Numpy: How to stack a single array into each row of a bigger array and turn it into a 2D array?

Question

I have a numpy array named heartbeats with 100 rows. Each row has 5 elements.

I also have a single array named time_index with 5 elements. I need to prepend the time index to each row of heartbeats.

heartbeats = np.array([
    [-0.58, -0.57, -0.55, -0.39, -0.40],
    [-0.31, -0.31, -0.32, -0.46, -0.46]
])
time_index = np.array([-2, -1, 0, 1, 2])

What I need:

array([-2, -0.58],
      [-1, -0.57],
      [0, -0.55],
      [1, -0.39],
      [2, -0.40],
      [-2, -0.31],
      [-1, -0.31],
      [0, -0.32],
      [1, -0.46],
      [2, -0.46])

I only wrote two rows of heartbeats to illustrate.

The operation you are looking for is "concatenate" or "stack" in numpy terminology, not "merge" — Mad Physicist
– Mad Physicist, Commented Jun 8, 2022 at 17:02
Please add a numpy tag and use np.... if you are using numpy. Otherwise, please indicate which array package you are using. Python does not support multidimensional arrays in the standard library. — Mad Physicist
– Mad Physicist, Commented Jun 8, 2022 at 17:10
Thanks for being responsive! It's a nice change from the usual "dump and walk away" approach some new users take. — Mad Physicist
– Mad Physicist, Commented Jun 8, 2022 at 17:15

Mad Physicist · Accepted Answer · 2022-06-08 17:06:59Z

2

Assuming you are using numpy, the exact output array you are looking for can be made by stacking a repeated version of time_index with the raveled version of heartbeats:

np.stack((np.tile(time_index, len(heartbeats)), heartbeats.ravel()), axis=-1)

answered Jun 8, 2022 at 17:06

Mad Physicist

116k29 gold badges202 silver badges292 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

Mad Physicist Over a year ago

@d.b. h.shape[0] is a good alternative, flatten is not because it copies the data unnecessarily, while ravel does not. It's rare that you want to use flatten over ravel. Also, doing the transpose will alter the layout of the array in memory over stacking on axis=-1.

hpaulj · Accepted Answer · 2022-06-08 18:58:12Z

Another approach, using broadcasting

In [13]: heartbeats = np.array([
    ...:     [-0.58, -0.57, -0.55, -0.39, -0.40],
    ...:     [-0.31, -0.31, -0.32, -0.46, -0.46]
    ...: ])
    ...: time_index = np.array([-2, -1, 0, 1, 2])

Make a target array:

In [14]: res = np.zeros(heartbeats.shape + (2,), heartbeats.dtype)

In [15]: res[:,:,1] = heartbeats     # insert a (2,5) into a (2,5) slot

In [17]: res[:,:,0] = time_index[None]   # insert a (5,) into a (2,5) slot

In [18]: res
Out[18]: 
array([[[-2.  , -0.58],
        [-1.  , -0.57],
        [ 0.  , -0.55],
        [ 1.  , -0.39],
        [ 2.  , -0.4 ]],

       [[-2.  , -0.31],
        [-1.  , -0.31],
        [ 0.  , -0.32],
        [ 1.  , -0.46],
        [ 2.  , -0.46]]])

and then reshape to 2d:

In [19]: res.reshape(-1,2)
Out[19]: 
array([[-2.  , -0.58],
       [-1.  , -0.57],
       [ 0.  , -0.55],
       [ 1.  , -0.39],
       [ 2.  , -0.4 ],
       [-2.  , -0.31],
       [-1.  , -0.31],
       [ 0.  , -0.32],
       [ 1.  , -0.46],
       [ 2.  , -0.46]])

[17] takes a (5,), expands it to (1,5), and then to (2,5) for the insert. Read up on broadcasting.

Ali_Sh · Accepted Answer · 2022-06-09 07:32:18Z

1

As an alternative way, you can repeat time_index by np.concatenate based on the specified times:

concatenated = np.concatenate([time_index] * heartbeats.shape[0])
# [-2 -1  0  1  2 -2 -1  0  1  2]

# result = np.dstack((concatenated, heartbeats.reshape(-1))).squeeze()
result = np.array([concatenated, heartbeats.reshape(-1)]).T

Using np.concatenate may be faster than np.tile. This solution is faster than Mad Physicist, but the fastest is using broadcasting as hpaulj's answer.

edited Jun 9, 2022 at 7:32

answered Jun 8, 2022 at 20:26

Ali_Sh

2,8364 gold badges45 silver badges70 bronze badges

Collectives™ on Stack Overflow

Numpy: How to stack a single array into each row of a bigger array and turn it into a 2D array?

3 Answers 3

1 Comment

Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

3 Answers 3

1 Comment

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related