How to access NumPy Array Indexing

Question

ICSM Computer · Answer

NumPy Array Indexing refers to the way you access or modify elements in a NumPy array. It’s similar to Python list indexing but much more powerful — supporting slicing, multi-dimensional access, boolean indexing, and fancy indexing.
1. Basic Indexing
Just like Python lists:
import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[0])   # 10
print(arr[-1])  # 50
2. 2D Array Indexing
Use [row, column]:
arr2d = np.array([[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]])
print(arr2d[0, 0])  # 1  (first row, first column)
print(arr2d[1, 2])  # 6  (second row, third column)
You can also access entire rows or columns:
print(arr2d[1])      # [4 5 6]  → 2nd row
print(arr2d[:, 2])   # [3 6 9]  → 3rd column
3. Slicing
You can extract a range of elements:
arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])   # [20 30 40]
print(arr[:3])    # [10 20 30]
print(arr[::2])   # [10 30 50] (step = 2)
For 2D arrays:
arr2d = np.array([[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]])
print(arr2d[0:2, 1:3])
# [[2 3]
#  [5 6]]
4. Integer (Fancy) Indexing
Use lists or arrays of indices:
arr = np.array([10, 20, 30, 40, 50])
indices = [0, 2, 4]
print(arr[indices])   # [10 30 50]
For 2D arrays:
arr2d = np.array([[10, 20], [30, 40], [50, 60]])
print(arr2d[[0, 2], [1, 0]])  # [20 50]
Explanation:
(0,1) and (2,0) are the chosen coordinates.
5. Boolean Indexing
Select elements that satisfy a condition:
arr = np.array([10, 20, 30, 40, 50])
print(arr[arr andgt; 25])  # [30 40 50]
You can also combine conditions:
print(arr[(arr andgt; 15) and (arr andlt; 45)])  # [20 30 40]
For 2D arrays:
arr2d = np.array([[1, 2, 3],
                  [4, 5, 6]])
print(arr2d[arr2d andgt; 3])
# [4 5 6]
6. Assigning Values
You can modify elements directly:
arr = np.array([10, 20, 30, 40, 50])
arr[1:4] = 0
print(arr)  # [10  0  0  0 50]
Boolean assignment:
arr[arr andgt; 25] = 99
print(arr)  # [10 20 99 99 99]
7. Using np.i() for Multi-dimensional Selection
If you want to select specific rows and columns:
arr = np.arange(1, 13).reshape(3, 4)
# arr =
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]
rows = [0, 2]
cols = [1, 3]
print(arr[np.i(rows, cols)])
# [[ 2  4]
#  [10 12]]
Summary Table
Type
Example
Description
Basic
arr[2]
Access single element
2D
arr[1, 2]
Row 1, Column 2
Slice
arr[1:4]
Range of elements
Fancy
arr[[0,2,4]]
Use list of indices
Boolean
arr[arr andgt; 5]
Filter by condition

interview

How to access NumPy Array Indexing

ICSM Computer

Can you answer this question?

1 Answers

1. Basic Indexing

2. 2D Array Indexing

3. Slicing

4. Integer (Fancy) Indexing

5. Boolean Indexing

6. Assigning Values

7. Using `np.i()` for Multi-dimensional Selection

Summary Table

Liked By

Type	Example	Description
Basic	`arr[2]`	Access single element
2D	`arr[1, 2]`	Row 1, Column 2
Slice	`arr[1:4]`	Range of elements
Fancy	`arr[[0,2,4]]`	Use list of indices
Boolean	`arr[arr > 5]`	Filter by condition