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 November 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.
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 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.
PYTHON FOR DATA SCIENCE: Guide to computer programming and web coding. Learn machine learning, artificial intelligence, NumPy and Pandas packages for data analysis. Step-by-step exercises included.
Author(s): Jason Test
Publisher: , Year: 2020, Size: 8 Mb, Download: epub
ID: 2548049
Python Programming: 3 BOOKS IN 1 Learn machine learning, data science and analysis with a crash course for beginners. Included coding exercises for artificial intelligence, Numpy, Pandas and Ipython.
Author(s): Test, Jason
Publisher: , Year: 2020, Size: 9 Mb, Download: epub
ID: 2578781
Python for Data Analysis: The Ultimate Beginner's Guide to Learn programming in Python for Data Science with Pandas and NumPy, Master Statistical Analysis, and Visualization
Author(s): Foster, Matt
Publisher: , Year: 2020, Size: 2 Mb, Download: epub
ID: 2611664
Python Machine Learning: The Beginner's Guide To Learn Python Machine Learning Including Keras, Numpy, Scikit Learn and PyTorch.
Author(s): Trinity, Lilly
Publisher: , Year: 2020, Size: 4 Mb, Download: epub
ID: 2616130
Python Data Analysis for Newbies: Numpy/pandas/matplotlib/scikit-learn/keras
Author(s): Joshua K. Cage
Publisher: , Year: 2020, Size: 3 Mb, Download: epub
ID: 2673370
Python Data Science: After work guide to start learning Data Science on your own. Avoid common beginners mistakes of coding. Approach Panda and NumPy to become a brilliant computer programmer.
Author(s): HOOD, CODING; Kölling, Michail
Publisher: , Year: 2020, Size: 4 Mb, Download: epub
ID: 2792847
Python Machine Learning for Beginners: Learning from Scratch Numpy, Pandas, Matplotlib, Seaborn, SKlearn and TensorFlow 2.0 for Machine Learning & Deep Learning- With Exercises and Hands-on Projects
Author(s): Publishing, AI
Publisher: AI Publishing LLC, Year: 2020, Size: 10 Mb, Download: epub
ID: 2795422
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
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.