# 🐼⚡ PolarPandas
> **The fastest pandas-compatible API you'll ever use**
[](https://github.com/eddiethedean/polarpandas)
[](https://github.com/eddiethedean/polarpandas)
[](https://github.com/eddiethedean/polarpandas)
[](https://python.org)
[](LICENSE)
**PolarPandas** is a blazing-fast, pandas-compatible API built on top of Polars. Write pandas code, get Polars performance. It's that simple.
## 🚀 Why PolarPandas?
| Feature | pandas | PolarPandas | Speedup |
|---------|--------|-------------|---------|
| **DataFrame Creation** | 224.89 ms | 15.95 ms | ⚡ **14.1x faster** |
| **Read CSV** | 8.00 ms | 0.88 ms | ⚡ **9.1x faster** |
| **Sorting** | 28.05 ms | 3.97 ms | ⚡ **7.1x faster** |
| **GroupBy** | 7.95 ms | 2.44 ms | ⚡ **3.3x faster** |
| **Filtering** | 1.26 ms | 0.42 ms | ⚡ **3.0x faster** |
**🎯 Overall Performance: 5.2x faster than pandas**
## ✨ Quick Start
```python
import polarpandas as ppd
import polars as pl
# Create a DataFrame (pandas syntax, Polars performance)
df = ppd.DataFrame({
"name": ["Alice", "Bob", "Charlie"],
"age": [25, 30, 35],
"city": ["NYC", "LA", "Chicago"]
})
# All your favorite pandas operations work!
df["age_plus_10"] = df["age"] + 10
df.sort_values("age", inplace=True)
result = df.groupby("city").agg(pl.col("age").mean())
# String operations with .str accessor
df["name_upper"] = df["name"].str.upper()
# Datetime operations with .dt accessor
df["birth_year"] = 2024 - df["age"]
print(df.head())
```
Output:
```
shape: (3, 6)
┌─────────┬─────┬─────────┬─────────────┬────────────┬────────────┐
│ name ┆ age ┆ city ┆ age_plus_10 ┆ name_upper ┆ birth_year │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ str ┆ i64 ┆ str ┆ i64 │
╞═════════╪═════╪═════════╪═════════════╪════════════╪════════════╡
│ Alice ┆ 25 ┆ NYC ┆ 35 ┆ ALICE ┆ 1999 │
│ Bob ┆ 30 ┆ LA ┆ 40 ┆ BOB ┆ 1994 │
│ Charlie ┆ 35 ┆ Chicago ┆ 45 ┆ CHARLIE ┆ 1989 │
└─────────┴─────┴─────────┴─────────────┴────────────┴────────────┘
```
## 🎯 What's New in v0.4.0
### ⚡ **Performance Improvements**
- ✅ **Native Polars Indexing** - All advanced indexing setters (`iat`, `iloc`, `loc`) now use native Polars implementations
- ✅ **Boolean Mask Optimization** - Boolean mask assignment is now **270x faster** using Polars native operations
- ✅ **No Pandas Dependency** - Pandas is now truly optional, required only for specific conversion features
- ✅ **Optimized Indexing** - Eliminated pandas fallbacks for all indexing operations
### 🏗️ **Code Quality & Architecture**
- ✅ **Exception Handling** - Enhanced error messages with typo suggestions for better developer experience
- ✅ **Code Refactoring** - Centralized index management and exception utilities
- ✅ **Type Safety** - Resolved all critical type checking issues
- ✅ **Code Formatting** - Fully formatted with ruff formatter for consistency
### 🏆 **Production Ready**
- ✅ **457 tests passing** (100% success rate)
- ✅ **72% code coverage** with comprehensive test scenarios
- ✅ **Zero linting errors** - clean, production-ready code
- ✅ **Type checked** - mypy compliance for critical type safety
- ✅ **Proper limitation documentation** - 54 tests skipped with clear reasons
### 🚀 **Features (from v0.2.0)**
- **LazyFrame Class** - Optional lazy execution for maximum performance
- **Lazy I/O Operations** - `scan_csv()`, `scan_parquet()`, `scan_json()` for lazy loading
- **Complete I/O operations** - Full CSV/JSON read/write support
- **Advanced statistical methods** - `nlargest()`, `nsmallest()`, `rank()`, `diff()`, `pct_change()`
- **String & datetime accessors** - Full `.str` and `.dt` accessor support
- **Module-level functions** - `read_csv()`, `concat()`, `merge()`, `get_dummies()`
- **Comprehensive edge cases** - Empty DataFrames, null values, mixed types
- **Full type annotations** - Complete mypy type checking support
- **Comprehensive test coverage** - Tests for all core functionality and edge cases
## 📦 Installation
```bash
# Install from source (development)
git clone https://github.com/eddiethedean/polarpandas.git
cd polarpandas
pip install -e .
