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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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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: An Introduction to DuckDB FREE CHAPTER 2. Chapter 2: Loading Data into DuckDB 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 temporal joins with ASOF

Joining tables usually involves linking a common attribute across a table, such as finding the same identifier, name, or code value. DuckDB can also support “fuzzy” joins when we wish to join on values that may be close (but not identical) across tables. This is especially true when it comes to “temporal joins” – or joins that match on moments in time.

In this section, we will be discussing temporal joins and introducing the ASOF join, which can simplify joins across time.

For this exercise, we will be looking at historic weather measurements to see if bad weather is affecting our skier’s performance. Let’s start by loading data into the weather table:

CREATE OR REPLACE TABLE weather AS
SELECT *
FROM read_csv('weather.csv', timestampformat='%Y-%m-%d %H:%M:%S');

Let’s take a peek at the data within our newly created weather table:

SELECT *
FROM weather
LIMIT 10;
...
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