Python Data Structures Dictionaries
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Python Data Structures: Dictionaries
Dictionaries are a common data structure in Python programming, designed to hold a sequence of key-value pairs. Dictionaries are dynamic, can be nested, and are preferable to lists in situations where you would want to search for and retrieve data with the same key. Being able to use dictionaries effectively is critical to mastering Python and create more efficient code. In this course, Deepa Muralidhar reviews the syntax and real-world use cases for dictionaries. Discover how to create a simple dictionary, iterate through the data, incorporate operations and comparators, and compare dictionaries to other common data structures such as lists, sets, and tuples. Plus, find out how to use dictionary comprehension to automate dictionary generation by merging lists or arrays.
High Performance Python
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
Python Data Structures Explained: A Practical Guide with Examples
This book offers a comprehensive guide to understanding and effectively utilizing data structures and algorithmic strategies in Python. It systematically presents each data structure, from basic arrays and lists to more complex entities like trees and graphs, ensuring that readers build a solid foundation in both theory and practical implementation. The content is designed to be accessible to beginners while still providing depth and insights that are valuable for experienced programmers. The text explains core concepts with clear, precise language, emphasizing the importance of choosing the right data structure for efficient problem solving. Detailed explanations of operations, performance considerations, and practical coding techniques provide readers with the tools they need to implement reliable and efficient solutions. The inclusion of debugging and optimization practices further supports the development of professional programming skills. Through a structured progression across multiple chapters, the book delivers a focused and advanced exploration of Python's capabilities in data organization and algorithmic efficiency. Readers will gain practical knowledge by engaging with clear examples, code snippets, and execution outputs that demonstrate the application of each concept. The guide is a valuable resource for anyone aiming to deepen their understanding of Python and its ecosystem of data structures and algorithms.