python - 组合 Pandas 中由 numpy 数组组成的列

Question

我有一个数据框，我需要添加一些列。我似乎无法正确理解。这就是我必须开始的:

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)

这给了我 DataFrame 的最后两列:

         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]

现在我想添加 ['x_car_move_route'] 和 ['x_car_move_route_1'] (后来还有 x_car_move_route_2 和 x_car_move_route_3)在一起，但我无法让它工作。我已经尝试过。

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)

最后我想要这个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]

有什么想法吗？

Answer 1

当您向我们展示一个列表但将其称为数组时，我在上一个问题中遇到了这个问题:

但是最简单的是np.concatenate

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

或者:

[*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())]