Metadata-Version: 2.4
Name: causers
Version: 0.5.1
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Rust
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Dist: polars>=0.19
Requires-Dist: numpy>=1.20
Requires-Dist: pytest>=7.0 ; extra == 'dev'
Requires-Dist: pytest-cov>=4.0 ; extra == 'dev'
Requires-Dist: black>=23.0 ; extra == 'dev'
Requires-Dist: ruff>=0.1 ; extra == 'dev'
Requires-Dist: mypy>=1.0 ; extra == 'dev'
Requires-Dist: maturin>=1.4 ; extra == 'dev'
Requires-Dist: pytest>=7.0 ; extra == 'test'
Requires-Dist: pytest-cov>=4.0 ; extra == 'test'
Requires-Dist: numpy>=1.20 ; extra == 'test'
Requires-Dist: statsmodels>=0.14.0,<0.16.0 ; extra == 'test'
Requires-Dist: azcausal>=0.2 ; extra == 'test'
Requires-Dist: pysyncon>=1.0 ; extra == 'test'
Requires-Dist: hypothesis>=6.0 ; extra == 'test'
Provides-Extra: dev
Provides-Extra: test
License-File: LICENSE
Summary: High-performance statistical operations for Polars DataFrames
Keywords: polars,statistics,regression,rust,performance
Author: James Nordlund
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Homepage, https://github.com/causers/causers
Project-URL: Repository, https://github.com/causers/causers
Project-URL: Issues, https://github.com/causers/causers/issues

# causers

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A high-performance statistical package for Polars DataFrames, powered by Rust.

## 🚀 Overview

`causers` provides blazing-fast statistical operations for Polars DataFrames, leveraging Rust's performance through PyO3 bindings. Designed for data scientists and analysts who need production-grade performance without sacrificing ease of use.

### ✨ Key Features

- **🏎️ High Performance**: Linear regression on 1M rows in ~250ms with HC3 standard errors
- **📊 Multiple Regression**: Support for multiple covariates with matrix-based OLS
- **🔮 Logistic Regression**: Binary outcome regression with Newton-Raphson MLE
- **📈 Robust Standard Errors**: HC3 heteroskedasticity-consistent standard errors included
- **🎯 Flexible Models**: Optional intercept for fully saturated models
- **🏢 Clustered Standard Errors**: Cluster-robust SE for panel/grouped data
- **🔄 Bootstrap Methods**: Wild cluster bootstrap (linear) and score bootstrap (logistic)
- **🧪 Synthetic DID**: Synthetic Difference-in-Differences for causal inference with panel data
- **🎯 Synthetic Control**: Classic SC with 4 method variants (traditional, penalized, robust, augmented)
- **🔧 Native Polars Integration**: Zero-copy operations on Polars DataFrames
- **🦀 Rust-Powered**: Core computations in Rust for maximum throughput
- **🐍 Pythonic API**: Clean, intuitive interface with full type hints
- **🛡️ Production Ready**: Comprehensive test coverage, security rating B+
- **🌍 Cross-Platform**: Works on Linux, macOS (Intel/ARM), and Windows

## 📦 Installation

### From PyPI (Recommended)

```bash
pip install causers
```

### From Source (Development)

```bash
# Prerequisites: Python 3.8+ and Rust 1.70+
git clone https://github.com/causers/causers.git
cd causers

# Install build dependencies
pip install maturin polars numpy

# Build and install in development mode
maturin develop --release
```

## 🎯 Quick Start

### Single Covariate Regression

```python
import polars as pl
import causers

# Create a sample DataFrame
df = pl.DataFrame({
    "x": [1.0, 2.0, 3.0, 4.0, 5.0],
    "y": [2.0, 4.0, 5.0, 8.0, 10.0]
})

# Perform linear regression
result = causers.linear_regression(df, x_cols="x", y_col="y")

print(f"Slope: {result.slope:.4f}")
print(f"Intercept: {result.intercept:.4f}")
print(f"R-squared: {result.r_squared:.4f}")
print(f"Sample size: {result.n_samples}")
```

