Gain hands-on experience with HDF5 for storing scientific data in Python This practical guide quickly gets you up to speed on the details best practices & pitfalls of using HDF5 to archive & share numerical datasets ranging in size from gigabytes to terabytes Through real-world examples & practical exercises you'll explore topics such as scientific datasets hierarchically organized groups user-defined metadata & interoperable files Examples are applicable for users of both Python 2 & Python 3 If you're familiar with the basics of Python data analysis this is an ideal introduction to HDF5 Get set up with HDF5 tools & create your first HDF5 file Work with datasets by learning the HDF5 Dataset object Understand advanced features like dataset chunking & compression Learn how to work with HDF5's hierarchical structure using groups Create self-describing files by adding metadata with HDF5 attributes Take advantage of HDF5's type system to create interoperable files Express relationships among data with references named types & dimension scales Discover how Python mechanisms for writing parallel code interact with HDF5 ...

Buggy " Python 2 voies 24 GHz so you can play as a group without interference Sturdy construction & ergonomically designed in the hands of children Single motor axle Snap on Front 26 mm height from the ground Direction digitale proportionnelle Powerful engine Coal (Brushed) More than 30 minutes Rolls up to 20 kmh Range approx 50 m Radio requires one 9 V monobloc battery not included The car is powered by standard batteries (6 AA LR06 15 V batteries (15 V NOT included) or by battery life " Available as an option Controlled Buggy remote control to pile up to 20 kmh at the GPSAll operation in 24 GHz radio so you can play as a group without interference for indoor & outdoor Can be pushed into the battery with the battery pack charger 46730 Revell Control Xtreme Direction & acceleration in proportion electronics protected from dust & splashes Batteries not included 4 LR6 1 9 V monobloc multilingual instruction manual (may not be in English) a development is Revell ...

For many researchers Python is a first-class tool mainly because of its libraries for storing manipulating & gaining insight from data Several resources exist for individual pieces of this data science stack but only with the Python Data Science Handbook do you get them all-IPython Num Py Pandas Matplotlib Scikit-Learn & other related tools Working scientists & data crunchers familiar with reading & writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues manipulating transforming & cleaning data; visualizing different types of data; & using data to build statistical or machine learning models Quite simply this is the must-have reference for scientific computing in Python With this handbook you'll learn how to use IPython & Jupyter provide computational environments for data scientists using Python Num Py

Includes:: the ndarray for efficient storage & manipulation of dense data arrays in Python Pandas features the Data Frame for efficient storage & manipulation of labeledcolumnar data in Python Matplotlib

Includes:: capabilities for a flexible range of data visualizations in Python Scikit-Learn for efficient & clean Python implementations of the most important & established machine learning algorithms



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Python Essential Reference is the definitive reference guide to the Python programming language
- the one authoritative handbook
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Get complete instructions for manipulating processing cleaning & crunching datasets in Python Updated for Python 36 the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively You'll learn the latest versions of pandas Num Py IPython & Jupyter in the process Written by Wes Mc Kinney the creator of the Python pandas project this book is a practical modern introduction to data science tools in Python It's ideal for analysts new to Python & for Python programmers new to data science & scientific computing Data files & related material are available on Git Hub Use the IPython shell & Jupyter notebook for exploratory computing Learn basic & advanced features in Num Py (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load clean transform merge & reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice dice & summarize datasets Analyze & manipulate regular & irregular time series data Learn how to solve real-world data analysis problems with thorough detailed examples ...

