Python Performance Tuning
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Mastering Performance Optimization in Python: Unlock the Secrets of Expert-Level Skills
Dive deep into the art and science of Python performance enhancement with "Mastering Performance Optimization in Python: Unlock the Secrets of Expert-Level Skills." This advanced guide transforms seasoned developers into Python performance experts by exploring sophisticated techniques that elevate coding prowess. From runtime environments and data handling to memory management and concurrency, this book provides the essential insights needed to navigate and conquer complex performance challenges. Each chapter meticulously breaks down intricate concepts, presenting clear, actionable strategies for real-world application. Discover how to leverage Python's built-in libraries, utilize asynchronous programming, and implement multithreading and multiprocessing for optimal efficiency. With a focus on profiling, benchmarking, and continuous monitoring, the book ensures that readers develop the skills to not only identify but also rectify performance bottlenecks, delivering applications that are both robust and scalable. "Mastering Performance Optimization in Python" is more than a technical manual—it's a key to unlocking efficiency. With practical examples and expert insights, this book empowers you to elevate your Python applications and redefine performance standards in your projects. Equip yourself with the tools and knowledge to push boundaries and achieve excellence in Python programming.
Python For Data Analysis
Author: Dr.Vidya Santosh Dhamdhere
language: en
Publisher: RK Publication
Release Date: 2024-07-25
Python for Data Analysis the essential tools and techniques for data manipulation, cleaning, and analysis in Python. It emphasizes the use of libraries like pandas, NumPy, and Matplotlib to efficiently handle and visualize data. Ideal for analysts and aspiring data scientists, the book provides practical insights, examples, and workflows for handling real-world datasets. Whether for beginners or experienced professionals, it delivers a solid foundation in Python's data analysis ecosystem.
Python Data Analysis
Author: Armando Fandango
language: en
Publisher: Packt Publishing Ltd
Release Date: 2017-03-27
Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book Find, manipulate, and analyze your data using the Python 3.5 libraries Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects. Who This Book Is For This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. What You Will Learn Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms Prepare and clean your data, and use it for exploratory analysis Manipulate your data with Pandas Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5 Visualize your data with open source libraries such as matplotlib, bokeh, and plotly Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian Understand signal processing and time series data analysis Get to grips with graph processing and social network analysis In Detail Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.