Rolling or Siding window for transpose of a matrix

Question

I have the following data:

input data

I want to obtain it this way:

output data

What we have manually done here is considered a 3×1 matrix and transposed it to obtain 1×3 matrix for all 4 columns: A1, A2, A3 & A4. I need to automate this process in python, and I believe rolling window is the solution. This needs to iterate through all rows (around 300 odd rows).

Reproducible input:

df = pd.DataFrame({'A1': [1,1,0,1],
                   'A2': [1,0,0,0],
                   'A3': [1,0,1,1],
                   'A4': [1,0,0,0],
                   })

Asked By: Addy

||

Source

Answer 1

You can use numpy for reshaping:

from numpy.lib.stride_tricks import sliding_window_view

N = 3
out = pd.DataFrame(
  sliding_window_view(df, (N,1))
  .reshape(-1,N*df.shape[1]),
)

print(out)

Output:

    0   1   2   3   4   5   6   7   8   9   10  11
0   1   1   0   1   0   0   1   0   1   1   0   0
1   1   0   1   0   0   0   0   1   1   0   0   0

With MultiIndex:

out = pd.DataFrame(
  sliding_window_view(df, (N,1))
  .reshape(-1,N*df.shape[1]),
  columns=pd.MultiIndex.from_product([df.columns, np.arange(N)])
)

Output:

   A1       A2       A3       A4      
   0  1  2  0  1  2  0  1  2  0  1  2
0  1  1  0  1  0  0  1  0  1  1  0  0
1  1  0  1  0  0  0  0  1  1  0  0  0

Used input:

df = pd.DataFrame({'A1': [1,1,0,1],
                   'A2': [1,0,0,0],
                   'A3': [1,0,1,1],
                   'A4': [1,0,0,0],
                   })

Answered By: mozway

Rolling or Siding window for transpose of a matrix

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