Polars Cookbook - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Polars Cookbook write by Yuki Kakegawa. This book was released on 2024-08-23. Polars Cookbook available in PDF, EPUB and Kindle. Leverage a lightning fast DataFrame library for efficient data wrangling in Python 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 DescriptionPolars Cookbook is a complete guide that not only helps you get started with Python Polars but also gives you effective solutions to your day-to-day data problems. Dive into the world of Polars, a high-performance DataFrame library designed for efficient data processing and analysis. This cookbook takes a practical approach to unlocking the full potential of Polars through detailed, step-by-step recipes. Starting with installation and basic operations, this book guides you through data manipulation, advanced querying, and performance optimization techniques. You’ll learn how to handle large datasets, perform complex transformations, and leverage Polars’ powerful features for data science tasks. As you progress, you’ll explore Polars’ integration with other tools and libraries, and discover how to deploy Polars in both onpremises and cloud environments. You’ll also explore use cases for data engineering, time series analysis, statistical analysis, and machine learning, providing essential strategies for securing and optimizing your Polars workflows. By the end of this book, you’ll have acquired the knowledge and skills to build scalable, efficient, and reliable data processing solutions using 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.
In-Memory Analytics with Apache Arrow
In-Memory Analytics with Apache Arrow - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook In-Memory Analytics with Apache Arrow write by Matthew Topol. This book was released on 2024-09-30. In-Memory Analytics with Apache Arrow available in PDF, EPUB and Kindle. 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.
Pandas Cookbook
Pandas Cookbook - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Pandas Cookbook write by Theodore Petrou. This book was released on 2017-10-23. Pandas Cookbook available in PDF, EPUB and Kindle. Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. Style and approach The author relies on his vast experience teaching pandas in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data.
The FODMAP Friendly Kitchen Cookbook
The FODMAP Friendly Kitchen Cookbook - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The FODMAP Friendly Kitchen Cookbook write by Emma Hatcher. This book was released on 2017-01-12. The FODMAP Friendly Kitchen Cookbook available in PDF, EPUB and Kindle. Chosen by the Telegraph and the Evening Standard as one of the best healthy eating books of 2017 FODMAPs are a collection of molecules found in foods, that can cause issues for some people. A low-FODMAP lifestyle is the only diet recommended by the NHS to treat IBS and its associated symptoms. Emma Hatcher, creator of the blog She Can't Eat What?!, brings you 100 beautiful, healthy and delicious low FODMAP recipes. Emma Hatcher has suffered from a sensitive gut for as long as she can remember. After years of horrible symptoms and endless frustration trying different diets and cutting out various foods, her GP recommended the Low FODMAP Diet. FODMAP changed Emma's life and she has never looked back since. Emma's book, based on her hugely popular food and lifestyle blog She Can't Eat What?! will take the frustration out of living with IBS, Crohn's disease, coeliac's disease, food intolerances and many other digestive disorders. It is for anyone who suffers from bloating, tummy pains, digestive issues or feelings of heaviness and discomfort, and for anyone who wants to feel healthy and happy after eating. Backed by the official FODMAP Friendly team and with more than 100 quick, easy and modern recipes, diet information and personal stories for those that have run out of answers and feel 'they can't eat anything', Emma shows you how to create delicious meals and look after your gut in today's stress-filled, modern lifestyle.
Python Feature Engineering Cookbook
Python Feature Engineering Cookbook - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Python Feature Engineering Cookbook write by Soledad Galli. This book was released on 2024-08-30. Python Feature Engineering Cookbook available in PDF, EPUB and Kindle. 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.
