首先，没有必要分配 y1 和 y2 对象，因为以后永远不会使用它们。另外，legend=True是默认值。

• 根据matplotlib.pyplot.subplots文档，ax的返回为：

ax：axes。Axes对象或Axes对象数组

• 根据pandas.DataFrame.plot，ax参数：

ax：matplotlib轴对象，默认为无
因此，首先初始化一个axis对象数组（默认为nrow=1和nrow=2），然后根据Pandas图对其进行赋值。现在，通常情况下，您会用ax=ax * 覆盖 * ax的赋值，但由于您使用了辅助y轴，图会彼此重叠：

# INITIALIZE FIG DIMENSION AND AXES OBJECTS
fig, axs = plt.subplots(figsize=(8,4))

# ASSIGN AXES OBJECTS ACCORDINGLY
speeds_df.plot(ax=axs, x='datetime', y='down', grid=True, label="DL", linewidth=2, ylim=(100,225))
speeds_df.plot(ax=axs, x='datetime', y='up', secondary_y=True, label="UL", linewidth=2, ylim=(100,225))

plt.show()

要说明如何扩展轴对象，请参阅下面的多个（非重叠）图。
使用nrows=2的多个子区示例：

# INITIALIZE FIG DIMENSION AND AXES OBJECTS
fig, axs = plt.subplots(nrows=2, figsize=(8,4))

# ASSIGN AXES OBJECTS WITH INDEXING AND NO Y LIMITS
speeds_df.plot(ax=axs[0], x='datetime', y='down', grid=True, label="DL", linewidth=2)
plt.subplots_adjust(hspace = 1)
speeds_df.plot(ax=axs[1], x='datetime', y='up', label="UL", linewidth=2)

plt.show()

使用ncols=2的多个绘图示例：

# INITIALIZE FIG DIMENSION AND AXES OBJECTS
fig, axs = plt.subplots(ncols=2, figsize=(12,4))

# ASSIGN AXES OBJECTS WITH INDEXING AND NO Y LIMITS
speeds_df.plot(ax=axs[0], x='datetime', y='down', grid=True, label="DL", linewidth=2)
speeds_df.plot(ax=axs[1], x='datetime', y='up', label="UL", linewidth=2)

plt.show()

您甚至可以在将日期/时间字段设置为索引后使用subplots=True：

# INITIALIZE FIG DIMENSION AND AXES OBJECTS
fig, axs = plt.subplots(figsize=(8,4))

# ASSIGN AXES OBJECT PLOTTING ALL COLUMNS
speeds_df.set_index('datetime').plot(ax=axs, subplots=True, grid=True, label="DL", linewidth=2)

plt.show()

所以感谢@Parfait，我希望我理解的东西是正确的。这里是工作代码：

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
###### Prepare the data to plot
# set field delimiter and set column names which will also cause reading from row 1
data = pd.read_csv('test.log', sep=';', names=[
                   'datetime', 'severity', 'down', 'up', 'loss', 'server'])
#  we need to filter out ERROR records (with 0 speeds)
indexNames = data[data['severity'] == 'ERROR'].index
data.drop(indexNames, inplace=True)
# convert datetime pandas object to datetime64
data['datetime'] = pd.to_datetime(data['datetime'])
# use a dataframe with just the data I need; cleaner
speeds_df = data[['datetime', 'down', 'up']]

# now plot the graph
fig, ax = plt.subplots()

color = 'tab:green'
ax.set_xlabel('thislabeldoesnotworkbutcolordoes', color=color)
ax.tick_params(axis='x', labelcolor=color)

color = 'tab:red'
speeds_df.plot(ax=ax, x='datetime', y='down', label="DL", legend=True, linewidth=2, color=color)
ax.set_ylabel('DL', color=color)
ax.tick_params(axis='y', labelcolor=color)

color = 'tab:blue'
ax2 = speeds_df.plot(ax=ax, x='datetime', y='up', secondary_y=True, label="UL", legend=True, linewidth=2, color=color)
ax2.set_ylabel('UL', color=color)
ax2.tick_params(axis='y', labelcolor=color)
# using ylim in the plot command params does not work the same
# cannot show a grid since the two scales are different
ax.set_ylim(10, 225)
ax2.set_ylim(15, 50)

plt.show()

其给出：

我还是不明白的是a）为什么x轴标签似乎只支持颜色而不支持字符串值：（b）为什么df图中的ylim=（n，m）参数不能很好地工作，我必须使用ax.set_ylim结构来代替