Efficient Data Science Workflows With Vaex
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Efficient Data Science Workflows With Vaex
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-18
Efficient Data Science Workflows With Vaex written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-18 with Computers categories.
"Efficient Data Science Workflows with Vaex" Efficient Data Science Workflows with Vaex delivers a comprehensive exploration of modern data science challenges and introduces Vaex as an innovative solution for handling and analyzing massive datasets at scale. The book presents a compelling case for the transition from traditional in-memory tools, such as pandas and NumPy, to more advanced, out-of-core solutions that effortlessly process data far exceeding physical memory constraints. Through detailed case studies and foundational principles, readers gain a deep understanding of both the limitations of legacy approaches and the critical requirements for building robust, reproducible, and scalable data pipelines. The book systematically guides practitioners through Vaex’s architecture, emphasizing its memory mapping, lazy evaluation, and columnar data handling capabilities. Practical chapters cover everything from efficient data ingestion and preprocessing, advanced transformation techniques, and high-performance analytics to seamless machine learning workflows and interactive visualization. Special attention is given to challenging aspects such as distributed and cloud-based analysis, incorporating strategies for parallelism, cloud-native deployments, and orchestration, all while maintaining security, scalability, and performance. Featuring real-world case studies and empirical benchmarks comparing Vaex to alternative frameworks, this book is an authoritative reference for data scientists and engineers seeking to maximize efficiency and throughput in their analytics workflows. Best practices, troubleshooting guidance, and insights into the growing Vaex ecosystem ensure that readers are equipped not only to master today’s large-scale data challenges but also to contribute to and shape the future of scalable data science.
Introduction To Text Analytics
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Author : Emily Ohman
language : en
Publisher: SAGE Publications Limited
Release Date : 2024-11-30
Introduction To Text Analytics written by Emily Ohman and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-30 with Reference categories.
Clear, nuanced introduction to digital text mining and data analysis specifically for students in digital humanities and computational social science.
Data Science
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Author : Dr.N.Rathina Kumar
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-03-23
Data Science written by Dr.N.Rathina Kumar and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-23 with Computers categories.
Dr.N.Rathina Kumar, Assistant Professor, Department of Artificial Intelligence and Data Sciences, SNS College of Engineering, Coimbatore, Tamil Nadu, India. Prof. Mrunalini U. Buradkar, Assistant Professor, Department of Electronics & Telecommunication Engineering, St. Vincent Pallotti College of Engineering & Technology, Nagpur, Maharashtra, India. Dr.P.Rama, Assistant Professor, Department of Computing Technologies, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, Tamil Nadu, India. Dr.Krishna Murthy Inumula, Associate Professor, Symbiosis Institute of International Business (SIIB), Symbiosis International (Deemed University), Pune, Maharashtra, India.
Pandas Essentials For Data Analysis
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-18
Pandas Essentials For Data Analysis written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-18 with Computers categories.
"Pandas Essentials for Data Analysis" Unlock the full power of data analysis with "Pandas Essentials for Data Analysis," a sophisticated and comprehensive resource for professionals, academics, and practitioners seeking mastery over the Pandas ecosystem. This book delves deeply into core structures such as Series and DataFrames, offering rigorous explanations of theoretical underpinnings, memory optimization, and performance nuances. Readers will gain practical fluency in advanced indexing, custom accessor creation, and seamless handling of diverse data types, preparing them to architect robust and efficient analytical pipelines. From high-performance data ingestion across heterogeneous sources to sophisticated data cleaning, transformation, and temporal analytics, the book provides actionable guidance on every aspect of the data workflow. Explore advanced topics such as imputation strategies, scalable join algorithms, and time series engineering, alongside best practices for ensuring data integrity, reproducibility, and automated validation. Extensive coverage is given to visualization, reporting, and the integration of Pandas with leading machine learning frameworks, ensuring your analyses are both insightful and production-ready. Through detailed case studies spanning finance, healthcare, web analytics, natural language processing, geospatial applications, and industrial IoT, "Pandas Essentials for Data Analysis" bridges the gap between foundational knowledge and real-world expertise. The final chapters expound on writing reliable, maintainable code and navigating evolving best practices in the Pandas and PyData landscape, equipping readers to confidently meet today’s demanding data challenges and tomorrow’s innovations.
