Polars Cookbook
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Polars Cookbook
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Author : Yuki Kakegawa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-23
Polars Cookbook written by Yuki Kakegawa 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 2024-08-23 with Computers categories.
Leverage Polars, a lightning-fast DataFrame library, to transform your Python-based data science projects with efficient data wrangling and manipulation Key Features Unlock the power of Python Polars for faster and more efficient data analysis workflows Master the fundamentals of Python Polars with step-by-step recipes Discover data manipulation techniques to apply across multiple data problems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Polars Cookbook is a comprehensive, hands-on guide to Python Polars, one of the first resources dedicated to this powerful data processing library. Written by Yuki Kakegawa, a seasoned data analytics consultant who has worked with industry leaders like Microsoft and Stanford Health Care, this book offers targeted, real-world solutions to data processing, manipulation, and analysis challenges. The book also includes a foreword by Marco Gorelli, a core contributor to Polars, ensuring expert insights into Polars' applications. From installation to advanced data operations, you’ll be guided through data manipulation, advanced querying, and performance optimization techniques. You’ll learn to work with large datasets, conduct sophisticated transformations, leverage powerful features like chaining, and understand its caveats. This book also shows you how to integrate Polars with other Python libraries such as pandas, numpy, and PyArrow, and explore deployment strategies for both on-premises and cloud environments like AWS, BigQuery, GCS, Snowflake, and S3. With use cases spanning data engineering, time series analysis, statistical analysis, and machine learning, Polars Cookbook provides essential techniques for optimizing and securing your workflows. By the end of this book, you'll possess the skills to design scalable, efficient, and reliable data processing solutions with Polars. What you will learn Read from different data sources and write to various files and databases Apply aggregations, window functions, and string manipulations Perform common data tasks such as handling missing values and performing list and array operations Discover how to reshape and tidy your data by pivoting, joining, and concatenating Analyze your time series data in Python Polars Create better workflows with testing and debugging Who this book is for This book is for data analysts, data scientists, and data engineers who want to learn how to use Polars in their workflows. Working knowledge of the Python programming language is required. Experience working with a DataFrame library such as pandas or PySpark will also be helpful.
Pandas Cookbook
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Author : William Ayd
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-10-31
Pandas Cookbook written by William Ayd 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 2024-10-31 with Computers categories.
From fundamental techniques to advanced strategies for handling big data, visualization, and more, this book equips you with skills to excel in real-world data analysis projects. Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features This book targets features in pandas 2.x and beyond Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book DescriptionUnlock the full power of pandas 2.x with this hands-on cookbook, designed for Python developers, data analysts, and data scientists who need fast, efficient solutions for real-world data challenges. This book provides practical, ready-to-use recipes to streamline your workflow. With step-by-step guidance, you'll master data wrangling, visualization, performance optimization, and scalable data analysis using pandas’ most powerful features. From importing and merging large datasets to advanced time series analysis and SQL-like operations, this cookbook equips you with the tools to analyze, manipulate, and visualize data like a pro. Learn how to boost efficiency, optimize memory usage, and seamlessly integrate pandas with NumPy, PyArrow, and databases. This book will help you transform raw data into actionable insights with ease. What you will learn The pandas type system and how to best navigate it Import/export DataFrames to/from common data formats Data exploration in pandas through dozens of practice problems Grouping, aggregation, transformation, reshaping, and filtering data Merge data from different sources through pandas SQL-like operations Leverage the robust pandas time series functionality in advanced analyses Scale pandas operations to get the most out of your system The large ecosystem that pandas can coordinate with and supplement Who this book is for This book is for Python developers, data scientists, engineers, and analysts. pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas
In Memory Analytics With Apache Arrow
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Author : Matthew Topol
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-30
In Memory Analytics With Apache Arrow written by Matthew Topol 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 2024-09-30 with Computers categories.
