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python - Pandas :删除带有日期的字符串

转载 作者:行者123 更新时间:2023-11-28 22:34:34 27 4
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我有 df:

ID,"address","used_at","active_seconds","pageviews"
71ecd2aa165114e5ee292131f1167d8c,"auto.drom.ru",2014-05-17 10:58:59,166,2
71ecd2aa165114e5ee292131f1167d8c,"auto.drom.ru",2016-07-17 17:34:07,92,4
70150aba267f671045f147767251d169,"avito.ru/*/avtomobili",2014-06-15 11:52:09,837,40
bc779f542049bcabb9e68518a215814e,"auto.yandex.ru",2014-01-16 22:23:56,8,1
bc779f542049bcabb9e68518a215814e,"avito.ru/*/avtomobili",2014-01-18 14:38:33,313,5
bc779f542049bcabb9e68518a215814e,"avito.ru/*/avtomobili",2016-07-18 18:12:07,20,1

我需要删除 used_at 超过 2016-06-30 的所有字符串。我该怎么做?

最佳答案

使用dt.dateboolean indexing :

print (df.used_at.dt.date > pd.to_datetime('2016-06-30').date())
0 False
1 True
2 False
3 False
4 False
5 True
Name: used_at, dtype: bool

print (df[df.used_at.dt.date > pd.to_datetime('2016-06-30').date()])
ID address \
1 71ecd2aa165114e5ee292131f1167d8c auto.drom.ru
5 bc779f542049bcabb9e68518a215814e avito.ru/*/avtomobili

used_at active_seconds pageviews
1 2016-07-17 17:34:07 92 4
5 2016-07-18 18:12:07 20 1

或者您可以通过定义日期时间:

print (df[df.used_at.dt.date > pd.datetime(2016, 6, 30).date()])
ID address \
1 71ecd2aa165114e5ee292131f1167d8c auto.drom.ru
5 bc779f542049bcabb9e68518a215814e avito.ru/*/avtomobili

used_at active_seconds pageviews
1 2016-07-17 17:34:07 92 4
5 2016-07-18 18:12:07 20 1

关于python - Pandas :删除带有日期的字符串,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38871775/

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