pytutorial/numpy/stack_split

Stack and Split

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The goal

Questions to David Rotermund

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numpy.column_stack

numpy.column_stack(tup)

Stack 1-D arrays as columns into a 2-D array.

Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first.

import numpy as np

a = np.arange(0, 10)
print(a) # -> [0 1 2 3 4 5 6 7 8 9]
print(a.shape)  # -> (10,)

b = np.column_stack((a, a))
print(b)
print(b.shape) # -> (10, 2)

Output:

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

numpy.row_stack

numpy.row_stack(tup, *, dtype=None, casting='same_kind')

Stack arrays in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.

np.row_stack is an alias for vstack. They are the same function.

import numpy as np

a = np.arange(0, 10)
print(a)  # -> [0 1 2 3 4 5 6 7 8 9]
print(a.shape)  # -> (10,)

b = np.row_stack((a, a))
print(b)
print(b.shape)  # -> (2, 10)

Output:

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