data-analysis

You can perform exploratory data analysis on Parquet files by loading the data into pandas, Polars, or DuckDB to inspect distributions, identify missing values, and apply standardized cleaning patterns for reproducible results.

You should use Polars instead of pandas when you need faster performance on larger datasets, while reserving pandas for smaller data and DuckDB for complex SQL-based queries across CSV and Parquet formats.

The best way to build reproducible data analysis pipelines in notebooks is to apply standardized templates and decision guides for tool selection, ensuring your data cleaning and transformation steps are auditable and repeatable.

Yes, you can use DuckDB to query CSV and Parquet data directly within your analysis workflow, enabling efficient SQL-based exploration and transformation alongside pandas and Polars for comprehensive data processing.

Yes, this approach works for cleaning and transforming large datasets by leveraging Polars and DuckDB for performance optimization, while providing standardized patterns to maintain reliability and auditability across your data projects.