Introduction to NumPy

NumPy (Numerical Python) is a powerful Python library designed for efficient numerical computation. It provides support for multi-dimensional arrays, mathematical operations, and vectorized computations, making it much faster and more memory-efficient than Python’s built-in lists. NumPy is widely used in data science, machine learning, scientific computing, and data analysis.

NumPy vs Python List

NumPy

• Optimized for numerical and scientific operations
• Stores elements in contiguous memory, improving speed
• Supports multi-dimensional arrays (ndarrays)
• Enables vectorized operations, which eliminate the need for explicit loops

Python List

• General-purpose and flexible
• Slower for numerical tasks
• Does not support mathematical operations directly
• Not memory-efficient for large-scale computations

NumPy Array Creation

np.array()

Creates a NumPy array from a Python list or nested list.

import numpy as np
          arr = np.array([1, 2, 3])
          

Iteration in NumPy

Basic Iteration

You can loop through array elements, but NumPy is optimized for vectorized operations, which are faster and preferred over loops.

np.nditer()

Used to iterate efficiently over multi-dimensional arrays.

for x in np.nditer(arr):
              print(x)
          

Slicing in NumPy

Slicing allows you to extract a subset of elements from a NumPy array. The general syntax is:

array[start : stop : step]
          

1D Array Slicing

Used to retrieve a range of elements:

• Start → beginning index (inclusive)
• Stop → ending index (exclusive)
• Step → interval between elements

Example:

arr[1:4]   # elements at index 1, 2, 3
          

2D Array Slicing

In 2D arrays, you specify slices for both rows and columns:

array[row_start:row_end , col_start:col_end]
          

Example:

arr[1:3, 0:2]   # rows 1–2, columns 0–1
          

Negative Indexing

You can also slice using negative indexes (e.g., -1 refers to the last element).