NumPy – Python Library Tutorial & Roadmap

Embark on an adventure into the heart of Python’s mathematical prowess with NumPy, the foundational library that transforms the way we handle numerical computations.

This guide is your compass to navigating the rich landscape of NumPy’s capabilities, from basic arithmetic to complex scientific computations.

NumPy, a versatile array-processing library for Python, offers a suite of methods and functions for handling multi-dimensional arrays. Known as “Numerical Python,” it comes packed with a variety of computational tools, including extensive mathematical functions and linear algebra routines.

Blending Python’s adaptability with the efficiency of optimized C code, NumPy stands out for its performance. Its straightforward syntax ensures that programmers of all levels can use it with ease.

Through this NumPy tutorial, you’ll be equipped to grasp the core aspects of NumPy, ranging from basic to advanced. You’ll learn how to manipulate NumPy arrays, generate and visualize random datasets, and utilize the breadth of NumPy’s functions.

The Power of NumPy:

NumPy, short for Numerical Python, is more than just a library—it’s a revolution in numerical computing. Its array object is the cornerstone of data analysis in Python, providing the efficiency and flexibility needed to crunch numbers like a pro.

Why Numpy ?

NumPy has transformed our approach to processing numerical data in Python. It was designed to overcome the shortcomings of Python’s traditional lists for numerical tasks.

Created by Travis Olliphant in 2005, NumPy introduces a robust array object that is both effective and adaptable. Its main purpose is to simplify intricate mathematical and scientific computations by offering array-based computing features.

The architecture of NumPy is crafted for easy compatibility with other scientific libraries, which accelerates the performance of numerical operations.

Consequently, NumPy has established itself as a fundamental element within the Python community, indispensable for data handling, machine learning, and scientific exploration.

Installation of Numpy Using PIP

Installing NumPy is your first step into a larger world. Once set up, you’ll be introduced to its primary structure—the ndarray. This multi-dimensional array will become your trusted ally in data manipulation.

Open your command prompt or terminal and run the following command:

Pip install numpy code emed

NumPy Tutorial Overview

¡》Basic Operations:

With NumPy, operations that once seemed tedious become trivial. You’ll learn how to perform element-wise calculations, slice and dice arrays, and harness the power of vectorization to speed up your workflows.

¡¡》Advanced Features:

As your journey progresses, you’ll encounter the advanced treasures of NumPy. We’ll explore linear algebra functions, statistical tools, and even dip our toes into the realm of Fourier transforms.

¡¡¡》Integration with Other Libraries:

NumPy doesn’t stand alone; it’s the bedrock upon which many other Python libraries are built. You’ll see how it integrates seamlessly with libraries like Pandas and Matplotlib, creating a cohesive ecosystem for data science.

Complete NumPy Tutorial

1. Introduction

  • Introduction to Numpy
  • Python NumPy
  • NumPy array in Python
  • Basics of NumPy Arrays
  • Python Lists VS Numpy Arrays
  • Numpy – ndarray
  • Data type Object (dtype) in NumPy Python

2. Creating NumPy Array

  • Numpy – Array Creation
  • The Arange Method
  • The Zero Method
  • Create a Numpy array filled with all ones
  • The linspace Method
  • The eye Method
  • Numpy Meshgrid function
  • Creating a one-dimensional NumPy array
  • How to create an empty and a full NumPy array?
  • Create a Numpy array filled with all zeros
  • Create a Numpy array filled with all ones
  • How to generate 2-D Gaussian array using NumPy?
  • How to create a vector in Python using NumPy
  • Create the record array from list of individual records

3. NumPy Array Manipulation

  • Copy and View in NumPy Array
  • How to Copy NumPy array into another array?
  • Appending values at the end of an NumPy array
  • How to swap columns of a given NumPy array?
  • Insert a new axis within a NumPy array
  • Stack the sequence of NumPy array horizontally
  • Stack the sequence of NumPy array vertically
  • Joining NumPy Array
  • Combining a one and a two-dimensional NumPy Array
  • Concatenate two arrays – np.ma.concatenate()
  • Combined array index by index
  • Splitting Arrays in NumPy
  • Compare two NumPy arrays
  • Find the union of two NumPy arrays
  • Find unique rows in a NumPy array
  • Get the unique values from an array
  • Trim the leading and/or trailing zeros from a 1-D array

4. Matrix in NumPy

  • Matrix manipulation in Python
  • Numpy matrix operations | empty() function
  • Numpy matrix operations | zeros() function
  • Numpy matrix operations | ones() function
  • Numpy matrix operations | eye() function
  • Numpy matrix operations | identity() function
  • Adding and Subtracting Matrices in Python
  • Matrix Multiplication in NumPy
  • Dot product of two arrays
  • NumPy | Vector Multiplication
  • How to calculate dot product of two vectors in Python?
  • Multiplication of two Matrices in Single line using Numpy in Python
  • Get the eigen values of a matrix
  • Calculate the determinant of a matrix using NumPy
  • Find the transpose of the matrix
  • Find the variance of a matrix
  • Compute the inverse of a matrix using NumPy

