🔬 Polarscope

One call, one table: a full profile of any Polars DataFrame. No pandas, anywhere.

import polarscope as ps
ps.xray(df, compact=True)

_{70,000 rows × 13 columns profiled in 21 ms — every column's dtype, nulls, quartiles, outliers, skew and distribution in a single call.}

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.

✨ Current Features

🔬 Data Inspection (xray)

• Summary statistics for numeric columns by default; include='all' covers every dtype.
• Rich string statistics for String/Categorical/Enum columns: top value and frequency, min/median/avg/max length, mode share, top-3 values, and value samples.
• Boolean columns analyzed as 0/1 (Mean = share of True); temporal columns report earliest/latest timestamps.
• Optional expanded output (expanded=True) with normality/uniformity tests, outlier metrics, usability flags, and correlation details.
• Inline distribution nanoplots and per-column data-quality flags.
• Great Tables output (great_tables=True) or plain Polars DataFrame output (great_tables=False).

With include="all", string and categorical columns get their own statistics alongside the numeric ones:

📊 Built-in Datasets

• Three datasets ship with the package: ps.titanic(), ps.diabetes(), and ps.cardio() (70k-row health records).
• Also available via from polarscope.datasets import titanic, diabetes, cardio, with loader helpers and dataset metadata.

🧹 Cleaning & Optimization (fix)

• ps.fix(df) cleans and optimizes in one call: column renaming (case="snake"/"camel"/"pascal"/"kebab"/"upper"/"lower"), whitespace stripping (empty strings → null), dtype shrinking, and empty-column removal — with a compact report of what changed.
• Opt-in extras: drop_duplicate_rows, missing_threshold, drop_constant_columns, outliers="iqr"/"zscore".
• Lossless by default: anything that removes or alters data must be switched on explicitly.
• Building blocks also available standalone: clean_column_names, convert_datatypes, drop_missing.

📈 Plots

• Correlation/missing/distribution/categorical plots (plotly or altair backends)

✅ Polars-Only Data Handling

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

🔍 How Polarscope compares

Where the others are the better choice:

• ydata-profiling produces a far deeper report — variable interactions, correlation matrices, alerts and warnings. If you want an exhaustive standalone HTML artifact and don't mind the 21 dependencies or the pandas conversion, it does more than polarscope does.
• skimpy also reads Polars natively and prints straight to the terminal, which is the nicer fit for CLI and script workflows. Polarscope targets the notebook.
• klib has a mature pandas cleaning and plotting suite. Polarscope's plotting API is openly modelled on it.

Where polarscope wins: it is the only one of the four with no pandas anywhere in its dependency tree, and the only one installable on Python 3.9 — which matters if your environment is locked down and you cannot pull in pandas, seaborn and numba just to look at a table.

_{Verified 2026-08-10 against each project's published PyPI metadata (requires_dist, requires_python) at the versions listed. Dependency counts exclude optional extras. Corrections welcome — please open an issue.}

🚀 Quick Start

Installation

pip install polarscope

Optional extras:

pip install "polarscope[altair]"   # optional Altair plotting backend
pip install "polarscope[plotly]"   # Kaleido support for static Plotly images
pip install "polarscope[scipy]"    # statistical tests
pip install "polarscope[all]"      # all optional features
pip install "polarscope[dev]"      # pytest + coverage tools

Plotly is included in the base installation and is the default plotting backend.

Basic Usage

import polars as pl
import polarscope as ps

# Use a built-in dataset or load your own
df = ps.titanic()          # also: ps.diabetes(), ps.cardio()
# df = pl.read_csv("your_data.csv")

# Get basic data summary (all column types)
ps.xray(df, include="all")

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

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

# Clean & optimize in one call (snake_case names, trimmed strings,
# shrunk dtypes, empty columns dropped - with a report)
df = ps.fix(df)

# Or opt in to deeper cleaning
df = ps.fix(df, case="camel", drop_duplicate_rows=True, missing_threshold=0.9)

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
)

🩺 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.

🤝 Contributing

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

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Inspired by klib and built with great_tables.

🔬 A simple tool for basic Polars data inspection.