Products related to Python:
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Data Structures using Python
Data Structures using Python provides an introduction to design, analysis, and implementation of data structures using the powerful language Python.This book is designed for a first course on the subject.It is written for the undergraduate engineering students of Computer Science, Information Technology and allied disciplines. The book begins with an overview of the concept, need, nomenclature followed by discussion of Arrays, Stacks, Queues, Double Ended Queues, Linked List - all under a chapter on Linear Data Structures.This is followed by a chapter on Non-linear Data Structures where Heap, Hash Table, Trie, and Disjoint Sets are discussed.Trees, though a non-linear data structure, is discussed as a separate chapter to emphasize upon its importance.The last few chapters of the book discusses Graphs, Searching, and Sorting finally ending with an appendix on Python built-in class functions.
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Data Wrangling with Python
How do you take your data analysis skills beyond Excel to the next level?By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that's initially too messy or difficult to access.You don't need to know a thing about the Python programming language to get started.Through various step-by-step exercises, you'll learn how to acquire, clean, analyze, and present data efficiently.You'll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process
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Data Structures & Algorithms in Python
LEARN HOW TO USE DATA STRUCTURES IN WRITING HIGH PERFORMANCE PYTHON PROGRAMS AND ALGORITHMS This practical introduction to data structures and algorithms can help every programmer who wants to write more efficient software.Building on Robert Lafore's legendary Java-based guide, this book helps you understand exactly how data structures and algorithms operate.You'll learn how to efficiently apply them with the enormously popular Python language and scale your code to handle today's big data challenges. Throughout, the authors focus on real-world examples, communicate key ideas with intuitive, interactive visualizations, and limit complexity and math to what you need to improve performance.Step-by-step, they introduce arrays, sorting, stacks, queues, linked lists, recursion, binary trees, 2-3-4 trees, hash tables, spatial data structures, graphs, and more.Their code examples and illustrations are so clear, you can understand them even if you're a near-beginner, or your experience is with other procedural or object-oriented languages. Build core computer science skills that take you beyond merely “writing code”Learn how data structures make programs (and programmers) more efficientSee how data organization and algorithms affect how much you can do with today's, and tomorrow's, computing resourcesDevelop data structure implementation skills you can use in any languageChoose the best data structure(s) and algorithms for each programming problem—and recognize which ones to avoid Data Structures & Algorithms in Python is packed with examples, review questions, individual and team exercises, thought experiments, and longer programming projects.It's ideal for both self-study and classroom settings, and either as a primary text or as a complement to a more formal presentation.
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Foundational Python for Data Science
Data science and machine learning—two of the world's hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines.Python, the world's #1 programming language, is also the most popular language for data science and machine learning.This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master.Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.
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How can communication between Node.js and Python occur?
Communication between Node.js and Python can occur through various methods such as using inter-process communication (IPC) mechanisms like child processes, sockets, or message queues. One common approach is to use a messaging protocol like JSON or XML to exchange data between the two languages. Additionally, libraries like ZeroMQ or gRPC can be used to establish communication channels between Node.js and Python applications. Overall, the key is to establish a clear communication protocol that both languages can understand and use to exchange information seamlessly.
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How do I program communication over Bluetooth with Python?
To program communication over Bluetooth with Python, you can use the PyBluez library, which provides a Python interface for Bluetooth. First, you will need to install the PyBluez library using pip. Then, you can use the library to scan for nearby Bluetooth devices, establish a connection with a specific device, and send/receive data over the Bluetooth connection. You can refer to the PyBluez documentation and examples to learn how to use the library for Bluetooth communication in Python.
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How can you store data in Python?
In Python, you can store data using variables. Variables are used to store values that can be accessed and manipulated throughout the program. You can assign a value to a variable using the assignment operator (=) and then use the variable to store and retrieve data as needed. Python also provides various data structures such as lists, dictionaries, tuples, and sets, which can be used to store and organize data in a structured way. These data structures allow you to store multiple values in a single variable and perform operations on them efficiently.
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How can one store data in Python?
One can store data in Python using variables. Variables are used to store values that can be accessed and manipulated throughout the program. To store data in a variable, you simply assign a value to it using the assignment operator (=). Python is a dynamically typed language, so you do not need to declare the data type of a variable before assigning a value to it. You can store different types of data such as integers, floats, strings, lists, dictionaries, etc., in variables in Python.
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Fast Python for Data Science
Fast Python for Data Science is a hands-on guide to writing Python code that can process more data, faster, and with less resources.It takes a holistic approach to Python performance, showing you how your code, libraries, and computing architecture interact and can be optimized together. Written for experienced practitioners, Fast Python for Data Science dives right into practical solutions for improving computation and storage efficiency.You'll experiment with fun and interesting examples such as rewriting games in lower-level Cython and implementing a MapReduce framework from scratch.Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the technologyFast, accurate systems are vital for handling the huge datasets and complex analytical algorithms that are common in modern data science.Python programmers need to boost performance by writing faster pure-Python programs, optimizing the use of libraries, and utilizing modern multi-processor hardware; Fast Python for Data Science shows you how.
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Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data.In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life.Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. Key Features:? Showcases the excellent data science environment in Python. ? Provides examples for readers to replicate, adapt, extend, and improve. ? Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts. Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.
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Learning Data Mining with Python
COM051360
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Data Science and Analytics with Python
Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning.This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook.In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI). This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy.Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play.This edition also addresses crucial issues of explainability, transparency, and fairness in AI.These topics have rightly gained significant attention in recent years.As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount.This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts. Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning.Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook.Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.
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What exactly is a data structure in Python?
In Python, a data structure is a way of organizing and storing data in a specific format. It provides a way to manage and manipulate the data efficiently. Examples of data structures in Python include lists, tuples, dictionaries, and sets. Each data structure has its own characteristics and methods for accessing and modifying the data it contains. Data structures are essential for writing efficient and organized code in Python.
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How can one read weather data using Python?
One can read weather data using Python by using various libraries such as requests, urllib, or BeautifulSoup to make HTTP requests to weather APIs such as OpenWeatherMap or Weatherstack. These APIs provide access to weather data such as temperature, humidity, wind speed, and more. Once the data is retrieved, it can be parsed and processed using Python to extract the relevant information and perform further analysis or visualization. Additionally, there are specific Python libraries like pyowm or pyweather that provide a more streamlined way to access weather data from APIs.
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What is the research question regarding animal experimentation?
The research question regarding animal experimentation is typically focused on understanding the potential benefits and ethical considerations of using animals in scientific research. Researchers may investigate the effectiveness of animal models in studying human diseases, the development of alternative methods to reduce or replace animal use, and the ethical implications of using animals in research. Additionally, the research question may also explore the impact of animal experimentation on animal welfare and the potential for translating findings from animal studies to human health outcomes.
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How can Python be used in computer science education?
Python can be used in computer science education as a beginner-friendly programming language that is easy to learn and understand. It can be used to teach fundamental programming concepts such as variables, data types, loops, and functions. Python's versatility allows students to work on a wide range of projects, from simple text-based programs to more complex applications like web development and data analysis. Additionally, Python's extensive libraries and resources make it a valuable tool for teaching various computer science topics such as artificial intelligence, machine learning, and cybersecurity.
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