Search results for "python in depth"
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Text Analysis in Python for Social Scientists
Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.
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Python Data Analysis
Understand data analysis pipelines using Python Data Analysis, machine learning, pandas, scikit-learn, and data visualization techniques. Build scalable workflows for time series, NLP, image analytics, and big data processing. Key FeaturesPrepare, clean, and transform data with Python, pandas, and exploratory data analysis techniquesApply machine learning with Python using regression, classification, clustering, PCA, and Bayesian methodsScale analytics workflows using Dask, Ray, Modin, and PySparkBook DescriptionModern data analysis goes beyond cleaning and visualizing data. Today's practitioners need to build scalable data pipelines, apply machine learning, work with text and image data, and understand emerging AI techniques such as Generative AI and Large Language Models (LLMs). This guide shows you how to tackle these challenges using Python's modern data ecosystem.Unlike books focused on a single library or technique, this book provides an end-to-end approach to Python data analysis. You'll learn how to move from data preparation and exploratory analysis to machine learning, NLP, image analytics, scalable processing, and AI-powered workflows.Starting with statistical foundations, you'll learn how to clean, transform, wrangle, and visualize data. You'll then explore time series analysis, signal processing, forecasting, and predictive analytics before applying machine learning techniques such as regression, classification, clustering, PCA, probabilistic methods, and Bayesian approaches.The book also covers graph analytics, sentiment analysis, NLP, image analytics, Generative AI, and LLMs. Finally, you'll learn to scale analytics workflows using Dask, Modin, Ray, and PySpark.By the end of the book, you'll be able to build end-to-end data analysis pipelines and apply modern data science and AI techniques to solve real-world challenges.What you will learnPrepare, clean, and transform data for exploratory data analysis and data wranglingAnalyze and visualize data using Python and pandasPerform time series analysis, forecasting, and signal processingApply machine learning with Python using scikit-learn techniquesUse regression, classification, clustering, PCA, and Bayesian methodsPerform sentiment analysis, NLP, graph analytics, and image analyticsAccelerate workflows using Dask, Modin, and RayBuild scalable big data analytics pipelines with PySparkWho this book is forThis book is for data analysts, data scientists, business analysts, statisticians, students, and academic professionals who want to strengthen their Python Data Analysis skills. It is ideal for readers looking to apply data science with Python to real-world problems involving data preparation, visualization, machine learning, NLP, image analytics, and big data processing. A basic understanding of mathematics and working knowledge of Python will help you get the most from this book.
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Programming with Python for Social Scientists
As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:the fundamentals of why and how to do your own programming in social scientific research,questions of ethics and research design,a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
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Learning Python Application Development
Take Python beyond scripting to build robust, reusable, and efficient applicationsAbout This Book• Get to grips with Python techniques that address commonly encountered problems in general application development.• Develop, package, and deploy efficient applications in a fun way.• All-practical coverage of the major areas of application development, including best practices, exception handling, testing, refactoring, design patterns, performance, and GUI application development.Who This Book Is ForDo you know the basics of Python and object oriented programming? Do you want to go an extra mile and learn techniques to make your Python application robust, extensible, and efficient? Then this book is for you.What You Will Learn• Build a robust application by handling exceptions.• Modularize, package, and release the source distribution.• Document the code and implement coding standards.• Create automated tests to catch bugs in the early development stage.• Identify and re-factor badly written code to improve application life.• Detect recurring problems in the code and apply design patterns.• Improve code efficiency by identifying performance bottlenecks and fixing them.• Develop simple GUI applications using Python.In DetailPython is one of the most widely used dynamic programming languages, supported by a rich set of libraries and frameworks that enable rapid development. But fast paced development often comes with its own baggage that could bring down the quality, performance, and extensibility of an application. This book will show you ways to handle such problems and write better Python applications.From the basics of simple command-line applications, develop your skills all the way to designing efficient and advanced Python apps. Guided by a light-hearted fantasy learning theme, overcome the real-world problems of complex Python development with practical solutions. Beginning with a focus on robustness, packaging, and releasing application code, you'll move on to focus on improving application lifetime by making code extensible, reusable, and readable. Get to grips with Python refactoring, design patterns and best practices. Techniques to identify the bottlenecks and improve performance are covered in a series of chapters devoted to performance, before closing with a look at developing Python GUIs.Style and approachThe book uses a fantasy game theme as a medium to explain various topics. Specific aspects of application development are explained in different chapters. In each chapter the reader is presented with an interesting problem which is then tackled using hands-on examples with easy-to-follow instructions.
