Introduction to Numpy

M3-R5.1 · Chapter 9: NumPy Basics · 4 min read

1. Introduction to NumPy

  • Numpyis a python package which stands for “Numerical Python
  • NumPy (Numerical Python) is the foundational library for scientific computing in Python.
  • NumPywas created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.
  • It provides support for large, multi-dimensional arrays and matrices, along with a collection of high-level mathematical functions to operate on these arrays.
  • It also has functions for working in domain of linear algebra, fourier transform, and matrices

1.1 Why NumPy?

Python lists are flexible but slow for numerical operations. NumPy arrays are:

  • Faster — stored as contiguous memory blocks (C-style)
  • Memory-efficient — all elements have the same datatype
  • Vectorized — operations apply element-wise without loops
  • Feature-rich — built-in functions for math, statistics, linear algebra

Feature

Python List

NumPy Array

Speed

Slow

Fast

Memory

More

Less

Data Types

Mixed allowed

Same type only

Math Functions

Limited

Extensive built-in

1.2 Installing and Importing NumPy

If you have Python and PIP already installed on a system, then installation of NumPy is very easy. Install it using this command: Ø

 C:\Users\Your Name>pip install numpy

If this command fails, then use a python distribution that already has NumPy installed like, Anaconda, Spyder etc.

Once NumPy is installed, import it in your applications by adding the import keyword:

import numpy

Now NumPy is imported and ready to use

import numpy

arr = numpy.array([1,2,3,4,5])

print(arr)

Alias Methos using as keyword----

import numpy as np

arr = numpy.array([1,2,3,4,5])

print(arr)

Note – In exams often ask about the standard alias used to import NumPy — always use 'np'.

2. Introduction to NumPy ndarray

The core object in NumPy is the ndarray (N-Dimensional Array).

It is a grid of values, all of the same type, indexed by a tuple of non-negative integers.

2.1 What is an ndarray?

  • nd stands for N-Dimensional
  • Homogeneous — all elements must be of the same type
  • Fixed size at creation (unlike Python lists)
  • Supports vectorized operations (no explicit loops needed)

2.2 Creating a Basic ndarray

import numpy as np

 

# 0D array (Scaler)

ar = np.array(66)

print(ar)    # 66

# 1D array (Vector)

ar2 = np.array([1, 2, 3, 4, 5])

print(ar2)          # [1 2 3 4 5]

 

# 2D array (Matrix)

ar3 = np.array([[1, 2, 3], [4, 5, 6]])

print(ar3)

# [[1 2 3]

#  [4 5 6]]

 

# 3D array

c = np.array([[[1,2],[3,4]], [[5,6],[7,8]]])

print(c.ndim)     # 3

Note  -  ndarray with 0 axis = 0D(Scaler)  1 axis = 1D (vector), 2 axes = 2D (matrix), 3+ axes = nd-array (tensor).