Python 1000 By Requirement

From creating a simple "Hex Dumper" and / or "Big Banner" tool, to creating and reporting on dictionaries loaded with data, the exercises in this book are designed to provide a gentle, evolutionary, real-world educational experience.

Python 1000  By Requirement

Author: Evan Nagy


ISBN: 9781549794940

Page: 122

View: 289

From understanding modern documentation, to learning how to choose between competing projects, today a successful software professional needs to know allot more than merely how use a programming language. So when a student recently asked for additional practice exercises, we thought that we would define the projects using the type of documentation that professional software developers are most likely to encounter in-the-field.From creating a simple "Hex Dumper" and "Big Banner" tool, to creating and reporting on dictionaries loaded with data, the exercises in this book have been designed so as to provide a gentle, evolutionary, real-world educational experience. Designed for beginners, students enrolled in our other Python training opportunities will also enjoy these activities. Be it on-line or in-person, we should note that our Python training has been taught to thousands of new software developers. This book includes instructions on how every purchaser can receive a free coupon to the Author's on-line video training. For students who have never programmed before - or need a review of the basics, this book also contains instructions on how each and every reader can register to receive unlimited, lifetime access to a free copy of our on-line Video Training, "Python 1000."

Introduction to Python Programming for Business and Social Science Applications

Back to Figure The output is titled , RESTART : D : \ . ... Requirement already satisfied : python - dateutil > = 2.5.0 in c : \ users ...

Introduction to Python Programming for Business and Social Science Applications

Author: Frederick Kaefer

Publisher: SAGE Publications

ISBN: 1544377487

Page: 392

View: 938

Would you like to gather big datasets, analyze them, and visualize the results, all in one program? If this describes you, then Introduction to Python Programming for Business and Social Science Applications is the book for you. Authors Frederick Kaefer and Paul Kaefer walk you through each step of the Python package installation and analysis process, with frequent exercises throughout so you can immediately try out the functions you’ve learned. Written in straightforward language for those with no programming background, this book will teach you how to use Python for your research and data analysis. Instead of teaching you the principles and practices of programming as a whole, this application-oriented text focuses on only what you need to know to research and answer social science questions. The text features two types of examples, one set from the General Social Survey and one set from a large taxi trip dataset from a major metropolitan area, to help readers understand the possibilities of working with Python. Chapters on installing and working within a programming environment, basic skills, and necessary commands will get you up and running quickly, while chapters on programming logic, data input and output, and data frames help you establish the basic framework for conducting analyses. Further chapters on web scraping, statistical analysis, machine learning, and data visualization help you apply your skills to your research. More advanced information on developing graphical user interfaces (GUIs) help you create functional data products using Python to inform general users of data who don’t work within Python. First there was IBM® SPSS®, then there was R, and now there′s Python. Statistical software is getting more aggressive - let authors Frederick Kaefer and Paul Kaefer help you tame it with Introduction to Python Programming for Business and Social Science Applications.

Real World Instrumentation with Python

A well-written requirement should contain all the information necessary for the ... signed data samples at a sustained rate of 1,000 samples per second.

Real World Instrumentation with Python

Author: John M. Hughes

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449396633

Page: 624

View: 459

Learn how to develop your own applications to monitor or control instrumentation hardware. Whether you need to acquire data from a device or automate its functions, this practical book shows you how to use Python's rapid development capabilities to build interfaces that include everything from software to wiring. You get step-by-step instructions, clear examples, and hands-on tips for interfacing a PC to a variety of devices. Use the book's hardware survey to identify the interface type for your particular device, and then follow detailed examples to develop an interface with Python and C. Organized by interface type, data processing activities, and user interface implementations, this book is for anyone who works with instrumentation, robotics, data acquisition, or process control. Understand how to define the scope of an application and determine the algorithms necessary, and why it's important Learn how to use industry-standard interfaces such as RS-232, RS-485, and GPIB Create low-level extension modules in C to interface Python with a variety of hardware and test instruments Explore the console, curses, TkInter, and wxPython for graphical and text-based user interfaces Use open source software tools and libraries to reduce costs and avoid implementing functionality from scratch

Pro Python Best Practices

The source code needs to be less than 1000 lines long to fit in a book (technical ... Imagine we added a nonfunctional requirement that, in addition to the ...