Harness the power of Apache Arrow to optimize tabular data processing and develop robust, high-performance data systems with its standardized, language-independent columnar memory format Key Features Explore Apache Arrow's data types and integration with pandas, Polars, and Parquet Work with Arrow libraries such as Flight SQL, Acero compute engine, and Dataset APIs for tabular data Enhance and accelerate machine learning data pipelines using Apache Arrow and its subprojects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionApache Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics. This book harnesses the author’s 15 years of experience to show you a standardized way to work with tabular data across various programming languages and environments, enabling high-performance data processing and exchange. This updated second edition gives you an overview of the Arrow format, highlighting its versatility and benefits through real-world use cases. It guides you through enhancing data science workflows, optimizing performance with Apache Parquet and Spark, and ensuring seamless data translation. You’ll explore data interchange and storage formats, and Arrow's relationships with Parquet, Protocol Buffers, FlatBuffers, JSON, and CSV. You’ll also discover Apache Arrow subprojects, including Flight, SQL, Database Connectivity, and nanoarrow. You’ll learn to streamline machine learning workflows, use Arrow Dataset APIs, and integrate with popular analytical data systems such as Snowflake, Dremio, and DuckDB. The latter chapters provide real-world examples and case studies of products powered by Apache Arrow, providing practical insights into its applications. By the end of this book, you’ll have all the building blocks to create efficient and powerful analytical services and utilities with Apache Arrow.What you will learn Use Apache Arrow libraries to access data files, both locally and in the cloud Understand the zero-copy elements of the Apache Arrow format Improve the read performance of data pipelines by memory-mapping Arrow files Produce and consume Apache Arrow data efficiently by sharing memory with the C API Leverage the Arrow compute engine, Acero, to perform complex operations Create Arrow Flight servers and clients for transferring data quickly Build the Arrow libraries locally and contribute to the community Who this book is for This book is for developers, data engineers, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. Whether you’re building utilities for data analytics and query engines, or building full pipelines with tabular data, this book can help you out regardless of your preferred programming language. A basic understanding of data analysis concepts is needed, but not necessary. Code examples are provided using C++, Python, and Go throughout the book.
Python Feature Engineering Cookbook
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Author : Soledad Galli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-30
Python Feature Engineering Cookbook written by Soledad Galli 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 2024-08-30 with Computers categories.
Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to production Key Features Learn Craft powerful features from tabular, transactional, and time-series data Develop efficient and reproducible real-world feature engineering pipelines Optimize data transformation and save valuable time Purchase of the print or Kindle book includes a free PDF eBook Book Description Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient. This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries. You’ll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data. The book explores feature extraction from complex data types such as dates, times, and text. You’ll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series. By the end, you’ll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance. What you will learn Discover multiple methods to impute missing data effectively Encode categorical variables while tackling high cardinality Find out how to properly transform, discretize, and scale your variables Automate feature extraction from date and time data Combine variables strategically to create new and powerful features Extract features from transactional data and time series Learn methods to extract meaningful features from text data Who this book is for If you're a machine learning or data science enthusiast who wants to learn more about feature engineering, data preprocessing, and how to optimize these tasks, this book is for you. If you already know the basics of feature engineering and are looking to learn more advanced methods to craft powerful features, this book will help you. You should have basic knowledge of Python programming and machine learning to get started.
Roskam S Airplane War Stories
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Author : Jan Roskam
language : en
Publisher: DARcorporation
Release Date : 2002
Roskam S Airplane War Stories written by Jan Roskam and has been published by DARcorporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Biography & Autobiography categories.
Microwave Cookbook For Microscopists
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Author : Lanbrecht Piet Kok
language : en
Publisher:
Release Date : 1992
Microwave Cookbook For Microscopists written by Lanbrecht Piet Kok and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Diathermy categories.
Microwave Cookbook Of Pathology
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Author : Mathilde E. Boon
language : en
Publisher:
Release Date : 1989
Microwave Cookbook Of Pathology written by Mathilde E. Boon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Cytology categories.
Encyclopedia Of Physical Science And Technology
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Author :
language : en
Publisher:
Release Date : 2002
Encyclopedia Of Physical Science And Technology written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Physical sciences categories.
Of the Encyclopedia of Physical Science and Technology: Has been completely updated with no less than 90% revised material and 50% new content throughout the volumes Presents eighteen volumes, nearly 800 authoritative articles and 14,500 pages Is lavishly illustrated with over 7,000 photographs, illustrations and tables Presents an increased emphasis on the hottest topics such as information processing, environmental science, biotechnology and biomedicine Includes a final Index Volume containing Thematic, Relational and Subject indexes.
California Farmer
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Author :
language : en
Publisher:
Release Date : 2002
California Farmer written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Agriculture categories.
Arts Humanities Citation Index
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Author :
language : en
Publisher:
Release Date : 1983
Arts Humanities Citation Index written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Arts categories.
A multidisciplinary index covering the journal literature of the arts and humanities. It fully covers 1,144 of the world's leading arts and humanities journals, and it indexes individually selected, relevant items from over 6,800 major science and social science journals.