polarscope 1.1.4

🔬 Professional data inspection and visualization toolkit for Polars - featuring advanced X-ray analysis, comprehensive plotting, and data quality assessment.

🔬 Polarscope

Professional data inspection and visualization toolkit for Polars 🐻‍❄️

Polarscope is a modern, fast, and comprehensive data analysis library built exclusively for Polars. It provides advanced X-ray analysis, beautiful visualizations, and comprehensive data quality assessment - all with native Polars performance.

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✨ Key Features

🔬 X-ray Analysis

• xray(df) → Comprehensive data quality assessment with beautiful Great Tables output
• Advanced statistics: skewness, kurtosis, outliers, normality tests, data quality flags
• Performance metrics: execution timing and memory usage tracking
• Customizable output: minimal or expanded views with professional formatting

📊 Multi-Backend Visualization

• corr_heatmap(df) → Correlation analysis with advanced filtering and target analysis
• dist_plot(df) → Distribution plotting with statistical overlays
• missingval_plot(df) → Missing value pattern analysis
• cat_plot(df) → Categorical data frequency analysis
• corr_plot(df) → Interactive correlation exploration
• 3 backends supported: Plotly (default), Seaborn, Altair

🧹 Data Processing

• clean_column_names(df) → Normalize and deduplicate column names
• data_cleaning(df) → Comprehensive automated data cleaning pipeline
• convert_datatypes(df) → Intelligent dtype optimization
• drop_missing(df) → Advanced missing value handling

🛠️ Utilities

• save_fig() → Universal figure saving (PNG, HTML, SVG, etc.)
• Native Polars performance - no Pandas dependency
• Type-safe with comprehensive docstrings

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🚀 Quick Start

Installation

# Basic installation
pip install polarscope

# With all optional dependencies
pip install polarscope[all]

# Individual extras
pip install polarscope[plotly,seaborn,great_tables]

Basic Usage

import polars as pl
import polarscope as plc

# Load your data
df = pl.read_csv("data.csv")

# 🔬 Get comprehensive X-ray analysis
plc.xray(df)

# 📊 Create beautiful visualizations
plc.corr_heatmap(df, backend="plotly")
plc.dist_plot(df, column="price", backend="seaborn")
plc.missingval_plot(df, backend="altair")

# 🧹 Clean and optimize your data
df_clean = plc.data_cleaning(df)
df_optimized = plc.convert_datatypes(df_clean)

Advanced X-ray Analysis

# Expanded analysis with custom settings
plc.xray(
    df,
    expanded=True,                    # Show all statistics
    corr_target="target_column",      # Correlation to specific column
    outlier_method="iqr",            # Outlier detection method
    decimals=3,                      # Formatting precision
    compact=True                     # Compact number formatting
)

# Custom percentiles and quality thresholds
plc.xray(
    df,
    percentiles=[0.1, 0.25, 0.5, 0.75, 0.9],
    missing_threshold=0.3,           # Flag high missingness
    outlier_threshold=0.05,          # Flag outlier-heavy columns
    normality_test=True,             # Include normality tests
    uniformity_test=True             # Include uniformity tests
)

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📈 Performance

Polarscope is built for speed and efficiency:

• Lightning fast: Native Polars operations throughout
• Memory efficient: Optimized for large datasets
• Scalable: Handles millions of rows with ease
• Professional: Comprehensive test suite with 100% pass rate

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🎯 Why Polarscope?

Polars-Native

• Built exclusively for Polars - no Pandas dependencies
• Native performance and memory efficiency
• Type-safe operations with Polars' query engine

Professional Quality

• Production-ready with comprehensive testing
• Beautiful, customizable output with Great Tables
• Scientific precision in statistical analysis

Multi-Backend Flexibility

• Choose your preferred visualization backend
• Consistent API across Plotly, Seaborn, and Altair
• Easy switching between backends

Comprehensive Analysis

• Goes beyond basic .describe() functionality
• Advanced statistics and quality assessment
• Data quality flags and recommendations

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📚 Documentation

Core Functions

Function	Description	Key Features
xray()	Comprehensive data analysis	Statistics, quality flags, performance metrics
corr_heatmap()	Correlation visualization	Multiple backends, filtering, target analysis
dist_plot()	Distribution analysis	Statistical overlays, multiple backends
missingval_plot()	Missing value patterns	Percentage/absolute counts, pattern analysis
data_cleaning()	Automated cleaning	Duplicates, missing values, optimization

Backends

• Plotly (default): Interactive, web-ready visualizations
• Seaborn: Statistical plotting with beautiful aesthetics
• Altair: Grammar of graphics approach

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🤝 Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

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📄 License

MIT License - see LICENSE file for details.

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🙏 Acknowledgments

Inspired by klib and ydata-profiling, but built from the ground up for Polars performance and modern data science workflows.

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🔬 Ready to X-ray your data? Install Polarscope today!