具有双y轴比例和x轴上日期时间的Pandas数据帧的Matplot线图

Question

我有一个日志，它描述了我的家庭ADSL速度。 日志条目采用以下格式，其中字段为datetime；数量减速；提高速度；测试主机：

2020-01-06 18:09:45;INFO;211.5;29.1;0;host:spd-pub-rm-01-01.fastwebnet.it
2020-01-06 18:14:39;WARNING;209.9;28.1;0;host:spd-pub-rm-01-01.fastwebnet.it
2020-01-08 10:51:27;INFO;211.6;29.4;0;host:spd-pub-rm-01-01.fastwebnet.it

（有关完整的示例文件->；https://www.dropbox.com/s/tfmj9ozxe5millx/test.log?dl=0，请下载以下代码）

我希望绘制一个matplot图，在左轴上显示下载速度、上传速度（在较小和较低的值范围内），并在x记号下缩短日期时间（可能为45度角）

"""Plots the adsl-log generated log."""
import matplotlib.pyplot as plt
# import matplotlib.dates as mdates
import pandas as pd

# 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 objecti 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']]
speeds_df.info() # this shows datetime column is really a datetime64 value now
# now let's plot
fig, ax = plt.subplots()
y1 = speeds_df.plot(ax=ax, x='datetime', y='down', grid=True, label="DL", legend=True, linewidth=2,ylim=(100,225))
y2 = speeds_df.plot(ax=ax, x='datetime', y='up', secondary_y=True, label="UL", legend=True, linewidth=2, ylim=(100,225))

plt.show()

我现在获得了我需要的图，但希望能澄清一下上述代码中ax、y1和y2轴的作用

Answer 1

多亏了@Parfait，我希望我能正确地理解事情。以下是工作代码：

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()

其中：

我仍然不明白的是： a） 为什么x轴标签似乎只尊重颜色而不尊重字符串值：( b） 为什么df图中的ylim=（n，m）参数不能很好地工作，而我必须使用ax.set_ylim构造

Answer 2

首先，分配y1和y2对象是不必要的，因为以后您将永远不会使用它们。另外，legend=True是默认值

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

  ax : axes.Axes object or array of Axes objects

• 对于pandas.DataFrame.plot，ax参数：

  ax : matplotlib axes object, default None

因此，首先要初始化轴对象数组（默认为一个项目nrow=1和nrow=2），然后根据图分配它/它们。现在，通常情况下，您将用ax=ax覆盖ax的赋值，但由于您使用了一个辅助y轴，因此相互重叠打印：

# 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，请参见下面的多个（非重叠）绘图

使用nrows=2的多个子批次示例：

# 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()

---

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()