Topic4:- What is Numpy?

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What is NumPy?

(Reading time 10-12minutes)

NumPy (Numerical Python), an open-source Python toolkit for numerical and scientific computation, is powerful. It supports massive multi-dimensional arrays and matrices and gives mathematical algorithms to effectively control them.

import numpy as np

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

Output:

[1 2 3 4 5]

Need of NumPy

Python lists are flexible but slow and inefficient for numerical operations (like matrix multiplication or element-wise addition).


Why Use NumPy?

Feature   Description
Speed   NumPy is much faster than Python lists for numerical tasks.
Convenience   Easy syntax for array and matrix operations.
Efficiency   Uses less memory and executes faster due to C backend.
Mathematical tools   Includes a large collection of built-in mathematical functions.
Integration   Works well with other libraries (Pandas, Matplotlib, Scikit-learn, etc.)

Common Functions Used with NumPy


Here’s a complete list of commonly used NumPy functions with their syntax and examples 

1. Creating Arrays

Function Description Syntax Example
np.array() Create an array from a list or tuple np.array(object) np.array([1,2,3]) → [1 2 3]
np.arange() Create array with range of values np.arange(start, stop, step) np.arange(0,10,2) → [0 2 4 6 8]
np.linspace() Evenly spaced numbers between range np.linspace(start, stop, num) np.linspace(0,1,5) → [0. 0.25 0.5 0.75 1.]
np.zeros() Create array of zeros np.zeros(shape) np.zeros((2,3)) → [[0. 0. 0.] [0. 0. 0.]]
np.ones() Create array of ones np.ones(shape) np.ones((3,3))
np.eye() Identity matrix np.eye(n) np.eye(3) → 3×3 identity
np.random.rand() Random numbers (0–1) np.random.rand(rows, cols) np.random.rand(2,2)

2. Array Attributes

Function     Description         Example
arr.shape     Returns dimensions     arr.shape → (2,3)
arr.size     Total number of elements     arr.size
arr.ndim     Number of dimensions     arr.ndim
arr.dtype     Data type     arr.dtype

3. Reshaping and Modifying Arrays

Function Description Syntax     Example
np.reshape() Change shape of array np.reshape(arr, new_shape)  np.reshape(arr, (3,2))
arr.flatten() Convert multi-d array to 1D arr.flatten()
np.concatenate() Join arrays np.concatenate((a,b))
np.hstack() Stack arrays horizontally np.hstack((a,b))
np.vstack() Stack arrays vertically np.vstack((a,b))

4. Mathematical Operations

Function     Description     Example
np.add(a,b)     Element-wise addition     [1,2,3] + [4,5,6] → [5 7 9]
np.subtract(a,b)     Subtraction     [5,6,7] - [1,2,3] → [4 4 4]
np.multiply(a,b)     Multiplication
np.divide(a,b)     Division
np.power(a,b)     Power function     np.power([2,3,4],2) → [4 9 16]
np.sqrt(a)     Square root     np.sqrt([4,9,16]) → [2. 3. 4.]

5. Statistical Functions

Function Description Example
np.mean(arr) Mean np.mean([1,2,3,4]) → 2.5
np.median(arr) Median np.median([1,2,3,4]) → 2.5
np.std(arr) Standard deviation
np.var(arr) Variance
np.sum(arr) Sum of elements
np.min(arr) Minimum value
np.max(arr) Maximum value

6. Indexing and Slicing

Operation     Example     Output
arr[0]     First element     1
arr[-1]     Last element
arr[1:4]     Elements from 1 to 3
arr[::2]     Every second element

7. Linear Algebra Operations

Function     Description     
np.dot(a,b)     Matrix multiplication
np.transpose(a)     Transpose of matrix
np.linalg.inv(a)     nverse of matrix
np.linalg.det(a)     Determinant
np.linalg.eig(a)     Eigenvalues & eigenvectors

8. Random Module Functions

Function Description Example
np.random.randint(low, high, size) Random integers np.random.randint(0,10,5)
np.random.randn(n) Random numbers (normal distribution)
np.random.choice(a) Random element from array

Example Program

import numpy as np

# Create array
a = np.array([1, 2, 3, 4, 5])

# Operations
print("Mean:", np.mean(a))
print("Sum:", np.sum(a))
print("Square Root:", np.sqrt(a))
print("Reshape:", np.arange(6).reshape(2,3))

Output:

Mean: 3.0
Sum: 15
Square Root: [1. 1.414 1.732 2. 2.236]
Reshape:
[[0 1 2]
 [3 4 5]]



 Summary

NumPy is the foundation of data science and machine learning in Python providing fast, efficient, and easy-to-use numerical computation tools.

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