pandas ValueError: view limit minimum -5.1000000000000005 小于 1 并且是无效的 Matplotlib 日期值
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ValueError: view limit minimum -5.1000000000000005 is less than 1 and is an invalid Matplotlib date value
提问by Timothy Pulliam
I have a pandas dataframe which contains some sar output that I would like to plot in matplotlib. Sample data is below.
我有一个 Pandas 数据框，其中包含一些我想在 matplotlib 中绘制的 sar 输出。示例数据如下。
>>> cpu_data.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 70 entries, 0 to 207
Data columns (total 8 columns):
00:00:01 70 non-null datetime64[ns]
CPU 70 non-null object
%user 70 non-null float64
%nice 70 non-null float64
%system 70 non-null float64
%iowait 70 non-null float64
%steal 70 non-null float64
%idle 70 non-null float64
dtypes: float64(6), object(2)
memory usage: 4.4+ KB
>>> cpu_data
00:00:01 CPU %user %nice %system %iowait %steal %idle
0 00:10:01 all 0.30 0.00 0.30 0.06 0.0 99.34
3 00:20:01 all 0.09 0.00 0.13 0.00 0.0 99.78
6 00:30:01 all 0.07 0.00 0.11 0.00 0.0 99.81
9 00:40:01 all 0.08 0.00 0.11 0.00 0.0 99.80
12 00:50:01 all 0.08 0.00 0.13 0.00 0.0 99.79
15 01:00:04 all 0.09 0.00 0.13 0.00 0.0 99.77
18 01:10:01 all 0.27 0.00 0.28 0.00 0.0 99.46
21 01:20:01 all 0.09 0.00 0.11 0.00 0.0 99.79
24 01:30:04 all 0.12 0.00 0.13 0.01 0.0 99.74
27 01:40:01 all 0.08 0.00 0.11 0.01 0.0 99.80
30 01:50:01 all 0.09 0.00 0.13 0.01 0.0 99.77
I want to plot using the timestamps as the x-axis. I have written the following code.
我想使用时间戳作为 x 轴进行绘图。我编写了以下代码。
import pandas as pd
import os
import matplotlib.pyplot as plt
import matplotlib.dates as md
import dateutil
import matplotlib.dates as mdates
cpu_data[cpu_data.columns[0]] = [dateutil.parser.parse(s) for s in cpu_data[cpu_data.columns[0]]]
plt.subplots_adjust(bottom=0.2)
plt.xticks( rotation=25 )
ax=plt.gca()
ax.xaxis_date()
xfmt = md.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
cpu_data.plot(ax=ax)
plt.show()
But I get the following error
但我收到以下错误
ValueError: view limit minimum -5.1000000000000005 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units
This doesn't make any sense because I manually converted all of the time stamp strings to datetime objects
这没有任何意义，因为我手动将所有时间戳字符串转换为日期时间对象
cpu_data[cpu_data.columns[0]] = [dateutil.parser.parse(s) for s in cpu_data[cpu_data.columns[0]]]
But they don't appear to be the correct data type
但它们似乎不是正确的数据类型
2018-09-30 00:10:01 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
2018-09-30 00:20:01 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
2018-09-30 00:30:01 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
2018-09-30 00:40:01 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
2018-09-30 00:50:01 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
2018-09-30 01:00:01 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
I have no idea how to fix this. I have tried manually setting the x-axis to start on a datetime object value using plt.xlim(cpu_data[cpu_data.columns[0]].iloc[0])but this produces the same error. I really am lost here. Any guidance would be appreciated. I can provide more information if it would help.
我不知道如何解决这个问题。我尝试手动设置 x 轴以使用日期时间对象值开始，plt.xlim(cpu_data[cpu_data.columns[0]].iloc[0])但这会产生相同的错误。我真的迷失在这里。任何指导将不胜感激。如果有帮助，我可以提供更多信息。
EDIT:
编辑：
I think the dates are not the correct data type (as indicated by the error). It seems like pandas keeps converting the data in the time column (column 0) to on object of type pandas._libs.tslibs.timestamps.Timestamp. I think it should be a datetime object as matplotlib complains about.
我认为日期不是正确的数据类型（如错误所示）。似乎Pandas不断将时间列（第 0 列）中的数据转换为 on 类型的对象pandas._libs.tslibs.timestamps.Timestamp。我认为它应该是 matplotlib 抱怨的日期时间对象。
回答by Timothy Pulliam
For those interested, this is how I ended up plotting the data using matplotlib
对于那些感兴趣的人，这就是我最终使用 matplotlib 绘制数据的方式
# Plot cpu
plt.figure(1)
plt.subplots_adjust(bottom=0.2)
plt.xticks(rotation=25)
ax=plt.gca()
ax.xaxis_date()
xfmt = md.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.title(f'CPU usage on {remote_host}')
lines = plt.plot(dates, cpu_data[cpu_data.columns[2:]])
ax.legend(lines, [str(col) for col in list(cpu_data.columns[2:])])
plot.show()
