NumPy Tutorial - Python Library
Last Updated :
27 Nov, 2025
NumPy is a core Python library for numerical computing, built for handling large arrays and matrices efficiently. It is significantly faster than Python's built-in lists because it uses optimized C-based operations and supports vectorized computations.
- ndarray object: Stores homogeneous data in n-dimensional arrays for fast processing.
- Vectorized operations: Perform element-wise calculations without explicit loops.
- Broadcasting: Apply operations across arrays of different shapes.
- Linear algebra functions: Matrix multiplication, inversion, eigenvalues, etc.
- Statistical tools: Mean, median, standard deviation, and more.
- Fourier transforms: Fast computation for signal and image processing.
- Integration with other libraries: Works seamlessly with Pandas, SciPy, and scikit-learn.
Important Facts to Know :
- NumPy arrays are homogeneous, meaning all elements must be the same type, allowing efficient computation.
- Vectorized operations in NumPy can be 10 to 100 times faster than equivalent Python loops.
What is NumPy Used for?
With NumPy, you can perform a wide range of numerical operations, including:
- Creating and manipulating arrays.
- Performing element-wise and matrix operations.
- Generating random numbers and statistical calculations.
- Conducting linear algebra operations.
- Working with Fourier transformations.
- Handling missing values efficiently in datasets.
Why Learn NumPy?
- NumPy speeds up math operations like addition and multiplication on large groups of numbers compared to regular Python..
- It’s good for handling large lists of numbers (arrays), so you don’t have to write complicated loops.
- It gives ready-to-use functions for statistics, algebra and random numbers.
- Libraries like Pandas, SciPy, TensorFlow and many others are built on top of NumPy.
- NumPy uses less memory and stores data more efficiently, which matters when working with lots of data.
NumPy Basics
This section covers the fundamentals of NumPy, including installation, importing the library and understanding its core functionalities. You will learn about the advantages of NumPy over Python lists and how to set up your environment for efficient numerical computing.
NumPy Arrays
NumPy arrays (ndarrays) are the backbone of the library. This section covers how to create and manipulate arrays effectively for data storage and processing
Python
import numpy as np
a = [9,3,3,5]
print(np.array(a)) # Converting list into numpy array
Mathematical Operations in NumPy
This section covers essential mathematical functions for array computations, including basic arithmetic, aggregation and mathematical transformations.
Python
import numpy as np
n = np.array([9,3,3,5])
print(n.mean()) # Calculating mean
Linear Algebra with NumPy
NumPy provides built-in functions for linear algebra operations essential for scientific computing and machine learning applications.
Python
import numpy as np
A = np.array([[1, 2],
[3, 4]])
res = np.dot(A, A) # Matrix multiplication
print(res)
Random Number Generation and Statistics
NumPy’s random module provides a list of functions for generating random numbers, which are essential for simulations, cryptography and machine learning applications. It supports various probability distributions, such as normal, uniform and Poisson and enable statistical analysis.
Python
import numpy as np
a = np.random.normal(0, 1, 5) # 5 values from normal distribution
print("Data:", a)
print("Mean:", np.mean(a))
OutputData: [ 0.50380896 1.56391917 -0.25398057 -0.31520396 0.4174568 ]
Mean: 0.38320007938038125
Advanced NumPy Operations
This section covers advanced NumPy techniques to enhance performance and handle complex computations. It includes vectorized operations for speed optimization, memory management strategies and integration with Pandas for efficient data analysis.
Python
import numpy as np
a = np.arange(5)
r = a * 10 # Vectorized operation (fast and efficient)
print(r)
NumPy Quiz
Test your knowledge of NumPy with this quiz, covering key topics such as array operations, mathematical functions and broadcasting.
Refer to Practice Exercises, Questions and Solutions for hands-on-numpy problems.
Numpy Tutorial for Beginners | Learn Python From Scratch
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