Pandas, dataframe with a datetime64 column, querying by hour

Question

I have a pandas dataframe df which has one column constituted by datetime64, e.g.

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1471 entries, 0 to 2940
Data columns (total 2 columns):
date    1471  non-null values
id      1471  non-null values
dtypes: datetime64[ns](1), int64(1)

I would like to sub-sample df using as criterion the hour of the day (independently on the other information in date). E.g., in pseudo code

df_sub = df[ (HOUR(df.date) > 8) & (HOUR(df.date) < 20) ]

for some function HOUR.

I guess the problem can be solved via a preliminary conversion from datetime64 to datetime. Can this be handled more efficiently?

Answer 1

Found a simple solution.

df['hour'] = df.date.apply(lambda x : x.hour)

df_sub = df[(df.hour > 8) & (df.hour) <20]

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EDIT:

There is a property dt specifically introduced to handle this problem. The query becomes:

df_sub = df[ (df.date.dt.hour > 8) 
              &  (df.date.dt.hour < 20) ]