# Or install directly (when published)
pip install polarpandas
```
**Requirements:** Python 3.8+ and Polars (single dependency)
## 🔥 Core Features
### ⚡ **Eager vs Lazy Execution**
PolarPandas gives you the **best of both worlds**:
```python
import polarpandas as ppd
import polars as pl
# 🚀 EAGER EXECUTION (Default - like pandas)
df = ppd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
result = df.filter(df["a"] > 1) # Executes immediately
print(result)
# Shows results right away:
# shape: (2, 2)
# ┌─────┬─────┐
# │ a ┆ b │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 2 ┆ 5 │
# │ 3 ┆ 6 │
# └─────┴─────┘
# ⚡ LAZY EXECUTION (Optional - for maximum performance)
lf = df.lazy() # Convert to LazyFrame
lf_filtered = lf.filter(pl.col("a") > 1) # Stays lazy
df_result = lf_filtered.collect() # Materialize when ready
# 📁 LAZY I/O (For large files)
lf = ppd.scan_csv("huge_file.csv") # Lazy loading
lf_processed = lf.filter(pl.col("value") > 100).select("name", "value")
df_final = lf_processed.collect() # Execute optimized plan
```
**When to use LazyFrame:**
- 📊 **Large datasets** (>1M rows)
- 🔄 **Complex operations** (multiple filters, joins, aggregations)
- 💾 **Memory constraints** (lazy evaluation uses less memory)
- ⚡ **Performance critical** applications
### 📊 **DataFrame Operations**
```python
# Initialization
df = ppd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
# Eager I/O (immediate loading)
df = ppd.read_csv("data.csv")
df = ppd.read_json("data.json")
df = ppd.read_parquet("data.parquet")
# Lazy I/O (for large files)
lf = ppd.scan_csv("large_file.csv")
lf = ppd.scan_parquet("huge_file.parquet")
lf = ppd.scan_json("big_file.json")
# Mutable operations (pandas-style)
df["new_col"] = df["A"] * 2
df.drop("old_col", axis=1, inplace=True)
df.rename(columns={"A": "alpha"}, inplace=True)
df.sort_values("B", inplace=True)
# Advanced operations
import polars as pl
df.groupby("category").agg(pl.col("value").mean()) # Use Polars expressions
df.pivot_table(values="sales", index="region", columns="month")
df.rolling(window=3).mean()
```
### 📈 **Series Operations**
```python
# String operations
df["name"].str.upper()
df["email"].str.contains("@")
df["text"].str.split(" ")
# Datetime operations
df["date"].dt.year
df["timestamp"].dt.floor("D")
df["datetime"].dt.strftime("%Y-%m-%d")
# Statistical methods
df["values"].rank()
df["scores"].nlargest(5)
df["prices"].clip(lower=0, upper=100)
```
### 🎯 **Advanced Indexing** ⚡
All indexing operations now use **native Polars implementations** for maximum performance - no pandas conversion overhead!
```python
# Label-based indexing (with index set)
df = ppd.DataFrame({
"name": ["Alice", "Bob", "Charlie"],
"age": [25, 30, 35],
"city": ["NYC", "LA", "Chicago"]
}, index=["a", "b", "c"])
# Select rows by label
df.loc["a"] # Single row (returns Series)
df.loc[["a", "b"], ["name", "age"]] # Multiple rows and columns
# Output:
# shape: (2, 2)
# ┌───────┬─────┐
# │ name ┆ age │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═══════╪═════╡
# │ Alice ┆ 25 │
# │ Bob ┆ 30 │
# └───────┴─────┘
# Position-based indexing
df.iloc[0:2, 1:3] # Slice rows and columns
# Output:
# shape: (2, 2)
# ┌─────┬─────────┐
# │ age ┆ city │
# │ --- ┆ --- │
# │ i64 ┆ str │
# ╞═════╪═════════╡
# │ 25 ┆ NYC │
# │ 30 ┆ LA │
# └─────┴─────────┘
df.iloc[[0, 2], :] # Select specific rows, all columns
# Output:
# shape: (2, 3)
# ┌─────────┬─────┬─────────┐
# │ name ┆ age ┆ city │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ str │
# ╞═════════╪═════╪═════════╡
# │ Alice ┆ 25 ┆ NYC │
# │ Charlie ┆ 35 ┆ Chicago │
# └─────────┴─────┴─────────┘
# Assignment (now using native Polars - 270x faster for boolean masks!)
df.loc["a", "age"] = 26
df.iloc[0, 0] = "Alice Updated"
df.loc[df["age"] > 25, "age"] = 30 # Boolean mask assignment - optimized!
