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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.
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. 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 DescriptionThe 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 as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. With this latest edition unlock the full potential of pandas 2.x onwards. Whether you're a beginner or an experienced data analyst, this book offers a wealth of practical recipes to help you excel in your data analysis projects. This cookbook covers everything from fundamental data manipulation tasks to advanced techniques for handling big data, visualization, and more. Each recipe is designed to address common real-world challenges, providing clear explanations and step-by-step instructions to guide you through the process. Explore cutting-edge topics such as idiomatic pandas coding, efficient handling of large datasets, and advanced data visualization techniques. Whether you're looking to sharpen or expand your skills, the "Pandas Cookbook" is your essential companion for mastering data analysis and manipulation with pandas 2.x, and beyond.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
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.
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.
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.
In recent decades crime fiction has enjoyed a creative boom. Although, as Alison Young argues in her book Imagining Crime (1996), crime stories remain strongly identified with specific locations, the genre has acquired a global reach, illuminating different corners of the world for the delectation of international audiences. The recent fashion for Nordic noir has highlighted the process by which the crime story may be franchised, as it is transposed from one culture to another. Crime fiction has thus become a vehicle for cultural exchange in the broadest of senses; not only does it move with apparent ease from one country to the next, and in and out of different languages, but it is also reproduced through various cultural media. What is involved in these processes of transference? Do stories lose or gain value? Or are they transformed into something else altogether? How does the crime story that originates in a specific society or culture come to articulate aspects of very different societies and cultures? And what are the repercussions of this cultural permeability?
Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to data visualization and how to choose the most appropriate for your needs Who This Book Is For If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you. What You Will Learn Introduce yourself to the essential tooling to set up your working environment Explore your data using the capabilities of standard Python Data Library and Panda Library Draw your first chart and customize it Use the most popular data visualization Python libraries Make 3D visualizations mainly using mplot3d Create charts with images and maps Understand the most appropriate charts to describe your data Know the matplotlib hidden gems Use plot.ly to share your visualization online In Detail Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. Style and approach A step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization.