Create "buffer" Matrix From Dataframe With Rolling Window?

Given a dataframe of just one column, how can I convert it into another dataframe 'buffer' (of size 2), described below: df = 0 0 1 1 2 2 3 3 4 4 4 5 5 6 5 expected_buff

Solution 1:

import pandas as pd

def buff(s, n):
    return (pd.concat([s.shift(-i) for i in range(n)], axis=1)
              .dropna().astype(int))

s = pd.Series([1,2,3,4,5])
print(buff(s, 2))

#    0  0
# 0  1  2
# 1  2  3
# 2  3  4
# 3  4  5

print(buff(s, 3))

#    0  0  0
# 0  1  2  3
# 1  2  3  4
# 2  3  4  5

print(buff(s, 4))

#    0  0  0  0
# 0  1  2  3  4
# 1  2  3  4  5

Solution 2:

def buff(df, past):
    a = np.concatenate([df.values[i:i-past] for i in range(past)], axis=1)
    return pd.DataFrame(a, columns=list(range(past)))

---

buff(df, 2)

buff(df, 3)

buff(df, 4)

buff(df, 5)