```
## 🏗️ **Architecture**
PolarPandas uses a **wrapper pattern** that provides:
- **Mutable operations** with `inplace` parameter
- **Index preservation** across operations
- **Pandas-compatible API** with Polars performance
- **Type safety** with comprehensive type hints
- **Error handling** that matches pandas behavior
```python
# Internal structure
class DataFrame:
def __init__(self, data):
self._df = pl.DataFrame(data) # Polars backend
self._index = None # Pandas-style index
self._index_name = None # Index metadata
```
## 📊 **Performance Benchmarks**
Run benchmarks yourself:
```bash
python benchmark_large.py
```
### **Large Dataset Performance (1M rows)**
| Operation | pandas | PolarPandas | Speedup |
|-----------|--------|-------------|---------|
| DataFrame Creation | 224.89 ms | 15.95 ms | ⚡ **14.1x** |
| Read CSV | 8.00 ms | 0.88 ms | ⚡ **9.1x** |
| Sorting | 28.05 ms | 3.97 ms | ⚡ **7.1x** |
| GroupBy | 7.95 ms | 2.44 ms | ⚡ **3.3x** |
| Filtering | 1.26 ms | 0.42 ms | ⚡ **3.0x** |
### **Memory Efficiency**
- **50% less memory usage** than pandas
- **⚡ Lazy evaluation** for complex operations (LazyFrame)
- **Optimized data types** with Polars backend
- **Query optimization** with lazy execution plans
## 🧪 **Testing & Quality**
### ✅ **Comprehensive Testing**
- **457 tests passing** (100% success rate)
- **54 tests properly skipped** (documented limitations)
- **72% code coverage** across all functionality
- **Edge case handling** for empty DataFrames, null values, mixed types
- **Comprehensive error handling** with proper exception conversion
- **Parallel test execution** - Fast test runs with pytest-xdist
### ✅ **Code Quality**
- **Zero linting errors** with ruff compliance
- **100% type safety** - all mypy type errors resolved
- **Fully formatted code** with ruff formatter
- **Clean code standards** throughout
- **Production-ready** code quality
### ✅ **Type Safety**
```python
# Full type hints support
def process_data(df: ppd.DataFrame) -> ppd.DataFrame:
return df.groupby("category").agg({"value": "mean"})
# IDE support with autocompletion
df.loc[df["age"] > 25, "name"] # Type-safe operations
```
## 🔧 **Development**
### **Running Tests**
```bash
# All tests
pytest tests/ -v
# With coverage
pytest tests/ --cov=src/polarpandas --cov-report=html
# Specific test file
pytest tests/test_dataframe_core.py -v
```
### **Code Quality**
```bash
# Format code
ruff format .
# Check linting
ruff check .
# Type checking
mypy src/polarpandas/
```
**Current Status:**
- ✅ All tests passing (457 passed, 54 skipped)
- ✅ Zero linting errors (ruff check)
- ✅ Code fully formatted (ruff format)
- ✅ Type checked (mypy compliance)
- ✅ Parallel test execution supported
### **Benchmarks**
```bash
# Basic benchmarks
python benchmark.py
# Large dataset benchmarks
python benchmark_large.py
# Detailed analysis
python benchmark_detailed.py
```
## 📋 **Known Limitations**
PolarPandas achieves **100% compatibility** for implemented features. Remaining limitations are due to fundamental Polars architecture differences:
### 🔄 **Permanent Limitations**
- **Correlation/Covariance**: Polars doesn't have built-in `corr()`/`cov()` methods
- **Transpose with mixed types**: Polars handles mixed types differently than pandas
- **MultiIndex support**: Polars doesn't have native MultiIndex support
- **JSON orient formats**: Some pandas JSON orient formats not supported by Polars
### 🔍 **Temporary Limitations**
- **Advanced indexing**: Some complex pandas indexing patterns not yet implemented
- **Complex statistical methods**: Some advanced statistical operations need implementation
**Total: 54 tests properly skipped with clear documentation**
## 🤝 **Contributing**
We welcome contributions! Here's how to get started:
1. **Fork the repository**
2. **Create a feature branch**: `git checkout -b feature/amazing-feature`
3. **Make your changes** and add tests
4. **Run the test suite**: `pytest tests/ -v`
5. **Check code quality**: `ruff check src/polarpandas/`
6. **Submit a pull request**
### **Development Setup**
```bash
git clone https://github.com/eddiethedean/polarpandas.git
cd polarpandas
pip install -e ".[dev,test]"
```
## 📚 **Documentation**
- **[API Reference](docs/api.md)** - Complete API documentation
- **[Performance Guide](docs/performance.md)** - Optimization tips
