data_analysis

Comprehensive data analysis toolkit using Polars - a blazingly fast DataFrame library. This skill provides instructions, reference documentation, and ready-to-use scripts for common data analysis tasks. Detailed API reference and patterns for specific operations: Executable Python scripts for common tasks: - Loading datasets (CSV, JSON, Parquet, Excel, databases) - Data cleaning, filtering, and transformation - Aggregations, grouping, and pivot tables - Statistical analysis and summary statistics - Time series analysis and resampling - Joining and merging multiple datasets - Creating visualizations and charts - Exporting results to various formats - reference/loading.md - Loading data from all supported formats - reference/transformations.md - Column operations, filtering, sorting, type casting - reference/aggregations.md - Group by, window functions, running totals - reference/time_series.md - Date parsing, resampling, lag features - reference/statistics.md - Correlations, distributions, hypothesis testing setup - reference/visualization.md - Creating charts with matplotlib/plotly - scripts/explore_data.py - Quick dataset exploration and profiling - scripts/summary_stats.py - Generate comprehensive statistics report - Use lazy evaluation for large datasets: pl.scan_csv() + .collect() - Filter early to reduce data volume before expensive operations - Select only needed columns...