polarscope 1.6.1

🔬 Simple data inspection tools for Polars - basic X-ray analysis and utilities for DataFrames.

🔬 Polarscope

Simple data inspection tools for Polars 🐻‍❄️

Polarscope is a basic data analysis library for Polars DataFrames. It provides an xray() function for data inspection and some plotting utilities. Still early in development with more features planned.

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✨ Current Features

🔬 Data Inspection (xray)

• Summary statistics for numeric columns by default.
• Optional include='all' support for mixed-type data.
• Optional expanded output (expanded=True) with normality/uniformity tests, outlier metrics, usability flags, and correlation details.
• Great Tables output (great_tables=True) or plain Polars DataFrame output (great_tables=False).

📊 Built-in Datasets

• from polarscope.datasets import titanic, diabetes
• Includes loader helpers and dataset metadata.

🧹 Utilities

• Column name cleaning (clean_column_names)
• Datatype optimization (convert_datatypes)
• Missing value dropping (drop_missing)
• Correlation/missing/distribution/categorical plots

✅ Polars-Only Data Handling

• Data handling is implemented with Polars.
• No Pandas is required for core data processing.

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🚀 Quick Start

Installation

pip install polarscope

Optional extras:

pip install "polarscope[all]"      # plotly + altair + scipy helpers
pip install "polarscope[dev]"      # pytest + coverage tools

Basic Usage

import polars as pl
import polarscope as ps
from polarscope.datasets import titanic

# Use a built-in dataset or load your own
df = titanic()
# df = pl.read_csv("your_data.csv")

# Get basic data summary
ps.xray(df)

# More detailed analysis
ps.xray(df, expanded=True)

# Custom title and correlation analysis
ps.xray(df, title="My Data Analysis", corr_target="Survived")

Common xray() Options

ps.xray(
    df,
    include="all",                # include all columns (not only numeric)
    expanded=True,                # additional stats/tests/quality fields
    corr_target="column_name",    # correlation against target column
    outlier_method="iqr",         # or "percentile"/"zscore"
    percentiles=[0.1, 0.5, 0.9],  # custom percentiles
    decimals=2,
    great_tables=False            # return Polars DataFrame
)

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🩺 Troubleshooting

Notebook still uses old code

If you changed source code locally but notebooks still show old behavior, reinstall in the same interpreter and restart kernel:

import sys, subprocess
subprocess.check_call([sys.executable, "-m", "pip", "install", "-e", "/path/to/polarscope"])

ValueError: Only the x-axis of a nanoplot allows strings

This comes from Great Tables nanoplot rendering when unsupported payloads reach the renderer. Update to latest local source and restart kernel.

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🤝 Contributing

This is a small project, but contributions are welcome! Feel free to report bugs or suggest improvements.

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📄 License

MIT License - see LICENSE file for details.

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🙏 Acknowledgments

Inspired by klib and built with great_tables.

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🔬 A simple tool for basic Polars data inspection.