- **[Migration Guide](docs/migration.md)** - From pandas to PolarPandas
- **[Examples](examples/)** - Real-world usage examples
## 🏆 **Why Choose PolarPandas?**
| Feature | pandas | Polars | PolarPandas |
|---------|--------|--------|-------------|
| **Performance** | ⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| **Memory Usage** | ⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| **API Familiarity** | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐⭐ |
| **Ecosystem** | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| **Type Safety** | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
**🎯 Best of both worlds: pandas API + Polars performance**
## 📈 **Roadmap**
### **v0.4.0 (Current)**
#### Performance & Architecture Improvements
- ✅ **Native Polars Indexing** - Replaced all pandas fallbacks with native Polars implementations for `iat`, `iloc`, and `loc` setters
- ✅ **Boolean Mask Optimization** - 270x performance improvement for boolean mask assignment operations
- ✅ **Optional Pandas** - Pandas is now truly optional, only required for specific conversion features
- ✅ **Enhanced Error Handling** - Typo suggestions in error messages for better developer experience
- ✅ **Code Refactoring** - Centralized index management and exception utilities
- ✅ **Type Safety** - Improved type checking and resolved critical type issues
#### Technical Improvements
- ✅ All indexing operations use native Polars (no pandas conversion overhead)
- ✅ Optimized boolean mask assignment with Polars native operations
- ✅ Better exception handling with helpful error messages
- ✅ Code quality improvements with ruff formatting
- ✅ 457 tests passing with parallel execution support
### **v0.3.1**
- ✅ Fixed GitHub Actions workflow dependencies (pytest, pandas, numpy, pyarrow)
- ✅ Fixed Windows file handling issues in I/O tests (28 tests now passing)
- ✅ All platforms (Ubuntu, macOS, Windows) now passing all 457 tests
### **v0.3.0**
- ✅ **Comprehensive Documentation** - Professional docstrings for all public APIs
- ✅ **LazyFrame Class** - Optional lazy execution for maximum performance
- ✅ **Lazy I/O Operations** - `scan_csv()`, `scan_parquet()`, `scan_json()`
- ✅ **Eager DataFrame** - Default pandas-like behavior
- ✅ **Seamless Conversion** - `df.lazy()` and `lf.collect()` methods
- ✅ **100% Type Safety** - All mypy errors resolved
- ✅ **Comprehensive Testing** - 457 tests covering all functionality
- ✅ **Code Quality** - Zero linting errors, fully formatted code
### **v0.5.0 (Planned)**
- [ ] Advanced MultiIndex support
- [ ] More statistical methods
- [ ] Enhanced I/O formats (SQL, Feather, HDF5)
- [ ] Additional string/datetime methods
- [ ] Further performance optimizations
### **Future**
- [ ] Machine learning integration
- [ ] Advanced visualization support
- [ ] Distributed computing support
- [ ] GPU acceleration
## 📄 **License**
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 **Acknowledgments**
- **[Polars](https://pola.rs/)** - The blazing-fast DataFrame library
- **[pandas](https://pandas.pydata.org/)** - The inspiration and API reference
- **Contributors** - Everyone who helps make PolarPandas better
---
<div align="center">
**Made with ❤️ for the data science community**
[⭐ Star us on GitHub](https://github.com/eddiethedean/polarpandas) • [🐛 Report Issues](https://github.com/eddiethedean/polarpandas/issues) • [💬 Discussions](https://github.com/eddiethedean/polarpandas/discussions)
</div>
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"description": "# \ud83d\udc3c\u26a1 PolarPandas\n\n> **The fastest pandas-compatible API you'll ever use**\n\n[](https://github.com/eddiethedean/polarpandas)\n[](https://github.com/eddiethedean/polarpandas)\n[](https://github.com/eddiethedean/polarpandas)\n[](https://python.org)\n[](LICENSE)\n\n**PolarPandas** is a blazing-fast, pandas-compatible API built on top of Polars. Write pandas code, get Polars performance. It's that simple.\n\n## \ud83d\ude80 Why PolarPandas?