Noobie Noobie - 2 months ago 21
Python Question

Pandas: how to compute the rolling sum of a variable over the last few days but only at a given hour?

I have a dataframe as follows

df = pd.DataFrame({ 'X' : np.random.randn(50000)}, index=pd.date_range('1/1/2000', periods=50000, freq='T'))

df.head(10)
Out[37]:
X
2000-01-01 00:00:00 -0.699565
2000-01-01 00:01:00 -0.646129
2000-01-01 00:02:00 1.339314
2000-01-01 00:03:00 0.559563
2000-01-01 00:04:00 1.529063
2000-01-01 00:05:00 0.131740
2000-01-01 00:06:00 1.282263
2000-01-01 00:07:00 -1.003991
2000-01-01 00:08:00 -1.594918
2000-01-01 00:09:00 -0.775230


I would like to create a variable that contains the
sum
of X


  • over the last 5 days (not including the current observation)

  • only considering observations that fall at the exact same hour as the current observation.



In other words:


  1. At index
    2000-01-01 00:00:00
    ,
    df['rolling_sum_same_hour']
    contains the sum the values of X observed at
    00:00:00
    during the last 5 days in the data (not including
    2000-01-01
    of course).

  2. At index
    2000-01-01 00:01:00
    ,
    df['rolling_sum_same_hour']
    contains the sum of of X observed at
    00:00:01
    during the last 5 days and so on.



The intuitive idea is that intraday prices have intraday seasonality, and I want to get rid of it that way.

I tried to use
df['rolling_sum_same_hour']=df.at_time(df.index.minute).rolling(window=5).sum()


with no success.
Any ideas?

Many thanks!

Answer

Behold the power of groupby!

df = # as you defined above
df['rolling_sum_by_time'] = df.groupby(df.index.time)['X'].apply(lambda x: x.shift(1).rolling(10).sum())

It's a big pill to swallow there, but we are grouping by time (as in python datetime.time), then getting the column we care about (else apply will work on columns - it now works on the time-groups), and then applying the function you want!

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