array1 = {[*unique ID's*, *date*, *number of hours*, *average output per hour*],
           ...}

array1 = {[10213, 19660101, 720, 36.6],
          [10215, 19550102, 430, 25.5],
          [10215, 19550202, 255, 30.1],
          ...}

unique_ID = np.unique(numpy_During[:, 0])
print("starting creation of sub arrays")
for x in np.nditer(unique_ID):
    temp = np.where(numpy_During[:, 0] == x)
    for value in np.nditer(temp):
        if str(int(x)) in list_of_ID:
            try:
                if max_Hours[str(int(x))] > 13140:
                    break
                new_Array = np.empty([int(numpy_During[value, 2]), 6])
                new_Array[:, :] = numpy_During[value, :]
                old_Array = numps[str(int(x))]
                my_Stacker = (old_Array, new_Array)
                combined_Array = np.vstack(my_Stacker)
                numps[str(int(x))] = combined_Array
                max_Hours[str(int(x))] += numpy_During[value, 2]
            except:
                print("volume period of NaN")
        else:
            try:
                list_of_ID.append(str(int(x)))
                new_Array = np.empty([int(numpy_During[value, 2]), 6])
                new_Array[:, :] = numpy_During[value, :]
                numps[str(int(x))] = new_Array
                max_Hours[str(int(x))] = numpy_During[value, 2]
            except:
                print("volume period of NaN")

panda['sum_calc'] = panda['per_hour'].rolling(window=size, center=False).sum()
calc = panda.loc[panda['sum_calc'] == panda['sum_calc'].max(), 'sum_calc']
calc_index = int(sum_calc._index.data[0]) # gets row number