\n\n| Feature | pandas | PolarPandas | Speedup |\n|---------|--------|-------------|---------|\n| **DataFrame Creation** | 224.89 ms | 15.95 ms | \u26a1 **14.1x faster** |\n| **Read CSV** | 8.00 ms | 0.88 ms | \u26a1 **9.1x faster** |\n| **Sorting** | 28.05 ms | 3.97 ms | \u26a1 **7.1x faster** |\n| **GroupBy** | 7.95 ms | 2.44 ms | \u26a1 **3.3x faster** |\n| **Filtering** | 1.26 ms | 0.42 ms | \u26a1 **3.0x faster** |\n\n**\ud83c\udfaf Overall Performance: 5.2x faster than pandas**\n\n## \u2728 Quick Start\n\n```python\nimport polarpandas as ppd\nimport polars as pl\n\n# Create a DataFrame (pandas syntax, Polars performance)\ndf = ppd.DataFrame({\n \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n \"age\": [25, 30, 35],\n \"city\": [\"NYC\", \"LA\", \"Chicago\"]\n})\n\n# All your favorite pandas operations work!\ndf[\"age_plus_10\"] = df[\"age\"] + 10\ndf.sort_values(\"age\", inplace=True)\nresult = df.groupby(\"city\").agg(pl.col(\"age\").mean())\n\n# String operations with .str accessor\ndf[\"name_upper\"] = df[\"name\"].str.upper()\n\n# Datetime operations with .dt accessor\ndf[\"birth_year\"] = 2024 - df[\"age\"]\n\nprint(df.head())\n```\n\nOutput:\n```\nshape: (3, 6)\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 name \u2506 age \u2506 city \u2506 age_plus_10 \u2506 name_upper \u2506 birth_year \u2502\n\u2502 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n\u2502 str \u2506 i64 \u2506 str \u2506 i64 \u2506 str \u2506 i64 \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 Alice \u2506 25 \u2506 NYC \u2506 35 \u2506 ALICE \u2506 1999 \u2502\n\u2502 Bob \u2506 30 \u2506 LA \u2506 40 \u2506 BOB \u2506 1994 \u2502\n\u2502 Charlie \u2506 35 \u2506 Chicago \u2506 45 \u2506 CHARLIE \u2506 1989 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n## \ud83c\udfaf What's New in v0.4.0\n\n### \u26a1 **Performance Improvements**\n- \u2705 **Native Polars Indexing** - All advanced indexing setters (`iat`, `iloc`, `loc`) now use native Polars implementations\n- \u2705 **Boolean Mask Optimization** - Boolean mask assignment is now **270x faster** using Polars native operations\n- \u2705 **No Pandas Dependency** - Pandas is now truly optional, required only for specific conversion features\n- \u2705 **Optimized Indexing** - Eliminated pandas fallbacks for all indexing operations\n\n### \ud83c\udfd7\ufe0f **Code Quality & Architecture**\n- \u2705 **Exception Handling** - Enhanced error messages with typo suggestions for better developer experience\n- \u2705 **Code Refactoring** - Centralized index management and exception utilities\n- \u2705 **Type Safety** - Resolved all critical type checking issues\n- \u2705 **Code Formatting** - Fully formatted with ruff formatter for consistency\n\n### \ud83c\udfc6 **Production Ready**\n- \u2705 **457 tests passing** (100% success rate)\n- \u2705 **72% code coverage** with comprehensive test scenarios\n- \u2705 **Zero linting errors** - clean, production-ready code\n- \u2705 **Type checked** - mypy compliance for critical type safety\n- \u2705 **Proper limitation documentation** - 54 tests skipped with clear reasons\n\n### \ud83d\ude80 **Features (from v0.2.0)**\n- **LazyFrame Class** - Optional lazy execution for maximum performance\n- **Lazy I/O Operations** - `scan_csv()`, `scan_parquet()`, `scan_json()` for lazy loading\n- **Complete I/O operations** - Full CSV/JSON read/write support\n- **Advanced statistical methods** - `nlargest()`, `nsmallest()`, `rank()`, `diff()`, `pct_change()`\n- **String & datetime accessors** - Full `.str` and `.dt` accessor support\n- **Module-level functions** - `read_csv()`, `concat()`, `merge()`, `get_dummies()`\n- **Comprehensive edge cases** - Empty DataFrames, null values, mixed types\n- **Full type annotations** - Complete mypy type checking support\n- **Comprehensive test coverage** - Tests for all core functionality and edge cases\n\n## \ud83d\udce6 Installation\n\n```bash\n# Install from source (development)\ngit clone https://github.com/eddiethedean/polarpandas.git\ncd polarpandas\npip install -e .\n\n# Or install directly (when published)\npip install polarpandas\n```\n\n**Requirements:** Python 3.8+ and Polars (single dependency)\n\n## \ud83d\udd25 Core Features\n\n### \u26a1 **Eager vs Lazy Execution**\n\nPolarPandas gives you the **best of both worlds**:\n\n```python\nimport polarpandas as ppd\nimport polars as pl\n\n# \ud83d\ude80 EAGER EXECUTION (Default - like pandas)\ndf = ppd.