NumPy Array Splitting
In this discussion, we will delve into the different techniques for NumPy Array Splitting, including the use of functions such as numpy.hsplit, and numpy.array_split.
This function takes the array that needs to be split and the number of splits desired as its arguments.
The purpose of this function is to break a single array into multiple smaller sub-arrays.
NumPy Array Split Examples
Divide the prime_arr array into four sections.
import numpy as npy
prime_arr = npy.array([2, 3, 5, 7, 11, 13, 17, 19])
mrx_arr = npy.array_split(prime_arr, 4)
print(mrx_arr)
Make three array partitions from the prime_arr array.
import numpy as npy
cube_arr = npy.array([1, 8, 27, 64, 125, 216])
mrx_arr = npy.array_split(cube_arr, 3)
print(mrx_arr)
Reminder: A output value consists of three arrays.
Numpy array split will modify from the end appropriately if the array has fewer elements than needed.
Separate the even_arr array into 4 subarrays:
import numpy as npy
even_arr = npy.array([0, 2, 4, 6, 8, 10, 12, 14])
mrx_arr = npy.array_split(even_arr, 5)
print(mrx_arr)
Make three subarrays from the even_arr array:
import numpy as npy
odd_arr = npy.array([1, 3, 5, 7, 9, 11, 13])
mrx_arr = npy.array_split(odd_arr, 3)
print(mrx_arr)
Reminder: As with array_split(), split() will not modify the items when there are less items in the source array for splitting. For example, array_split() executed successfully but split() was unsuccessful.
Split Into Arrays
When we talk about Numpy array split, the array_split() method produces an array including every split.
Retrieve the array which is divided:
import numpy as npy
fibonacci_arr = npy.array([ 0, 1, 1, 2, 3, 5, 8, 13])
mrx_arr = npy.array_split(fibonacci_arr, 4)
print(mrx_arr[0])
print(mrx_arr[1])
print(mrx_arr[2])
print(mrx_arr[3])
The array that is separated can be obtained as follows:
import numpy as npy
factorial_arr = npy.array([ 1, 2, 6, 24, 120, 720, 5040])
mrx_arr = npy.array_split(factorial_arr, 5)
print(mrx_arr[0])
print(mrx_arr[1])
print(mrx_arr[2])
print(mrx_arr[3])
print(mrx_arr[4])
Numpy Array Splitting 2-D
Split 2-D arrays utilizing the identical syntax. Simply pass in the array you need to split and the number of splits you need to make to the array_split() method.
Make five two-dimensional arrays out of one two-dimensional array.
import numpy as npy
rational_arr = npy.array([[2.7, 3.84], [1.54, 3.22], [6.49, 5.11], [7.34, 8.96], [3.29,
7.33], [9.39, 9.99], [8.91, 6.16]])
mrx_arr = npy.array_split(rational_arr, 5)
print(mrx_arr)
From one palindrome_arr two-dimensional array, create five two-dimensional arrays.
import numpy as npy
palindrome_arr = npy.array([["101", "252"], ["25252", "89098"], ["1845481", "1010101"], ["3456543", "989"], ["9672769",
"24542"], ["01110", "225535522"], ["800008", "61216"]])
mrx_arr = npy.array_split(palindrome_arr, 5)
print(mrx_arr)
The outputs of the above two examples provide five two-dimensional arrays.
Obtain four two-dimensional arrays by partitioning the two-dimensional array in half.
import numpy as npy
even_arr = npy.array([[0, 2, 4], [6, 8, 10], [12, 14, 16], [20, 22, 24], [26, 28, 30]])
mrx_arr = npy.array_split(even_arr, 4)
print(mrx_arr)
Divide the silentWords_arr two-dimensional array into four two-dimensional arrays.
import numpy as npy
silentWords_arr = npy.array([['aplomb', 'bomb', 'abscess'], ['conscience', 'conscience', 'edge'], ['assign', 'align', 'handful'], ['descent', 'doubt', 'handkerchief'], ['fluorescent', 'breathe', 'muscle']])
mrx_arr = npy.array_split(silentWords_arr, 4)
print(mrx_arr)
There are four two-dimensional arrays generated by the above two examples.
You can also indicate which axis you need to divide around.
Assemble three two-dimensional arrays in rows from the two-dimensional array.
import numpy as npy
odd_arr = npy.array([[1, 3, 5], [7, 9, 11], [13, 15, 17], [19, 21, 23], [25, 27, 29]])
mrx_arr = npy.array_split(odd_arr, 3, axis=1)
print(mrx_arr)
Separate the two-dimensional array into three two-dimensional arrays in rows.
import numpy as npy
prime_arr = npy.array([[2, 3, 5], [7, 11, 13], [17, 19, 23], [27, 31, 33], [37, 41, 43]])
mrx_arr = npy.array_split(prime_arr, 3, axis=1)
print(mrx_arr)
The better approach is to utilize hsplit() instead of hstack()
To divide the two-dimensional array into three two-dimensional arrays according to rows, invoke the hsplit() method.
import numpy as npy
odd_arr = npy.array([[1, 3, 5], [7, 9, 11], [13, 15, 17], [19, 21, 23], [25, 27, 29]])
mrx_arr = npy.hsplit(odd_arr, 3)
print(mrx_arr)
By utilizing the hsplit() method, you can seperate a two-dimensional array into three two-dimensional arrays based on rows.
import numpy as npy
prime_arr = npy.array([[2, 3, 5], [7, 11, 13], [17, 19, 23], [27, 31, 33], [37, 41, 43]])
mrx_arr = npy.hsplit(prime_arr, 3)
print(mrx_arr)
Example Explanation
In above example we have import the NumPy library using the alias npy.
Then we have created a 2D NumPy array called prime_arr, which has 5 rows and 3 columns. Each row contains 3 prime numbers.
The npy.hsplit() function is used to split the prime_arr array horizontally into 3 arrays.
This function splits the array along its columns, creating 3 separate arrays. These arrays are then assigned to the variable mrx_arr.
Finally, the code prints the value of mrx_arr. This should output a list of 3 arrays, where each array has 5 rows and 1 column, containing the prime numbers that were originally in the corresponding columns of prime_arr.
Reminder: A suitable replacement for vstack() and dstack() is vsplit() and dsplit().
