DuckDB over Pandas/Polars

Paul Gross discusses his experience using DuckDB for data analysis compared to Polars and Pandas. He initially attempted to analyze financial CSV files using Polars but found its syntax confusing and cumbersome, particularly when it came to selecting and transforming columns, parsing date formats, and using lambda functions. In contrast, he found DuckDB more intuitive due to his familiarity with SQL, allowing him to write queries that were easier to understand and execute. He highlights the simplicity of using SQL for tasks such as summing amounts and joining multiple CSV files, noting that DuckDB automatically handles date parsing. Gross concludes that DuckDB is a powerful and enjoyable tool for data analysis, especially for users who are more accustomed to SQL than to the syntax of libraries like Polars.

- DuckDB is preferred by users familiar with SQL for its intuitive syntax.

- Polars can be complex for casual users due to its syntax and transformation requirements.

- DuckDB simplifies data parsing and manipulation tasks compared to other libraries.

- The ability to join multiple CSVs and apply complex conditions is a significant advantage of DuckDB.

- Users transitioning from SQL to DuckDB may find it easier to perform data analysis tasks.