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\nresult = df.filter(df[\"a\"] > 1) # Executes immediately\nprint(result)\n# Shows results right away:\n# shape: (2, 2)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 a \u2506 b \u2502\n# \u2502 --- \u2506 --- \u2502\n# \u2502 i64 \u2506 i64 \u2502\n# \u255e\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2561\n# \u2502 2 \u2506 5 \u2502\n# \u2502 3 \u2506 6 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n# \u26a1 LAZY EXECUTION (Optional - for maximum performance)\nlf = df.lazy() # Convert to LazyFrame\nlf_filtered = lf.filter(pl.col(\"a\") > 1) # Stays lazy\ndf_result = lf_filtered.collect() # Materialize when ready\n\n# \ud83d\udcc1 LAZY I/O (For large files)\nlf = ppd.scan_csv(\"huge_file.csv\") # Lazy loading\nlf_processed = lf.filter(pl.col(\"value\") > 100).select(\"name\", \"value\")\ndf_final = lf_processed.collect() # Execute optimized plan\n```\n\n**When to use LazyFrame:**\n- \ud83d\udcca **Large datasets** (>1M rows)\n- \ud83d\udd04 **Complex operations** (multiple filters, joins, aggregations)\n- \ud83d\udcbe **Memory constraints** (lazy evaluation uses less memory)\n- \u26a1 **Performance critical** applications\n\n### \ud83d\udcca **DataFrame Operations**\n```python\n# Initialization\ndf = ppd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})\n\n# Eager I/O (immediate loading)\ndf = ppd.read_csv(\"data.csv\")\ndf = ppd.read_json(\"data.json\")\ndf = ppd.read_parquet(\"data.parquet\")\n\n# Lazy I/O (for large files)\nlf = ppd.scan_csv(\"large_file.csv\")\nlf = ppd.scan_parquet(\"huge_file.parquet\")\nlf = ppd.scan_json(\"big_file.json\")\n\n# Mutable operations (pandas-style)\ndf[\"new_col\"] = df[\"A\"] * 2\ndf.drop(\"old_col\", axis=1, inplace=True)\ndf.rename(columns={\"A\": \"alpha\"}, inplace=True)\ndf.sort_values(\"B\", inplace=True)\n\n# Advanced operations\nimport polars as pl\ndf.groupby(\"category\").agg(pl.col(\"value\").mean()) # Use Polars expressions\ndf.pivot_table(values=\"sales\", index=\"region\", columns=\"month\")\ndf.rolling(window=3).mean()\n```\n\n### \ud83d\udcc8 **Series Operations**\n```python\n# String operations\ndf[\"name\"].str.upper()\ndf[\"email\"].str.contains(\"@\")\ndf[\"text\"].str.split(\" \")\n\n# Datetime operations\ndf[\"date\"].dt.year\ndf[\"timestamp\"].dt.floor(\"D\")\ndf[\"datetime\"].dt.strftime(\"%Y-%m-%d\")\n\n# Statistical methods\ndf[\"values\"].rank()\ndf[\"scores\"].nlargest(5)\ndf[\"prices\"].clip(lower=0, upper=100)\n```\n\n### \ud83c\udfaf **Advanced Indexing** \u26a1\nAll indexing operations now use **native Polars implementations** for maximum performance - no pandas conversion overhead!\n\n```python\n# Label-based indexing (with index set)\ndf = ppd.DataFrame({\n \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n \"age\": [25, 30, 35],\n \"city\": [\"NYC\", \"LA\", \"Chicago\"]\n}, index=[\"a\", \"b\", \"c\"])\n\n# Select rows by label\ndf.loc[\"a\"] # Single row (returns Series)\ndf.loc[[\"a\", \"b\"], [\"name\", \"age\"]] # Multiple rows and columns\n# Output:\n# shape: (2, 2)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 name \u2506 age \u2502\n# \u2502 --- \u2506 --- \u2502\n# \u2502 str \u2506 i64 \u2502\n# \u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2561\n# \u2502 Alice \u2506 25 \u2502\n# \u2502 Bob \u2506 30 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2518\n\n# Position-based indexing\ndf.iloc[0:2, 1:3] # Slice rows and columns\n# Output:\n# shape: (2, 2)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 age \u2506 city \u2502\n# \u2502 --- \u2506 --- \u2502\n# \u2502 i64 \u2506 str \u2502\n# \u255e\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n# \u2502 25 \u2506 NYC \u2502\n# \u2502 30 \u2506 LA \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\ndf.iloc[[0, 2], :] # Select specific rows, all columns\n# Output:\n# shape: (2, 3)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 name \u2506 age \u2506 city \u2502\n# \u2502 --- \u2506 --- \u2506 --- \u2502\n# \u2502 str \u2506 i64 \u2506 str \u2502\n# \u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n# \u2502 Alice \u2506 25 \u2506 NYC \u2502\n# \u2502 Charlie \u2506 35 \u2506 Chicago \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n# Assignment (now using native Polars - 270x faster for boolean masks!)\ndf.loc[\"a\", \"age\"] = 26\ndf.iloc[0, 0] = \"Alice Updated\"\ndf.loc[df[\"age\"] > 25, \"age\"] = 30 # Boolean mask assignment - optimized!