NumPy Array Slicing

✂️ NumPy — Array Slicing

Array slicing allows you to extract a portion (subset) of an array using syntax:

start → Starting index (default = 0)
end → End index (not included)
step → Step size (default = 1)

1. Slicing a 1-D Array

import numpy as np

arr = np.array([10, 20, 30, 40, 50, 60])

print(arr[1:4]) # 20, 30, 40
print(arr[:3]) # From beginning → 10, 20, 30
print(arr[2:]) # Till end → 30, 40, 50, 60
print(arr[::2]) # Step = 2 → 10, 30, 50

import numpy as np

arr = np.array([10, 20, 30, 40, 50, 60])

print(arr[1:4]) # 20, 30, 40

print(arr[:3]) # From beginning → 10, 20, 30

print(arr[2:]) # Till end → 30, 40, 50, 60

print(arr[::2]) # Step = 2 → 10, 30, 50

🔄 Negative Index Slicing (Reverse)

2. Slicing a 2-D Array

Syntax:

Example:

Result:

Extracting Entire Rows or Columns

Slice Alternate Rows/Columns

Output:

3. Slicing a 3-D Array

Syntax:

Example:

Special Slicing Patterns

Slice Expression	Meaning
`arr[:]`	Full array
`arr[start:]`	From index to end
`arr[:end]`	Start to index
`arr[::-1]`	Reverse array
`arr[::step]`	Take elements with step

🧠 Key Difference: Indexing vs Slicing

Method	Example	Returns
Indexing	`arr[2]`	Single value
Slicing	`arr[2:5]`	Sub-array

📌 Summary

Slicing allows:

✔ Extracting specific rows/columns
✔ Reversing arrays
✔ Selecting elements with step size
✔ Working with multi-dimensional arrays efficiently

NumPy Array Slicing