How to count the number of rows in a given time interval in python pandas? -


i have pandas dataframe numer of columns contain timestamps events can happen objects, object ids index rows.

obj_id | event1  |  event2  |  event3  |  ... 1      | datetime| datetime |  nat     |  ... ...    | ...     | ...      |  ...     |  ...

i want count number of occurences of event on course of day (discarding date), in intervals specify.

sor far, solve recunstructing number of minutes since midnight using datetime.hour , datetime.minute:

i = 5    # number of minutes in interval i'm interested in ev1_counts = df.groupby(                         df.event1.apply(lambda x: * ((60*x.hour + x.minute)//i))                         )['event1'].count()

this job, seems unpythonic , i'm sure there better way. how?

i have seen this question, trying

time_series = pd.datetimeindex(df.event1) ts_df =  pd.series([1]*len(time_series), index=time_series) ev1_counts = ts_df.groupby(pd.timegrouper(freq = '{:d}min'.format(i)).count()

keeps date information, want discard. converting pd.datetime objects .time() method seems problematic, since result can not treated datetime object.

it seems can omit apply , simplify solution to:

ev1_counts = df.groupby((60*df.event1.dt.hour+df.event1.dt.minute)//i * i)['event1'].count()

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