Loading Pandas DataFrames into QuestDB

QuestDB is an open-source time-series database designed for high-demand workloads, offering ultra-low latency, high ingestion throughput, and a multi-tier storage engine, with native support for Parquet and SQL to keep data portable and AI-ready. While the Pandas library is essential for data scientists working with Python due to its intuitive data manipulation capabilities, it struggles with large datasets that exceed available machine memory. By integrating Pandas DataFrames with QuestDB, users can leverage the database's robust data processing capabilities to efficiently handle and analyze large datasets. This tutorial demonstrates how to load NYC taxi trip records into QuestDB using Docker and Python, overcoming memory constraints by ingesting data one file at a time through QuestDB’s Python client. The method allows for scalable data analysis and manipulation, offering insights such as average passenger payments, and can be customized for specific needs, highlighting the potential of combining Pandas and QuestDB for big data tasks.