Combine columns in Pandas consisting of numpy arrays

Question

I have a dataframe and I need to add some columns togeather. I cant seem to get it right. This is what I have to start with:

cars = pd.DataFrame({'x_now':    np.repeat(1,10),
                     'y_now':    np.arange(1,11), 

                     'x_1_goal': np.repeat(1,10),
                     'y_1_goal': np.repeat(10,10),

                     'x_2_goal': np.repeat(4, 10),
                     'y_2_goal': np.repeat(10, 10),

                     'x_3_goal': np.repeat(4, 10),
                     'y_3_goal': np.arange(22,12,-1)})

def route(row, var,variabel_text_1, variabel_text_2):
    var2 = 'y' if var == 'x' else 'x'

    now, now2   = row[f'{var}{variabel_text_1}'], row[f'{var2}{variabel_text_1}']
    goal, goal2 = row[f'{var}{variabel_text_2}'], row[f'{var2}{variabel_text_2}']
    diff, diff2 = goal - now, goal2 - now2

    if diff == 0:
        result = np.array([now] * abs(diff2)).astype(int)
    else:
        result = 1 + np.arange(now, goal, diff / abs(diff)).astype(int)
    return result

cars['x_car_move_route']   = cars.apply(route, args=('x','_now'   , '_1_goal'), axis=1)
cars['x_car_move_route_1'] = cars.apply(route, args=('x','_1_goal', '_2_goal'), axis=1)

This gives me these last 2 columns of my DataFrame:

         x_car_move_route          x_car_move_route_1
0  [1, 1, 1, 1, 1, 1, 1, 1, 1]          [2, 3, 4]
1     [1, 1, 1, 1, 1, 1, 1, 1]          [2, 3, 4]
2        [1, 1, 1, 1, 1, 1, 1]          [2, 3, 4]
3           [1, 1, 1, 1, 1, 1]          [2, 3, 4]
4              [1, 1, 1, 1, 1]          [2, 3, 4]
5                 [1, 1, 1, 1]          [2, 3, 4]
6                    [1, 1, 1]          [2, 3, 4]
7                       [1, 1]          [2, 3, 4]
8                          [1]          [2, 3, 4]
9                           []          [2, 3, 4]

Now I want to add the ['x_car_move_route'] and ['x_car_move_route_1'] ( later also the x_car_move_route_2 and x_car_move_route_3) togeather and I can't get it to work. I have tried.

cars['x_car_route_total'] = cars['x_car_move_route'] + cars['x_car_move_route_1']
cars['x_car_route_total'] = cars['x_car_move_route','x_car_move_route_1','x_car_move_route_2'].sum(1)

at the end I want this DataFrame

                  x_car_route_total          
    0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4]
    1     [1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4]
    2        [1, 1, 1, 1, 1, 1, 1, 2, 3, 4]
    3           [1, 1, 1, 1, 1, 1, 2, 3, 4]
    4              [1, 1, 1, 1, 1, 2, 3, 4]
    5                 [1, 1, 1, 1, 2, 3, 4]
    6                    [1, 1, 1, 2, 3, 4]
    7                       [1, 1, 2, 3, 4]
    8                          [1, 2, 3, 4]
    9                             [2, 3, 4]

Any ideas?

anky · Accepted Answer · 2021-03-27 17:22:34Z

I had this question in your prev question when you showed us a list but called it an array:

However the simplest is np.concatenate

cars[['x_car_move_route','x_car_move_route_1']].apply(np.concatenate,axis=1)

Or:

[*map(np.concatenate,cars[['x_car_move_route','x_car_move_route_1']].to_numpy())]

0    [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4]
1       [1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4]
2          [1, 1, 1, 1, 1, 1, 1, 2, 3, 4]
3             [1, 1, 1, 1, 1, 1, 2, 3, 4]
4                [1, 1, 1, 1, 1, 2, 3, 4]
5                   [1, 1, 1, 1, 2, 3, 4]
6                      [1, 1, 1, 2, 3, 4]
7                         [1, 1, 2, 3, 4]
8                            [1, 2, 3, 4]
9                               [2, 3, 4]
dtype: object

#cars['x_car_route_total'] = (cars[['x_car_move_route','x_car_move_route_1']]
#                             .apply(np.concatenate,axis=1))

#cars['x_car_route_total'] = [*map(np.concatenate,                      
#cars[['x_car_move_route','x_car_move_route_1']].to_numpy())]

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