I have a dataframe like this:
2014-01-17 15:03:55.073616,description,53.5,61.8
2014-01-17 15:03:55.789405,description,54.0,62.4
2014-01-17 15:03:56.604489,description,54.2,62.5
2014-01-17 15:03:57.345481,description,54.2,62.5
2014-01-17 15:03:58.072992,description,54.3,62.6
2014-01-17 15:03:58.805325,description,54.6,62.9
2014-01-17 15:03:59.585869,description,57.3,65.4
2014-01-17 15:04:00.292370,description,57.3,65.4
2014-01-17 15:04:01.030217,description,57.1,65.2
2014-01-17 15:04:01.836544,description,57.1,65.2
2014-01-17 15:04:02.559560,description,56.7,64.9
2014-01-17 15:04:03.259607,description,56.7,64.9
2014-01-17 15:04:03.968458,description,56.2,64.4
2014-01-17 15:04:04.695971,description,56.3,64.5
2014-01-17 15:04:05.447393,description,56.3,64.5
...
I would like to slice it by minutes , for example a slice between the third minute and the fifth, looking at the doc it seems that I would have to use searchsorted
, but I don't want to be providing the entire date everytime, since my dataframe only contains hourly data, I would just like to slice using an int for start/end minutes .
thanks in advance
Assuming the datetime is the index, you can access the minute:
# df1 = pd.read_csv('foo.csv', sep=',', header=None, parse_dates=[0], index_col=0)
In [11]: df1.index.minute
Out[11]: array([3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4], dtype=int32)
And grab those between 3rd and 5th minute:
In [12]: df1.iloc[(3 <= df1.index.minute) & (df1.index.minute < 5)]
Out[12]:
1 2 3
0
2014-01-17 15:03:55.073616 description 53.5 61.8
2014-01-17 15:03:55.789405 description 54.0 62.4
2014-01-17 15:03:56.604489 description 54.2 62.5
2014-01-17 15:03:57.345481 description 54.2 62.5
2014-01-17 15:03:58.072992 description 54.3 62.6
2014-01-17 15:03:58.805325 description 54.6 62.9
2014-01-17 15:03:59.585869 description 57.3 65.4
2014-01-17 15:04:00.292370 description 57.3 65.4
2014-01-17 15:04:01.030217 description 57.1 65.2
2014-01-17 15:04:01.836544 description 57.1 65.2
2014-01-17 15:04:02.559560 description 56.7 64.9
2014-01-17 15:04:03.259607 description 56.7 64.9
2014-01-17 15:04:03.968458 description 56.2 64.4
2014-01-17 15:04:04.695971 description 56.3 64.5
2014-01-17 15:04:05.447393 description 56.3 64.5
[15 rows x 3 columns]
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