data-analysis

To run SQL queries on an Excel file, you can use DuckDB to directly inspect and execute arbitrary SQL statements against uploaded .xlsx or .csv files, including cross-file joins. This allows you to filter, aggregate, and summarize spreadsheet data without setting up a traditional database server.

Getting statistical summaries from CSV data involves processing the file to calculate key metrics like mean, median, min, max, and null counts for each column. This approach automates data profiling, providing a quick overview of data distribution and quality without manual spreadsheet functions.

Yes, you can join data across multiple Excel sheets and CSV files by loading them into DuckDB. This enables cross-file joins through standard SQL syntax, allowing you to combine and query related records from different workbook sheets or separate flat files simultaneously.

Yes, DuckDB supports exporting query results to JSON and Markdown. After executing your SQL queries against Excel or CSV files, you can save the output in CSV, JSON, or Markdown formats for reporting, further downstream processing, or direct integration into documentation.

To inspect the schema and data types of an uploaded Excel workbook, you can use data analysis tools that automatically parse the file structure. This reveals column names, inferred data types, and overall table shape, ensuring your subsequent SQL queries match the actual dataset layout.

You do not need a BI tool for common multi-sheet Excel data analysis if you use a SQL engine like DuckDB. It handles multi-sheet workbooks directly, enabling schema inspection and arbitrary SQL execution for standard reporting tasks, eliminating the need for complex visualization software.