# Groggy Table Operations - Implementation Status

## Overview
Comprehensive analysis of table operations comparing current Groggy implementation against standard data processing libraries (pandas, polars, SQL, dplyr).

**Legend:**
- ✅ **Implemented** - Available and working
- 🔄 **Partial** - Basic implementation exists, needs enhancement
- ❌ **Missing** - Not implemented, needs development
- 🎯 **Priority** - High-impact operation for users

---

## 1. **BASIC TABLE OPERATIONS**

### Table Structure & Metadata
- ✅ `table.shape` - Get (rows, cols) dimensions
- ✅ `table.nrows` / `table.ncols` - Get row/column counts
- ✅ `table.columns` / `table.column_names` - List column names
- ✅ `table.has_column(name)` - Check if column exists
- ❌ `table.dtypes` - Get column data types 🎯
- ❌ `table.info()` - Summary of table structure 🎯
- ❌ `table.describe()` - Statistical summary of numeric columns 🎯
- ❌ `table.memory_usage()` - Memory footprint per column

### Basic Access & Display
- ✅ `table.head(n)` - First n rows
- ✅ `table.tail(n)` - Last n rows
- ✅ `table.__getitem__` - Column/row selection
- ✅ `table.__iter__` - Row iteration
- ❌ `table.sample(n)` - Random sample of rows 🎯
- ❌ `table.sample(frac=0.1)` - Percentage sample
- ❌ `table.at[row, col]` - Fast scalar access
- ❌ `table.iat[row, col]` - Fast integer scalar access

---

## 2. **COLUMN OPERATIONS**

### Column Management
- ✅ `table.select(columns)` - Select specific columns
- ✅ `table.drop_columns(columns)` - Remove columns
- ✅ `table.assign(new_columns)` - Add/update columns
- ✅ `table.set_column(name, values)` - Set entire column
- ❌ `table.rename(mapping)` - Rename columns 🎯
- ❌ `table.reorder(columns)` - Reorder columns
- ❌ `table.add_prefix(prefix)` - Add prefix to all columns
- ❌ `table.add_suffix(suffix)` - Add suffix to all columns

### Column Types & Conversion
- ❌ `table.astype(dtype_mapping)` - Convert column types 🎯
- ❌ `table[col].cast(dtype)` - Cast single column
- ❌ `table.infer_dtypes()` - Auto-detect optimal types
- ❌ `table.to_numeric(col)` - Convert to numeric with error handling

---

## 3. **ROW OPERATIONS**

### Row Selection & Filtering
- ✅ `table.filter(predicate)` - Boolean filtering
- ✅ `table.slice(start, end)` - Slice rows by position
- ❌ `table.query(expression)` - SQL-like string queries 🎯
- ❌ `table.where(condition)` - Conditional selection
- ❌ `table.mask(condition)` - Inverse of where
- ❌ `table.between(col, low, high)` - Range filtering
- ❌ `table.isin(col, values)` - Value membership filtering 🎯

### Row Modification
- ✅ `table.set_value(row, col, value)` - Set single value
- ✅ `table.set_values_by_mask(mask, col, value)` - Conditional updates
- ✅ `table.set_values_by_range(start, end, col, value)` - Range updates
- ❌ `table.drop(indices)` - Remove specific rows
- ❌ `table.drop_duplicates()` - Remove duplicate rows 🎯
- ❌ `table.reset_index()` - Reset row indices
- ❌ `table.set_index(col)` - Set column as index

### Row Insertion & Deletion
- ❌ `table.append(row_dict)` - Add single row 🎯
- ❌ `table.insert(position, row_dict)` - Insert row at position
- ❌ `table.extend(other_table)` - Add multiple rows
- ❌ `table.pop(index)` - Remove and return row

---

## 4. **SORTING & ORDERING**

### Basic Sorting
- ✅ `table.sort_by(column, ascending)` - Sort by single column
- ❌ `table.sort_values(columns, ascending)` - Multi-column sort 🎯
- ❌ `table.sort_index()` - Sort by index
- ❌ `table.nlargest(n, col)` - N largest values
- ❌ `table.nsmallest(n, col)` - N smallest values

### Ranking & Order
- ❌ `table[col].rank()` - Rank values in column
- ❌ `table[col].argsort()` - Indices that would sort array
- ❌ `table.reindex(new_order)` - Reorder with custom index

---

## 5. **AGGREGATION & STATISTICS**

### Current Implementation
- ✅ `table.aggregate(agg_specs)` - Basic aggregation with specs
- ✅ `table.agg(agg_specs)` - Alias for aggregate

### Missing Core Statistics - ALL MISSING 🎯
- ❌ `table[col].sum()` - Column sum
- ❌ `table[col].mean()` - Column average
- ❌ `table[col].median()` - Column median
- ❌ `table[col].std()` - Standard deviation
- ❌ `table[col].var()` - Variance
- ❌ `table[col].min()` - Minimum value
- ❌ `table[col].max()` - Maximum value
- ❌ `table[col].count()` - Non-null count
- ❌ `table[col].nunique()` - Unique value count
- ❌ `table[col].mode()` - Most frequent value