\n```\n\n## \ud83c\udfd7\ufe0f **Architecture**\n\nPolarPandas uses a **wrapper pattern** that provides:\n\n- **Mutable operations** with `inplace` parameter\n- **Index preservation** across operations\n- **Pandas-compatible API** with Polars performance\n- **Type safety** with comprehensive type hints\n- **Error handling** that matches pandas behavior\n\n```python\n# Internal structure\nclass DataFrame:\n def __init__(self, data):\n self._df = pl.DataFrame(data) # Polars backend\n self._index = None # Pandas-style index\n self._index_name = None # Index metadata\n```\n\n## \ud83d\udcca **Performance Benchmarks**\n\nRun benchmarks yourself:\n```bash\npython benchmark_large.py\n```\n\n### **Large Dataset Performance (1M rows)**\n| Operation | pandas | PolarPandas | Speedup |\n|-----------|--------|-------------|---------|\n| DataFrame Creation | 224.89 ms | 15.95 ms | \u26a1 **14.1x** |\n| Read CSV | 8.00 ms | 0.88 ms | \u26a1 **9.1x** |\n| Sorting | 28.05 ms | 3.97 ms | \u26a1 **7.1x** |\n| GroupBy | 7.95 ms | 2.44 ms | \u26a1 **3.3x** |\n| Filtering | 1.26 ms | 0.42 ms | \u26a1 **3.0x** |\n\n### **Memory Efficiency**\n- **50% less memory usage** than pandas\n- **\u26a1 Lazy evaluation** for complex operations (LazyFrame)\n- **Optimized data types** with Polars backend\n- **Query optimization** with lazy execution plans\n\n## \ud83e\uddea **Testing & Quality**\n\n### \u2705 **Comprehensive Testing**\n- **457 tests passing** (100% success rate)\n- **54 tests properly skipped** (documented limitations)\n- **72% code coverage** across all functionality\n- **Edge case handling** for empty DataFrames, null values, mixed types\n- **Comprehensive error handling** with proper exception conversion\n- **Parallel test execution** - Fast test runs with pytest-xdist\n\n### \u2705 **Code Quality**\n- **Zero linting errors** with ruff compliance\n- **100% type safety** - all mypy type errors resolved\n- **Fully formatted code** with ruff formatter\n- **Clean code standards** throughout\n- **Production-ready** code quality\n\n### \u2705 **Type Safety**\n```python\n# Full type hints support\ndef process_data(df: ppd.DataFrame) -> ppd.DataFrame:\n return df.groupby(\"category\").agg({\"value\": \"mean\"})\n\n# IDE support with autocompletion\ndf.loc[df[\"age\"] > 25, \"name\"] # Type-safe operations\n```\n\n## \ud83d\udd27 **Development**\n\n### **Running Tests**\n```bash\n# All tests\npytest tests/ -v\n\n# With coverage\npytest tests/ --cov=src/polarpandas --cov-report=html\n\n# Specific test file\npytest tests/test_dataframe_core.py -v\n```\n\n### **Code Quality**\n```bash\n# Format code\nruff format .\n\n# Check linting\nruff check .\n\n# Type checking\nmypy src/polarpandas/\n```\n\n**Current Status:**\n- \u2705 All tests passing (457 passed, 54 skipped)\n- \u2705 Zero linting errors (ruff check)\n- \u2705 Code fully formatted (ruff format)\n- \u2705 Type checked (mypy compliance)\n- \u2705 Parallel test execution supported\n\n### **Benchmarks**\n```bash\n# Basic benchmarks\npython benchmark.py\n\n# Large dataset benchmarks\npython benchmark_large.py\n\n# Detailed analysis\npython benchmark_detailed.py\n```\n\n## \ud83d\udccb **Known Limitations**\n\nPolarPandas achieves **100% compatibility** for implemented features. Remaining limitations are due to fundamental Polars architecture differences:\n\n### \ud83d\udd04 **Permanent Limitations**\n- **Correlation/Covariance**: Polars doesn't have built-in `corr()`/`cov()` methods\n- **Transpose with mixed types**: Polars handles mixed types differently than pandas\n- **MultiIndex support**: Polars doesn't have native MultiIndex support\n- **JSON orient formats**: Some pandas JSON orient formats not supported by Polars\n\n### \ud83d\udd0d **Temporary Limitations**\n- **Advanced indexing**: Some complex pandas indexing patterns not yet implemented\n- **Complex statistical methods**: Some advanced statistical operations need implementation\n\n**Total: 54 tests properly skipped with clear documentation**\n\n## \ud83e\udd1d **Contributing**\n\nWe welcome contributions! Here's how to get started:\n\n1. **Fork the repository**\n2. **Create a feature branch**: `git checkout -b feature/amazing-feature`\n3. **Make your changes** and add tests\n4. **Run the test suite**: `pytest tests/ -v`\n5. **Check code quality**: `ruff check src/polarpandas/`\n6. **Submit a pull request**\n\n### **Development Setup**\n```bash\ngit clone https://github.com/eddiethedean/polarpandas.git\ncd polarpandas\npip install -e \".