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Python Scripting for ArcGIS Pro
Unlock the power of Python in ArcGIS® Pro with this definitive, easy-to-follow guide designed for users with limited programming or scripting experience.Get started learning to write Python scripts to automate tasks in ArcGIS Pro with Python Scripting for ArcGIS Pro. This book begins with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. You’ll learn how to use geoprocessing tools; describe, create, and update data; and execute specialized tasks. With step-by-step instructions, practical examples, and insightful guidance, you’ll be able to write scripts that will automate and improve your ArcGIS Pro workflows.This third edition has been revised for ArcGIS Pro 3.2 and Python 3.9.18 and includes updated images; a fully updated chapter 2; and expanded chapters 4, 8, 9, and 10.The key topics you will learn include:Python fundamentalsSetting up a Python editorAutomating geoprocessing tasks using ArcPy™Exploring and manipulating spatial and tabular dataWorking with geometries using cursorsWorking with rasters and map algebraMap scriptingDebugging and error handlingHelpful points to remember, key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and tutorials are available online.Whether you’re new to Python or already have some experience, Python Scripting for ArcGIS Pro is the go-to resource for learning the versatility of Python coding to solve problems and enhance productivity and efficiency in ArcGIS Pro.
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Hyperparameter Tuning with Python
Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model’s finest detailsKey FeaturesGain a deep understanding of how hyperparameter tuning worksExplore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methodsLearn which method should be used to solve a specific situation or problemBook DescriptionHyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements. You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter. By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.What you will learnDiscover hyperparameter space and types of hyperparameter distributionsExplore manual, grid, and random search, and the pros and cons of eachUnderstand powerful underdog methods along with best practicesExplore the hyperparameters of popular algorithmsDiscover how to tune hyperparameters in different frameworks and librariesDeep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAPGet to grips with best practices that you can apply to your machine learning models right awayWho this book is forThis book is for data scientists and ML engineers who are working with Python and want to further boost their ML model’s performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required.
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Practical Python Data Visualization
Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations.You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets.The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. With Practical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface.What You'll LearnReview practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with PandasWho This Book Is ForData Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.
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Python Workout
Python Workout presents 50 exercises designed to deepen the reader’s skill with Python. Readers will not only tackle exercises using built-in data structures, but also more advanced techniques, such as functional programming, object-oriented programming, iterators, and generators. With each engaging challenge, readers will practice a new skill and learn how to apply it to everyday coding tasks. Key Features 50 hands-on exercises and solutions Basic Python sequence types Python dictionaries and sets Functional programming in Python Creating your own classes Working with Python objects Generator functions Intended for readers with basic Python skills. About the technology Python is a versatile, elegant, general purpose programming language. Essential for data analysis, web development, artificial intelligence, games, desktop apps, and more, Python skills are a hot commodity. Reuven M. Lerner, an independent consultant for more than two decades, teaches Python, data science, and Git to companies around the world. His Better developers newsletter and blog are read by thousands of Python developers each week. Reuven has written a monthly column, “At the Forge,” for Linux Journal since 1996 and is a panellist on the weekly Freelancers Show podcast. Reuven lives with his wife and three children in Modi’in, Israel, and can be reached at https://lerner.co.il/ or on Twitter at @reuvenmlerner.
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Python Unlocked
Become more fluent in Python—learn strategies and techniques for smart and high-performance Python programmingAbout This Book• Write smarter, bug-free, high performance code with minimal effort• Uncover the best tools and options available to Python developers today• Deploy decorators, design patters, and various optimization techniques to use Python 3.5 effectivelyWho This Book Is ForIf you are a Python developer and you think that you don't know everything about the language yet, then this is the book for you. We will unlock the mysteries and re-introduce you to the hidden features of Python to write efficient programs, making optimal use of the language.What You Will Learn• Manipulate object creation processes for instances, classes, and functions• Use the best possible language constructs to write data structures with super speed and maintainability• Make efficient use of design patterns to decrease development time and make your code more maintainable• Write better test cases with an improved understanding of the testing framework of Python and unittests, and discover how to develop new functionalities in it• Write fully-optimized code with the Python language by profiling, compiling C modules, and more• Unlock asynchronous programming to build efficient and scalable applicationsIn DetailPython is a versatile programming language that can be used for a wide range of technical tasks—computation, statistics, data analysis, game development, and more. Though Python is easy to learn, it's range of features means there are many aspects of it that even experienced Python developers don't know about. Even if you're confident with the basics, its logic and syntax, by digging deeper you can work much more effectively with Python – and get more from the language.Python Unlocked walks you through the most effective techniques and best practices for high performance Python programming - showing you how to make the most of the Python language. You'll get to know objects and functions inside and out, and will learn how to use them to your advantage in your programming projects. You will also find out how to work with a range of design patterns including abstract factory, singleton, strategy pattern, all of which will help make programming with Python much more efficient. Finally, as the process of writing a program is never complete without testing it, you will learn to test threaded applications and run parallel tests.If you want the edge when it comes to Python, use this book to unlock the secrets of smarter Python programming.Style and approachThis is book had been created to help you to “unlock” the best ways to tackle the challenges and performance bottlenecks that many Python developers face today. The keys are supported with program examples to help you understand the concepts better and see them in action.
