Arrays can be combined in various ways. This process in NumPy is referred to as stacking. Stacking can take various forms, including horizontal, vertical, and depth-wise stacking. To demonstrate this, we will use the following two arrays (a and b):
In [59]: # creating two arrays for examples a = np.arange(9).reshape(3, 3) b = (a + 1) * 10 a Out[59]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) In [60]: b Out[60]: array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
Horizontal stacking combines two arrays in a manner where the columns of the second array are placed to the right of those in the first array. The function actually stacks the two items provided in a two-element tuple. The result is a new array with data copied from the two that are specified:
In [61]: # horizontally stack the two arrays # b becomes columns of a to the right of a's columns np.hstack((a, b)) Out[61]: array([[ 0, 1, 2, 10, 20, 30], [ 3, 4, 5, 40, 50, 60], [ 6, 7, 8, 70, 80, 90]])
This functionally is equivalent to using the np.concatenate() function while specifying axis = 1:
In [62]: # identical to concatenate along axis = 1 np.concatenate((a, b), axis = 1) Out[62]: array([[ 0, 1, 2, 10, 20, 30], [ 3, 4, 5, 40, 50, 60], [ 6, 7, 8, 70, 80, 90]])
Vertical stacking returns a new array with the contents of the second array as appended rows of the first array:
In [63]: # vertical stack, adding b as rows after a's rows np.vstack((a, b)) Out[63]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [10, 20, 30], [40, 50, 60], [70, 80, 90]])
Like np.hstack(), this is equivalent to using the concatenate function, except specifying axis=0:
In [64]: # concatenate along axis=0 is the same as vstack np.concatenate((a, b), axis = 0) Out[64]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [10, 20, 30], [40, 50, 60], [70, 80, 90]])
Depth stacking takes a list of arrays and arranges them in order along an additional axis referred to as the depth:
In [65]: # dstack stacks each independent column of a and b np.dstack((a, b)) Out[65]: rray([[[ 0, 10], [ 1, 20], [ 2, 30]], [[ 3, 40], [ 4, 50], [ 5, 60]], [[ 6, 70], [ 7, 80], [ 8, 90]]])
Column stacking performs a horizontal stack of two one-dimensional arrays, making each array a column in the resulting array:
In [66]: # set up 1-d array one_d_a = np.arange(5) one_d_a Out[66]: array([0, 1, 2, 3, 4]) In [67]: # another 1-d array one_d_b = (one_d_a + 1) * 10 one_d_b Out[67]: array([10, 20, 30, 40, 50]) In [68]: # stack the two columns np.column_stack((one_d_a, one_d_b)) Out[68]: array([[ 0, 10], [ 1, 20], [ 2, 30], [ 3, 40], [ 4, 50]])
Row stacking returns a new array where each one-dimensional array forms one of the rows of the new array:
In [69]: # stack along rows np.row_stack((one_d_a, one_d_b)) Out[69]: array([[ 0, 1, 2, 3, 4], [10, 20, 30, 40, 50]])