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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.vsplit
numpy.vsplit(ary, indices_or_sections)[source]
Split an array into multiple sub-arrays vertically (row-wise).
vsplit is equivalent to split with axis=0 (default), the array is always split along the first axis regardless of the array dimension.
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.hsplit
numpy.hsplit(ary, indices_or_sections)[source]
Split an array into multiple sub-arrays horizontally (column-wise).
hsplit is equivalent to split with axis=1, the array is always split along the second axis except for 1-D arrays, where it is split at axis=0.
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)
numpy.dsplit
numpy.dsplit(ary, indices_or_sections)
Split array into multiple sub-arrays along the 3rd axis (depth).
dsplit is equivalent to split with axis=2, the array is always split along the third axis provided the array dimension is greater than or equal to 3.
numpy.stack
numpy.stack(arrays, axis=0, out=None, *, dtype=None, casting='same_kind')
Join a sequence of arrays along a new axis.
The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.
import numpy as np
a = np.zeros((6, 8, 10))
print(a.shape) # -> (6, 8, 10)
b = np.stack((a, a), axis=0)
print(b.shape) # -> (2, 6, 8, 10)
b = np.stack((a, a), axis=1)
print(b.shape) # -> (6, 2, 8, 10)
b = np.stack((a, a), axis=2)
print(b.shape) # -> (6, 8, 2, 10)
b = np.stack((a, a), axis=3)
print(b.shape) # -> (6, 8, 10, 2)
b = np.stack((a, a), axis=4) # AxisError: axis 4 is out of bounds for array of dimension 4
numpy.split and numpy.array_split
numpy.split(ary, indices_or_sections, axis=0)
Split an array into multiple sub-arrays as views into ary.
import numpy as np
a = np.arange(0, 20).reshape(10, 2)
print(a)
print(a.shape) # -> (10, 2)
print()
b = np.split(a, 2, axis=0)
print(len(b)) # -> 2
print(b[0])
print(b[0].shape) # -> (5, 2)
print()
print(b[1])
print(b[1].shape) # -> (5, 2)
b = np.split(a, 3, axis=0) # ValueError: array split does not result in an equal division
Output:
[[ 0 1]
[ 2 3]
[ 4 5]
[ 6 7]
[ 8 9]
[10 11]
[12 13]
[14 15]
[16 17]
[18 19]]
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]
[[10 11]
[12 13]
[14 15]
[16 17]
[18 19]]
numpy.array_split(ary, indices_or_sections, axis=0)
Split an array into multiple sub-arrays.
The only difference between these functions is that array_split allows indices_or_sections to be an integer that does not equally divide the axis. For an array of length l that should be split into n sections, it returns l % n sub-arrays of size l//n + 1 and the rest of size l//n.