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The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are made specifically for the web, others are for the desktop only, some deal with 3D and large data, while others target flawless 2D rendering. In this landscape, Matplotlib has a very special place. It is a versatile and powerful library that allows you to design very high quality figures, suitable for scientific publishing. It also offers a simple and intuitive...

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[Stan Blank] Python Programming in OpenGL

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The goal of this book is to reconcile Python programmers with OpenGL, providing both an introduction to modern OpenGL and a set of basic and advanced techniques in order to achieve both fast, scalable & beautiful scientific visualizations.

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This open access book explains the foundations of modern solvers for ordinary differential equations (ODEs). Explicit and implicit methods are motivated and explained, as well as methods for error control and automatic time step selection, and all the solvers are implemented as a class hierarchy in Python.

This book is open access under a CC BY license.

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This book contains all the example programs used in my CoderDojo class to teach Python programming. The primary goal of the class is to teach pro- gramming using action games used to make learning more interesting. Some of the examples are entirely focused on introducing new language concepts or showing how the Pygame Zero API works, but most are a mixture of both.

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Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library.

Summary
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core...

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Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting...

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This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.

Key Features:

● Showcases the excellent data science environment in Python.
● Provides examples for readers to replicate...

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During the next three intensive days you will learn how to deal with spatial data and analyze it using “pure” Python.

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This book introduces the basics of Python programming and geographic data analysis for all “geo-minded” people (geographers, geologists and others using spatial data). You'll learn how to apply Python GIS geospatial processes to a variety of problems, and work with remote sensing data.

About the Authors

• Henrikki Tenkanen is an Assistant Professor of Geoinformation Technology at the Department of Built Environment, Aalto University (Finland).

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This textbook acts as a hands-on introduction to the areas of Digital Signal Processing (DSP), Software-Defined Radio (SDR), and wireless communications. Think of this textbook like a gateway into the world of DSP and SDR.

About the Authors

• Dr. Marc Lichtman is a wireless communications researcher who specializes in SDR, machine learning, LTE/5G-NR, and spectrum sensing. He is an Adjunct Professor at the University of Maryland, where he created and taught a course that served as the basis of this textbook.

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Advanced Guide to Python 3 Programming 2nd Edition delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level.

This second edition has been significantly updated with two new sections on advanced Python language concepts and data analytics and machine learning. The GUI chapters have been rewritten to use the Tkinter UI library and a chapter on performance monitoring and profiling has been added. In total there are 18 new chapters, and...

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Whether you have never written a line of code or you have some programming experience, this book is the right choice because it will inevitably put you in front of new, high-paying job opportunities, ...

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This book is an introduction to mathematical computing including:

• Jupyter notebooks, markdown and LaTex
• Basic Python programming: datatypes, variables, logic, loops and functions
• Scientific computing with NumPy, SciPy and Matplotlib
• Applications in calculus, linear algebra and differential equations

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This book is an introduction to mathematical computing including:

• Jupyter notebooks, markdown and LaTex
• Basic Python programming: datatypes, variables, logic, loops and functions
• Scientific computing with NumPy, SciPy and Matplotlib
• Applications in calculus, linear algebra and differential equations

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You’re living in an era of large amounts of data, powerful computers, and artificial intelligence. This is just the beginning. Data science and machine learning are driving image recognition, development of autonomous vehicles, decisions in the financial and energy sectors, advances in medicine, the rise of social networks, and more. Linear regression is an important part of this. Linear regression is one of the fundamental statistical and machine learning techniques. Whether you want to do statistics, machine learning, or scientific computing, there’s a good chance that you’ll need it. It’s best to build a solid foundation first and...

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Become competent at implementing Regression Analysis in Python Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

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This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students.

The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The...

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[Natasha Samoylenko] Python for Network Engineers

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the...

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This book provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

in recent years Python has become a popular language for data science, hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

About the Authors
Gareth James is the E. Morgan Stanley Chair in Business Administration and a professor of data sciences...

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Many books and websites have aggressive programming challenges for top coders. However, this book is for the rest of us. We want challenges that improve our coding skills, not leave us confused and discouraged. Other tutorials and books have taught you the basics of Python, but the 42 programming exercises in this book let you practice what you've learned. Selected for their simplicity, these programming problems include gentle explanations of the problem, the prerequisite coding concepts you’ll need to understand the solution, and helpful templates to put together the programs if you have trouble starting from scratch.

This is the...

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A guide to completing Python projects for those ready to take their skills to the next level

Python Projects is the ultimate resource for the Python programmer with basic skills who is ready to move beyond tutorials and start building projects.

The preeminent guide to bridge the gap between learning and doing, this book walks readers through the "where" and "how" of real-world Python programming with practical, actionable instruction. With a focus on real-world functionality, Python Projects details the ways that Python can be used to complete daily tasks and bring efficiency to businesses and individuals alike.

Python Projects is...

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This book demonstrates how to combine different libraries and frameworks to build amazing things. It picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you'll use every day.

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Best-selling author Al Sweigart shows you how to easily build over 80 fun programs with minimal code and maximum creativity.

If you’ve mastered basic Python syntax and you’re ready to start writing programs, you’ll find The Big Book of Small Python Projects both enlightening and fun. This collection of 81 Python projects will have you making digital art, games, animations, counting pro- grams, and more right away. Once you see how the code works, you’ll practice re-creating the programs and experiment by adding your own custom touches.

These simple, text-based programs are 256 lines of code or less. And whether it’s a vintage...