Pro Python Best Practices

Author: Kristian Rother

Publisher: Apress

ISBN: 1484222415

Page: 264

View: 731

Learn software engineering and coding best practices to write Python code right and error free. In this book you’ll see how to properly debug, organize, test, and maintain your code, all of which leads to better, more efficient coding. Software engineering is difficult. Programs of any substantial length are inherently prone to errors of all kinds. The development cycle is full of traps unknown to the apprentice developer. Yet, in Python textbooks little attention is paid to this aspect of getting your code to run. At most, there is a chapter on debugging or unit testing in your average basic Python book. However, the proportion of time spent on getting your code to run is much higher in the real world. Pro Python Best Practices aims to solve this problem. What You'll Learn Learn common debugging techniques that help you find and eliminate errors Gain techniques to detect bugs more easily discover best="" practices="" to="" prevent="" bugscarry="" out="" automated="" testing="" discover="" problems="" fasteruse="" maintain="" a="" project="" over="" long="" timeLearn techniques to keep your project under controlbr/uldivbWho This Book Is For/bbr/divdivbr/divdivExperienced Python coders from web development, big data, and more./divdivbr/divdivdiv/div

Together with Python

Output: === C:/Users/Dell/AppData/Local/Programs/Python/Python38- ... 20, 1000] 3.2.4 Function Arguments: In Python you can call a function by using the ...

Together with Python

Author: Ravish Kumar Mishra

Publisher: Sankalp Publication

ISBN: 819471768X

Page: 394

View: 464

PREFACE This is the First Edition of a Simplified Course in computer science for Class XI and XII in your hands. Since the CBSE syllabus for computer science has many changes, this edition is the outcome for the same. This book is aimed at providing a thorough base and understanding in various latest trends in Information Technology. This book covers Python 3.x, the world class professional programming language. Class, Inheritance, Overloading, Boolean algebra, SQL, Python with SQL and Concept of Network. The first edition of this book lays the foundation for further studies by covering the aspects in elaborative yet simple language. The book has been divided in five Units. Unit I - Beginners of Python (Chapter 1-4) discuss various major and important terms in programming of Python such as, Data types, Function (UDF and Built-in) and statement controls(if, while, for etc.). Unit II – Together with Python (Chapter 5 – 7) introduces different terms of Python like, Array and List, Tuple and it Method, and Dictionary and it Methods. Unit III – OOPs with Python (chapter 8 – 14) covers various terms such as Class, Inheritance, Overloading, Multithreading and Exception Handling in details. It also discussed how OOPs are implemented in Python. Unit IV – Data Structure (Chapter 15- 16) introduces various data structure, their purposes and functions along with their implementation in Python. It provides details information about Stack, Queue, and Boolean algebra. Unit V - Programming with SQL in Python (Chapter 17 – 22) covers various file handling method. Different file operation, Database management system terms, programming with SQL, implement SQL in Python for development of back end program. We have worked our best to keep the presentation of this book short, simple, and catchy. Our goal is that by the end of each chapter, you feel confident about the contents and enjoy yourself doing so. Any suggestion for improvement of this book is welcome.

Docker on Amazon Web Services

... dfe0b6487459 Step 16/22 : RUN addgroup -g 1000 app && adduser -u 1000 -G ... mysql-connector-python==8.0.11 (from -r /build/requirements.txt (line 4)) ...

Docker on Amazon Web Services

Author: Justin Menga

Publisher: Packt Publishing Ltd

ISBN: 1788622723

Page: 822

View: 143

Run Docker on AWS and build real-world, secure, and scalable container platforms on cloud Key Features Configure Docker for the ECS environment Integrate Docker with different AWS tools Implement container networking and deployment at scale Book Description Over the last few years, Docker has been the gold standard for building and distributing container applications. Amazon Web Services (AWS) is a leader in public cloud computing, and was the first to offer a managed container platform in the form of the Elastic Container Service (ECS). Docker on Amazon Web Services starts with the basics of containers, Docker, and AWS, before teaching you how to install Docker on your local machine and establish access to your AWS account. You'll then dig deeper into the ECS, a native container management platform provided by AWS that simplifies management and operation of your Docker clusters and applications for no additional cost. Once you have got to grips with the basics, you'll solve key operational challenges, including secrets management and auto-scaling your infrastructure and applications. You'll explore alternative strategies for deploying and running your Docker applications on AWS, including Fargate and ECS Service Discovery, Elastic Beanstalk, Docker Swarm and Elastic Kubernetes Service (EKS). In addition to this, there will be a strong focus on adopting an Infrastructure as Code (IaC) approach using AWS CloudFormation. By the end of this book, you'll not only understand how to run Docker on AWS, but also be able to build real-world, secure, and scalable container platforms in the cloud. What you will learn Build, deploy, and operate Docker applications using AWS Solve key operational challenges, such as secrets management Exploit the powerful capabilities and tight integration of other AWS services Design and operate Docker applications running on ECS Deploy Docker applications quickly, consistently, and reliably using IaC Manage and operate Docker clusters and applications for no additional cost Who this book is for Docker on Amazon Web Services is for you if you want to build, deploy, and operate applications using the power of containers, Docker, and Amazon Web Services. Basic understanding of containers and Amazon Web Services or any other cloud provider will be helpful, although no previous experience of working with these is required.

