从 Pandas 数据框列中删除“秒”和“分钟”
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Remove 'seconds' and 'minutes' from a Pandas dataframe column
提问by Dustin Helliwell
Given a dataframe like:
给定一个数据框，如：
import numpy as np
import pandas as pd
df = pd.DataFrame(
{'Date' : pd.date_range('1/1/2011', periods=5, freq='3675S'),
'Num' : np.random.rand(5)})
Date Num
0 2011-01-01 00:00:00 0.580997
1 2011-01-01 01:01:15 0.407332
2 2011-01-01 02:02:30 0.786035
3 2011-01-01 03:03:45 0.821792
4 2011-01-01 04:05:00 0.807869
I would like to remove the 'minutes' and 'seconds' information.
我想删除“分钟”和“秒”信息。
The following (mostly stolen from: How to remove the 'seconds' of Pandas dataframe index?) works okay,
以下（主要来自：How to remove the 'seconds' of Pandas dataframe index?）工作正常，
df = df.assign(Date = lambda x: pd.to_datetime(x['Date'].dt.strftime('%Y-%m-%d %H')))
Date Num
0 2011-01-01 00:00:00 0.580997
1 2011-01-01 01:00:00 0.407332
2 2011-01-01 02:00:00 0.786035
3 2011-01-01 03:00:00 0.821792
4 2011-01-01 04:00:00 0.807869
but it feels strange to convert a datetime to a string then back to a datetime. Is there a way to do this more directly?
但是将日期时间转换为字符串然后再转换回日期时间感觉很奇怪。有没有办法更直接地做到这一点？
回答by piRSquared
dt.round
dt.round
This is how it should be done... use dt.round
这就是它应该如何完成...使用 dt.round
df.assign(Date=df.Date.dt.round('H'))
Date Num
0 2011-01-01 00:00:00 0.577957
1 2011-01-01 01:00:00 0.995748
2 2011-01-01 02:00:00 0.864013
3 2011-01-01 03:00:00 0.468762
4 2011-01-01 04:00:00 0.866827
OLD ANSWER
旧答案
One approach is to set the index and use resample
一种方法是设置索引并使用 resample
df.set_index('Date').resample('H').last().reset_index()
Date Num
0 2011-01-01 00:00:00 0.577957
1 2011-01-01 01:00:00 0.995748
2 2011-01-01 02:00:00 0.864013
3 2011-01-01 03:00:00 0.468762
4 2011-01-01 04:00:00 0.866827
Another alternative is to strip the dateand hourcomponents
另一种选择是剥离date和hour组件
df.assign(
Date=pd.to_datetime(df.Date.dt.date) +
pd.to_timedelta(df.Date.dt.hour, unit='H'))
Date Num
0 2011-01-01 00:00:00 0.577957
1 2011-01-01 01:00:00 0.995748
2 2011-01-01 02:00:00 0.864013
3 2011-01-01 03:00:00 0.468762
4 2011-01-01 04:00:00 0.866827