Efficient Scientific Programming With Spyder
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-18
Efficient Scientific Programming With Spyder written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-18 with Computers categories.
"Efficient Scientific Programming with Spyder" "Efficient Scientific Programming with Spyder" is a definitive guide for scientists, engineers, and researchers seeking to elevate their computational workflows using the powerful Spyder IDE. This comprehensive resource delves into advanced facets of the Spyder ecosystem, including its modular architecture, extensibility through plugins, seamless integration with the scientific Python stack, and best practices for customizing and optimizing the development environment. Readers are equipped to handle large-scale, complex scientific projects, leveraging environment management, high-performance computing, and distributed workflows directly from within Spyder. The book systematically covers all aspects of the scientific programming lifecycle using Python, from scripting patterns and automated refactoring to rigorous type checking, test-driven development, and collaborative code quality maintenance. Advanced chapters focus on numerical methods—such as efficient vectorization, parallelization, GPU computing, and native language integration—as well as efficient data management strategies for scientific formats, real-time acquisition, data privacy, and validation. Additionally, it explores cutting-edge scientific visualization, offering guidance on creating publication-quality plots, interactive dashboards, complex 3D visualizations, and custom analytical GUIs. Beyond technical mastery, the text addresses the real-world needs of modern scientific teams: from automating experiments and orchestrating robust data workflows, to integrating machine learning pipelines, and ensuring research reproducibility, collaboration, and open science practices. Through detailed case studies and explorations of future trends—including cloud, HPC, and community-driven development—this book empowers scientists to build, extend, and manage end-to-end, scalable, and reproducible research solutions with Spyder at the core of their computational toolset.
Cleaning Data For Effective Data Science
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Author : David Mertz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-31
Cleaning Data For Effective Data Science written by David Mertz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Mathematics categories.
Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.
Getting Started With Duckdb
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Author : Simon Aubury
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-24
Getting Started With Duckdb written by Simon Aubury and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-24 with Computers categories.
Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL database Key Features Use DuckDB to rapidly load, transform, and query data across a range of sources and formats Gain practical experience using SQL, Python, and R to effectively analyze data Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB’s versatile capabilities Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.What you will learn Understand the properties and applications of a columnar in-process database Use SQL to load, transform, and query a range of data formats Discover DuckDB's rich extensions and learn how to apply them Use nested data types to model semi-structured data and extract and model JSON data Integrate DuckDB into your Python and R analytical workflows Effectively leverage DuckDB's convenient SQL enhancements Explore the wider ecosystem and pathways for building DuckDB-powered data applications Who this book is for If you’re interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R.
Python For Excel
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Author : Felix Zumstein
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-03-04
Python For Excel written by Felix Zumstein and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-04 with Business & Economics categories.
While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently. Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started. Use Python without extensive programming knowledge Get started with modern tools, including Jupyter notebooks and Visual Studio code Use pandas to acquire, clean, and analyze data and replace typical Excel calculations Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports Use xlwings to build interactive Excel tools that use Python as a calculation engine Connect Excel to databases and CSV files and fetch data from the internet using Python code Use Python as a single tool to replace VBA, Power Query, and Power Pivot
High Performance Python
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Author : Micha Gorelick
language : en
Publisher: O'Reilly Media
Release Date : 2020-04-30
High Performance Python written by Micha Gorelick and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-30 with Computers categories.
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
Think Dsp
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Author : Allen B. Downey
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-07-12
Think Dsp written by Allen B. Downey and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-12 with Technology & Engineering categories.
If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: Periodic signals and their spectrums Harmonic structure of simple waveforms Chirps and other sounds whose spectrum changes over time Noise signals and natural sources of noise The autocorrelation function for estimating pitch The discrete cosine transform (DCT) for compression The Fast Fourier Transform for spectral analysis Relating operations in time to filters in the frequency domain Linear time-invariant (LTI) system theory Amplitude modulation (AM) used in radio Other books in this series include Think Stats and Think Bayes, also by Allen Downey.