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Getting Started with DuckDB

You're reading from   Getting Started with DuckDB A practical guide for accelerating your data science, data analytics, and data engineering workflows

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781803241005
Length 382 pages
Edition 1st Edition
Languages
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Authors (2):
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Ned Letcher Ned Letcher
Author Profile Icon Ned Letcher
Ned Letcher
Simon Aubury Simon Aubury
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Simon Aubury
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: An Introduction to DuckDB 2. Chapter 2: Loading Data into DuckDB FREE CHAPTER 3. Chapter 3: Data Manipulation with DuckDB 4. Chapter 4: DuckDB Operations and Performance 5. Chapter 5: DuckDB Extensions 6. Chapter 6: Semi-Structured Data Manipulation 7. Chapter 7: Setting up the DuckDB Python Client 8. Chapter 8: Exploring DuckDB’s Python API 9. Chapter 9: Exploring DuckDB’s R API 10. Chapter 10: Using DuckDB Effectively 11. Chapter 11: Hands-On Exploratory Data Analysis with DuckDB 12. Chapter 12: DuckDB – The Wider Pond 13. Index 14. Other Books You May Enjoy

Using INSERT effectively

For this section, we will create an index on our skiers table to ensure each skier’s name is unique:

CREATE UNIQUE INDEX skier_unique
ON skiers (skier_first_name);

With this index created, we can be assured that skier_first_name is a unique value.

Imagine that we have a new skier called Kim who has a blue helmet. We can use the conventional INSERT syntax to list the columns and values:

INSERT INTO skiers(skier_first_name, skier_helmet_color)
SELECT 'Kim' AS skier_first_name, 'blue' AS skier_helmet_color;

This isn’t very difficult, but we do need to ensure we keep the positional order of the column names (skier_first_name, skier_helmet_color) so that they match the order of the data provided ('Kim', 'blue'). This can become tedious if we are dealing with very wide tables with numerous columns and need to keep the ordering consistent.

DuckDB allows us to use the BY NAME directive to signify...

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