[PR #2633] [CLOSED] Add DuckDB: A fast in-process OLAP database for analytics #1949

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GiteaMirror · 2025-11-06T13:26:26-06:00

GiteaMirror commented

2025-11-06 13:26:26 -06:00

📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/2633
Author: @zukizukizuki
Created: 12/23/2024
Status: ❌ Closed

Base: master ← Head: feature/1

📝 Commits (1)

08a4e8a Add DuckDB: A fast in-process OLAP database for analytics

📊 Changes

1 file changed (+90 additions, -87 deletions)

View changed files

📝 README.md (+90 -87)

📄 Description

What is this Python project?

This pull request proposes adding the Python library DuckDB to the "Data Analysis" section of the awesome-python list.
DuckDB is a fast, in-process OLAP database management system specifically designed for analytical workloads. Key features include:

High-performance analytical queries: It executes SQL queries efficiently, even on large datasets.
Seamless Pandas integration: It allows for easy data exchange and querying directly on Pandas DataFrames.
In-process operation: It runs directly within your Python process, simplifying setup and management.
Standard SQL support: It supports a wide range of SQL syntax.
Direct querying of external data sources: It can query data directly from files (like CSV and Parquet) and cloud storage.

What's the difference between this Python project and similar ones?

Compared to other data analysis tools and databases, DuckDB offers several advantages:

Compared to Pandas:
- Speed: DuckDB generally provides faster query execution, especially on larger datasets.
- Memory Efficiency: Features like lazy execution can lead to better memory utilization.
- SQL Capabilities: It enables more complex and expressive SQL queries.
- External Data Access: It can directly query data in files and cloud storage without loading everything into memory.
Compared to embedded databases like SQLite:
- Optimized for Analytics: DuckDB is specifically built for OLAP workloads, offering superior performance for analytical queries.
- Stronger Pandas Integration: It provides tighter integration and more seamless interoperability with Pandas DataFrames.
Compared to larger RDBMS like PostgreSQL:
- In-process and Lightweight: DuckDB is easy to set up and requires minimal overhead, making it suitable for local development and embedding.

--

Anyone who agrees with this pull request could submit an Approve review to it.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/2633 **Author:** [@zukizukizuki](https://github.com/zukizukizuki) **Created:** 12/23/2024 **Status:** ❌ Closed **Base:** `master` ← **Head:** `feature/1` --- ### 📝 Commits (1) - [`08a4e8a`](https://github.com/vinta/awesome-python/commit/08a4e8ae60f859c9b331fd4eb09c2a17e9356db8) Add DuckDB: A fast in-process OLAP database for analytics ### 📊 Changes **1 file changed** (+90 additions, -87 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+90 -87) </details> ### 📄 Description ## What is this Python project? This pull request proposes adding the Python library DuckDB to the "Data Analysis" section of the awesome-python list. DuckDB is a fast, in-process OLAP database management system specifically designed for analytical workloads. Key features include: - High-performance analytical queries: It executes SQL queries efficiently, even on large datasets. - Seamless Pandas integration: It allows for easy data exchange and querying directly on Pandas DataFrames. - In-process operation: It runs directly within your Python process, simplifying setup and management. - Standard SQL support: It supports a wide range of SQL syntax. - Direct querying of external data sources: It can query data directly from files (like CSV and Parquet) and cloud storage. ## What's the difference between this Python project and similar ones? Compared to other data analysis tools and databases, DuckDB offers several advantages: - Compared to Pandas: - Speed: DuckDB generally provides faster query execution, especially on larger datasets. - Memory Efficiency: Features like lazy execution can lead to better memory utilization. - SQL Capabilities: It enables more complex and expressive SQL queries. - External Data Access: It can directly query data in files and cloud storage without loading everything into memory. - Compared to embedded databases like SQLite: - Optimized for Analytics: DuckDB is specifically built for OLAP workloads, offering superior performance for analytical queries. - Stronger Pandas Integration: It provides tighter integration and more seamless interoperability with Pandas DataFrames. - Compared to larger RDBMS like PostgreSQL: - In-process and Lightweight: DuckDB is easy to set up and requires minimal overhead, making it suitable for local development and embedding. -- Anyone who agrees with this pull request could submit an Approve review to it. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2025-11-06 13:26:26 -06:00

GiteaMirror closed this issue

2025-11-06 13:26:26 -06:00

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Reference: github-starred/awesome-python#1949