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Learning to Program in Python
This book is intended for individuals and students learning to program. You may already have done some programming in other languages, but not be familiar with Python. Novice programmers should work through the book sequentially, starting at Chapter 1. It will also be a useful reference book for students on a programming course or anyone working on a programming project. It teaches basic syntax and programming techniques, and introduces three inbuilt Python modules: "> Tkinter, used for building a graphical user interface, which is an option that some users may like to include in their project work. "> SQLite, which enables the creation and processing of a database from within a Python program. This provides an alternative to writing to a text file when data needs to be stored and retrieved "> .pdb, Python's debugging module, which can be used to help find elusive logic errors. Questions and exercises are included in every chapter. Answers to these as well as over 120 Python programs for all the examples and exercises given in the book may be downloaded from www.pgonline.co.uk.These programs enable users of the book to try out the in-text examples and check possible solutions to the exercises.
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Mastering Python for Networking and Security
Tackle security and networking issues using Python libraries such as Nmap, requests, asyncio, and scapyKey FeaturesEnhance your Python programming skills in securing systems and executing networking tasksExplore Python scripts to debug and secure complex networksLearn to avoid common cyber events with modern Python scriptingBook DescriptionIt's now more apparent than ever that security is a critical aspect of IT infrastructure, and that devastating data breaches can occur from simple network line hacks. As shown in this book, combining the latest version of Python with an increased focus on network security can help you to level up your defenses against cyber attacks and cyber threats. Python is being used for increasingly advanced tasks, with the latest update introducing new libraries and packages featured in the Python 3.7.4 recommended version. Moreover, most scripts are compatible with the latest versions of Python and can also be executed in a virtual environment. This book will guide you through using these updated packages to build a secure network with the help of Python scripting. You'll cover a range of topics, from building a network to the procedures you need to follow to secure it. Starting by exploring different packages and libraries, you'll learn about various ways to build a network and connect with the Tor network through Python scripting. You will also learn how to assess a network's vulnerabilities using Python security scripting. Later, you'll learn how to achieve endpoint protection by leveraging Python packages, along with writing forensic scripts. By the end of this Python book, you'll be able to use Python to build secure apps using cryptography and steganography techniques.What you will learnCreate scripts in Python to automate security and pentesting tasksExplore Python programming tools that are used in network security processesAutomate tasks such as analyzing and extracting information from serversUnderstand how to detect server vulnerabilities and analyze security modulesDiscover ways to connect to and get information from the Tor networkFocus on how to extract information with Python forensics toolsWho this book is forThis Python network security book is for network engineers, system administrators, or any security professional looking to overcome networking and security challenges. You will also find this book useful if you're a programmer with prior experience in Python. A basic understanding of general programming structures and the Python programming language is required before getting started.
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Python Unit Test Automation
Learn how to automate unit tests of Python 3 with automation libraries, such as doctest, unittest, nose, nose2, pytest, and selenium. This book explores important concepts in software test automation and demonstrates how to automate, organize, and execute unit tests with Python. It also introduces readers to the concepts of web browser automation and logging.This new edition starts with an introduction to Python 3. Next, it covers doctest and pydoc. This is followed by a discussion on unittest, a framework that comes packaged with Python 3 itself. There is a dedicated section on creating test suites, followed by an explanation of how nose2 provides automatic test module discovery. Moving forward, you will learn about pytest, the most popular third-party library and testrunner for Python. You will see how to write and execute tests with pytest. You’ll also learn to discover tests automatically with pytest.This edition features two brand new chapters, the first of which focuses on the basics of web browser automation with Selenium. You’ll learn how to use Selenium with unittest to write test cases for browser automation and use the Selenium IDE with web browsers such as Chrome and Firefox. You’ll then explore logging frameworks such as Python’s built-in logger and the third-party framework loguru.The book concludes with an exploration of test-driven development with pytest, during which you will execute a small project using TDD methodology.What You Will LearnStart testing with doctest and unittestUnderstand the idea of unit testingGet started with nose 2 and pytestLearn how to use logger and loguruWork with Selenium and test driven development Who This Book Is ForPython developers, software testers, open source enthusiasts, and contributors to the Python community.
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