In this post, we have prepared a curated top list of reading recommendations for beginners and experienced. This hand-picked list of the best Numpy books and tutorials can help fill your brain this October and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2012)
- Raspberry Pi Supercomputing and Scientific Programming (2017)
- Python for Data Science For Dummies (For Dummies (Computer/Tech)) (2015)
- Python Data Science Handbook: Essential Tools for Working with Data (2016)
- High Performance Python: Practical Performant Programming for Humans (2014)
- Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series) (2018)
- Learning NumPy Array (2014)
- Scientific Computation (2015)
- NumPy Essentials (2016)
- Guide to NumPy: 2nd Edition (2015)
- Geoprocessing with Python (2016)
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2012)
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications.
Author(s): Wes McKinney
Raspberry Pi Supercomputing and Scientific Programming (2017)
Build an inexpensive cluster of multiple Raspberry Pi computers and install all the required libraries to write parallel and scientific programs in Python 3. This book covers setting up your Raspberry Pis, installing the necessary software, and making a cluster of multiple Pis.Once the cluster is built, its power has to be exploited by means of programs to run on it. So, Raspberry Pi Supercomputing and Scientific Programming teaches you to code the cluster with the MPI4PY library of Python 3.
Author(s): Ashwin Pajankar
Python for Data Science For Dummies (For Dummies (Computer/Tech)) (2015)
Unleash the power of Python for your data analysis projects with For Dummies!Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns.
Author(s): John Paul Mueller, Luca Massaron
Python Data Science Handbook: Essential Tools for Working with Data (2016)
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data.
Author(s): Jake VanderPlas
High Performance Python: Practical Performant Programming for Humans (2014)
Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Python’s implementation. You’ll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs.How can you take advantage of multi-core architectures or clusters?
Author(s): Micha Gorelick, Ian Ozsvald
Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series) (2018)
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python.Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex.
Author(s): Daniel Y. Chen
Learning NumPy Array (2014)
Supercharge your scientific Python computations by understanding how to use the NumPy library effectively.This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.NumPy is an extension of Python, which provides highly optimized arrays and numerical operations.
Author(s): Ivan Idris
Scientific Computation (2015)
This is a book about hacking, but not just any kind of hacking. It is about mathematical hacking, or scientific computing. If you like math and want to use computers to do math or solve mathematical problems, then this book is the for you.This is version 3, which includes over 80 additional pages, with extensive additions on Python classes, iPython with jupyter notebooks, magic functions, random numbers, and probability and statistics. The emphasis throughout the text is on programming in the iPython notebook. Version 3.0.3 (2017) is revised and corrected.
Author(s): Bruce E Shapiro
NumPy Essentials (2016)
In today’s world of science and technology, it’s all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need.This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory.
Author(s): Leo (Liang-Huan) Chin, Tanmay Dutta
Guide to NumPy: 2nd Edition (2015)
This is the second edition of Travis Oliphant’s A Guide to NumPy originally published electronically in 2006. It is designed to be a reference that can be used by practitioners who are familiar with Python but want to learn more about NumPy and related tools. In this updated edition, new perspectives are shared as well as descriptions of new distributed processing tools in the ecosystem, and how Numba can be used to compile code using NumPy arrays.
Author(s): Travis E. Oliphant PhD
Geoprocessing with Python (2016)
Geoprocessing with Python teaches you how to use the Python programming language, along with free and open source tools, to read, write, and process geospatial data.This book is about the science of reading, analyzing, and presenting geospatial data programmatically, using Python. Thanks to dozens of open source Python libraries and tools, you can take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo.
Author(s): Chris Garrard
You might also be interested in: Raspberry pi, Coffeescript, Nodejs, Erlang, Tensorflow, VOIP, Keras, Sitecore, Opencart, Spark Books.
Best Books to Learn Numpy
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Library Genesis and download some Numpy books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Data Analysis with Python: Introducing NumPy, Pandas, Matplotlib, and Essential Elements of Python Programming
Author(s): Rituraj Dixit
ID: 3368829, Publisher: BPB Publications, Year: 2023, Size: 8 Mb, Format: epub
Python NumPy for Beginners: NumPy Specialization for Data Science
Author(s): AI Publishing
ID: 3196190, Publisher: AI Publishing LLC, Year: 2022, Size: 7 Mb, Format: epub
Raspberry Pi Image Processing Programming: With NumPy, SciPy, Matplotlib, and OpenCV
Author(s): Ashwin Pajankar
ID: 3336912, Publisher: Apress, Year: 2022, Size: 5 Mb, Format: epub
Please note that this booklist is not definite. Some books are truly best-sellers according to The New York Times, others are drafted by unknown writers. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant links you could recommend? Drop a comment if you have any feedback on the list.