In this post, we have prepared a curated top list of reading recommendations for beginners and experienced. This hand-picked list of the best Scipy books and tutorials can help fill your brain this July and ensure you’re getting smarter. We have also mentioned the brief introduction of each book based on the relevant Amazon or Reddit descriptions.
- SciPy Recipes: A cookbook with over 110 proven recipes for performing mathematical and scientific computations (2017)
- Raspberry Pi Image Processing Programming: Develop Real-Life Examples with Python, Pillow, and SciPy (2017)
- Raspberry Pi Supercomputing and Scientific Programming: MPI4PY, NumPy, and SciPy for Enthusiasts (2017)
- Python Data Science Handbook (2016)
- Elegant SciPy: The Art of Scientific Python (2017)
- Learning SciPy for Numerical and Scientific Computing Second Edition (2015)
- Python for Data Science For Dummies (For Dummies (Computer/Tech)) (2015)
- Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series) (2018)
- Data Analysis with Open Source Tools: A Hands-On Guide for Programmers (2010)
- PySpark Recipes: A Problem-Solution Approach with PySpark2 (2017)
- Mastering SciPy (2015)
- Introducing Data Science (2016)
SciPy Recipes: A cookbook with over 110 proven recipes for performing mathematical and scientific computations (2017)
With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease. This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others.
Author(s): L. Felipe Martins, Ruben Oliva Ramos
Raspberry Pi Image Processing Programming: Develop Real-Life Examples with Python, Pillow, and SciPy (2017)
Write your own Digital Image Processing programs with the use of pillow, scipy.ndimage, and matplotlib in Python 3 with Raspberry Pi 3 as the hardware platform. This concise quick-start guide provides working code examples and exercises. Learn how to interface Raspberry Pi with various image sensors.
Author(s): Ashwin Pajankar
Raspberry Pi Supercomputing and Scientific Programming: MPI4PY, NumPy, and SciPy for Enthusiasts (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.
Author(s): Ashwin Pajankar
Python Data Science Handbook (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.
Author(s): Jake VanderPlas
Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand.
Author(s): Juan Nunez-Iglesias, Stéfan van der Walt
Quick solutions to complex numerical problems in physics, applied mathematics, and science with SciPy. This book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy. SciPy is an open source Python library used to perform scientific computing. The SciPy (Scientific Python) package extends the functionality of NumPy with a substantial collection of useful algorithms.
Author(s): Sergio J. Rojas G., Erik A Christensen
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
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.
Author(s): Daniel Y. Chen
These days it seems like everyone is collecting data. But all of that data is just raw information — to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless.
Author(s): Philipp K. Janert
Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems.
Author(s): Raju Kumar Mishra
Mastering SciPy (2015)
If you are a professional with a proficiency in Python and familiarity with IPython, this book is for you. Some basic knowledge of numerical methods in scientific computing would be helpful. The SciPy stack is a collection of open source libraries of the powerful scripting language Python, together with its interactive shells.
Author(s): Francisco J. Blanco-Silva
Introducing Data Science (2016)
Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you’ll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.
Author(s): Davy Cielen, Arno Meysman
Best Scipy Books You Must Read
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 Scipy 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 Beginner To Pro: Python Tutorial, File Handling, Python NumPy, Python Matplotlib, Python SciPy, Machine Learning, Python MySQL,Python MySQL, Python Reference, Module Reference, Python Examples
Author(s): KUMAR, N KRISHNA
ID: 2856291, Publisher: , Year: 2020, Size: 3 Mb, Format: epub
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Author(s): Robert Johansson
ID: 2302028, Publisher: Apress, Year: 2019, Size: 23 Mb, Format: pdf
DataCamp SciPy Cheat Sheet
ID: 2947936, Publisher: iBooker it-ebooks, Year: 2018, Size: 146 Kb, Format: pdf
Please note that this booklist is not absolute. Some books are absolutely chart-busters according to USA Today, others are composed 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 books you could recommend? Leave a comment if you have any feedback on the list.