SciPy recipes : a cookbook with over 110 proven recipes for performing mathematical and scientific computations / L. Felipe Martins, Ruben Oliva Ramos, V. Kishore Ayyadevara.

Format:

Book

Author/Creator:

Martins, Luiz Felipe, author.

Oliva Ramos, Rubén, author.

Ayyadevara, V. Kishore, author.

Language:

English

Subjects (All):

Python (Computer program language).

Numerical analysis.

Mathematics--Data processing.

Mathematics.

Physical Description:

1 online resource (386 pages)

Edition:

First edition

Place of Publication:

Birmingham, England : Packt, 2017.

System Details:

text file

Summary:

Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy About This Book 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 Who This Book Is For Python developers, aspiring data scientists, and analysts who want to get started with scientific computing using Python will find this book an indispensable resource. If you want to learn how to manipulate and visualize your data using the SciPy Stack, this book will also help you. A basic understanding of Python programming is all you need to get started. What You Will 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 In Detail 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. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk th...

Contents:

Cover

Title Page

Copyright

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Getting to Know the Tools

Introduction

Installing Anaconda on Windows

How to do it...

Installing Anaconda on macOS

Installing Anaconda on Linux

Checking the Anaconda installation

Installing SciPy from a binary distribution on Windows

Installing Python

Installing the SciPy stack

Installing SciPy from a binary distribution on macOS

Installing the Xcode command-line tools

Installing Homebrew

Installing Python 3

Installing SciPy from source on Linux

Installing optional packages with conda

Getting ready

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

Getting started

Chapter 2: Getting Started with NumPy

Creating NumPy arrays

How to do it…

Creating an array from a list.

Specifying the data type for elements in an array

Creating an empty array with a given shape

Creating arrays of zeros and ones with a single value

Creating arrays with equally spaced values

Creating an array by repeating elements

Creating an array by tiling another array

Creating an array with the same shape as another array

Using object arrays to store heterogeneous data

See also

Querying and changing the shape of an array

Storing and retrieving NumPy arrays

Storing a NumPy array in text format

Storing a NumPy array in CSV format

Loading an array from a text file

Storing a single array in binary format

Storing several arrays in binary format

Loading arrays stored in NPY binary format

Indexing

Accessing sub arrays using slices

Selecting subarrays using an index list

Indexing with Boolean arrays

Operations on arrays

Computing a function for all elements of an array

Doing array operations

Computing matrix products

Using masked arrays to represent invalid data

Creating a masked array from an explicit mask

Creating a masked array from a condition

Defining, symbolically, a function operating on arrays

How it works...

Chapter 3: Using Matplotlib to Create Graphs

Creating two-dimensional plots of functions and data

How it works…

Generating multiple plots in a single figure

Setting line styles and markers

Using different backends to display graphs

How it works….

Saving plots to disk

Annotating graphs

Generating histograms and box plots

Creating three-dimensional plots

Generating interactive displays in the Jupyter Notebook

Object-oriented graph creation using Artist objects

Creating a map with cartopy

Chapter 4: Data Wrangling with pandas

Creating Series objects

Creating DataFrame objects

Inserting and deleting columns to a DataFrame

Inserting and deleting rows to a DataFrame

Selecting items by row indexes and column labels

Selecting items by integer location

Selecting items using mixed indexing

Accessing, selecting, and modifying data

Selecting rows using Boolean selection

Reading and storing data in different formats

Working with CSV, text/tabular, and format data

Reading a CSV file into a DataFrame

Specifying the index column when reading a CSV file

Reading and writing data in Excel format

Reading and writing JSON files

Reading HTML data from the web

Accessing CSV data on the web.

Reading and writing from/to SQL databases

Data displays employing different kinds of visual representation

How to apply numerical functions and operations to Series and DataFrame objects

Computing statistical functions on Series and DataFrame objects

Retrieving summary descriptive statistics

Calculating the mean

Calculating variance and standard deviation

How to sort data in Series and DataFrame objects

Performing merging, joins, concatenation, and grouping

Merging data from multiple pandas objects

Chapter 5: Matrices and Linear Algebra

Matrix operations and functions on two-dimensional arrays

Solving linear systems using matrices

Calculating the null space of a matrix

Calculating the LU decompositions of a matrix

Calculating the QR decomposition of a matrix

Calculating the eigenvalue and eigenvector of a matrix

Diagonalizing a matrix

Calculating the Jordan form of a matrix

Calculating the singular value decomposition of a matrix

Creating a sparse matrix

Computations on top of a sparse matrix

Chapter 6: Solving Equations and Optimization

Non-linear equations and systems

System of equations and how to solve it

Choosing the solver used to find the solution of equations.

Getting ready

Solving constrained non-linear optimization problems in several variables

Solving one-dimensional optimization problems

Solving multidimensional non-linear equations using the Newton-Krylov method

Solving multidimensional non-linear equations using the Anderson method

Finding the best linear fit for a set of data

How it works ...

Doing non-linear regression for a set of data

Regression

Chapter 7: Constants and Special Functions

Physical and mathematical constants available in SciPy

Getting ready...

Using constants in the CODATA database

Bessel functions

Error functions

Orthogonal polynomials functions

Gamma function

The Riemann zeta function

Airy and Bairy functions

The Bessel and Struve functions

There's more

Chapter 8: Calculus, Interpolation, and Differential Equations

Integration

Computing integrals using the Newton-Cotes method

Computing integrals using a Gaussian quadrature

How to do it.

How it works.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed January 29, 2018).

OCLC:

1018168528