Cara menggunakan split 2d array python

How to split an array into multiple arrays in Numpy? Use Python NumPy array split() function to split an array into more than one (multiple) sub arrays as views. This function divides the array into subarrays along with a specified axis. The function takes three parameters array,

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

In this article, I will explain with examples how to split Python NumPy array by using

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

1. Quick Examples of Python NumPy Array Split Function

If you are in a hurry, below are some quick examples of how to use Python NumPy array split() function.

# Below are a quick example

# Example 1: use numpy.split() function
arr2 = np.split(arr,4) 

# Example 2: use numpy.split() function to split 1-D numpy array
arr2 = np.split(arr,[2,3])  

# Example 3: Use split 2-D numpy array use numpy.split() function
arr = np.array([[15,28,57,65],[25,37,55,88]])
arr2 = np.split(arr, 2, axis=0)  

# Example 4: Use split array along axis=1
arr2 = np.split(arr, 2, axis=1)  

# Example 5: Use numpy.split() function to slicing
arr2 = np.split(arr, (2,3), axis=1)

2. Python NumPy split() Syntax

Following is the syntax of the

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

2.1 Parameters of split()

Following are the parameters of split() function.

• 
  # pthon numpy.split() syntax
  numpy.split(arr, indices_or_sections, axis=0)
  

  5 – Array to be divided into sub-arrays.

• 
  # pthon numpy.split() syntax
  numpy.split(arr, indices_or_sections, axis=0)
  

  0 – The parameter can be an integer value or 1-D sorted Numpy integer array. indicating the number of equal-sized subarrays to be created from the input array. If this parameter is a 1-D array, the entries indicate the points at which a new subarray is to be created.

• 
  # pthon numpy.split() syntax
  numpy.split(arr, indices_or_sections, axis=0)
  

  1 – To specify the axis along which to perform the split. By default, axis=0.

2.2 Return Value of split()

It returns a list of sub-arrays as views into arr. If indices_or_sections is given as an integer, but its unable to split in equal division, it raises a ValueError.

3. Use numpy.split() Function

You can split the NumPy array as many parts as you want using the

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

import numpy as np
 
# creating an input array
arr = np.array([5,7,9,11,13,19,23,27])

# use numpy.split() function
arr2 = np.split(arr,4)  
print(arr2)

# OutPut
# [array([5, 7]), array([ 9, 11]), array([13, 19]), array([23, 27])]

You can access the element of the split array by using its index

import numpy as np
 
# creating an input array
arr = np.array([5,7,9,11,13,19,23,27])

# use numpy.split() function
arr2 = np.split(arr,4)  
print(arr2)

# OutPut
# [array([5, 7]), array([ 9, 11]), array([13, 19]), array([23, 27])]

4. Use split() Function to Split 1-D Array

To split the array at positions indicated in the 1-Dimensional NumPy array.

# use numpy.split() function to split 1-D numpy array
arr2 = np.split(arr,[2,3])  
print(arr2)

# OutPut
# [array([5, 7]), array([9]), array([11, 13, 19, 23, 27])]

5. Split 2-D Array Use split() Function

You can use

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

import numpy as np
 
# creating an input array
arr = np.array([5,7,9,11,13,19,23,27])

# use numpy.split() function
arr2 = np.split(arr,4)  
print(arr2)

# OutPut
# [array([5, 7]), array([ 9, 11]), array([13, 19]), array([23, 27])]

import numpy as np
 
# creating an 2D input array
arr = np.array([[15,28,57,65],[25,37,55,88]])

# Use split array along axis = 0
arr2 = np.split(arr, 2, axis=0)  
print(arr2)

# OutPut
# [array([[15, 28, 57, 65]]), array([[25, 37, 55, 88]])]

You can also use

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

import numpy as np
 
# creating an input array
arr = np.array([5,7,9,11,13,19,23,27])

# use numpy.split() function
arr2 = np.split(arr,4)  
print(arr2)

# OutPut
# [array([5, 7]), array([ 9, 11]), array([13, 19]), array([23, 27])]

# Use split array along axis=1
arr2 = np.split(arr, 2, axis=1)  
print(arr2)

# OutPut
# [array([[15, 28],
#        [25, 37]]), array([[57, 65],
#       [55, 88]])]

To split

# pthon numpy.split() syntax
numpy.split(arr, indices_or_sections, axis=0)

# Use numpy.split() function to slicing
arr2 = np.split(arr, (2,3), axis=1)
print(arr2)

# OutPut
# [array([[15, 28],
#        [25, 37]]), array([[57],
#        [55]]), array([[65],
#       [88]])]

6. Split() Returning ValueError

If split() function is unable to split in an equal division it returns a ValueError: array split does not result in an equal division. In our example below, I am trying to split 8 elements by 5 slices which is not possible hence it returns an error.

# creating an input array
arr = np.array([5,7,9,11,13,19,23,27])

# use numpy.split() function
arr2 = np.split(arr,5)  
print(arr2)

# Output
# ValueError: array split does not result in an equal division

7. Conclusion

In this article, I have explained how to use NumPy array split() function to split an array into multiple sub-arrays as views into an array with examples.