Use Python to sum the data every 24 hours and filter for the data with the maximum sum

I have three rows of data, representing a time series for a whole year at an hourly resolution (3,8760).
I want to sum the values for every 24 hours/columns and filter the row with the maximum sum.

For example:
A = (1,2,3,4,5,6)
B = (0,0,4,5,6,7)
C = (0,0,2,6,14,0)

If I want to sum the values every 2 hours/columns and filter the row with the maximum sum,
the expected output would be
(1,2,4,5,14,0).

Currently, I am only trying to input the data into Python and create it in the form of a dataframe.

Taking your simplified example with shape=(6,3):

import numpy as np
a = np.array([(1,2,3,4,5,6), (0,0,4,5,6,7), (0,0,2,6,14,0)])

array([[ 1,  2,  3,  4,  5,  6],
       [ 0,  0,  4,  5,  6,  7],
       [ 0,  0,  2,  6, 14,  0]])

First reshape the array and sum along new axis (2 in simplified example, 24 in your full case):

b = a.reshape((3,3,2)).sum(axis=2)

array([[ 3,  7, 11],
       [ 0,  9, 13],
       [ 0,  8, 14]])

This gives you all partial sums. Now you can get indexes where there sum is biggest for a given column:

idx = np.argmax(b, axis=0)

array([0, 1, 2], dtype=int64)

Now you can select values from the initial array according to this index:

a.reshape((3,3,2))[idx, range(a.shape[1]//2), :].reshape((6,))

array([ 1,  2,  4,  5, 14,  0])

Which gives the answer you wanted.

Final solution

You can write it all in this routine:

def filter_row_cols(a, period = 2):
  rshape = (a.shape[0], a.shape[1]//period, period)
  idx = np.argmax(a.reshape(rshape).sum(axis=2), axis=0)
  result = a.reshape(rshape)[idx, range(a.shape[1]//period), :].reshape((a.shape[1],))
  return result

array([ 1,  2,  4,  5, 14,  0])