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.
1. 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. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author…
Author(s): Wes McKinney
2. 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. Along the way, you will learn the concepts of the Message Passing Interface (MPI) standards…
Author(s): Ashwin Pajankar
3. 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
4. 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. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues…
Author(s): Jake VanderPlas
5. 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? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these…
Author(s): Micha Gorelick, Ian Ozsvald
6. 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. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.Pandas for Everyone brings together practical…
Author(s): Daniel Y. Chen
7. 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. NumPy replaces a lot of the functionality of Matlab and Mathematica specifically vectorized operations, but in contrast…
Author(s): Ivan Idris
8. 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
9. 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. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each…
Author(s): Leo (Liang-Huan) Chin, Tanmay Dutta
10. 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. Travis Oliphant is the co-founder and CEO of Continuum Analytics. Continuum Analytics develops Anaconda…
Author(s): Travis E. Oliphant PhD
11. 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.
An open-access book on numpy vectorization techniques, Nicolas P. Rougier, 2017 – rougier/from-python-to-numpy Numpy and Scipy Documentation¶ Welcome! This is the documentation for Numpy and Scipy. For contributors: Acknowledgements¶ Large parts of this manual originate from Travis E. Oliphant’s book Guide to NumPy (which generously entered Public Domain in August 2008). After October 31, 2010 this book may be freely copied in any format and used as source material for other books as long as ac-knowledgement of the original author is given. Your support of this temporary distribution restriction plays an essential role in allowing the author and others like him to produce more quality books and software. 1
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 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.
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Author(s): Robert Johansson
Publisher: Apress, Year: 2019, Size: 23 Mb, Download: pdf
ID: 2302028
Python Data Science: The Ultimate Handbook for Beginners on How to Explore NumPy for Numerical Data, Pandas for Data Analysis, IPython, Scikit-Learn and Tensorflow for Machine Learning and Business
Author(s): Steve Blair
Publisher: Steve Blair, Year: 2019, Size: 2 Mb, Download: epub
ID: 2401143
Python Data Analytics: With Pandas, NumPy, and Matplotlib
Author(s): Fabio Nelli
Publisher: Apress, Year: 2018, Size: 14 Mb, Download: pdf
ID: 2268922
Python Data Analytics with Pandas, NumPy and Matplotlib [2nd ed.]
Author(s): Fabio Nelli
Publisher: Apress, Year: 2018, Size: 10 Mb, Download: pdf
ID: 2268961
Python Data Analytics With Pandas, NumPy, and Matplotlib
Author(s): Fabio Nelli
Publisher: Apress, Year: 2018, Size: 15 Mb, Download: epub
ID: 2269641
Mastering Numerical Computing With NumPy: Master Scientific Computing and Perform Complex Operations With Ease
Author(s): Mert Cuhadaroglu & Umit Mert Cakmak [Mert Cuhadaroglu]
Publisher: Packt Publishing, Year: 2018, Size: 4 Mb, Download: epub
ID: 2289644
Hands-On Data Analysis with NumPy and pandas
Author(s): Curtis Miller [Curtis Miller]
Publisher: Packt Publishing, Year: 2018, Size: 7 Mb, Download: epub
ID: 2289648
Data Analysis From Scratch With Python: Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and Matplotlib
Author(s): Peters Morgan
Publisher: AI Sciences LLC, Year: 2018, Size: 3 Mb, Download: pdf
ID: 2357677
Raspberry Pi Supercomputing and Scientific Programming. MPI4PY, NumPy, and SciPy for Enthusiasts
Author(s): Ashwin Pajankar
Publisher: Apress, Year: 2017, Size: 5 Mb, Download: pdf
ID: 1685037
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.