data-analysis

You can analyze Excel and CSV files using SQL queries by leveraging DuckDB, an in-process SQL engine. This approach allows you to directly inspect schemas, execute complex queries, and generate statistical summaries without needing a separate database server.

Yes, you can join data across multiple CSV or Excel files in a single query. The Skill provides multi-file support within the same query context, enabling cross-file joins to combine and analyze related datasets efficiently.

To generate a statistical summary for numeric columns, the Skill processes your data to calculate metrics like mean, median, and standard deviation. This provides a quick overview of your dataset's distribution and central tendencies.

The best way to export SQL query results is by using the built-in export functionality, which supports saving your analyzed data in CSV, JSON, or Markdown formats. This allows for seamless integration into reports or downstream data pipelines.

Yes, you need basic SQL knowledge to use this data analysis tool effectively. The Skill is designed for users who understand SQL syntax to query their Excel and CSV files and extract specific insights using DuckDB.

To process Excel files with DuckDB, you need the openpyxl and pandas Python dependencies installed. These libraries work alongside DuckDB to read, process, and analyze your spreadsheet data within the Python environment.