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.
Author: Evan Nagy
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."
Back to Figure The output is titled , RESTART : D : \ survey_1000.py . ... Requirement already satisfied : python - dateutil > = 2.5.0 in c : \ users ...
Author: Frederick Kaefer
Publisher: SAGE Publications
Category: Business & Economics
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.
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.
Author: John M. Hughes
Publisher: "O'Reilly Media, Inc."
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
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 ...
Author: Kristian Rother
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
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 ...
Author: Ravish Kumar Mishra
Publisher: Sankalp Publication
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.
Formally, the only requirement is that the price vectors of the two financial assets in ... To this end, 1,000 random portfolio compositions are simulated.
Author: Yves Hilpisch
Publisher: "O'Reilly Media, Inc."
Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance. Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other. Draw upon mathematics to learn the foundations of financial theory and Python programming Learn about financial theory, financial data modeling, and the use of Python for computational finance Leverage simple economic models to better understand basic notions of finance and Python programming concepts Use both static and dynamic financial modeling to address fundamental problems in finance, such as pricing, decision-making, equilibrium, and asset allocation Learn the basics of Python packages useful for financial modeling, such as NumPy, pandas, Matplotlib, and SymPy
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 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 ...
Author: Nilabh Nishchhal
Publisher: Notion Press
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
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 ...
Author: Micha Gorelick
Publisher: "O'Reilly Media, Inc."
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
Author: Abhishek VijayvargiaPublish On: 2018-06-02
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
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
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 ...
Author: Steven F. Lott
Publisher: Packt Publishing Ltd
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.
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 = .
Author: Serge Kruk
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.
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, ...
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.
Unlike the Perl constructor , the Python __init __ ( ) function is required in ... 2000 ) To deposit money in the account : bank.deposit ( 1000 ) Finally ...
Author: Martin C. Brown
Publisher: Pearson Education
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.
... block us from doing array_pointer and reading other (random) memory. ... because of the fixed sizes and the requirement of declaring the type ...
Author: Rick van Hattem
Publisher: Packt Publishing Ltd
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.
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 ...
Author: Steven F. Lott
Publisher: Packt Publishing Ltd
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.
... (1000,), dtype='f4') idx = rank*250 # This will be our starting index. ... With HDF5, the main requirement is this: modifications tofile metadata must ...
Author: Andrew Collette
Publisher: "O'Reilly Media, Inc."
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
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 ...
Author: Svein Linge
Publisher: Springer Nature
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.