### Advanced Statistics
- ❌ `table[col].quantile(q)` - Quantiles/percentiles
- ❌ `table[col].skew()` - Skewness
- ❌ `table[col].kurtosis()` - Kurtosis
- ❌ `table.corr()` - Correlation matrix
- ❌ `table.cov()` - Covariance matrix
- ❌ `table[col].cumsum()` - Cumulative sum
- ❌ `table[col].cumprod()` - Cumulative product
- ❌ `table[col].pct_change()` - Percentage change

---

## 6. **GROUPING OPERATIONS**

### Current Implementation
- 🔄 `table.group_by(columns)` - Returns PyTableArray (basic)
- ✅ `table.group_by_agg(group_cols, agg_specs)` - Group + aggregate

### Missing GroupBy Features - HIGH PRIORITY 🎯
- ❌ `table.groupby(col).sum()` - Fluent group-aggregate API
- ❌ `table.groupby(col).mean()` - Group means
- ❌ `table.groupby(col).count()` - Group counts
- ❌ `table.groupby(col).apply(func)` - Apply custom function
- ❌ `table.groupby(col).transform(func)` - Transform with broadcasting
- ❌ `table.groupby(col).filter(func)` - Filter groups by condition
- ❌ `table.groupby(col).size()` - Group sizes
- ❌ `table.groupby(col).first()` - First value per group
- ❌ `table.groupby(col).last()` - Last value per group

### Window Functions
- ❌ `table[col].rolling(window).mean()` - Rolling statistics
- ❌ `table[col].expanding().sum()` - Expanding window operations
- ❌ `table[col].shift(periods)` - Lag/lead operations

---

## 7. **JOIN & MERGE OPERATIONS**

### Current Implementation
- 🔄 `table.inner_join(other, left_on, right_on)` - Basic inner join
- 🔄 `table.left_join(other, left_on, right_on)` - Basic left join
- ✅ `table.union(other)` - Union operation
- ✅ `table.intersect(other)` - Intersection

### Missing Join Types 🎯
- ❌ `table.right_join(other, on)` - Right join
- ❌ `table.outer_join(other, on)` - Full outer join
- ❌ `table.cross_join(other)` - Cartesian product
- ❌ `table.merge(other, how='inner', on=None)` - Pandas-style merge
- ❌ `table.join(other, on=None, how='left')` - Index-based join

### Advanced Join Features
- ❌ Multi-column joins: `on=['col1', 'col2']`
- ❌ Different column names: `left_on='a', right_on='b'`
- ❌ Suffix handling for duplicate columns
- ❌ Join validation (one-to-one, one-to-many, etc.)

---

## 8. **RESHAPING & PIVOTING**

### ALL MISSING - MAJOR GAP 🎯
- ❌ `table.pivot(index, columns, values)` - Pivot table
- ❌ `table.pivot_table(...)` - Aggregating pivot table
- ❌ `table.melt(id_vars, value_vars)` - Unpivot/melt
- ❌ `table.transpose()` - Transpose table
- ❌ `table.stack()` - Stack columns to rows
- ❌ `table.unstack()` - Unstack rows to columns
- ❌ `table.wide_to_long()` - Reshape wide to long format

---

## 9. **MISSING VALUE HANDLING**

### ALL MISSING - CRITICAL GAP 🎯
- ❌ `table.isna()` - Detect missing values
- ❌ `table.notna()` - Detect non-missing values
- ❌ `table.dropna()` - Remove rows with missing values
- ❌ `table.fillna(value)` - Fill missing values
- ❌ `table[col].bfill()` - Backward fill
- ❌ `table[col].ffill()` - Forward fill
- ❌ `table.interpolate()` - Interpolate missing values
- ❌ `table.replace(old, new)` - Replace specific values

---

## 10. **STRING OPERATIONS**

### ALL MISSING - TEXT PROCESSING GAP 🎯
- ❌ `table[col].str.upper()` - Convert to uppercase
- ❌ `table[col].str.lower()` - Convert to lowercase
- ❌ `table[col].str.title()` - Title case
- ❌ `table[col].str.strip()` - Remove whitespace
- ❌ `table[col].str.replace(old, new)` - String replacement
- ❌ `table[col].str.contains(pattern)` - Pattern matching
- ❌ `table[col].str.startswith(prefix)` - Prefix check
- ❌ `table[col].str.endswith(suffix)` - Suffix check
- ❌ `table[col].str.split(delimiter)` - String splitting
- ❌ `table[col].str.len()` - String length
- ❌ `table[col].str.extract(pattern)` - Regex extraction