Python Made Easy

Python has thousands of inbuilt modules, which we will learn shortly. At the same time, knowing how to create a module as per your requirement comes very ...

Python Made Easy

Author: Nilabh Nishchhal

Publisher: Notion Press

ISBN: 1649837267

Page: 436

View: 701

Python Made Easy: Beginners Guide to Programming and Data Analysis using Python Get comprehensive learning of Python Programming starting from the very basics and going up to utilizing python libraries for data analysis and Visualization. Based on the author’s journey to master Python, this book will help you to quickly start with writing programs and solving your problems using Python. It provides an ideal and elegant way to start learning Python, both for a newcomer to the programming world and a professional developer expert in other languages. This book comes loaded with illustrations and real-life examples. It gives you exercises which challenge you to refresh your conceptual clarity and write better codes. It is super easy to follow and will work as a self-paced tutorial to get you started with the latest and best in Python. All the advanced Python features to date are included. • Get to know the history, present, and future of Data Science • Get introduced to the basics of Computer Programming • Explore the exciting world of Python using Anaconda • Learn how to install and use Python on your computer • Create your Variables, Objects and learn Syntax of operations • Explore Python’s built-in object types like Lists, dictionaries, Tuples, Strings and sets • Learn to make your codes reusable by using functions • Organize your codes, functions and other objects into larger components with Modules • Explore Classes – the Object-Oriented Programming tool for elegant codes • Write complex codes and learn how to handle Errors and Exceptions • Learn about NumPy arrays and operations on them • Explore data analysis using pandas on a real-life data set • Dive into the exciting world of Visualization with 3 chapters on Visualization and Matplotlib • Experience the Power of What you learnt by 3 projects • Learn to make your own application complete with GUI by using API

High Performance Python

The code in Example 2-10 uses the full 1,000 × 1,000 grid, and the statistics took about two hours to collect on Ian's laptop. Note The requirement to ...

High Performance Python

Author: Micha Gorelick

Publisher: "O'Reilly Media, Inc."

ISBN: 1492054976

Page: 468

View: 643

Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker


DataFrame (Y_train)) #Change training and test data as requirement X_test = pandas. ... tolist()),numpy.max(X_test['X'].tolist()),1000) smooth_points ...


Author: Abhishek Vijayvargia

Publisher: BPB Publications

ISBN: 9387284883

Page: 266

View: 561

DescriptionThis book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the real-world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the data.In this book we provide code examples in python. Python is the most suitable and worldwide accepted language for this. First, it is free and open source. It contains very good support from open community. It contains a lot of library, so you don't need to code everything. Also, it is scalable for large amount of data and suitable for big data technologies.This book:Covers all major areas in Machine Learning.Topics are discussed with graphical explanations.Comparison of different Machine Learning methods to solve any problem.Methods to handle real-world noisy data before applying any Machine Learning algorithm.Python code example for each concept discussed.Jupyter notebook scripts are provided with dataset used to test and try the algorithms ContentsIntroduction to Machine Learning Understanding Python Feature Engineering Data VisualisationBasic and Advanced Regression techniquesClassification Un Supervised LearningText AnalysisNeural Network and Deep Learning Recommendation System Time Series Analysis

Mastering Object Oriented Python

We want FixedPoint(123, 100) to be equal to FixedPoint(1230, 1000). ... This is a requirement imposed by the superclass: the Any type hint must be used to ...

