Table of Contents

    NumPy Basics

    Rumman Ansari May 25, 2026 1 views

    NumPy is one of the most important Python libraries used in Machine Learning (ML), Data Science, Artificial Intelligence, Scientific Computing, and Deep Learning.

    The word NumPy stands for:

    Numerical Python

    NumPy provides powerful tools for:

    • Numerical computation
    • Array operations
    • Matrix manipulation
    • Statistical analysis
    • Mathematical functions

    Why NumPy is Important in ML

    Machine Learning algorithms work with large amounts of numerical data.

    NumPy makes data processing:

    • Faster
    • More efficient
    • Memory optimized
    • Easy to manage

    Most ML libraries such as:

    • Scikit-learn
    • TensorFlow
    • PyTorch
    • Pandas

    are built using NumPy.

    Installing NumPy

    NumPy can be installed using pip. 

    
pip install numpy

    Importing NumPy

    NumPy is commonly imported using the alias np. 

    
import numpy as np

    What is an Array?

    An array is a collection of elements stored in a structured format.

    NumPy arrays are:

    • Fast
    • Efficient
    • Memory optimized

    Creating a NumPy Array

    NumPy provides the array() function.

    Example

    
import numpy as np

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

print(arr)

    Output

    
[1 2 3 4]

    Advantages of NumPy Arrays

    • Faster than Python lists
    • Efficient mathematical operations
    • Supports multidimensional data
    • Less memory usage

    Checking Array Type

    
print(type(arr))

    Output

    
<class 'numpy.ndarray'>

    Dimensions in NumPy Arrays

    NumPy supports:

    • 1D Arrays
    • 2D Arrays
    • 3D Arrays

    1D Array

    A one-dimensional array contains a single row of elements. 

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

    2D Array

    A two-dimensional array contains rows and columns. 

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

print(arr)

    Output

    
[[1 2]
 [3 4]]

    Matrix Representation

    :contentReference[oaicite:0]{index=0}

    3D Array

    Three-dimensional arrays are useful in Deep Learning and image processing. 

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

    Checking Array Dimensions

    NumPy provides the ndim property. 

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

print(arr.ndim)

    Output

    
2

    Checking Array Shape

    The shape property returns rows and columns. 

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

print(arr.shape)

    Output

    
(2, 2)

    Checking Array Size

    
print(arr.size)

    Output

    
4

    Checking Data Type of Array

    
print(arr.dtype)

    Output

    
int64

    Creating Arrays with Special Functions

    Array of Zeros

    
arr = np.zeros((2, 3))

print(arr)

    Output

    
[[0. 0. 0.]
 [0. 0. 0.]]

    Array of Ones

    
arr = np.ones((2, 2))

print(arr)

    Identity Matrix

    Identity matrices are important in linear algebra and ML. 

    
arr = np.eye(3)

print(arr)

    Identity Matrix Formula

    :contentReference[oaicite:1]{index=1}

    Using arange()

    The arange() function creates arrays with sequences. 

    
arr = np.arange(1, 10)

print(arr)

    Output

    
[1 2 3 4 5 6 7 8 9]

    Using linspace()

    The linspace() function generates evenly spaced numbers. 

    
arr = np.linspace(0, 1, 5)

print(arr)

    Output

    
[0.   0.25 0.5  0.75 1.  ]

    Array Indexing

    Indexing accesses specific elements. 

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

print(arr[1])

    Output

    
20

    2D Array Indexing

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

print(arr[1, 0])

    Output

    
3

    Array Slicing

    Slicing extracts parts of arrays. 

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

print(arr[1:4])

    Output

    
[2 3 4]

    NumPy Mathematical Operations

    NumPy supports fast mathematical calculations.

    Addition Example

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

print(arr + 5)

    Output

    
[6 7 8]

    Multiplication Example

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

print(arr * 2)

    Output

    
[2 4 6]

    Vector Representation

    :contentReference[oaicite:2]{index=2}

    Statistical Functions

    NumPy provides statistical functions useful in Machine Learning.

    Function Description
    mean() Average value
    median() Middle value
    std() Standard deviation
    sum() Total sum

    Mean Example

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

print(np.mean(arr))

    Output

    
20.0

    Mean Formula

    :contentReference[oaicite:3]{index=3}

    Standard Deviation

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

print(np.std(arr))

    Standard Deviation Formula

    :contentReference[oaicite:4]{index=4}

    Array Reshaping

    Reshaping changes array dimensions. 

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

new_arr = arr.reshape(2, 2)

print(new_arr)

    Output

    
[[1 2]
 [3 4]]

    Random Numbers in NumPy

    Random data is important in Machine Learning. 

    
arr = np.random.rand(3)

print(arr)

    NumPy in Machine Learning

    NumPy is heavily used in:

    • Data preprocessing
    • Feature engineering
    • Matrix operations
    • Deep Learning
    • Scientific computing

    NumPy and Linear Algebra

    Linear Algebra is the foundation of Machine Learning.

    Dot Product Example

    
a = np.array([1, 2])
b = np.array([3, 4])

print(np.dot(a, b))

    Output

    
11

    Dot Product Formula

    :contentReference[oaicite:5]{index=5}

    Advantages of NumPy

    • Fast computation
    • Efficient memory usage
    • Supports multidimensional arrays
    • Powerful mathematical functions
    • Foundation for ML libraries

    Limitations of NumPy

    • Limited support for labeled data
    • Complex for beginners initially
    • Less suitable for extremely large distributed datasets

    Best Practices

    • Use vectorized operations
    • Avoid unnecessary loops
    • Use appropriate array dimensions
    • Understand broadcasting properly

    Real-World Example

    In image processing systems, images are stored as NumPy arrays.

    Machine Learning models use NumPy arrays to process:

    • Pixels
    • Features
    • Audio signals
    • Sensor data

    Future of NumPy

    NumPy remains one of the most important libraries in AI and Data Science.

    Future improvements include:

    • Better performance
    • GPU acceleration support
    • Advanced scientific computation
    • Cloud integration

    Conclusion

    NumPy is the foundation of numerical computing in Python.

    It provides:

    • Efficient arrays
    • Fast mathematical operations
    • Linear algebra support
    • Statistical analysis tools

    Mastering NumPy is essential for becoming successful in:

    • Machine Learning
    • Data Science
    • Artificial Intelligence
    • Deep Learning