A Hands On Introduction To Using Python In The Atmospheric And Oceanic Sciences
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A Hands On Introduction To Using Python In The Atmospheric And Oceanic Sciences
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Author : Johnny Wei-Bing Lin
language : en
Publisher: Lulu.com
Release Date : 2012
A Hands On Introduction To Using Python In The Atmospheric And Oceanic Sciences written by Johnny Wei-Bing Lin and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
This book is a mini-course for researchers in the atmospheric and oceanic sciences. "We assume readers will already know the basics of programming... in some other language." - Back cover.
An Introduction To Python Programming For Scientists And Engineers
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Author : Johnny Wei-Bing Lin
language : en
Publisher: Cambridge University Press
Release Date : 2022-07-07
An Introduction To Python Programming For Scientists And Engineers written by Johnny Wei-Bing Lin and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-07 with Computers categories.
Textbook that uses examples and Jupyter notebooks from across the sciences and engineering to teach Python programming.
Earth Observation Using Python
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Author : Rebekah B. Esmaili
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-24
Earth Observation Using Python written by Rebekah B. Esmaili and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-24 with Science categories.
Learn basic Python programming to create functional and effective visualizations from earth observation satellite data sets Thousands of satellite datasets are freely available online, but scientists need the right tools to efficiently analyze data and share results. Python has easy-to-learn syntax and thousands of libraries to perform common Earth science programming tasks. Earth Observation Using Python: A Practical Programming Guide presents an example-driven collection of basic methods, applications, and visualizations to process satellite data sets for Earth science research. Gain Python fluency using real data and case studies Read and write common scientific data formats, like netCDF, HDF, and GRIB2 Create 3-dimensional maps of dust, fire, vegetation indices and more Learn to adjust satellite imagery resolution, apply quality control, and handle big files Develop useful workflows and learn to share code using version control Acquire skills using online interactive code available for all examples in the book The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. Find out more about this book from this Q&A with the Author
Statistical Methods For Climate Scientists
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Author : Timothy DelSole
language : en
Publisher: Cambridge University Press
Release Date : 2022-02-24
Statistical Methods For Climate Scientists written by Timothy DelSole and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-24 with Mathematics categories.
An accessible introduction to statistical methods for students in the climate sciences.
Pandas Cookbook
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Author : Theodore Petrou
language : en
Publisher:
Release Date : 2017-10-23
Pandas Cookbook written by Theodore Petrou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-23 with Computers categories.
Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysisAbout This Book* Use the power of pandas to solve most complex scientific computing problems with ease* Leverage fast, robust data structures in pandas to gain useful insights from your data* Practical, easy to implement recipes for quick solutions to common problems in data using pandasWho This Book Is ForThis book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory.What You Will Learn* Master the fundamentals of pandas to quickly begin exploring any dataset* Isolate any subset of data by properly selecting and querying the data* Split data into independent groups before applying aggregations and transformations to each group* Restructure data into tidy form to make data analysis and visualization easier* Prepare real-world messy datasets for machine learning* Combine and merge data from different sources through pandas SQL-like operations* Utilize pandas unparalleled time series functionality* Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and SeabornIn DetailThis book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way.The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.Many advanced recipes combine several different features across the pandas library to generate results.Style and approachThe author relies on his vast experience teaching pandas in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data.
Cleaning Data For Effective Data Science
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Author : David Mertz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-31
Cleaning Data For Effective Data Science written by David Mertz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Mathematics categories.
Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.
Global Warming Science
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Author : Eli Tziperman
language : en
Publisher: Princeton University Press
Release Date : 2022-03-15
Global Warming Science written by Eli Tziperman and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-15 with Mathematics categories.
A quantitative, broad, hands-on introduction to the cutting-edge science of global warming This textbook introduces undergraduates to the concepts and methods of global warming science, covering topics that they encounter in the news, ranging from the greenhouse effect and warming to ocean acidification, hurricanes, extreme precipitation, droughts, heat waves, forest fires, the cryosphere, and more. This book explains each of the issues based on basic statistical analysis, simple ordinary differential equations, or elementary chemical reactions. Each chapter explains the mechanisms behind an observed or anticipated change in the climate system and demonstrates the tools used to understand and predict them. Proven in the classroom, Global Warming Science also includes “workshops” with every chapter, each based on a Jupyter Python notebook and an accompanying small data set, with supplementary online materials and slides for instructors. The workshop can be used as an interactive learning element in class and as a homework assignment. Provides a clear, broad, quantitative yet accessible approach to the science of global warming Engages students in the analysis of climate data and models, examining predictions, and dealing with uncertainty Features workshops with each chapter that enhance learning through hands-on engagement Comes with supplementary online slides, code, and data files Requires only elementary undergraduate-level calculus and basic statistics; no prior coursework in science is assumed Solutions manual available (only to instructors)
Problemas De Meteorolog A Y Climatolog A
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Author : ZÚÑIGA LÓPEZ Ignacio
language : es
Publisher: Editorial UNED
Release Date : 2017-02-24
Problemas De Meteorolog A Y Climatolog A written by ZÚÑIGA LÓPEZ Ignacio and has been published by Editorial UNED this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-24 with Science categories.
Este libro de problemas está dirigido a los estudiantes de Meteorología y Climatología del grado en Ciencias Ambientales de la UNED, y puede ser de utilidad para estudiantes de las Facultades de Ciencias o de otros programas de enseñanza de carácter científico-técnico en los que se impartan asignaturas similares. Los problemas se encuentran clasificados en 9 capítulos que incluyen: la composición de la atmósfera, la temperatura de la Tierra, la termodinámica de la atmósfera, la estabilidad atmosférica, dinámica y circulación atmosféricas, oceanografía y clasificación climática. El propósito general en la elección y resolución de los problemas ha sido reducir en lo posible los desarrollos matemáticos y hacer énfasis tanto en los mecanismos físicos que intervienen en los procesos como en la interpretación de los resultados que se obtienen. En cada uno de los capítulos se ofrece una breve introducción, los problemas resueltos correspondientes y una serie de problemas propuestos sin resolver junto con el resultado. Al final del libro se incluyen un glosario, esquemas de cada capítulo con fórmulas y bibliografía complementaria.
Python For Programmers
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Author : Paul Deitel
language : en
Publisher: Prentice Hall
Release Date : 2019-03-15
Python For Programmers written by Paul Deitel and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Computers categories.
The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Accompanying code examples are available here: http://ptgmedia.pearsoncmg.com/imprint_downloads/informit/bookreg/9780135224335/9780135224335_examples.zip. Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.
Artificial Intelligence Methods In The Environmental Sciences
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Author : Sue Ellen Haupt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-11-28
Artificial Intelligence Methods In The Environmental Sciences written by Sue Ellen Haupt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-28 with Science categories.
How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.