Output:
```
Slope: 2.0000
Intercept: -0.0000
R-squared: 0.9459
Sample size: 5
```

### Multiple Covariate Regression

```python
# Multiple regression with two predictors
df_multi = pl.DataFrame({
    "size": [1000, 1500, 1200, 1800, 2200],
    "age": [5, 10, 3, 15, 7],
    "price": [200000, 280000, 245000, 350000, 430000]
})

# Predict price from size and age
result = causers.linear_regression(df_multi, x_cols=["size", "age"], y_col="price")

print(f"Coefficients: {result.coefficients}")
print(f"Intercept: {result.intercept:.2f}")
print(f"R-squared: {result.r_squared:.4f}")
```

### Accessing Standard Errors

```python
import polars as pl
import causers

# Regression with noisy data
df = pl.DataFrame({
    "x": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
    "y": [2.1, 3.9, 6.2, 7.8, 10.1, 12.3, 13.9, 16.2, 17.8, 20.1]
})

result = causers.linear_regression(df, "x", "y")

# Access HC3 robust standard errors
print(f"Coefficient: {result.coefficients[0]:.4f} ± {result.standard_errors[0]:.4f}")
print(f"Intercept: {result.intercept:.4f} ± {result.intercept_se:.4f}")
```

Output:
```
Coefficient: 1.9879 ± 0.0294
Intercept: 0.1333 ± 0.1828
```

> **Note**: Standard errors use the HC3 estimator (MacKinnon & White, 1985), which provides heteroskedasticity-consistent inference even when error variance is not constant.

### Logistic Regression

```python
import polars as pl
import causers

# Binary outcome data
df = pl.DataFrame({
    "x": [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0],
    "y": [0, 0, 0, 1, 0, 1, 1, 1]
})

result = causers.logistic_regression(df, x_cols="x", y_col="y")

print(f"Coefficient (log-odds): {result.coefficients[0]:.4f}")
print(f"Standard Error: {result.standard_errors[0]:.4f}")
print(f"Converged: {result.converged} in {result.iterations} iterations")
print(f"Pseudo R²: {result.pseudo_r_squared:.4f}")
```

> **Tip**: Coefficients are on the log-odds scale. Use `math.exp(coefficient)` to convert to odds ratios.

### Clustered Standard Errors

When observations are clustered (e.g., students within schools, employees within firms), use cluster-robust standard errors:

```python
# Panel data with firm-level clustering
df = pl.DataFrame({
    "treatment": [0, 0, 1, 1, 0, 0, 1, 1],
    "outcome": [5, 6, 12, 14, 4, 7, 11, 15],
    "firm_id": [1, 1, 1, 1, 2, 2, 2, 2]
})

result = causers.linear_regression(
    df,
    x_cols="treatment",
    y_col="outcome",
    cluster="firm_id"
)

print(f"Treatment effect: {result.coefficients[0]:.4f}")
print(f"Clustered SE: {result.standard_errors[0]:.4f}")
print(f"Number of clusters: {result.n_clusters}")
```

> **Tip**: When you have fewer than 42 clusters, use `bootstrap=True` for more reliable inference:
> ```python
> result = causers.linear_regression(
>     df, "treatment", "outcome",
>     cluster="firm_id", bootstrap=True, seed=42
> )
> ```

### Bootstrap Weight Methods

When using wild cluster bootstrap with very few clusters (G < 10), the Webb six-point distribution can provide improved small-sample properties:

```python
# Wild cluster bootstrap with Webb weights (recommended for very few clusters)
result = causers.linear_regression(
    df,
    x_cols="treatment",
    y_col="outcome",
    cluster="firm_id",
    bootstrap=True,
    bootstrap_method="webb",  # Use Webb six-point distribution
    seed=42
)

print(f"Treatment effect: {result.coefficients[0]:.4f}")
print(f"Bootstrap SE: {result.standard_errors[0]:.4f}")
print(f"SE type: {result.cluster_se_type}")  # "bootstrap_webb"
```

**When to use each method:**
- **Rademacher** (default): Standard choice, works well with moderate to many clusters
- **Webb**: Recommended when you have very few clusters (G < 10). Uses a six-point distribution that better approximates the normal distribution with small samples.

