NumPy Array Slicing

Machine Learning Using Python 3

Slicing in NumPy arrays is similar to how it works with a Python list.  

Program 1:

import numpy as np
arr = np.array([[1,2,3,4,5], [4,5,6,7,8], [9,8,7,6,5]]) 
print("The NumPy Array is:")
print(arr) 
arr1 = arr[1:3, :3] # row 1 to 3 (not inclusive) and first 3 columns 
print("The Sliced NumPy Array is:")
print(arr1) 

Output:

The NumPy Array is: 
[[1 2 3 4 5] 
[4 5 6 7 8] 
[9 8 7 6 5]] 
The Sliced NumPy Array is: 
[[4 5 6]  
[9 8 7]]

Slicing in python means taking elements from one given index to another given index.

• We pass slice instead of index like this: [start: end].

• We can also define the step, like this: [start: end: step].

• If we don't pass start its considered 0.

• If we don't pass end it’s considered length of array in that dimension.

• If we don't pass step it’s considered 1.

Program 2: Slice elements from index 1 to index 5 from the following array.

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[1:5])

Output:

[2 3 4 5]

Note: The result includes the start index, but excludes the end index.

Program 3: Slice elements from index 4 to the end of the array.

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[4:])

Output:

[5 6 7]

Program 4: Slice elements from the beginning to index 4 (not included).

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[:4])

Output:

[1 2 3 4]

Negative Slicing: Use the minus operator to refer to an index from the end.

Program 5: Slice from the index 3 from the end to index 1 from the end.

import numpy as np 
arr = np.array([1, 2, 3, 4, 5, 6, 7]) 
print(arr[-3:-1])

Output:

[5 6]

STEP: Use the step value to determine the step of the slicing.

Program 6: Return every other element from index 1 to index 5.

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[1:5:2])

Output:

[2 4]

Program 7: Return every other element from the entire array.

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[::2])

Output:

[1 3 5 7]

Slicing 2-D Array’s

Program 8: From the second element, slice elements from index 1 to index 4 (not included).

import numpy as np
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[1, 1:4])

Output:

[7 8 9]

Note: Remember that second element has index 1.

Program 9: From both elements, return index 2.

import numpy as np
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[0:2, 2])

Output:

[3 8]

Program 10: From both elements, slice index 1 to index 4 (not included), this will return a 2-D array.

import numpy as np
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[0:2, 1:4])

Output:

[[2 3 4]
 [7 8 9]]

NumPy Slice as a reference: It is noteworthy that the result of a NumPy slice is a reference and not a copy of the original array.

Program 11:

import numpy as np
arr = np.array([[1,2,3,4,5], [4,5,6,7,8], [9,8,7,6,5]])
print("The NumPy Array is:")
print(arr)
arr1 = arr[1:, 2:] # row 1 onwards and column 2 onwards , arr1 is now pointing to a subset of arr.
print("The Sliced NumPy Array is:")
print(arr1)

Output:

The NumPy Array is:
[[1 2 3 4 5]
 [4 5 6 7 8]
 [9 8 7 6 5]]
The Sliced NumPy Array is:
[[6 7 8]
 [7 6 5]]

Here, arr1 is actually a reference to the original array arr. Hence, if we change one of the elements in arr1, then:

Program 12:

import numpy as np
arr = np.array([[1,2,3,4,5], [4,5,6,7,8], [9,8,7,6,5]])
print("The NumPy Array is:")
print(arr)
arr1 = arr[1:, 2:] # row 1 onwards and column 2 onwards , arr1 is now pointing to a subset of arr.
print("The Sliced NumPy Array is:")
print(arr1)
arr1[0,2]=88
print("Updates NumPy Array:")
print(arr)

Output:

The NumPy Array is:
[[1 2 3 4 5]
 [4 5 6 7 8]
 [9 8 7 6 5]]
The Sliced NumPy Array is:
[[6 7 8]
 [7 6 5]]
Updates NumPy Array:
[[ 1  2  3  4  5]
 [ 4  5  6  7 88]
 [ 9  8  7  6  5]]

Another salient point to note is that the result of the slicing is dependent on how we slice it.

Program 13:

import numpy as np
arr = np.array([[1,2,3,4,5], [4,5,6,7,8], [9,8,7,6,5]])
print("The NumPy Array is:")
print(arr)
arr1 = arr[2:, :] # row 2 onwards and all columns 
print("The Sliced NumPy Array is:")
print(arr1) 
print("The shape of Sliced NumPy Array is:")
print(arr1.shape)

Output:

The NumPy Array is:
[[1 2 3 4 5]
 [4 5 6 7 8]
 [9 8 7 6 5]]
The Sliced NumPy Array is:
[[9 8 7 6 5]]
The shape of Sliced NumPy Array is:
(1, 5)

Program 14:

import numpy as np
arr = np.array([[1,2,3,4,5], [4,5,6,7,8], [9,8,7,6,5]])
print("The NumPy Array is:")
print(arr)
arr1 = arr[2, :] # row 2 onwards and all columns 
print("The Sliced NumPy Array is:")
print(arr1) 
print("The shape of Sliced NumPy Array is:")
print(arr1.shape)

Output:

The NumPy Array is:
[[1 2 3 4 5]
 [4 5 6 7 8]
 [9 8 7 6 5]]
The Sliced NumPy Array is:
[9 8 7 6 5]
The shape of Sliced NumPy Array is:
(5,)