pandas 如何按熊猫中的时间戳排序？
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How to sort by timestamps in pandas?
提问by J Doe
So, I have timestamps that look like the following:
所以，我有如下所示的时间戳：
20140804:10:00:13.281486
20140804:10:00:13.400113
20140804:10:00:13.555512
20140804:10:00:13.435677
I have them in a DataFrame and I am trying to sort them in an ascending order. I have tried the following. But, it doesn't seem to work
我把它们放在一个 DataFrame 中，我试图按升序对它们进行排序。我尝试了以下方法。但是，它似乎不起作用
df['yyyymmdd'] = pd.to_numeric(df['yyyymmdd'], errors='coerce')
df['hh'] = pd.to_numeric(df['hh'], errors='coerce')
df['mm'] = pd.to_numeric(df['mm'], errors='coerce')
df['ss'] = pd.to_numeric(df['ss'], errors='coerce')
df=df.sort(['yyyymmdd', 'hh','mm','ss'], ascending=[True, True,True,True])
Any help is appreciated.
任何帮助表示赞赏。
回答by miradulo
You just have to ensure you denote the format specification properly, and you can use pd.to_datetimeto convert them to actualdatetimes before sort_values.
你只需要确保你表示格式规范正确，您可以使用pd.to_datetime将它们转换为实际的前日期时间sort_values。
pd.to_datetime(stamps, format="%Y%m%d:%H:%M:%S.%f").sort_values()
This is much more direct than decomposing the timestamps in components and performing a multiple-criteria sort as you were attempting.
这比在您尝试时分解组件中的时间戳并执行多标准排序要直接得多。
Demo
演示
>>> stamps
0 20140804:10:00:13.281486
1 20140804:10:00:13.400113
2 20140804:10:00:13.555512
3 20140804:10:00:13.435677
dtype: object
>>> pd.to_datetime(stamps, format="%Y%m%d:%H:%M:%S.%f").sort_values()
0 2014-08-04 10:00:13.281486
1 2014-08-04 10:00:13.400113
3 2014-08-04 10:00:13.435677
2 2014-08-04 10:00:13.555512
dtype: datetime64[ns]
