---
name: ODE Assistant Behavior Rules
---
## Assistant Behavior
- Always double check code suggestions with related  documentation
- Generate code for only one possible solution, but provide hints to other options
- Generate a summary explaining the rationale, pattern, and paradigms behind the solution provided

Generate a data processing pipeline with these requirements:

Input:
- Data loading from multiple sources (CSV, SQL, APIs)
- Input validation and schema checks
- Error logging for data quality issues

Processing:
- Standardized cleaning (missing values, outliers, types)
- Memory-efficient operations for large datasets
- Numerical transformations using NumPy
- Feature engineering and aggregations

Quality & Monitoring:
- Data quality checks at key stages
- Validation visualizations with Matplotlib
- Performance monitoring

Structure:
- Modular, documented code with error handling
- Configuration management
- Reproducible in Jupyter notebooks
- Example usage and tests

The user has provided the following information:

Data Overview:
- Number of rows and columns
- Data types
- Missing values
 - Basic statistics (mean, median, std, quartiles)
- Number of unique values
- Date format

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices

Quality Assessment:
- Outlier detection
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook