Matplotlib子图无法显示低分辨率数据问题求助

问题背景 #

使用Matplotlib绘制共享X轴的双Y轴子图，展示两组时序数据：

• stw：每60秒一个数据点，空值为nan
• Trim corrected：每秒一个数据点

但低分辨率的stw数据完全无法显示，仅能看到Trim corrected的数据；当更换为同分辨率的sog和stw数据时，两组数据均可正常显示。

初始未成功代码 #

fig, ax = plt.subplots(figsize=(15,5))

ax.plot(df1['Time corrected'],df1['stw'], color = 'green')
ax.tick_params(axis='y', labelcolor='green')

ax1=ax.twinx()

ax1.plot(df1['Time corrected'], df1['Trim corrected'], color= 'purple')
ax1.tick_params(axis='y', labelcolor='purple')

运行后仅显示Trim corrected的数据，stw无任何显示。

验证测试 #

1. 同分辨率数据测试 #

将Trim corrected替换为同分辨率的sog数据，两组数据均可正常显示：

import pandas as pd
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(15,5))

ax.plot(df1['Time corrected'],df1['stw'], color = 'green')
ax.tick_params(axis='y', labelcolor='green')

ax1=ax.twinx()

ax1.plot(df1['Time corrected'], df1['sog'], color= 'purple')
ax1.tick_params(axis='y', labelcolor='purple')

2. 单独绘制stw数据 #

使用df.plot方法单独绘制stw数据，可正常显示：

import pandas as pd
import matplotlib.pyplot as plt

df1.plot(figsize= (15,5),
          kind= 'line',
          color= 'green',     
          x= 'tsp',
          y= 'stw', 
          sharex= False,
          title= 'Speed over Time',
          ylabel= 'Speed Through Water [kn]',
          xlabel= 'Time [UTC]',
          grid = True)
plt.rcParams['figure.dpi'] = 200
plt.show()

成功解决方案（Seaborn实现） #

通过Seaborn库实现两组数据的正常展示，代码如下：

df1 = df1.rename({'Trim corrected':'Pitch Angle [°]'}, axis = 'columns')
df1 = df1.rename({'stw':'Speed through Water [kn]'}, axis = 'columns')
df1 = df1.rename({'Time corrected':'Time [UTC]'}, axis = 'columns')

import seaborn as sns

fig, ax = plt.subplots(figsize=(15,5))
ax1= sns.lineplot(x=df1['Time [UTC]'],y=df1.loc[:,'Pitch Angle [°]'], color='purple', label='Pitch Angle [°]', ax = ax)
ax1.set_ylim(-1.5,2.2)
ax2 = ax1.twinx()

sns.lineplot(x=df1['Time [UTC]'],y=df1['Speed through Water [kn]'], color='green', ax = ax2,linewidth=3, label= 'Speed Through Water [kn]') 
plt.rcParams['figure.dpi'] = 200

ax.legend()

运行后可同时展示低分辨率的stw和高分辨率的Trim corrected数据。

内容的提问来源于stack exchange，提问作者johannesv