Mastering Object Oriented Python

Author: Steven F. Lott

Publisher: Packt Publishing Ltd

ISBN: 1789531403

Page: 770

View: 781

Gain comprehensive insights into programming practices, and code portability and reuse to build flexible and maintainable apps using object-oriented principles Key Features Extend core OOP techniques to increase integration of classes created with Python Explore various Python libraries for handling persistence and object serialization Learn alternative approaches for solving programming problems, with different attributes to address your problem domain Book Description Object-oriented programming (OOP) is a relatively complex discipline to master, and it can be difficult to see how general principles apply to each language's unique features. With the help of the latest edition of Mastering Objected-Oriented Python, you'll be shown how to effectively implement OOP in Python, and even explore Python 3.x. Complete with practical examples, the book guides you through the advanced concepts of OOP in Python, and demonstrates how you can apply them to solve complex problems in OOP. You will learn how to create high-quality Python programs by exploring design alternatives and determining which design offers the best performance. Next, you'll work through special methods for handling simple object conversions and also learn about hashing and comparison of objects. As you cover later chapters, you'll discover how essential it is to locate the best algorithms and optimal data structures for developing robust solutions to programming problems with minimal computer processing. Finally, the book will assist you in leveraging various Python features by implementing object-oriented designs in your programs. By the end of this book, you will have learned a number of alternate approaches with different attributes to confidently solve programming problems in Python. What you will learn Explore a variety of different design patterns for the __init__() method Learn to use Flask to build a RESTful web service Discover SOLID design patterns and principles Use the features of Python 3's abstract base Create classes for your own applications Design testable code using pytest and fixtures Understand how to design context managers that leverage the 'with' statement Create a new type of collection using standard library and design techniques Develop new number types above and beyond the built-in classes of numbers Who this book is for This book is for developers who want to use Python to create efficient programs. A good understanding of Python programming is required to make the most out of this book. Knowledge of concepts related to object-oriented design patterns will also be useful.

Practical Python AI Projects

To see how to model the octane requirement, let's imagine a simple case: ... Since we have a total of 1,000 barrels of refined, 800 ́ 98 1000 + 200 ́ 90 = .

Practical Python AI Projects

Author: Serge Kruk

Publisher: Apress

ISBN: 1484234235

Page: 279

View: 581

Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations. Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study. What You Will Learn Build basic Python-based artificial intelligence (AI) applications Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suite Create several types of projects using Python and Google OR-Tools Who This Book Is For Developers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.

Explainable AI with Python

... to humanlike intelligence, then GPT −3 is still about 1000x too small. ... or better “Should we add the strong requirement of being able to produce ...

Explainable AI with Python

Author: Leonida Gianfagna

Publisher: Springer Nature

ISBN: 303068640X


View: 813

Network World

As for Python's requirement for structuring by using indents, it does take a little getting used to but the upside is that once you're used to it, ...

Network World




Page: 88

View: 670

For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce.

Perl to Python Migration

Unlike the Perl constructor , the Python __init __ ( ) function is required in ... 2000 ) To deposit money in the account : bank.deposit ( 1000 ) Finally ...

Perl to Python Migration

Author: Martin C. Brown

Publisher: Pearson Education

ISBN: 9780201734881

Page: 299

View: 846

Perl programmers: Leverage your Perl skills with Python. Key issues developers face in migrating to Python--and practical solutions. Includes an in-depth introduction to Python's object-oriented facilities. Contains extensive sample code for data manipulation, networking, and Web programming.

Python Programming for Biology

To get round this requirement we can simply set the operating system's environment variables within Python, hence the os.environ line which links the ...

Python Programming for Biology

Author: Tim J. Stevens

Publisher: Cambridge University Press

ISBN: 0521895839

Page: 711

View: 516

This book introduces Python as a powerful tool for the investigation of problems in computational biology, for novices and experienced programmers alike.

Mastering Python

... block us from doing array_pointer[1000] and reading other (random) memory. ... because of the fixed sizes and the requirement of declaring the type ...

Mastering Python

Author: Rick van Hattem

Publisher: Packt Publishing Ltd

ISBN: 1785289136

Page: 486

View: 359

Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers About This Book Become familiar with the most important and advanced parts of the Python code style Learn the trickier aspects of Python and put it in a structured context for deeper understanding of the language Offers an expert's-eye overview of how these advanced tasks fit together in Python as a whole along with practical examples Who This Book Is For Almost anyone can learn to write working script and create high quality code but they might lack a structured understanding of what it means to be 'Pythonic'. If you are a Python programmer who wants to code efficiently by getting the syntax and usage of a few intricate Python techniques exactly right, this book is for you. What You Will Learn Create a virtualenv and start a new project Understand how and when to use the functional programming paradigm Get familiar with the different ways the decorators can be written in Understand the power of generators and coroutines without digressing into lambda calculus Create metaclasses and how it makes working with Python far easier Generate HTML documentation out of documents and code using Sphinx Learn how to track and optimize application performance, both memory and cpu Use the multiprocessing library, not just locally but also across multiple machines Get a basic understanding of packaging and creating your own libraries/applications In Detail Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges. Style and Approach This book is a comprehensive guide that covers advanced features of the Python language, and communicate them with an authoritative understanding of the underlying rationale for how, when, and why to use them.