---

## 11. **TIME SERIES OPERATIONS**

### ALL MISSING - TIME HANDLING GAP
- ❌ `table.set_datetime_index(col)` - Set datetime index
- ❌ `table.resample(freq)` - Time-based resampling
- ❌ `table.asfreq(freq)` - Convert to frequency
- ❌ `table[col].dt.year` - Extract year from datetime
- ❌ `table[col].dt.month` - Extract month
- ❌ `table[col].dt.dayofweek` - Day of week
- ❌ `table.between_time(start, end)` - Time range filtering

---

## 12. **I/O OPERATIONS**

### Current Implementation
- ✅ `table.to_csv(path)` - Export to CSV
- ✅ `table.from_csv(path)` - Import from CSV
- ✅ `table.to_parquet(path)` - Export to Parquet
- ✅ `table.from_parquet(path)` - Import from Parquet
- ✅ `table.to_json(path)` - Export to JSON
- ✅ `table.from_json(path)` - Import from JSON
- ✅ `table.to_pandas()` - Convert to pandas DataFrame

### Missing I/O Features
- ❌ `table.to_excel(path)` - Export to Excel
- ❌ `table.from_excel(path)` - Import from Excel
- ❌ `table.to_sql(connection, table_name)` - Export to database
- ❌ `table.from_sql(query, connection)` - Import from database
- ❌ `table.to_dict()` - Convert to dictionary
- ❌ `table.to_records()` - Convert to record array

### I/O Options & Parameters
- ❌ CSV options: `sep`, `header`, `index`, `encoding`
- ❌ JSON options: `orient`, `lines`, `compression`
- ❌ Parquet options: `compression`, `engine`

---

## 13. **ADVANCED OPERATIONS**

### Mathematical Operations
- ❌ `table[col] + table[col2]` - Element-wise arithmetic 🎯
- ❌ `table[col] * scalar` - Broadcasting operations
- ❌ `table.apply(func)` - Apply function to columns/rows 🎯
- ❌ `table.applymap(func)` - Apply function element-wise
- ❌ `table.eval(expression)` - Evaluate string expressions

### Conditional Operations
- ❌ `table.where(condition, value)` - Conditional replacement
- ❌ `table.clip(lower, upper)` - Clip values to range
- ❌ `table.round(decimals)` - Round numeric values
- ❌ `np.select(conditions, choices)` - Multiple condition selection

---

## 14. **VALIDATION & QUALITY**

### ALL MISSING - DATA QUALITY GAP
- ❌ `table.validate_schema(schema)` - Schema validation
- ❌ `table.check_duplicates()` - Duplicate analysis
- ❌ `table.check_nulls()` - Missing value analysis
- ❌ `table.check_dtypes()` - Data type validation
- ❌ `table.profile()` - Data profiling report
- ❌ `table.outliers(method='iqr')` - Outlier detection

---

## **PRIORITY IMPLEMENTATION ROADMAP**

### 🔥 **Phase 1: Core Statistics (Immediate Need)**
1. Individual column statistics: `sum()`, `mean()`, `min()`, `max()`, `count()`
2. `table.describe()` - Statistical summary
3. `table[col].unique()` and `table[col].nunique()`
4. Basic missing value detection: `isna()`, `notna()`

### 🎯 **Phase 2: Essential Operations (High Impact)**
1. `table.sample(n)` - Random sampling
2. `table.rename(mapping)` - Column renaming
3. `table.drop_duplicates()` - Duplicate removal
4. `table.query(expression)` - String-based filtering
5. GroupBy fluent API: `table.groupby(col).sum()`

### 📊 **Phase 3: Data Manipulation**
1. `table.append(row)` - Row insertion
2. `table.sort_values(columns)` - Multi-column sorting
3. String operations: `.str.upper()`, `.str.contains()`, etc.
4. Missing value handling: `fillna()`, `dropna()`

### 🔄 **Phase 4: Advanced Analytics**
1. Pivot/melt operations for reshaping
2. Window functions: `rolling()`, `expanding()`
3. Advanced joins: `right_join()`, `outer_join()`
4. Time series operations

### 🏗️ **Phase 5: Infrastructure**
1. Data quality validation tools
2. Enhanced I/O with options
3. Performance optimizations
4. Advanced mathematical operations

---

## **GAPS ANALYSIS SUMMARY**

**Major Missing Categories:**
1. **Individual column statistics** - Critical for data exploration
2. **Missing value handling** - Essential for data cleaning
3. **String operations** - Needed for text processing
4. **Pivot/reshape operations** - Important for data transformation
5. **Enhanced GroupBy API** - Needed for aggregation workflows
6. **Row insertion/deletion** - Required for data manipulation
7. **Data validation tools** - Important for data quality

**Current Strengths:**
- Solid foundation with basic operations
- Good I/O support (CSV, Parquet, JSON)
- Basic joins and aggregation framework
- Integration with pandas via `to_pandas()`

**Immediate User Pain Points:**
- Cannot compute basic statistics like `table['col'].sum()`
- No simple way to remove duplicates
- No string manipulation capabilities
- No missing value handling
- Limited GroupBy functionality

This analysis shows Groggy has a solid foundation but is missing many essential table operations that users expect from modern data processing libraries.