I have got a time series with a float index representing minutes from start of an experiment . The observations are not perfectly equally spaced. I am doing a rolling mean . Here some example data:
S = pd.Series([0,3,2,6,4,7,7,9,11,13,12,12,11,9,6,7,3,5,4],
index=[0.01,0.13,0.2,0.29,0.4,0.5,0.59,0.68,0.79,0.9,1.0,1.1,1.19,1.29,1.4,1.5,1.6,1.71,1.8])
Sr = S.rolling(3, win_type='triang', center=True).mean()
In my real data the window spans several hundred data points. Thus, i would like it to always span the same time (in index units) , instead of a fixed number of observations. I found that this is possible on datetime indexes, however I need the index to be float for further calculation. Is there any way of doing this without having to convert the index to datetime and back again?
Pseudo-function:
Sr = S.rolling(0.3, win_type='triang', center=True, *on=index*).mean()
Expected output for this example:
for each index i: mean over window from i-0.15 to i+0.15 (with triangular weighting according to distance from i)
I do not think it can be done with the rolling
method.
Out of interest, it can be done manually as follows:
from scipy.signal.windows import triang
import numpy as np
import pandas as pd
def triangular(a):
n = a.size
b = triang(n) / (n - 1)
return b @ a
S = pd.Series([0,3,2,6,4,7,7,9,11,13,12,12,11,9,6,7,3,5,4],
index=[0.01,0.13,0.2,0.29,0.4,0.5,0.59,0.68,0.79,0.9,1.0,1.1,1.19,1.29,1.4,1.5,1.6,1.71,1.8])
df = pd.DataFrame({'S': S})
df['neighbours'] = df.index.to_series().apply(lambda x: [df.loc[index][0] for index in df.index if x - 0.15 < index <= x + 0.15])
df['rolling_mean'] = df.neighbours.apply(lambda x: triangular(np.array(x)))
df.drop('neighbours', axis=1, inplace=True)
print(df)
Output:
S rolling_mean
0.01 0 1.50
0.13 3 2.00
0.20 2 3.25
0.29 6 4.50
0.40 4 5.25
0.50 7 6.25
0.59 7 7.50
0.68 9 9.00
0.79 11 11.00
0.90 13 12.25
1.00 12 12.25
1.10 12 11.75
1.19 11 10.75
1.29 9 8.75
1.40 6 7.00
1.50 7 5.75
1.60 3 4.50
1.71 5 4.25
1.80 4 4.50
I doubt, however, that this is simpler than converting the float index into datetime and then back.
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