Pandas - Using `.rolling()` on multiple columns

Question

Consider a pandas DataFrame which looks like the one below

      A     B     C
0  0.63  1.12  1.73
1  2.20 -2.16 -0.13
2  0.97 -0.68  1.09
3 -0.         


        

Answer 1

One solution is to stack the data and then multiply your window size by the number of columns and slice the result by the number of columns. Also, since you want a forward looking window, reverse the order of the stacked DataFrame

wsize = 3
cols = len(df.columns)

df.stack(dropna=False)[::-1].rolling(window=wsize*cols).quantile(0.75)[cols-1::cols].reset_index(-1, drop=True).sort_index()

Output:

0    1.12
1    0.97
2    0.97
3     NaN
4     NaN
dtype: float64

In the case of many columns and a small window:

import pandas as pd
import numpy as np

wsize = 3
df2 = pd.concat([df.shift(-x) for x in range(wsize)], 1)
s_quant = df2.quantile(0.75, 1)

# Only necessary if you need to enforce sufficient data. 
s_quant[df2.isnull().any(1)] = np.NaN

Output: s_quant

0    1.12
1    0.97
2    0.97
3     NaN
4     NaN
Name: 0.75, dtype: float64

Answer 2

You can use numpy ravel. Still you may have to use for loops.

for i in range(0,3):
    print(df.iloc[i:i+3].values.ravel())

If your t steps in 3s, you can use numpy reshape function to create a n*9 dataframe.