由于您只想了解分布情况，只需将 df["No. of Trades"] 缩放到范围 [0, 1] 并将其按 df["Day_Perc_Change"] 的最大值进行缩放。

import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
sns.set(style="darkgrid")


scaledvolume =  df["No. of Trades"] - df["No. of Trades"].min()
scaledvolume = scaledvolume/scaledvolume.max() * df.Day_Perc_Change.max()

fig, ax = plt.subplots(figsize=(12, 6))

ax.stem(df.index, df.Day_Perc_Change , 'b', markerfmt='bo', label='Percente Change')
ax.plot(df.index, scaledvolume, 'k', label='Volume')

ax.set_xlabel('Date')
plt.legend(loc=2)

plt.tight_layout()
plt.xticks(plt.xticks()[0], df.index.date, rotation=45)
plt.show()

应该得到，输出：

我认为你正在寻找的是所谓的twinx。我只是使用了给出的示例之一并稍微修改了输入参数：

import numpy as np
import matplotlib.pyplot as plt

date_name = "Date"
stem_name = "Day_Perc_Change"
plot_name = "No. of Trades"
t = df[date_name]
data1 = df[stem_name]
data2 = df[plot_name]

fig, ax1 = plt.subplots()

color = 'tab:blue'
ax1.set_xlabel(date_name)
ax1.set_ylabel(stem_name, color=color)
ax1.stem(t, data1)
ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

color = 'tab:red'
ax2.set_ylabel(plot_name, color=color)  # we already handled the x-label with ax1
ax2.plot(t, data2, color=color)
ax2.tick_params(axis='y', labelcolor=color)

fig.tight_layout()  # otherwise the right y-label is slightly clipped
plt.show()