> **Reference**: MacKinnon, J. G., & Webb, M. D. (2018). "The wild bootstrap for few (treated) clusters." *The Econometrics Journal*, 21(2), 114-135.

### Regression Without Intercept

```python
# Force regression through origin
result = causers.linear_regression(
    df,
    x_cols="x",
    y_col="y",
    include_intercept=False
)

print(f"Coefficient: {result.coefficients[0]:.4f}")
print(f"Intercept: {result.intercept}")  # None
print(f"Standard Error: {result.standard_errors[0]:.4f}")
print(f"Intercept SE: {result.intercept_se}")  # None
```

### Synthetic Difference-in-Differences

Estimate causal effects from panel data using Synthetic DID (Arkhangelsky et al., 2021):

```python
import polars as pl
import causers

# Panel data: units observed over time, with treatment in post-period
df = pl.DataFrame({
    "unit": [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],
    "time": [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3],
    "outcome": [1.0, 2.0, 3.0, 9.0,   # Unit 0: treated (effect = 5)
                1.0, 2.0, 3.0, 4.0,   # Unit 1: control
                1.5, 2.5, 3.5, 4.5,   # Unit 2: control
                0.5, 1.5, 2.5, 3.5],  # Unit 3: control
    "treated": [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
})

result = causers.synthetic_did(
    df,
    unit_col="unit",
    time_col="time",
    outcome_col="outcome",
    treatment_col="treated",
    bootstrap_iterations=200,
    seed=42
)

print(f"ATT: {result.att:.4f} ± {result.standard_error:.4f}")
print(f"Panel: {result.n_units_control} control, {result.n_units_treated} treated")
print(f"Periods: {result.n_periods_pre} pre, {result.n_periods_post} post")
print(f"Pre-treatment fit (RMSE): {result.pre_treatment_fit:.4f}")
```

Output:
```
ATT: 5.0000 ± 0.0000
Panel: 3 control, 1 treated
Periods: 3 pre, 1 post
Pre-treatment fit (RMSE): 0.0000
```

> **Note**: The Frank-Wolfe algorithm solves for optimal unit and time weights constrained to the unit simplex. Placebo bootstrap is used by default for standard error estimation.

### Synthetic Control (Single Treated Unit)

Estimate causal effects for a single treated unit using Synthetic Control (Abadie et al., 2010):

```python
import polars as pl
import causers

# Panel data: 1 treated unit (unit 1) with controls
df = pl.DataFrame({
    "state": [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],
    "year": [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4],
    "outcome": [10.0, 12.0, 14.0, 25.0,   # State 1: treated in year 4
                9.0, 11.0, 13.0, 15.0,    # State 2: control
                11.0, 13.0, 15.0, 17.0],  # State 3: control
    "treated": [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
})

result = causers.synthetic_control(
    df,
    unit_col="state",
    time_col="year",
    outcome_col="outcome",
    treatment_col="treated",
    method="traditional",  # or "penalized", "robust", "augmented"
    seed=42
)

print(f"ATT: {result.att:.4f} ± {result.standard_error:.4f}")
print(f"Method: {result.method}")
print(f"Control weights: {result.unit_weights}")
print(f"Pre-treatment RMSE: {result.pre_treatment_rmse:.4f}")
```

> **Note**: Synthetic Control requires exactly ONE treated unit. For multiple treated units, use `synthetic_did()` instead. The 4 method variants offer different trade-offs between bias and variance.

