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 ...

Python is one of the most powerful easy-to-read programming languages around but it does have its limitations This general purpose high-level language that can be extended & embedded is a smart option for many programming problems but a poor solution to others Python For Dummies is the quick-&-easy guide to getting the most out of this robust program This hands-on book will show you everything you need to know about building programs debugging code & simplifying development as well as defining what actions it can perform You ll wrap yourself around all of its advanced features & become an expert Python user in no time This guide gives you the tools you need to Master basic elements & syntax Document design & debug programs Work with strings like a pro Direct a program with control structures Integrate integers complex numbers & modules Build lists stacks & queues Create an organized dictionary Handle functions data & namespace Construct applications with modules & packages Call create extend & override classes Access the Internet to enhance your library Understand the new features of Python 25 Packed with critical idioms & great resources to maximize your productivity Python For Dummies is the ultimate one-stop information guide In a matter of minutes you ll be familiar with Python s building blocks strings dictionaries & sets; & be on your way to writing the program that you ve dreamed about! ...

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 ...

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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Taking the same approach as he did in his best-selling C Without Fear Brian Overland offers the perfect guide to Python for anyone moving from another language or seeking to go beyond basic Python skills they've already mastered Overland's Python Without Fear reveals the "whys" "tricks" & especially the "gotchas" behind each essential Python language feature You'll learn how to use Python the right way avoiding mistakes that trip up programmers coming from other languages -- & avoiding hours of unnecessary debugging too Best of all you'll enjoy learning Python That's because Overland teaches Python by showing how to leverage its power to solve fascinating challenging puzzles & games Along the way you'll learn how to use mutable & immutable objects; work with strings functions local & global variables lists data inheritance properties files exceptions enumerators generators iterators & even multi-file apps Throughout you'll never get bogged down in unnecessarily complex examples or pointless discussions Overland keeps you 100 focused on learning what works -- & what matters ...

Delays in the production of the first German main battle tanks led to the use of the armoured fighting vehicle II After successful deployment in Poland & France at the beginning of the Second World War it was also used in Russia & North Africa although in the meantime it had become inferior to the enemy tanks The F version developed in 1941 had reinforced front & side armour & with a weight of 95 tonnes reached a speed of 40 kmh Model kit contain 61 pieces Model kits are designed for children aged 10 years & upwards After built it measures 63 cm in length Reaches a maximum speed of over 40 kmh ...