pandas select rows asof time, instead of datetime

Question

Given a dataframe:

df = pd.DataFrame({'a': [10, 20, 40, 50,60,70,80,100]},index = pd.DatetimeIndex(['2018-02-27 09:01:00','2018-02-27 09:02:00','2018-02-27 09:04:00','2018-02-27 09:05:00','2018-03-27 09:01:00','2018-03-27 09:02:00','2018-03-27 09:03:00','2018-03-27 09:05:00']))

I hope to get the values as of 09:04, in which case it should return 2 rows with 'a' value to be 40 and 80. It seems pandas only takes datetime as input for asof() and I have to do some pre-processing to create all the filters myself. I wonder if there is already such functionality in pandas where input like 09:04 would suffice, or other simple ways to get the result.

EDIT: Sorry I put in the wrong df in the beginning - there should not be 09:04 for the 2nd day, thus need asof().

Do you need asof because possible 40 and 90 for 09:04:30 ? — jezrael
– jezrael, Commented Apr 29, 2019 at 13:24

BENY · Accepted Answer · 2019-04-29 13:44:00Z

1

I am using strftime

df[df.index.strftime('%H:%M')=='09:04']
Out[652]: 
                      a
2018-02-27 09:04:00  40
2018-03-27 09:04:00  90

answered Apr 29, 2019 at 13:44

BENY

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Comments

ALollz · Accepted Answer · 2019-04-29 14:42:34Z

1

If need to disregard the seconds for a minute, can use .between_time

df.between_time('09:04', '09:05', include_end=False)
#                      a
#2018-02-27 09:04:00  40
#2018-03-27 09:04:00  90

Or if you want to make this a bit more functional:

time = '09:04'
df.between_time(time, (pd.to_datetime(time)+pd.Timedelta('1min')).time(), include_end=False)

edited Apr 29, 2019 at 14:42

answered Apr 29, 2019 at 13:55

ALollz

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Comments

Scott Boston · Accepted Answer · 2019-04-29 13:23:44Z

0

You can try using boolean index:

df[(df.index.hour == 9) & (df.index.minute == 4)]

Output:

                      a
2018-02-27 09:04:00  40
2018-03-27 09:04:00  90

answered Apr 29, 2019 at 13:23

Scott Boston

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