# CSV Processor - Application Design
## Project Description
A **Rust library and CLI tool** for CSV data analysis. Features automatic type inference, embedded statistical operations, and a professional module architecture following industry patterns from Polars and Apache Arrow.
## Dual Purpose Design
- **📚 Rust Library** - Clean API for embedding CSV analysis in applications
- **🖥️ CLI Tool** - Command-line interface for direct usage
## Architecture
### Core Principles
- **Industry-aligned**: Module structure following Polars/Arrow patterns
- **Embedded Analysis**: Statistical operations integrated into column types (no separate analyzer)
- **Functional design**: Immutable data flow with pure functions
- **Single responsibility**: Each module handles one concern
- **Ergonomic API**: Direct method calls on trait objects without complex downcasting
### Data Flow
```
CLI Args → Config → DataFrame (self-analyzing columns) → Formatted Output
```
## Project Structure
```
src/
├── lib.rs # ✅ Library interface with documentation
├── bin/
│ └── csv_processor.rs # ✅ CLI binary (separated from library)
├── config.rs # ✅ CLI parsing (exported for advanced use)
├── types.rs # ✅ Core types (Dtype, CsvError)
├── series/ # ✅ Column-oriented data structures (Polars pattern)
│ ├── mod.rs # ✅ Re-exports for series functionality
│ └── array.rs # ✅ ColumnArray trait with embedded statistical operations
├── frame/ # ✅ DataFrame operations and I/O
│ ├── mod.rs # ✅ DataFrame struct with headers, typed columns
│ ├── error.rs # ✅ DataFrameError enum with proper error handling
│ └── io.rs # ✅ CSV file loading with load_dataframe()
├── scalar/ # ✅ Cell-level operations and values
│ └── mod.rs # ✅ CellValue enum with utility methods
└── reporter.rs # ✅ Statistical report generation (wide/long formats)
```
### Library + Binary Configuration
```toml
# Cargo.toml
[[bin]]
name = "csv_processor"
path = "src/bin/csv_processor.rs"
[lib]
name = "csv_processor"
path = "src/lib.rs"
```
## Key Design Decisions
### Unified Column System (Following Polars Patterns)
- `ColumnArray` trait provides both data access AND statistical operations
- Self-analyzing columns with embedded statistical methods
- Automatic type inference (Integer → Float → Boolean → String)
- All statistical operations return `Option<f64>` for consistency
- No separate analyzer - analysis is embedded in column types
### Error Handling
- Custom `DataFrameError` enum with specific variants (HeadersColumnsLengthMismatch, ColumnsLengthMismatch, RowLengthMismatch, CsvError, IoError)
- `Result<T, E>` pattern throughout for explicit error handling
- Proper error conversion with `map_err` and `?` operator
- Display and Error trait implementations for user-friendly messages
### Core Components
**Library API:**
- **DataFrame**: Main data container with self-analyzing typed columns and Display formatting
- **ColumnArray**: Unified trait for polymorphic column access AND statistical operations
- **CellValue**: Enhanced enum with utility methods (is_null, data_type, Display)
- **Reporter**: Statistical report generation functions (generate_info_report, generate_na_report)
**Module Organization:**
- **Series Module**: Column-oriented structures following Polars/Arrow patterns
- **Frame Module**: DataFrame operations, CSV I/O, and formatted display
- **Scalar Module**: Cell-level operations and conversions
- **Config Module**: CLI parsing (exported for advanced library use)
**Library Interface (src/lib.rs):**
```rust
// Core exports for library users
pub use frame::DataFrame;
pub use scalar::CellValue;
pub use series::ColumnArray;
pub use types::{CsvError, Dtype};
// CLI exports (optional for library users)
pub use config::{Command, Config, ConfigError, parse_command, parse_config};
```
## Current Status
- ✅ **Foundation & Data Loading**: Complete with typed column system
- ✅ **Module Architecture**: Reorganized following Polars/Arrow patterns
- ✅ **Column System**: Complete with unified `ColumnArray` trait with `is_empty()` method
- ✅ **Statistical Operations**: Complete for all column types (Integer, Float, Boolean, String)
- All types implement: `sum()`, `min()`, `max()`, `mean()` returning `Option<f64>`
- Proper null handling and NaN filtering
- Boolean mean calculation (proportion of true values)
- Type conversion traits with explicit integer type handling
- ✅ **Analysis Architecture**: Complete - embedded in column trait system (no separate analyzer)
- ✅ **API Design**: Ergonomic trait object interface with direct method calls
- ✅ **DataFrame Display**: Complete with formatted table output and proper truncation
- ✅ **Statistical Reporting**: Complete with wide and long format report generation
- ✅ **Error Handling**: Complete with proper Result types throughout DataFrame operations
- Custom `DataFrameError` enum with specific error variants
- Display and Error trait implementations for user-friendly error messages
- Proper error conversion and propagation using `map_err` and `?` operator
- Clean module organization with `frame/error.rs` and public re-exports
- ✅ **Memory Optimization**: Complete - removed duplicate row storage from DataFrame
- ✅ **Testing Framework**: Complete with comprehensive test suites for all core functionality (39 tests passing)
- All statistical operations verified including boolean calculations
- Type conversion and trait implementation coverage
- Robust test architecture with proper type handling
- ✅ **Code Quality**: Idiomatic Rust patterns following clippy recommendations
- ✅ **CLI Integration**: Complete with `na` and `info` commands, help system, and publication-ready
- ✅ **Library Refactoring**: Clean library + binary separation with comprehensive documentation
## Progress Assessment
### **Current Status: 10/10 - COMPLETE** 🎉
**Major Achievements:**
- **Sophisticated Architecture**: Polars/Arrow-inspired design with professional module organization
- **Self-Analyzing Statistical Engine**: Embedded operations in column types with unified trait interface
- **Complete Display System**: Formatted DataFrame output with proper truncation and wide/long reports
- **Comprehensive Testing**: Well-structured test coverage across all core modules (39 tests passing)
- **Excellent API Design**: Ergonomic trait-based polymorphism enabling direct method calls
- **Production-Ready Code Quality**: Idiomatic Rust patterns, comprehensive error handling, and clippy compliance
- **Professional Error Handling**: Complete Result-based error system with proper conversion and user-friendly messages
**All architectural and implementation work is complete.** The project is now 100% finished and ready for publication.
### **Completion Status**
**🎉 PROJECT COMPLETE**:
✅ **CLI Integration** - Full command routing with `na` and `info` commands, comprehensive help system
✅ **NA Analysis Function** - Integrated into unified reporting system
✅ **Error Handling & UX** - Production-ready error messages with comprehensive DataFrameError system
✅ **Publication Ready** - Crates.io metadata, documentation, and clean repository
✅ **Professional Help** - `--help`, `-h`, `help` flags with usage examples
✅ **Library + Binary** - Clean separation with comprehensive API documentation
**📋 Future Enhancements (Optional)**:
- **Advanced Statistics** - median, mode, variance operations
- **Extended CLI Features** - Additional output formats, configuration options
**🔮 Future Roadmap (Optional)**:
- **Performance Optimizations** - Large file handling, streaming support
- **Output Format Options** - JSON, CSV export capabilities
- **Advanced Analytics** - Correlation analysis, statistical significance testing