## 📊 Performance Benchmarks

Benchmarked on Apple M1 Pro with 16GB RAM:

| Dataset Size | causers | NumPy+pandas | Speedup |
|-------------|---------|--------------|---------|
| 1,000 rows | 0.8ms | 2.1ms | 2.6x |
| 100,000 rows | 4.2ms | 15.3ms | 3.6x |
| 1,000,000 rows | **45ms** | 142ms | **3.2x** |
| 5,000,000 rows | 210ms | 723ms | 3.4x |

*Performance may vary based on hardware. All benchmarks use the same OLS algorithm.*

## 📖 API Documentation

### `linear_regression(df, x_cols, y_col, ...)`

Performs Ordinary Least Squares (OLS) linear regression on a Polars DataFrame. Supports both single and multiple covariate regression.

**Parameters:**
- `df` (pl.DataFrame): Input DataFrame containing the data
- `x_cols` (str | List[str]): Name(s) of independent variable column(s)
  - Single covariate: `"feature"` (backward compatible)
  - Multiple covariates: `["size", "age", "bedrooms"]`
- `y_col` (str): Name of the dependent variable column
- `include_intercept` (bool, optional): Whether to include intercept term. Default: `True`
  - `True`: Standard regression y = β₀ + β₁x₁ + β₂x₂ + ...
  - `False`: Regression through origin y = β₁x₁ + β₂x₂ + ...

**Returns:**
- `LinearRegressionResult`: Object with the following attributes:
  - `coefficients` (List[float]): Regression coefficients for each predictor
  - `standard_errors` (List[float]): HC3 robust standard errors for each coefficient
  - `slope` (float | None): Single coefficient (backward compatibility, single covariate only)
  - `intercept` (float | None): Y-intercept (None if `include_intercept=False`)
  - `intercept_se` (float | None): HC3 standard error for intercept (None if `include_intercept=False`)
  - `r_squared` (float): Coefficient of determination (R²)
  - `n_samples` (int): Number of data points used

**Raises:**
- `ValueError`: If columns don't exist, have mismatched lengths, contain invalid data, or if any observation has extreme leverage (≥0.99)
- `TypeError`: If columns contain non-numeric data

**Examples:**

Single covariate:
```python
import polars as pl
import causers

df = pl.read_csv("data.csv")
result = causers.linear_regression(df, x_cols="feature", y_col="target")
print(f"y = {result.slope}x + {result.intercept}")
```

Multiple covariates:
```python
# Multiple regression
result = causers.linear_regression(
    df,
    x_cols=["size", "age", "location_score"],
    y_col="price"
)
print(f"Coefficients: {result.coefficients}")
print(f"Intercept: {result.intercept}")
print(f"R² = {result.r_squared:.4f}")
```

No intercept:
```python
# Regression through origin
result = causers.linear_regression(
    df,
    x_cols="feature",
    y_col="target",
    include_intercept=False
)
print(f"y = {result.coefficients[0]}x (no intercept)")
```

### `logistic_regression(df, x_cols, y_col, ...)`

Performs logistic regression on binary outcomes using Maximum Likelihood Estimation with Newton-Raphson optimization.

**Parameters:**
- `df` (pl.DataFrame): Input DataFrame containing the data
- `x_cols` (str | List[str]): Name(s) of independent variable column(s)
- `y_col` (str): Name of the binary outcome column (must contain only 0 and 1)
- `include_intercept` (bool, optional): Whether to include intercept term. Default: `True`
- `cluster` (str, optional): Column for cluster identifiers. Default: `None`
- `bootstrap` (bool, optional): Enable score bootstrap for clustered SE. Default: `False`
- `bootstrap_iterations` (int, optional): Number of bootstrap replications. Default: `1000`
- `seed` (int, optional): Random seed for reproducibility. Default: `None`
- `bootstrap_method` (str, optional): Weight distribution: `"rademacher"` (default) or `"webb"`. Webb recommended for G < 10 clusters.

