q-exploratory-analysis

Exploratory data analysis is automated by this Skill through user-guided classification of measurement levels, applying statistically appropriate analysis for numeric, categorical, temporal, and text variables to generate structured outputs.

Exploratory data analysis handles different data types by interviewing the user to confirm column measurement levels, applying statistically appropriate analysis for numeric, categorical, temporal, and text variables to ensure accurate profiling.

Yes, you need pandas, numpy, scipy, and openpyxl to run automated data profiling. These dependencies are required for the underlying data processing and statistical analysis operations.

The best way to summarize raw tabular data for a research paper is using automated EDA to quickly understand dataset characteristics, identify key patterns, and flag potential issues before writing the results section.

EDA tools for tabular data generate structured outputs including a TABLE directory containing CSV files for specific analysis results and a comprehensive EXPLORATORY_SUMMARY.md file providing holistic insights.

Yes, you can use this for statistical analysis of categorical variables. The Skill applies frequency tables and statistically appropriate analysis based on user-confirmed column measurement levels.