The NumPy split()
method splits an array into multiple sub-arrays.
Example
import numpy as np
# create a 1-D array
array1 = np.array([0, 1, 2, 3])
# split into two equal length sub-arrays
subArrays= np.split(array1, 2)
print(subArrays)
'''
Output: [array([0, 1]), array([2, 3])]
'''
split() Syntax
The syntax of split()
is:
numpy.split(array, indices_or_sections, axis = 0)
split() Arguments
The split()
method takes the following arguments:
array
- the array to splitindices
- defines indices (asint
or1-D array
) in which the array is splitaxis
(optional) - axis along which the array is to be split
Notes:
- If
indices
are an integer(N), the array is divided into N equal parts.- If N equal divisions are not possible, an error is raised.
- If
indices
is a 1-D array, the entries indicate the indices where the input array is divided.
split() Return Value
The split()
method returns the list of sub-arrays.
Example 1: Split an Array into Three Arrays
import numpy as np
array1 = np.array( [[1, 2], [3, 4], [5, 6]] )
# split into 3 arrays
splitArrays = np.split(array1, 3)
print(splitArrays)
Output
[array([[1, 2]]), array([[3, 4]]), array([[5, 6]])]
Example 2: Split an Array by Index
import numpy as np
array1 = np.array( [1, 2, 3, 4, 5, 6] )
# indices at which array is split
splitIndices = [2, 5, 8]
# split into subarrays
splitArrays = np.split(array1, splitIndices)
print(splitArrays)
Output
[array([1, 2]), array([3, 4, 5]), array([6]), array([], dtype=int64)]
When an index exceeds the dimension of the array, the function returns an empty subarray.
Example 3: Split an Array Across Different Axes
The third parameter is used to split NumPy arrays across different axes. By default, axis
is set to 0 (column-wise).
import numpy as np
# create a 3D array
array1 = np.array([[[1, 2], [3, 4]],
[[5, 6], [7, 8]]])
# split the array column-wise (axis = 0 by default)
splitArrays = np.split(array1, 2)
print("\nSplit array column-wise:")
print(splitArrays)
# split the array row-wise
splitArrays = np.split(array1, 2, axis = 1)
print("Split array row-wise:")
print(splitArrays)
# split the array depth-wise
splitArrays = np.split(array1, 2, axis = 2)
print("\nSplit array depth-wise:")
print(splitArrays)
Output
Split array column-wise: [array([[[1, 2], [3, 4]]]), array([[[5, 6], [7, 8]]])] Split array row-wise: [array([[[1, 2]], [[5, 6]]]), array([[[3, 4]], [[7, 8]]])] Split array depth-wise: [array([[[1], [3]], [[5], [7]]]), array([[[2], [4]], [[6], [8]]])] >
Example 4: Split an Array into Uneven Sub-arrays
The split()
function cannot split an array into uneven arrays.
import numpy as np
array1 = np.array( [[1, 2], [3, 4], [5, 6]] )
# split into 2 arrays
splitArrays = np.split(array1, 2)
print(splitArrays)
Output
ERROR! raise ValueError( ValueError: array split does not result in an equal division
To split an array into unequal subarrays, you can use array_split()
instead. Let's see an example.
import numpy as np
array1 = np.array( [[1, 2], [3, 4], [5, 6]] )
# split into 2 arrays
splitArrays = np.array_split(array1, 2)
for array in splitArrays:
print(array,'\n')
Output
[[1 2] [3 4]] [[5 6]]
Related Methods
We can also split an array along different axes using the following methods:
- NumPy
hsplit()
- split an array into multiple sub-arrays horizontally - NumPy
vsplit()
- split an array into multiple sub-arrays vertically - NumPy
dsplit()
- split an array into multiple sub-arrays along the 3rd axis (depth)
Let's see an example.
import numpy as np
# create a 3D array
array1 = np.array([[[1, 2], [3, 4]],
[[5, 6], [7, 8]]])
# split the array column-wise
splitArrays = np.vsplit(array1, 2)
print("\nSplit array column-wise:")
for arr in splitArrays:
print(arr)
# split the array row-wise
splitArrays = np.hsplit(array1, 2)
print("Split array row-wise:")
for arr in splitArrays:
print(arr)
# split the array depth-wise
splitArrays = np.dsplit(array1, 2)
print("\nSplit array depth-wise:")
for arr in splitArrays:
print(arr)
Output
Split array column-wise: [[[1 2] [3 4]]] [[[5 6] [7 8]]] Split array row-wise: [[[1 2]] [[5 6]]] [[[3 4]] [[7 8]]] Split array depth-wise: [[[1] [3]] [[5] [7]]] [[[2] [4]] [[6] [8]]]