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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

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781803241005

Length 382 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Authors (2):

Ned Letcher

Simon Aubury

View More author details

Table of Contents (15) Chapters

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

Why subscribe?

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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Authors (2)

Simon Aubury

Simon Aubury

Simon Aubury has been working in the IT industry since 2000 as a data engineering specialist. He has an extensive background in building large, flexible, highly available distributed data systems. Simon has delivered critical data systems for finance, transport, healthcare, insurance, and telecommunications clients in Australia, Europe, and Asia Pacific. In 2019, Simon joined Thoughtworks as a principal data engineer and today is associate director of data platforms at Simple Machines in Sydney, Australia. Simon is active in the data community, a regular conference speaker, and the organizer of local and international meetups and data engineering conferences.

See other products by Simon Aubury

Ned Letcher

Ned Letcher

Ned Letcher has worked as a data science and software engineering consultant since completing his PhD in computational linguistics in 2018 and currently works at Thoughtworks. He has designed and developed data-powered products and services across a range of industries and helped organizations and teams improve the effectiveness of their data processes and workflows. Ned has also worked as a Python trainer, supporting both tertiary students and data professionals across various organizations. He is active in the data community, speaking at and helping organize meetups and conferences, as well as contributing to a range of open source projects.

See other products by Ned Letcher

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