5. Operations on NumPy Array

  • Numpy – Binary Operations
  • Numpy – Mathematical Function
  • Numpy – String Operations

6. Reshaping NumPy Array

  • Reshape NumPy Array
  • Resize the shape of the given matrix
  • Reshape the shape of the given matrix
  • Get the Shape of NumPy Array
  • Change the dimension of a NumPy array
  • Change shape and size of array in-place
  • Flatten a Matrix in Python using NumPy
  • Flatten a matrix – matrix.ravel()
  • Move axes of an array to new positions
  • Interchange two axes of an array
  • Swap the axes a matrix
  • Split an array into multiple sub-arrays vertically
  • Split an array into multiple sub-arrays horizontally
  • Give a new shape to the masked array without changing its data
  • Squeeze the size of a matrix

7. Indexing NumPy Array

  • Basic Slicing and Advanced Indexing in NumPy Python
  • Get selected slices of an array along mentioned axis
  • Accessing Data Along Multiple Dimensions Arrays in Python Numpy
  • How to access different rows of a multidimensional NumPy array?
  • Get the indices for the lower-triangle of an (n, m) array

8. Arithmetic operations on NumPy Array

  • Broadcasting with NumPy Arrays
  • Estimation of Variable
  • Python: Operations on Numpy Arrays
  • How to use the NumPy sum function?
  • Divide the NumPy array element wise
  • Computes the inner product of two arrays
  • Absolute Deviation and Absolute Mean Deviation using NumPy
  • Find the standard deviation a matrix
  • Calculate the GCD of the NumPy array

9. Linear Algebra in NumPy Array

  • Numpy | Linear Algebra
  • Get the QR factorization of a given NumPy array
  • How to get the magnitude of a vector in NumPy?
  • Compute the eigenvalues and right eigenvectors of a given square array using NumPy?

10. NumPy and Random Data

  • Random sampling in numpy | ranf() function
  • Random sampling in numpy | random() function
  • Random sampling in numpy | random_sample() function
  • Random sampling in numpy | sample() function
  • Random sampling in numpy | random_integers() function
  • Random sampling in numpy | randint() function
  • Get random elements from NumPy – random.choice()
  • How to choose elements from the list with different probability using NumPy?
  • How to get weighted random choice in Python?
  • How to get the random positioning of different integer values?
  • Get Random Elements form geometric distribution
  • Get Random samples of a sequence of permutation

11. Sorting and Searching in NumPy Array

  • Searching in a NumPy array
  • How to sort a Numpy Array
  • Numpy – Sorting, Searching and Counting
  • Variations in different Sorting techniques in Python
  • Sort a complex array
  • Get the minimum value of masked array
  • Sort the values in a matrix
  • Sort the elements in the given matrix having one or more dimension

12. Universal Functions

  • Numpy ufunc | Universal functions
  • Create your own universal function in NumPy

13. Working With Images

  • Create a white image using NumPy in Python
  • Convert a NumPy array to an image
  • How to Convert images to NumPy array?
  • Convert an image to NumPy array and save it to CSV file using Python?

14. Projects and Applications with NumPy

  • Print checkerboard pattern of nxn using numpy
  • Implementation of neural network from scratch using NumPy
  • Analyzing selling price of used cars using Python

15. Python Numpy Exercises

  • Python NumPy – Practice Exercises, Questions, and Solutions
  • Python MCQ (Multiple Choice Questions) with Answers

Numpy Program Examples

Python

import numpy as np 
  
# Create two NumPy arrays 
array1 = np.array([1, 2, 3]) 
array2 = np.array([4, 5, 6]) 
  
# Perform element-wise addition 
result_addition = array1 + array2 
  
# Display the original arrays and the results 
print("Array 1:", array1) 
print("Array 2:", array2) 
print("Element-wise Addition:", result_addition)

Also Practice, Important Numpy programs

  • Python | Check whether a list is empty or not
  • Python | Get unique values from a list
  • Python | Multiply all numbers in the list (3 different ways)
  • Transpose a matrix in Single line in Python
  • Multiplication of two Matrices in Single line using Numpy in Python
  • Python program to print checkerboard pattern of nxn using numpy
  • Graph Plotting in Python |
  • Set 1
  • ,
  • Set 2
  • ,
  • Set 3

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Conclusion:

Your expedition through NumPy’s landscape concludes here, but your journey in numerical computing is just beginning. Armed with the knowledge from this tutorial, you’re now equipped to tackle any challenge with confidence and skill. NumPy isn’t just a tool; it’s your gateway to mastering Python’s data science capabilities.

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