It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.You’ll explore:- Python syntax basics and running Python codeBasic semantics of Python variables, objects, and operators- Built-in simple types and data structures- Control flow statements for executing code blocks conditionally- Methods for creating and using reusable functionsIterators, list comprehensions, and generators- String manipulation and regular expressions- Python’s standard library and third-party modules- Python’s core data science tools- Recommended resources to help you learn more

    Falling apart and bowing down to the miseries of a broken family and a daunting past, at 24, they decided to move on. But life pulled out one trick after another to bring them back to each other and give one last chance to do what they couldn't do in the past twelve years. A choice had to be made and there was no going back. Could they learn from the echoing footsteps of their parents and speak their heart out or the fear of saying the unsaid killed it once again for them? Did their time to fall in love come, or like always, they met, greeted and departed to part forever?

    You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.Get a straightforward synopsis of the social web landscapeUse adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, LinkedIn, and Google+Learn how to employ easy-to-use Python tools to slice and dice the data you collectExplore social connections in microformats with the XHTML Friends NetworkApply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detectionBuild interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    Three modern day Tom Sawyers, with no caving expertise but an abundance of bravado, made Hannibal ground zero for a terrifying calamity that would leave its traumatic mark for half a century. Joel Hoag, his brother Billy, and their friend Craig Dowell vanished after exploring a vast and complex maze cave system that had been exposed by highway construction. Fifty years later, their fate remains the ultimate unsolved mystery.

    

    ever wanted to quit your job...?‘I’d Sooner Starve’ is the amazing true story of one man's quest to escape his monotonous nine-to-five existence and open a charming delicatessen and restaurant in a delightful market town.With honesty, humour and breathtaking naïveté, it records his steep learning curve, radical lifestyle change and the immediate revelation that the customer is not always right!Amidst tales of bulimia, public menstruation, endless abuse and hilarious customer encounters, this eye-opening story unveils what happens after you walk into the boss and say: “I quit!”‘I’d Sooner Starve’ is a shockingly comical tale of culinary highs, customer lows and one woman's unhealthy fixation with thigh-warmed Stilton…‘An absolute hoot!’ ~ Anonymous celebrity chef‘This hits the nail on the head so much I can't believe it! So much so, I can't possibly put my name to it!’~ UK chef‘I am delighted that Mark has written this book. Maybe now people will believe me when I tell them the stories!’ ~ Matt, co-ownerI'd Sooner Starve - the book the celebrity chefs were too afraid to endorse!

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    It was a sea in name only. It was actually a bone dry, ancient dusty basin pockmarked with craters and littered with rocks and boulders. Somewhere in that 500 mile diameter basin, the astronauts would attempt to make Mankind’s first landing on the Moon. Neil Armstrong would pilot the Lunar Module “Eagle” during its twelve minute descent from orbit down to a landing. Col. Edwin “Buzz” Aldrin would assist him. On the way down they would encounter a host of problems, any one of which could have potentially caused them to have to call off the landing, or, even worse, die making the attempt. The problems were all technical-communications problems, computer problems, guidance problems, sensor problems. Armstrong and Aldrin faced the very real risk of dying by the very same technical sword that they had to live by in order to accomplish the enormous task of landing on the Moon for the first time. Yet the human skills Armstrong and Aldrin employed would be more than equal to the task. Armstrong’s formidable skills as an aviator, honed from the time he was a young boy, would serve him well as he piloted Eagle down amidst a continuing series of systems problems that might have fatally distracted a lesser aviator. Armstrong’s brilliant piloting was complemented by Aldrin’s equally remarkable discipline and calmness as he stoically provided a running commentary on altitude and descent rate while handling systems problems that threatened the landing. Finally, after a harrowing twelve and a half minutes, Armstrong gently landed Eagle at “Tranquility Base”, a name he had personally chosen to denote the location of the first Moon landing. In “Landing Eagle-Inside the Cockpit During the First Moon Landing”, author Mike Engle gives a minute by minute account of the events that occurred throughout Eagle’s descent and landing on the Moon. Engle, a retired NASA engineer and Mission Control flight controller, uses NASA audio files of actual voice recordings made inside Eagle’s cockpit during landing to give the reader an “inside the cockpit” perspective on the first Moon landing. Engle’s transcripts of these recordings, along with background material on the history and technical details behind the enormous effort to accomplish the first Moon landing, give a new and fascinating insight into the events that occurred on that remarkable day fifty years ago.

    The story of information begins in a time profoundly unlike our own, when every thought and utterance vanishes as soon as it is born. From the invention of scripts and alphabets to the long-misunderstood talking drums of Africa, Gleick tells the story of information technologies that changed the very nature of human consciousness. He provides portraits of the key figures contributing to the inexorable development of our modern understanding of information: Charles Babbage, the idiosyncratic inventor of the first great mechanical computer; Ada Byron, the brilliant and doomed daughter of the poet, who became the first true programmer; pivotal figures like Samuel Morse and Alan Turing; and Claude Shannon, the creator of information theory itself. And then the information age arrives. Citizens of this world become experts willy-nilly: aficionados of bits and bytes. And we sometimes feel we are drowning, swept by a deluge of signs and signals, news and images, blogs and tweets. The Information is the story of how we got here and where we are heading.

    Fast Tract Digestion Heartburn is the first book to define and address the real cause of acid reflux. Acid reflux occurs as a result of digestive malabsorption of five difficult-to-digest carbohydrates. It leads to the overgrowth of gas-producing bacteria in the small intestine and the gas pressure drives acid reflux. Dropping a Mentos into a bottle of Coke is the perfect illustration of this phenomenon. Unfortunately, PPI drugs do not address the real cause, but also cause serious side effects including vitamin and mineral malabsorption, bone fractures, pneumonia, low blood magnesium levels, bacterial overgrowth, C diff infection and pneumonia as explained in the book.Fast Tract Digestion Heartburn offers science-based food choices and recipes (Fast Tract Diet) to limit the five difficult-to-digest carbohydrates, so that you can feel relief within a few days Also, it enables you to self-manage all of the symptoms of acid reflux going forward. The Fast Tract Diet is based on a scientific formula called Fermentation Potential (FP). FP is the key measure regardless of the carbohydrate count. Limiting foods with high FP will help control the overgrowth of bacteria in the small intestine and, as a result, acid reflux. The Fast Tract Diet was clinically tested on reflux sufferers in the Boston area. The results were overwhelming and proved the effectiveness of the diet as an alternative to proton pump inhibitor drugs.