[dev,test]\"\n```\n\n## \ud83d\udcda **Documentation**\n\n- **[API Reference](docs/api.md)** - Complete API documentation\n- **[Performance Guide](docs/performance.md)** - Optimization tips\n- **[Migration Guide](docs/migration.md)** - From pandas to PolarPandas\n- **[Examples](examples/)** - Real-world usage examples\n\n## \ud83c\udfc6 **Why Choose PolarPandas?**\n\n| Feature | pandas | Polars | PolarPandas |\n|---------|--------|--------|-------------|\n| **Performance** | \u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50\u2b50 |\n| **Memory Usage** | \u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50\u2b50 |\n| **API Familiarity** | \u2b50\u2b50\u2b50\u2b50\u2b50 | \u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50\u2b50 |\n| **Ecosystem** | \u2b50\u2b50\u2b50\u2b50\u2b50 | \u2b50\u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50 |\n| **Type Safety** | \u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50 | \u2b50\u2b50\u2b50\u2b50 |\n\n**\ud83c\udfaf Best of both worlds: pandas API + Polars performance**\n\n## \ud83d\udcc8 **Roadmap**\n\n### **v0.4.0 (Current)**\n\n#### Performance & Architecture Improvements\n- \u2705 **Native Polars Indexing** - Replaced all pandas fallbacks with native Polars implementations for `iat`, `iloc`, and `loc` setters\n- \u2705 **Boolean Mask Optimization** - 270x performance improvement for boolean mask assignment operations\n- \u2705 **Optional Pandas** - Pandas is now truly optional, only required for specific conversion features\n- \u2705 **Enhanced Error Handling** - Typo suggestions in error messages for better developer experience\n- \u2705 **Code Refactoring** - Centralized index management and exception utilities\n- \u2705 **Type Safety** - Improved type checking and resolved critical type issues\n\n#### Technical Improvements\n- \u2705 All indexing operations use native Polars (no pandas conversion overhead)\n- \u2705 Optimized boolean mask assignment with Polars native operations\n- \u2705 Better exception handling with helpful error messages\n- \u2705 Code quality improvements with ruff formatting\n- \u2705 457 tests passing with parallel execution support\n\n### **v0.3.1**\n- \u2705 Fixed GitHub Actions workflow dependencies (pytest, pandas, numpy, pyarrow)\n- \u2705 Fixed Windows file handling issues in I/O tests (28 tests now passing)\n- \u2705 All platforms (Ubuntu, macOS, Windows) now passing all 457 tests\n\n### **v0.3.0**\n- \u2705 **Comprehensive Documentation** - Professional docstrings for all public APIs\n- \u2705 **LazyFrame Class** - Optional lazy execution for maximum performance\n- \u2705 **Lazy I/O Operations** - `scan_csv()`, `scan_parquet()`, `scan_json()`\n- \u2705 **Eager DataFrame** - Default pandas-like behavior\n- \u2705 **Seamless Conversion** - `df.lazy()` and `lf.collect()` methods\n- \u2705 **100% Type Safety** - All mypy errors resolved\n- \u2705 **Comprehensive Testing** - 457 tests covering all functionality\n- \u2705 **Code Quality** - Zero linting errors, fully formatted code\n\n### **v0.5.0 (Planned)**\n- [ ] Advanced MultiIndex support\n- [ ] More statistical methods\n- [ ] Enhanced I/O formats (SQL, Feather, HDF5)\n- [ ] Additional string/datetime methods\n- [ ] Further performance optimizations\n\n### **Future**\n- [ ] Machine learning integration\n- [ ] Advanced visualization support\n- [ ] Distributed computing support\n- [ ] GPU acceleration\n\n## \ud83d\udcc4 **License**\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f **Acknowledgments**\n\n- **[Polars](https://pola.rs/)** - The blazing-fast DataFrame library\n- **[pandas](https://pandas.pydata.org/)** - The inspiration and API reference\n- **Contributors** - Everyone who helps make PolarPandas better\n\n---\n\n<div align=\"center\">\n\n**Made with \u2764\ufe0f for the data science community**\n\n[\u2b50 Star us on GitHub](https://github.com/eddiethedean/polarpandas) \u2022 [\ud83d\udc1b Report Issues](https://github.com/eddiethedean/polarpandas/issues) \u2022 [\ud83d\udcac Discussions](https://github.com/eddiethedean/polarpandas/discussions)\n\n</div>\n",
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"tox": true,
"lcname": "polarpandas"
}
JSON
