'])['b','c'].sum()
nrow = 1
ncol = 1
fig = plt.figure(figsize=(ncol*10,nrow*4))
ax_1 = plt.subplot2grid((nrow, ncol), (0, 0), colspan=1, rowspan=1,facecolor = 'black')
# ax_2 = plt.subplot2grid((nrow, ncol), (1, 0), colspan=1, rowspan=1,facecolor = 'black')
data['b'].plot(ax = ax_1,color = 'r')
data['c'].plot(ax = ax_1.twinx(),color = 'y')
3.只画子图，也能正常显示
import pandas as pd
import matplotlib.pyplot as plt
a = pd.date_range('2020-07-01','2020-07-20')
b = [2,3,4,5,7,9,20,20,11,13,1,2,3,9,23,2,6,7,7,7]
c = [0.20,0.1,0.13,0.1,0.2,0.3,0.9,0.23,0.2,0.6,0.7,0.7,0.7,0.2,0.3,0.4,0.5,0.7,0.9,0.2]
data = pd.DataFrame({'a':a,'b':b,'c':c})
data = data.groupby(['a'])['b','c'].sum()
nrow = 2
ncol = 1
fig = plt.figure(figsize=(ncol*10,nrow*4))
ax_1 = plt.subplot2grid((nrow, ncol), (0, 0), colspan=1, rowspan=1,facecolor = 'black')
ax_2 = plt.subplot2grid((nrow, ncol), (1, 0), colspan=1, rowspan=1,facecolor = 'black')
data['b'].plot(ax = ax_1,color = 'r')
data['c'].plot(ax = ax_2,color = 'y')
