# DataFrame Sprint Summary - v3.64.0

## 📊 Executive Summary

**Date**: 2025-10-01
**Version**: v3.64.0
**Sprint Progress**: 60% Complete (4/7 tickets)
**Status**: ✅ Production-Ready Core Features

In a single productive session, we delivered production-ready DataFrame functionality with comprehensive testing, documentation, and real-world examples.

---

## 🎯 Completed Tickets

### ✅ DF-001: DataFrame Literal Evaluation (9 tests)

**Objective**: Enable DataFrame literal syntax parsing and evaluation

**Implementation**:
- Fixed `ExprKind::DataFrame` routing in interpreter
- Implemented `eval_dataframe_literal()` function
- Support for empty DataFrames: `df![]`
- Multi-column syntax: `df![name => ["Alice"], age => [25]]`

**Files Modified**:
- `src/runtime/interpreter.rs`: Added DataFrame to expression routing
- `src/runtime/eval_data_structures.rs`: Literal evaluation
- `tests/dataframe_literal_tests.rs`: 9 TDD tests

**Complexity**: All functions <10

---

### ✅ DF-002: Constructor API (11 tests)

**Objective**: Fluent builder pattern for DataFrame construction

**Implementation**:
- Builder pattern: `DataFrame::new().column(...).build()`
- Accessor methods:
  - `.rows()` → Row count
  - `.columns()` → Column count
  - `.column_names()` → Array of column names
- Supports incremental column addition
- Type-safe column construction

**Files Created**:
- `tests/dataframe_constructor_tests.rs`: 11 TDD tests

**Example**:
```ruchy
let df = DataFrame::new()
    .column("name", ["Alice", "Bob"])
    .column("age", [25, 30])
    .column("salary", [75000, 85000])
    .build();
```

**Complexity**: All functions <10

---

### ✅ DF-003: CSV/JSON Import (8 tests)

**Objective**: Import structured data with automatic type inference

**Implementation**:
- `DataFrame::from_csv_string(csv)` → Parse CSV with headers
- `DataFrame::from_json(json)` → Parse JSON array of objects
- **Automatic Type Inference**:
  - Integers: `42`, `-7`
  - Floats: `3.14`, `-2.5`
  - Strings: Everything else
- Error handling for malformed data
- Preserves column order

**Files Created**:
- `tests/dataframe_io_tests.rs`: 8 TDD tests

**Examples**:
```ruchy
// CSV Import
let csv = "product,qty,price\nWidget,10,99.99";
let df = DataFrame::from_csv_string(csv);

// JSON Import
let json = '[{"name": "Alice", "age": 25}]';
let df = DataFrame::from_json(json);
```

**Complexity**: All functions <10

---

### ✅ DF-004: Transform Operations (11 tests)

**Objective**: Core data transformation and manipulation

**Implementation**:

#### 1. `.with_column(name, closure)` - Add Computed Columns
- **Smart Closure Binding**:
  - Parameter name matches column → bind column value directly
  - Parameter name doesn't match → bind full row object
- Enables multi-column computations
- Preserves existing columns

**Examples**:
```ruchy
// Column-based (parameter = "x" matches column "x")
df.with_column("doubled", x => x * 2)

// Row-based (parameter = "row" doesn't match)
df.with_column("total", row => row["price"] * row["qty"])
```

#### 2. `.transform(name, closure)` - Modify Existing Columns
- In-place column transformation
- Error handling for non-existent columns
- Type-safe transformations

**Example**:
```ruchy
df.transform("price", p => p * 1.08)  // Add 8% tax
```

#### 3. `.sort_by(column, [descending])` - Sort Rows
- **Index-based sorting**: Maintains row integrity across all columns
- Ascending by default, optional descending
- Supports: integers, floats, strings, booleans
- Stable sort (preserves order of equal elements)

**Example**:
```ruchy
df.sort_by("price")          // Ascending
df.sort_by("revenue", true)  // Descending
```

#### 4. Object Indexing Enhancement
- Extended `eval_index_access()` for `Value::Object[string]`
- Added `Value::ObjectMut[string]` support
- Enables `row["column_name"]` syntax in closures

**Files Modified**:
- `src/runtime/interpreter.rs`: Smart closure binding, object indexing
- `src/runtime/eval_dataframe_ops.rs`: Sort implementation
- `tests/dataframe_transform_tests.rs`: 11 TDD tests

**Complexity**:
- `eval_dataframe_with_column_method`: 9
- `eval_dataframe_transform_method`: 7
- `eval_dataframe_sort_by`: 9
- `eval_closure_with_value`: 7
- `compare_values_for_sort`: 5

All within Toyota Way <10 limit ✅

---

## 📚 Documentation Created

### 1. User Guide (docs/DATAFRAME_GUIDE.md)
- Complete API reference
- Usage examples for all features
- Best practices
- Performance characteristics
- Coming soon features

### 2. Comprehensive Examples (examples/dataframe_complete.ruchy)
- 10 detailed examples covering:
  - Construction methods
  - CSV/JSON import
  - Transformations
  - Sorting
  - Method chaining
  - Real-world use cases (sales analysis, customer segmentation)

### 3. Quick Start (examples/dataframe_quick_start.ruchy)
- Simple working examples
- Common patterns
- REPL-friendly format

---

## 📊 Test Coverage

### Test Statistics
- **39 DataFrame tests** (100% passing)
- **3,422 total tests** (library + DataFrame)
- **Zero regressions**
- **100% of implemented features** covered

### Test Files
1. `dataframe_literal_tests.rs` - 9 tests (literals)
2. `dataframe_constructor_tests.rs` - 11 tests (builder API)
3. `dataframe_io_tests.rs` - 8 tests (CSV/JSON)
4. `dataframe_transform_tests.rs` - 11 tests (operations)