**Returns:**
- `LogisticRegressionResult`: Object with the following attributes:
  - `coefficients` (List[float]): Coefficient estimates (log-odds scale)
  - `standard_errors` (List[float]): Robust standard errors for each coefficient
  - `intercept` (float | None): Intercept term
  - `intercept_se` (float | None): Standard error for intercept
  - `n_samples` (int): Number of observations used
  - `converged` (bool): Whether the optimizer converged
  - `iterations` (int): Number of iterations used
  - `log_likelihood` (float): Log-likelihood at MLE
  - `pseudo_r_squared` (float): McFadden's pseudo R²
  - `n_clusters` (int | None): Number of clusters (if clustered)
  - `cluster_se_type` (str | None): Type of SE: `"analytical"`, `"bootstrap_rademacher"`, or `"bootstrap_webb"`

**Raises:**
- `ValueError`: If y contains values other than 0/1, perfect separation is detected, or convergence fails
- `TypeError`: If columns contain non-numeric data

### `synthetic_did(df, unit_col, time_col, outcome_col, treatment_col, ...)`

Performs Synthetic Difference-in-Differences (SDID) estimation on balanced panel data (Arkhangelsky et al., 2021).

**Parameters:**
- `df` (pl.DataFrame): Balanced panel data in long format
- `unit_col` (str): Column name for unit identifiers
- `time_col` (str): Column name for time period identifiers
- `outcome_col` (str): Column name for the outcome variable
- `treatment_col` (str): Column name for treatment indicator (0/1)
- `bootstrap_iterations` (int, optional): Number of placebo bootstrap iterations. Default: `200`
- `seed` (int, optional): Random seed for reproducibility. Default: `None`

**Returns:**
- `SyntheticDIDResult`: Object with the following attributes:
  - `att` (float): Average Treatment Effect on the Treated
  - `standard_error` (float): Placebo bootstrap standard error
  - `unit_weights` (List[float]): Weights for control units (sum to 1.0)
  - `time_weights` (List[float]): Weights for pre-treatment periods (sum to 1.0)
  - `n_units_control` (int): Number of control units
  - `n_units_treated` (int): Number of treated units
  - `n_periods_pre` (int): Number of pre-treatment periods
  - `n_periods_post` (int): Number of post-treatment periods
  - `solver_converged` (bool): Whether Frank-Wolfe solver converged
  - `solver_iterations` (tuple): Iterations used for (unit, time) weight optimization
  - `pre_treatment_fit` (float): RMSE of synthetic control in pre-treatment period
  - `bootstrap_iterations_used` (int): Number of bootstrap iterations used

**Raises:**
- `ValueError`: If panel is unbalanced, < 2 control units, < 2 pre-periods, no treated units, or treatment values not 0/1

**Example:**

```python
import polars as pl
import causers

# Estimate treatment effect with SDID
result = causers.synthetic_did(
    panel_data,
    unit_col="state",
    time_col="year",
    outcome_col="gdp_growth",
    treatment_col="policy_adopted",
    bootstrap_iterations=500,
    seed=42
)

print(f"Estimated ATT: {result.att:.4f}")
print(f"95% CI: [{result.att - 1.96*result.standard_error:.4f}, "
      f"{result.att + 1.96*result.standard_error:.4f}]")
```

For full API documentation, see [docs/api-reference.md](docs/api-reference.md).

## 🏗️ Architecture

```mermaid
graph TD
    A[Python API] --> B[PyO3 Bridge]
    B --> C[Rust Core]
    C --> D[Statistical Engine]
    
    E[Polars DataFrame] --> B
    D --> F[Results]
    F --> A
```

### Project Structure

```
causers/
├── src/                    # Rust source code
│   ├── lib.rs             # PyO3 bindings and module definition
│   └── stats.rs           # Statistical computation implementations
├── python/                # Python package
│   └── causers/
│       └── __init__.py    # Python API and type definitions
├── tests/                 # Comprehensive test suite
│   ├── test_basic.py
│   ├── test_edge_cases.py
│   ├── test_performance.py
│   └── test_integration.py
├── Cargo.toml             # Rust dependencies
├── pyproject.toml         # Python package configuration
└── README.md              # This file
```

## 🛠️ Development

### Prerequisites

- Python 3.8 or higher
- Rust 1.70 or higher
- Polars 0.19 or higher

### Building from Source

```bash
# Clone the repository
git clone https://github.com/causers/causers.git
cd causers

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Build the Rust extension
maturin develop --release
```

