CODEWITHSUNDEEP
pythonpandas
ℕʘʘḆḽḘ
ℕʘʘḆḽḘ

Reputation: 19395

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

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!

Upvotes: 4

Views: 2732

Answers (2)

StarFox
StarFox

Reputation: 637

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!

Upvotes: 3

Ami Tavory
Ami Tavory

Reputation: 76366

IIUC, what you want is to perform a rolling sum, but only on the observations grouped by the exact same time of day. This can be done by

df.X.groupby([df.index.hour, df.index.minute]).apply(lambda g: g.rolling(window=5).sum())

(Note that your question alternates between 5 and 10 periods.) For example:

In [43]: df.X.groupby([df.index.hour, df.index.minute]).apply(lambda g: g.rolling(window=5).sum()).tail()
Out[43]: 
2000-02-04 17:15:00   -2.135887
2000-02-04 17:16:00   -3.056707
2000-02-04 17:17:00    0.813798
2000-02-04 17:18:00   -1.092548
2000-02-04 17:19:00   -0.997104
Freq: T, Name: X, dtype: float64

Upvotes: 2

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