The financial industry has adopted Python at a tremendous rate recently with some of the largest investment banks & hedge funds using it to build core trading & risk management systems This hands-on guide helps both developers & quantitative analysts get started with Python & guides you through the most important aspects of using Python for quantitative finance Using practical examples through the book author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives & risk analytics based on a large realistic case study Much of the book uses interactive IPython Notebooks with topics that include Fundamentals Python data structures Num Py array handling time series analysis with pandas visualization with matplotlib high performance IO operations with Py Tables datetime information handling & selected best practices Financial topics mathematical techniques with Num Py Sci Py & Sym Py such as regression & optimization; stochastics for Monte Carlo simulation value-at-Risk & Credit-value-at-Risk calculations; statistics for normality tests mean-variance portfolio optimization principal component analysis (PCA) & Bayesian regression Special topics performance Python for financial algorithms such as vectorization & parallelization integrating Python with Excel & building financial applications based on Web technologies ...

Useful in many roles from design & prototyping to testing deployment & maintenance Python is consistently ranked among today's most popular programming languages The third edition of this practical book provides a quick reference to the language-including Python 35 27 & highlights of 36-commonly used areas of its vast standard library & some of the most useful third-party modules & packages Ideal for programmers with some Python experience & those coming to Python from other programming languages this book covers a wide range of application areas including web & network programming XML handling database interactions & high-speed numeric computing Discover how Python provides a unique mix of elegance simplicity practicality & sheer power This edition covers Python syntax Object-Oriented Python standard library modules & third-party Python packages Python's support for file & text operations persistence & databases concurrent execution & numeric computations Networking basics event-driven programming & client-side network protocol modules Python extension modules & tools for packaging & distributing extensions modules & applications ...

Updated for both Python 34 & 27 this convenient pocket guide is the perfect on-the-job quick reference You'll find concise need-to-know information on Python types & statements special method names built-in functions & exceptions commonly used standard library modules & other prominent Python tools The handy index lets you pinpoint exactly what you need Written by Mark Lutz
- widely recognized as the world's leading Python trainer
- Python Pocket Reference is an ideal companion to O' Reilly's classic Python tutorials Learning Python & Programming Python also written by Mark This fifth edition covers Built-in object types including numbers lists dictionaries & more Statements & syntax for creating & processing objects Functions & modules for structuring & reusing code Python's object-oriented programming tools Built-in functions exceptions & attributes Special operator overloading methods Widely used standard library modules & extensions Comm&-line options & development tools Python idioms & hints The Python SQL Database API

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If you are new to programming with Python & are looking for a solid introduction this is the book for you Developed by computer science instructors books in the for the absolute beginner series teach the principles of programming through simple game creation You will acquire the skills that you need for practical Python programming applications & will learn how these skills can be put to use in real-world scenarios Throughout the chapters you will find code samples that illustrate concepts presented At the end of each chapter you will find a complete game that demonstrates the key ideas in the chapter a summary of the chapter & a set of challenges that tests your newfound knowledge By the time you finish this book you'll be well versed in Python & be able to apply the basic programming principles you've learned to the next programming language you tackle ...

Do less work when testing your Python code but be just as expressive just as elegant & just as readable The pytest testing framework helps you write tests quickly & keep them readable & maintainable
- with no boilerplate code Using a robust yet simple fixture model it's just as easy to write small tests with pytest as it is to scale up to complex functional testing for applications packages & libraries This book shows you how For Python-based projects pytest is the undeniable choice to test your code if you're looking for a full-featured API-independent flexible & extensible testing framework With a full-bodied fixture model that is unmatched in any other tool the pytest framework gives you powerful features such as assert rewriting & plug-in capability
- with no boilerplate code With simple step-by-step instructions & sample code this book gets you up to speed quickly on this easy-to-learn & robust tool Write short maintainable tests that elegantly express what you're testing Add powerful testing features & still speed up test times by distributing tests across multiple processors & running tests in parallel Use the built-in assert statements to reduce false test failures by separating setup & test failures Test error conditions & corner cases with expected exception testing & use one test to run many test cases with parameterized testing Extend pytest with plugins connect it to continuous integration systems & use it in tandem with tox mock coverage unittest & doctest Write simple maintainable tests that elegantly express what you're testing & why What You Need The examples in this book are written using Python 36 & pytest 30 However pytest 30 supports Python 26 27 & Python 33-36

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