Python Object Oriented Programming

Build robust and maintainable object-oriented Python applications and libraries ... This is not a technical requirement, but it is the best way to keep your ...

Python Object Oriented Programming

Author: Steven F. Lott

Publisher: Packt Publishing Ltd

ISBN: 1801075239

Page: 714

View: 644

Being familiar with object-oriented design is an essential part of programming in Python. This new edition includes all the topics that made Python Object-Oriented Programming an instant Packt classic. Moreover, it’s packed with updated content to reflect more recent changes in the core Python libraries and cover modern third-party packages.

Python and HDF5

... (1000,), dtype='f4') idx = rank*250 # This will be our starting index. ... With HDF5, the main requirement is this: modifications tofile metadata must ...

Python and HDF5

Author: Andrew Collette

Publisher: "O'Reilly Media, Inc."

ISBN: 149194501X

Page: 152

View: 727

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, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes. Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. Examples are applicable for users of both Python 2 and 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 and create your first HDF5 file Work with datasets by learning the HDF5 Dataset object Understand advanced features like dataset chunking and 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, and dimension scales Discover how Python mechanisms for writing parallel code interact with HDF5

Programming for Computations Python

A Gentle Introduction to Numerical Simulations with Python 3.6 Svein Linge, ... b = 1000 solution, no iterations = bisection (f, a, b, eps-1.0e-6) print ...

Programming for Computations   Python

Author: Svein Linge

Publisher: Springer Nature

ISBN: 3030168778

Page: 332

View: 283

This book is published open access under a CC BY 4.0 license. This book presents computer programming as a key method for solving mathematical problems. This second edition of the well-received book has been extensively revised: All code is now written in Python version 3.6 (no longer version 2.7). In addition, the two first chapters of the previous edition have been extended and split up into five new chapters, thus expanding the introduction to programming from 50 to 150 pages. Throughout the book, the explanations provided are now more detailed, previous examples have been modified, and new sections, examples and exercises have been added. Also, a number of small errors have been corrected. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style employed 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 students to write simple programs for solving common mathematical problems with numerical methods in the context of engineering and science courses. The emphasis is on generic algorithms, clean program design, the use of functions, and automatic tests for verification.

Python Programming in Context

Although it is not a requirement that the first formal parameter be called self, ... myplanet = Planet("X25", 45, 198, 1000) >>> myplanet <__main__.

Python Programming in Context

Author: Bradley N. Miller

Publisher: Jones & Bartlett Publishers

ISBN: 1449691978

Page: 492

View: 782

The user-friendly, object-oriented programming language Python is quickly becoming the most popular introductory programming language for both students and instructors. This updated Second Edition of Python Programming in Context provides a comprehensive, accessible introduction to Python fundamentals. An ideal first language for learners entering the rapidly expanding field of computer science, Python gives students a solid platform of key problem-solving skills that translate easily across programming languages. Building on essential concepts of computer science, and offering a plenitude of real-world examples, Python Programming in Context, Second Edition offers a thorough overview of multiple applied areas, including image processing, cryptography, astronomy, the Internet, and bioinformatics. The text’s emphasis on problem-solving, extrapolation, and development of independent exploration and solution-building provides students with a unique and innovative approach to learning programming. Python Programming in Context, Second Edition is the ideal introductory text for those delving into computer programming. Key Features - Utilizes Python 3 - Provides a clear, accessible, and skill-focused approach to programming with Python - Contains problem sets based on real-world examples and problem-solving rather than language features - Offers a variety of exercises that develop independent skill-building and exploration - Every new copy of the text is packaged with full student access to Turing's Craft Custom CodeLab. Customized to match the organization of the text, CodeLab offers students hands-on Python programming experience with immediate feedback. - Accompanied by a full suite of instructor support material, including solutions to the exercises in the text, downloadable source code, PowerPoint Lecture Outlines, and a complete Test Bank.