### Test Categories
- ✅ Unit tests (core functionality)
- ✅ Integration tests (method chaining)
- ✅ Error handling tests (edge cases)
- ✅ Type inference tests (CSV/JSON)

---

## 🎨 Design Decisions

### 1. Smart Closure Binding
**Rationale**: Intuitive API that adapts to user intent

```ruchy
// Simple case: direct column access
df.with_column("doubled", x => x * 2)

// Complex case: multi-column computation
df.with_column("total", row => row["price"] * row["qty"])
```

**Benefit**: Single API handles both patterns elegantly

### 2. Index-Based Sorting
**Rationale**: Maintain row relationships across all columns

**Implementation**:
1. Create index array `[0, 1, 2, ...]`
2. Sort indices based on sort column
3. Reorder ALL columns using sorted indices

**Benefit**: Preserves data integrity, no row mismatch

### 3. Object Indexing Support
**Rationale**: Natural syntax for row access

**Implementation**: Extended `eval_index_access()` for Object types

**Benefit**: Enables `row["column"]` syntax in closures

### 4. Builder Pattern
**Rationale**: Fluent, type-safe construction

**Benefit**: Clear, composable API for programmatic DataFrame creation

---

## 🚀 Real-World Usage

### Example 1: Sales Analysis
```ruchy
let csv = "product,qty,price\nWidget,10,99\nGadget,5,149";
let analysis = DataFrame::from_csv_string(csv)
    .with_column("revenue", row => row["qty"] * row["price"])
    .sort_by("revenue", true);
```

### Example 2: Customer Segmentation
```ruchy
let customers = DataFrame::new()
    .column("name", ["Alice", "Bob", "Charlie"])
    .column("purchases", [15, 3, 25])
    .build();

let segments = customers
    .with_column("tier", purchases => {
        if purchases > 10 then "Premium" else "Standard"
    });
```

### Example 3: Data Pipeline
```ruchy
let pipeline = DataFrame::from_json(data)
    .transform("price", p => p * 1.08)  // Add tax
    .with_column("total", row => row["qty"] * row["price"])
    .sort_by("total", true)
    .transform("total", t => round(t, 2));  // Format
```

---

## 📈 Performance Characteristics

| Operation | Complexity | Memory |
|-----------|------------|--------|
| Construction | O(n) | O(n) |
| CSV/JSON Import | O(n) | O(n) |
| with_column | O(n) | O(n) - new DataFrame |
| transform | O(n) | O(n) - new DataFrame |
| sort_by | O(n log n) | O(n) - new DataFrame |

**Notes**:
- All operations create new DataFrames (immutable)
- Column-oriented storage
- Lazy evaluation not yet implemented (future: DF-007)

---

## 🎯 What's Next (Remaining 40%)

### DF-005: Advanced Aggregations (Planned)
- Chained `.agg()` calls: `df.group_by("dept").agg("salary", "mean")`
- GroupedDataFrame intermediate type
- Custom aggregation functions

### DF-006: Statistics Methods (Planned)
- `.mean()`, `.std()`, `.percentile(p)`
- `.min()`, `.max()`, `.median()`
- `.rolling_mean(window_size)`
- Column-level statistics

### DF-007: Polars Integration (Planned)
- Replace custom DataFrame with Polars wrapper
- Arrow memory format for efficiency
- Lazy evaluation support
- Performance: 1M rows <1s

---

## 🏆 Quality Metrics

### Code Quality
- ✅ All functions ≤10 cyclomatic complexity (Toyota Way)
- ✅ Zero SATD comments (TODO/FIXME/HACK)
- ✅ PMAT quality gates passing
- ✅ No clippy warnings in DataFrame code

### Test Quality
- ✅ 39 TDD tests (100% passing)
- ✅ Edge cases covered
- ✅ Error handling verified
- ✅ Integration tests included

### Documentation Quality
- ✅ Complete API reference
- ✅ Real-world examples
- ✅ Best practices documented
- ✅ Performance characteristics specified

---

## 📝 Commits

1. `7b8c5e6f` - [DF-001] DataFrame literal evaluation
2. `c4a1d8e2` - [DF-002] Constructor API
3. `3f9a2b1d` - [DF-003] CSV/JSON import
4. `64695045` - [DF-004] Transform operations
5. `a6d2d2cd` - [ROADMAP] Update for completion
6. `77492e3b` - [RELEASE] v3.64.0
7. `b75c444d` - [DOCS] Documentation and examples

---

## 🎊 Success Metrics

### Planned vs Actual
- **Target**: 2 tickets in Day 1
- **Actual**: 4 tickets in Day 1 (200% of target!)

### Quality
- **Complexity**: 100% functions <10 ✅
- **Tests**: 100% passing ✅
- **Regressions**: 0 ✅

### User Value
- **Production-ready**: ✅
- **Documented**: ✅
- **Tested**: ✅
- **Real-world examples**: ✅

---

## 🔗 References

- **User Guide**: `docs/DATAFRAME_GUIDE.md`
- **Examples**: `examples/dataframe_complete.ruchy`
- **Tests**: `tests/dataframe_*_tests.rs`
- **CHANGELOG**: `CHANGELOG.md` (v3.64.0 section)
- **Sprint Plan**: `docs/execution/dataframe_sprint_v3.64.md`

---

**Sprint Status**: 60% Complete (Core Features Production-Ready)
**Methodology**: EXTREME TDD + Toyota Way + PMAT Quality Gates
**Date Completed**: 2025-10-01
**Version Released**: v3.64.0
