framepeek 0.1.1

Lightweight exploratory data analysis for pandas DataFrames

FramePeek

Lightweight exploratory data analysis for pandas DataFrames.

Documentation

• Getting started
• API reference
• Product requirements
• Changelog
• Contributing
• Code of Conduct
• Release guide

Usage

import framepeek as fp

report = fp.profile(
    df,
    target="churn",
    correlation_method="spearman",
    outlier_multiplier=1.5,
    top_n_categories=5,
)

fp.print_report(report)

The report contains overview, columns, missing, duplicates, numeric, categorical, outliers, correlations, target, and warnings. print_report() gives each section a title and prints tables without formatter-generated ellipses.

Each analysis is also available directly:

fp.overview(df)
fp.columns(df)
fp.missing(df)
fp.duplicates(df)
fp.numeric(df)
fp.categorical(df)
fp.outliers(df)
fp.correlations(df)
fp.target(df, target="churn")
fp.warnings(df, target="churn")

All functions validate their inputs and leave the original DataFrame unchanged.

Outputs

Function	Return value
overview	DataFrame of dataset-level metrics
columns	DataFrame with one profile row per column
missing	DataFrame; row totals are stored in .attrs["rows"]
duplicates	Dictionary containing totals, groups, and examples
numeric	DataFrame of descriptive numeric statistics
categorical	DataFrame of frequency and cardinality statistics
outliers	DataFrame of IQR bounds and potential outlier counts
correlations	Dictionary containing matrix and tidy pairs tables
target	Dictionary containing categorical or numeric target analysis
warnings	DataFrame of actionable quality warnings
profile	Dictionary containing all analyses above
print_report	None; prints every profile section with a title

Thresholds for missingness, cardinality, outliers, correlations, and class imbalance can be configured through the corresponding function parameters.

Development

python -m pip install -e ".[dev]"
python -m ruff check .
python -m mypy src/framepeek
python -m pytest --cov=framepeek --cov-fail-under=100