CODEWITHSUNDEEP
pythonpandas
user369210
user369210

Reputation: 133

Pandas rolling function with shifted indices

The code

s = pd.Series([0,1,2,3,4])
sr = s.rolling(3)
sr.apply(np.sum)

returns the series with indices [0,1,2,3,4] and values [NaN, NaN, 3, 6, 9]. Is there a quick hack, specifically using pandas rolling functions, so that it returns the rolling sum from the following 3 indices, i.e. so that the series values are [3, 6, 9, NaN, NaN]?

Upvotes: 1

Views: 612

Answers (3)

BENY
BENY

Reputation: 323396

Adding iloc[::-1]

s = pd.Series([0,1,2,3,4])
sr = s.iloc[::-1].rolling(3)
sr.sum().iloc[::-1]

0    3.0
1    6.0
2    9.0
3    NaN
4    NaN
dtype: float64

Upvotes: 1

llllllllll
llllllllll

Reputation: 16434

The only difference is a shift by -2:

w = 3
s.rolling(w).sum().shift(-w + 1)

0    3.0
1    6.0
2    9.0
3    NaN
4    NaN
dtype: float64

Upvotes: 3

harpan
harpan

Reputation: 8641

You need numpy.roll()

s = pd.Series([0,1,2,3,4])
sr = s.rolling(3)
pd.Series(np.roll(sr.apply(np.sum),3))

Output:

0    3.0
1    6.0
2    9.0
3    NaN
4    NaN
dtype: float64

Upvotes: 1

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