Multiple shifts on dataframe

Question

I would like to repeatedly shift select columns of a a dataframe based on the number of shifts specified in array nShiftsPerCol. How to generate the output dataframe DFO that contains the columns with nonzero shifts specified and each of those columns shifted multiple times. Note, the first shift is zero or no shift. Append the shift number to the column name.

import pandas as pd 
import numpy as np 

df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [2, 3, 4, 5, 6], 'C': [3, 4, 5, 6, 7]})
print(df)
nCols = df.shape[0]
nShiftsPerCol = np.zeros(nCols)
nShiftsPerCol[0]=3 # shift column A 3 times
nShiftsPerCol[2]=2 # shift column C 2 times

Original dataframe

   A  B  C
0  1  2  3
1  2  3  4
2  3  4  5
3  4  5  6
4  5  6  7

Desired output

   A_0  A_1  A_2  C_0   C_1
0  1    2    3    3     4
1  2    3    4    4     5
2  3    4    5    5     6
3  4    5    NA   6     7
4  5    NA   NA   7     NA

Answer 1

First create Series with filtering out 0 values:

#for columns need shape[1]
nCols = df.shape[1]
nShiftsPerCol = np.zeros(nCols)
nShiftsPerCol[0]=3 # shift column A 3 times
nShiftsPerCol[2]=2 # shift column C 2 times

print (nShiftsPerCol)

s = pd.Series(nShiftsPerCol, df.columns).astype(int)
s = s[s!=0]
print (s)
A    3
C    2
dtype: int32

and then loop and create new columns:

for i, x in s.items():
    for y in range(x):
        df['{}_{}'.format(i, y)] = df[i].shift(-y)

print (df)
   A  B  C  A_0  A_1  A_2  C_0  C_1
0  1  2  3    1  2.0  3.0    3  4.0
1  2  3  4    2  3.0  4.0    4  5.0
2  3  4  5    3  4.0  5.0    5  6.0
3  4  5  6    4  5.0  NaN    6  7.0
4  5  6  7    5  NaN  NaN    7  NaN

Another solution for store columns names and shift number is list of tuples:

L = list(zip(df.columns, nShiftsPerCol.astype(int)))
L = [x for x in L if x[1] != 0]
print (L)
[('A', 3), ('C', 2)]

for i, x in L:
    for y in range(x):
        df['{}_{}'.format(i, y)] = df[i].shift(-y)

print (df)
   A  B  C  A_0  A_1  A_2  C_0  C_1
0  1  2  3    1  2.0  3.0    3  4.0
1  2  3  4    2  3.0  4.0    4  5.0
2  3  4  5    3  4.0  5.0    5  6.0
3  4  5  6    4  5.0  NaN    6  7.0
4  5  6  7    5  NaN  NaN    7  NaN