select 数据帧行 (datetimeindex) 按 datetime.date 列表
select dataframe rows (datetimeindex) by a list of datetime.date
df 看起来像这样:
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-08-10 05:00:00 393.0
2017-08-10 06:00:00 522.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0
2017-10-10 09:00:00 585.0
问题如何select在日期列表中行:
['2017-07-10', '2017-09-10']
拥有:
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0
谢谢
假设日期时间是索引,请尝试以下操作:
to_search=['2017-07-10', '2017-09-10']
df[df.index.to_series().dt.date.astype(str).isin(to_search)]
1
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0
鉴于您列表中的日期最多包含每日信息,您可以从地板开始(Series.dt.floor
) the DatetimeIndex
up to the daily level and indexing with the list of datetime objects using isin
:
t = [pd.to_datetime('2017-07-10'), pd.to_datetime('2017-09-10')]
df.index= pd.to_datetime(df.index)
df[df.index.floor('d').isin(t)]
输出
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0
df 看起来像这样:
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-08-10 05:00:00 393.0
2017-08-10 06:00:00 522.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0
2017-10-10 09:00:00 585.0
问题如何select在日期列表中行:
['2017-07-10', '2017-09-10']
拥有:
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0
谢谢
假设日期时间是索引,请尝试以下操作:
to_search=['2017-07-10', '2017-09-10']
df[df.index.to_series().dt.date.astype(str).isin(to_search)]
1
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0
鉴于您列表中的日期最多包含每日信息,您可以从地板开始(Series.dt.floor
) the DatetimeIndex
up to the daily level and indexing with the list of datetime objects using isin
:
t = [pd.to_datetime('2017-07-10'), pd.to_datetime('2017-09-10')]
df.index= pd.to_datetime(df.index)
df[df.index.floor('d').isin(t)]
输出
DateTime
2017-07-10 03:00:00 288.0
2017-07-10 04:00:00 306.0
2017-09-10 07:00:00 487.0
2017-09-10 08:00:00 523.0