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Stack and Split
{:.no_toc}
* TOC {:toc}The goal
Questions to David Rotermund
{: .topic-optional} This is an optional topic!
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]]
numpy.vstack
numpy.vstack(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.zeros((2, 3, 4))
print(a.shape) # -> (2, 3, 4)
b = np.vstack((a, a))
print(b.shape) # -> (4, 3, 4)
numpy.hstack
numpy.hstack(tup, *, dtype=None, casting='same_kind')
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.
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.
import numpy as np
a = np.zeros((2, 3, 4))
print(a.shape) # -> (2, 3, 4)
b = np.hstack((a, a))
print(b.shape) # -> (2, 6, 4)
numpy.dstack
numpy.dstack(tup)
Stack arrays in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D arrays of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape (N,) have been reshaped to (1,N,1). Rebuilds arrays divided by dsplit.
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.
import numpy as np
a = np.zeros((2, 3, 4))
print(a.shape) # -> (2, 3, 4)
b = np.dstack((a, a))
print(b.shape) # -> (2, 3, 8)