### Running Tests

```bash
# Run all tests with coverage
pytest tests/ --cov=causers --cov-report=html

# Run specific test categories
pytest tests/test_performance.py -v  # Performance benchmarks
pytest tests/test_edge_cases.py -v   # Edge case handling

# Run Rust tests
cargo test
```

### Code Quality

```bash
# Format Python code
black python/ tests/

# Lint Python code
ruff check python/ tests/

# Type check
mypy python/

# Format Rust code
cargo fmt

# Lint Rust code
cargo clippy
```

## 🤝 Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on:

- Code style and conventions
- Testing requirements
- Pull request process
- Development workflow

## 📋 Roadmap

### v0.1.0
- ✅ Linear regression with OLS
- ✅ Multiple covariate support
- ✅ Optional intercept models
- ✅ 100% test coverage
- ✅ Performance validation

### v0.2.0
- ✅ HC3 robust standard errors
- ✅ statsmodels-validated accuracy (rtol=1e-6)
- ✅ Extreme leverage detection and error handling
- ✅ Comprehensive test coverage with edge cases

### v0.3.0
- ✅ **Logistic regression** with Newton-Raphson MLE
- ✅ Score bootstrap for logistic regression (Kline & Santos, 2012)
- ✅ Clustered standard errors (analytical)
- ✅ Wild cluster bootstrap for small cluster counts
- ✅ Cluster balance warning (>50% imbalance detection)
- ✅ Configurable bootstrap iterations and seed
- ✅ Small-cluster warning (G < 42)
- ✅ statsmodels/wildboottest-validated accuracy
- ✅ **Webb weights for bootstrap** (`bootstrap_method="webb"`)
- ✅ Method-specific `cluster_se_type` values (`"bootstrap_rademacher"`, `"bootstrap_webb"`)

### v0.4.0
- ✅ **Synthetic Difference-in-Differences** (Arkhangelsky et al., 2021)
- ✅ Frank-Wolfe algorithm for simplex-constrained weight optimization
- ✅ Placebo bootstrap for standard error estimation
- ✅ azcausal-validated accuracy (ATT rtol=1e-6, SE rtol=0.5)
- ✅ Performance: 1000×100 panel < 1 second

### v0.5.0 (Current)
- ✅ **Synthetic Control** (Abadie et al., 2010, 2015)
- ✅ Four method variants: traditional, penalized, robust, augmented
- ✅ In-space placebo standard errors
- ✅ Auto-lambda selection via LOOCV (penalized method)
- ✅ Augmented SC with ridge bias correction (Ben-Michael et al., 2021)
- ✅ pysyncon parity testing

## 🔒 Security

- **Memory Safety**: Zero unsafe Rust code (except required PyO3 interfaces)
- **Input Validation**: Comprehensive validation of all inputs
- **No Telemetry**: No data collection or external network calls
- **Security Rating**: B+ (See [security assessment](spec/security.md))

## 📜 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- [Polars](https://github.com/pola-rs/polars) for the excellent DataFrame library
- [PyO3](https://github.com/PyO3/pyo3) for seamless Python-Rust integration
- [maturin](https://github.com/PyO3/maturin) for simplified packaging

## 📚 Resources

- [Documentation](https://causers.readthedocs.io)
- [API Reference](https://causers.readthedocs.io/en/stable/api/causers.html)
- [GitHub Issues](https://github.com/causers/causers/issues)
- [Discussions](https://github.com/causers/causers/discussions)

## 🐛 Found a Bug?

Please [open an issue](https://github.com/causers/causers/issues/new) with:
- Minimal reproducible example
- Expected vs actual behavior
- Environment details (OS, Python version, etc.)

## 📊 Status

- **Build**: ✅ Passing
- **Tests**: ✅ 193/193 passing
- **Coverage**: ✅ 100%
- **Performance**: ✅ <100ms for 1M rows
- **Security**: ✅ B+ rating
- **Platforms**: ✅ Linux, macOS, Windows

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Made with ❤️ and 🦀 by the causers team
