NumPy Tutorial – Python Library

NumPy is a general-purpose array-processing Python library which provides handy methods/functions for working n-dimensional arrays. NumPy is a short form for “Numerical Python“. It provides various computing tools such as comprehensive mathematical functions, and linear algebra routines.

• NumPy provides both the flexibility of Python and the speed of well-optimized compiled C code.
• Its easy-to-use syntax makes it highly accessible and productive for programmers from any background.

This NumPy tutorial helps you learn the fundamentals of NumPy from Basics to Advanced, like operations on NumPy array, creating and plotting random data sets, and working with NumPy functions.

Why Numpy ?

NumPy revolutionized the way we handle numerical data in Python. It is created to address the limitations of traditional Python lists when it comes to numerical computing. It is developed by Travis Olliphant in 2005.

NumPy provides a powerful array object that is both efficient and flexible. Its primary goal is to facilitate complex mathematical and scientific operations by introducing array-oriented computing capabilities. NumPy’s design allows for seamless integration with other scientific libraries, enabling faster execution of numerical tasks.

As a result, NumPy has become a cornerstone in the Python ecosystem, essential for data manipulation, machine learning, and scientific research.

Installation of Numpy Using PIP

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

pip install numpy

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

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

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

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

Operations on NumPy Array

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

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

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

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

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?

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

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

Universal Functions

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

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?

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

Python Numpy Exercises

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

Numpy Program Examples

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

Conclusion

From simple calculations to complex data manipulation, NumPy empowers you to tackle any numerical challenge, no matter the dimensionality. Its intuitive syntax and powerful functionality make you feel like you’re working with single numbers, even when manipulating vast datasets.

This Numpy tutorial has equipped you with the fundamental skills to:

• Grasp the fundamental concepts of data science using NumPy.
• Create NumPy arrays using various methods.
• Masterfully manipulate Numpy arrays to perform critical calculations.
• Practice your Numpy knowledge to solve real-world data problems.

Also Explore, Recent Articles on NumPy !!

FAQs: NumPy Tutorial

Q1. What is the main use of NumPy library?

NumPy is primarily used for numerical computing in Python. It provides a powerful, efficient, and flexible way to work with large arrays and matrices, making it ideal for tasks like:

• Scientific computing: Performing complex mathematical calculations, simulations, and data analysis.
• Machine learning: Manipulating and preparing data for machine learning algorithms.
• Image processing: Analyzing and processing images for tasks like filtering and segmentation.
• Financial modeling: Performing financial calculations, risk analysis, and portfolio optimization.
• Data analysis and visualization: Cleaning, aggregating, and preparing data for analysis and visualization.

Q2. What is the benefit of using NumPy?

There are many benefits to using NumPy:

• Efficiency: NumPy’s arrays are stored in contiguous memory, allowing for faster access and manipulation compared to traditional Python lists.
• Vectorized operations: NumPy allows you to perform operations on entire arrays at once, significantly improving performance.
• Versatility: NumPy provides a wide range of mathematical functions, data types, and array manipulation tools.
• Ease of use: NumPy’s syntax is straightforward and intuitive, making it easy to learn and use.
• Integration: NumPy integrates seamlessly with other popular scientific libraries like SciPy, Pandas, and Matplotlib.

Q3. What should I know before learning NumPy?

Before learning NumPy, it’s helpful to have a basic understanding of:

• Python programming: Familiarity with Python syntax and data structures is essential.
• Linear algebra: Basic knowledge of linear algebra concepts like vectors, matrices, and operations will be beneficial.
• Data types: Understanding different data types like integers, floats, and strings is important for working with arrays efficiently.

Q4. What makes NumPy faster?

There are several factors that contribute to NumPy’s speed:

• C-based code: NumPy is written in C, a compiled language, which makes it inherently faster than pure Python code.
• Vectorized operations: Performing computations on entire arrays at once significantly reduces overhead compared to looping through individual elements.
• Optimized data structures: NumPy’s arrays are stored in contiguous memory, allowing for efficient memory access and manipulation.
• Hardware acceleration: NumPy can leverage hardware acceleration features like SIMD instructions to further improve performance on modern processors.