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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 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

Effective data analysis using Jupyter Notebooks

In this section, we’re going to briefly cover two open source tools that will assist us in performing our data analysis within a Jupyter Notebook. The first is JupySQL, which provides us with a convenient way to run SQL queries in Jupyter notebooks. The second is Plotly, a comprehensive data visualization library that produces interactive visualizations with strong support for running inside Jupyter Notebooks. Let’s get started.

Convenient SQL queries with JupySQL

JupySQL is an open source Python package for streamlining the process of writing and running SQL queries in Jupyter Notebooks. To understand how it can help our data analysis workflow, let’s have a look at how we can use the Relational API to query our pedestrian_counts table using SQL. Say we wanted to count the total number of pedestrian readings for the Melbourne Central sensor for 2022. We might write the following query and run it using the sql...

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