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SciPy Recipes

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Learn
  • Get a solid foundation in scientific computing using Python
  • Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib
  • Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy
  • Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack
  • Implement data wrangling tasks efficiently using pandas
  • Visualize your data through various graphs and charts using matplotlib
About

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. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.

Features
  • Covers a wide range of data science tasks using SciPy, NumPy, pandas, and matplotlib
  • Effective recipes on advanced scientific computations, statistics, data wrangling, data visualization, and more
  • A must-have book if you're looking to solve your data-related problems using SciPy, on-the-go
Page Count 386
Course Length 11 hours 34 minutes
ISBN 9781788291460
Date Of Publication 20 Dec 2017
Introduction
Installing Anaconda on Windows
Installing Anaconda on macOS
Installing Anaconda on Linux
Checking the Anaconda installation
Installing SciPy from a binary distribution on Windows
Installing SciPy from a binary distribution on macOS
Installing SciPy from source on Linux
Installing optional packages with conda
Installing packages with pip
Setting up a virtual environment with conda
Creating a virtual environment for development with conda 
Creating a conda environment with a different version of a package
Using conda environments to run different versions of Python
Creating virtual environments with venv
Running SciPy in a script
Running SciPy in Jupyter
Running SciPy in Spyder
Running SciPy in PyCharm
Introduction
Creating NumPy arrays
Querying and changing the shape of an array
Storing and retrieving NumPy arrays
Indexing
Operations on arrays
Using masked arrays to represent invalid data
Using object arrays to store heterogeneous data
Defining, symbolically, a function operating on arrays

Authors

L. Felipe Martins

L. Felipe Martins has a PhD in applied mathematics from Brown University and is currently an associate professor in the Department of Mathematics at Cleveland State University. His main research areas are applied probability and scientific computing. He has taught applied mathematics courses at all levels, including linear algebra, differential equations, probability, and optimization, and uses Python as an instructional tool in all courses. He is the author of two books, IPython Notebook Essentials and Mastering Python Data Analysis.

Ruben Oliva Ramos

Ruben Oliva Ramos is a computer systems engineer from Tecnologico de Leon Institute, with a master's degree in computer and electronic systems engineering and a specialization in teleinformatics and networking from the University of Salle Bajio in Leon, Guanajuato, Mexico. He has more than 5 years of experience of developing web applications to control and monitor devices connected with Arduino and Raspberry Pi, using web frameworks and cloud services to build the Internet of Things applications.

He is a mechatronics teacher at the University of Salle Bajio and teaches students of the master's degree in design and engineering of mechatronics systems. Ruben also works at Centro de Bachillerato Tecnologico Industrial 225 teaching subjects such as electronics, robotics and control, automation, and microcontrollers. He is a consultant and developer for projects in areas such as monitoring systems and datalogger data using technologies (such as Android, iOS, HTML5, and ASP.NET), databases (such as SQlite, MongoDB, and MySQL), web servers, hardware programming, and control and monitor systems for data acquisition and programming.

V Kishore Ayyadevara

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams.

Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes.

Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.