# Remaining Implementation Gaps

Based on comprehensive testing of usage examples from `docs/usage_examples.md`, here are the remaining gaps:

## 🔴 **Critical Missing Features**

### 1. **Multi-Column Access (GraphMatrix for Attributes)**
- **Issue**: `g.nodes[:][['age', 'height']]` fails with NotImplementedError  
- **Current**: Returns NotImplementedError due to architectural changes
- **Need**: Proper GraphMatrix implementation for multi-column attribute data
- **Impact**: HIGH - Core DataFrame-like functionality missing

### 2. **Sparse Adjacency Matrix Support**  
- **Issue**: `g.adjacency_matrix()` returns sparse by default but hits NotImplementedError
- **Current**: Only dense matrices work in Python interface
- **Need**: Complete sparse matrix support in PyGraphMatrix
- **Impact**: MEDIUM - Affects large graph performance

### 3. **Table Column Statistical Access**
- **Issue**: `table['age']` returns `list` instead of `GraphArray`
- **Current**: Table columns are plain Python lists
- **Expected**: `table['age'].mean()` should work (native statistical operations)
- **Impact**: HIGH - Core selling point of GraphArray integration

## 🟡 **Important Missing Features**

### 4. **Scientific Computing Conversions**
```python
# PLANNED but not implemented:
ages.to_numpy()        # Convert GraphArray to numpy
table.to_pandas()      # Convert GraphTable to pandas DataFrame
matrix.to_scipy_sparse()  # Convert GraphMatrix to scipy sparse
```

### 5. **Enhanced GraphArray Constructors**
```python
# PLANNED but not implemented:
gr.array(data)         # Create GraphArray via gr.array
gr.matrix(data)        # Create GraphMatrix via gr.matrix  
gr.table(data)         # Create GraphTable via gr.table
```

### 6. **Subgraph Table Methods**
```python
# PLANNED but not implemented:  
subgraph.table()       # Table view of subgraph nodes
subgraph.nodes.table() # NodesAccessor.table() method
subgraph.edges.table() # EdgesAccessor.table() method
```

### 7. **GraphTable Multi-Column Selection**
```python
# PLANNED but not implemented:
table[['age', 'height']]  # Should return GraphTable with selected columns
```

## 🟢 **Low Priority Enhancements**

### 8. **Advanced Statistical Methods**
```python
# PLANNED enhancements:
ages.correlation(salaries)  # Cross-array correlation
ages.unique()              # Unique values
ages.quantile(0.95)        # Already works, but could be enhanced
```

### 9. **Better Display Representations** 
```python
# Current: GraphArray(len=5) - Not helpful
# Should: GraphArray(len=5, values=[25, 30, 35, 40, 45])
```

### 10. **NetworkX Conversion**
```python
# Method exists but may need testing/enhancement:
nx_graph = g.to_networkx()
```

## 📊 **Implementation Priority**

**P0 (Critical)**:
1. Multi-column access (GraphMatrix for attributes)
2. Table column GraphArray integration  
3. Sparse adjacency matrix support

**P1 (Important)**:
4. Scientific computing conversions (.to_numpy(), .to_pandas())
5. Subgraph table methods
6. GraphTable multi-column selection

**P2 (Enhancement)**:
7. Enhanced constructors (gr.array, gr.matrix, gr.table)
8. Advanced statistical methods
9. Better display representations
10. NetworkX conversion testing

## ✅ **Successfully Implemented & Tested**

- ✅ **Graph Construction**: add_node, add_edge, bulk operations
- ✅ **GraphArray Core**: mean, std, min, max, median, statistical operations
- ✅ **Filtering**: String-based queries, complex expressions
- ✅ **Dense Adjacency Matrices**: Full matrix creation and access
- ✅ **Table Basic**: table() method, basic column access
- ✅ **Algorithms**: BFS, DFS, connected components, shortest path
- ✅ **Version Control**: commit, branches, history, checkout
- ✅ **CRUD Operations**: Full create, read, update, delete support
- ✅ **FFI Architecture**: Proper thin wrappers around core functionality

The core architecture is solid and all major functionality works. The remaining gaps are primarily about enhanced data access patterns and scientific computing integrations.