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  • Book Overview & Buying Getting Started with DuckDB
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Getting Started with DuckDB

Getting Started with DuckDB

By : Simon Aubury, Ned Letcher
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Getting Started with DuckDB

Getting Started with DuckDB

5 (1)
By: Simon Aubury, Ned Letcher
Buy this Book

Overview of this book

DuckDB 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.
Table of Contents (15 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Conventions used
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Get in touch
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Share Your Thoughts
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Download a free PDF copy of this book
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1
Chapter 1: An Introduction to DuckDB
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Chapter 1: An Introduction to DuckDB
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Technical requirements
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What is DuckDB?
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Why use DuckDB?
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DuckDB deployment options and installation
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A short SQL primer
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Summary
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Chapter 2: Loading Data into DuckDB
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Chapter 2: Loading Data into DuckDB
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Technical requirements
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Loading CSV files
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Loading JSON files
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Working with Parquet files
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Exploring public datasets
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Exporting data
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Summary
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Chapter 3: Data Manipulation with DuckDB
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Chapter 3: Data Manipulation with DuckDB
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Technical requirements
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Data wrangling with DuckDB – cleaning and reshaping data
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Data import and manipulation
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Altering tables and creating views
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Aggregate functions and common table expressions
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Joining data from multiple tables
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Summary
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Chapter 4: DuckDB Operations and Performance
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Chapter 4: DuckDB Operations and Performance
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Technical requirements
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Exploring DuckDB indexes
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Optimizing the file read performance of DuckDB
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Working with timestamps
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Time and window functions
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Summary
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Chapter 5: DuckDB Extensions
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Chapter 5: DuckDB Extensions
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Technical requirements
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Expanding DuckDB capabilities with extensions
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Working with geospatial data
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Summary
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Chapter 6: Semi-Structured Data Manipulation
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Chapter 6: Semi-Structured Data Manipulation
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Technical requirements
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Exploring nested data types
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Working with lists
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Working with JSON
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Querying JSON data from an API
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Summary
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Chapter 7: Setting up the DuckDB Python Client
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Chapter 7: Setting up the DuckDB Python Client
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Technical requirements
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Connecting to DuckDB in Python
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Summary
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Chapter 8: Exploring DuckDB’s Python API
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Chapter 8: Exploring DuckDB’s Python API
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Technical requirements
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Working with the Relational API
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Working with the Python DB-API
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Integration with Python packages and language features
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Summary
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Chapter 9: Exploring DuckDB’s R API
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Chapter 9: Exploring DuckDB’s R API
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Technical requirements
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Working with DuckDB using R’s DBI
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Registering R objects as virtual tables
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Querying DuckDB with dplyr
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Summary
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Chapter 10: Using DuckDB Effectively
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Chapter 10: Using DuckDB Effectively
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Technical requirements
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Selecting columns effectively
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Applying function chaining
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Using INSERT effectively
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Leveraging positional joins
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Using temporal joins with ASOF
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Recursive queries and macros
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Summary
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Chapter 11: Hands-On Exploratory Data Analysis with DuckDB
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Chapter 11: Hands-On Exploratory Data Analysis with DuckDB
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Technical requirements
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Preparing the pedestrian traffic dataset for analysis
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Effective data analysis using Jupyter Notebooks
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Analyzing pedestrian traffic through Melbourne CBD
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Summary
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Chapter 12: DuckDB – The Wider Pond
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Chapter 12: DuckDB – The Wider Pond
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Tools and services for working with DuckDB effectively
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DuckDB integration
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Alternative DuckDB query interfaces
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DuckDB-powered data apps
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Keeping up to speed with the DuckDB ecosystem
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Chapter and book summary
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Index
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Index
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Why subscribe?
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Other Books You May Enjoy
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Other Books You May Enjoy
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Packt is searching for authors like you
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Share Your Thoughts
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Download a free PDF copy of this book

Joining data from multiple tables

We have learned the basics of data wrangling with DuckDB. Now, let’s move on to a more detailed example. We’re going to combine datasets from multiple tables in real-world taxi passenger-trip data, in order to analyze passenger movement and tipping behavior.

New York taxi data

The NYC Taxi and Limousine Commission provides a collection of data that contains information about the trips taken by yellow and green taxis in New York City. The dataset includes variables such as pickup and drop-off locations, dates and times, passenger counts, trip distances, fares, tips, tolls, and payment types. The dataset is publicly available and is interesting for analyzing traffic patterns and evaluating tipping behavior.

The dataset is updated monthly and can be accessed through the New York City website (https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page). For this exercise, we will be using the data from January 2023 for yellow taxi...

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Tech Concepts
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Programming languages
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