Books: **7**

Cover | Title | Year |
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Develop Real-Life Examples with Python, Pillow, and SciPy 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.
What more » You'll Learn
•Understand Raspberry Pi concepts and setup
•Understand digital image processing concepts
•Study pillow, the friendly PIL fork
•Explore scipy.ndimage and matplotlib
•Master use of the Pi camera and webcam
Who This Book Is For
Raspberry Pi and IoT enthusiasts, digital image processing enthusiasts, Python and Open Source enthusiasts and professionals « less / Hardware | 2017 | |

Key Features
Your ultimate resource for getting up and running with Python numerical computations Explore numerical computing and mathematical libraries using Python 3.x code with SciPy and NumPy modules A hands-on guide to implementing mathematics with Python, with complete coverage of all the key more » concepts
Book Description
Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
What you will learn
* The principal syntactical elements of Python
* The most important and basic types in Python
* The essential building blocks of computational mathematics, linear algebra, and related Python objects
* Plot in Python using matplotlib to create high quality figures and graphics to draw and visualize your results
* Define and use functions and learn to treat them as objects
* How and when to correctly apply object-oriented programming for scientific computing in Python
* Handle exceptions, which are an important part of writing reliable and usable code
* Two aspects of testing for scientific programming: Manual and Automatic
About the Author
Claus Fuhrer is a professor of scientific computations at Lund University, Sweden. He has an extensive teaching record that includes intensive programming courses in numerical analysis and engineering mathematics across various levels in many different countries and teaching environments. Claus also develops numerical software in research « less | 2017 | |

James McCaffrey’s SciPy Programming Succinctly offers readers a quick, thorough grounding in knowledge of the Python open source extension SciPy. The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices. Each section presents more » a complete demo program for programmers to experiment with, carefully chosen examples to best illustrate each function, and resources for further learning. Use this e-book to install and edit SciPy, and use arrays, matrices, and combinatorics in Python programming. « less | 2017 | |

Summary
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.
About the Technology
This book is about the science of reading, analyzing, and presenting geospatial data programmatically, more » 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. The book shows you how.
About the Book
Geoprocessing with Python teaches you how to access available datasets to make maps or perform your own analyses using free tools like the GDAL, NumPy, and matplotlib Python modules. Through lots of hands-on examples, you’ll master core practices like handling multiple vector file formats, editing geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. The book also covers how to manipulate, resample, and analyze raster data, such as aerial photographs and digital elevation models.
What's Inside
* Geoprocessing from the ground up
* Read, write, process, and analyze raster data
* Visualize data with matplotlib
* Write custom geoprocessing tools
* Three additional appendixes available online
About the Reader
To read this book all you need is a basic knowledge of Python or a similar programming language.
About the Author
Chris Garrard works as a developer for Utah State University and teaches a graduate course on Python programming for GIS.Table of Contents
1. Introduction
2. Python basics
3. Reading and writing vector data
4. Working with different vector file formats
5. Filtering data with OGR
6. Manipulating geometries with OGR
7. Vector analysis with OGR
8. Using spatial reference systems
9. Reading and writing raster data
10. Working with raster data
11. Map algebra with NumPy and SciPy
12. Map classification
13. Visualizing data
14. Appendixes
* A - Installation
* B - References
* C - OGR - online only
* D - OSR - online only
* E - GDAL - online only « less | 2016 | |

Become an expert at using Python for advanced statistical analysis of data using real-world examples ABOUT THIS BOOK
* Clean, format, and explore data using graphical and numerical summaries
* Leverage the IPython environment to efficiently analyze data with Python
* Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data
WHO THIS BOOK IS FOR
If more » you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed.
WHAT YOU WILL LEARN
* Read, sort, and map various data into Python and Pandas
* Recognise patterns so you can understand and explore data
* Use statistical models to discover patterns in data
* Review classical statistical inference using Python, Pandas, and SciPy
* Detect similarities and differences in data with clustering
* Clean your data to make it useful
* Work in Jupyter Notebook to produce publication ready figures to be included in reports
IN DETAIL
Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!
Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.
You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.
STYLE AND APPROACH
This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists. « less | 2016 | |

Quick solutions to complex numerical problems in physics, applied mathematics, and science 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.
The book starts with a brief description of the SciPy libraries, followed by a chapter that is a more » fun and fast-paced primer on array creation, manipulation, and problem-solving. You will also learn how to use SciPy in linear algebra, which includes topics such as computation of eigenvalues and eigenvectors. Furthermore, the book is based on interesting subjects such as definition and manipulation of functions, computation of derivatives, integration, interpolation, and regression. You will also learn how to use SciPy in signal processing and how applications of SciPy can be used to collect, organize, analyze, and interpret data.
By the end of the book, you will have fast, accurate, and easy-to-code solutions for numerical and scientific computing applications. « less | 2015 | |

Over 60 practical recipes to help you explore Python and its robust data science capabilities ABOUT THIS BOOK
* The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action
* Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python
* Get up to speed on machine learning algorithms more » with the help of easy-to-follow, insightful recipes
WHO THIS BOOK IS FOR
This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience.
WHAT YOU WILL LEARN
* Explore the complete range of Data Science algorithms
* Get to know the tricks used by industry engineers to create the most accurate data science models
* Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively
* Create meaningful features to solve real-world problems
* Take a look at Advanced Regression methods for model building and variable selection
* Get a thorough understanding of the underlying concepts and implementation of Ensemble methods
* Solve real-world problems using a variety of different datasets from numerical and text data modalities
* Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on
IN DETAIL
Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way.
This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly.
The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional.
STYLE AND APPROACH
This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms. « less | 2015 |