onekit 2.1.16

One-liner utilities for the Python data science stack

OneKit

One-liner utilities for the Python data science stack OneKit eliminates repetitive boilerplate that every data scientist rewrites from scratch. It wraps NumPy, Pandas, Matplotlib, Seaborn, and SciPy with smart defaults, transparent reporting, and full parameter control.

Installation

pip install onekit

Quick Start

import onekit as ok
import pandas as pd
df = pd.read_csv('sales.csv')
# One‑call EDA
dk.quick_eda(df)
# Clean it up
df = dk.clean_columns(df)
df = dk.fix_dtypes(df)
df = dk.auto_impute(df)
# Visualise
dk.plot_distributions(df)
dk.plot_corr(df)
# Compare two groups
result = dk.compare_groups(
    df[df['region'] == 'North']['revenue'],
    df[df['region'] == 'South']['revenue']
)
print(result['interpretation'])

Features

Function	What it does	Module
auto_impute()	Fill / drop missing values intelligently	pandas
profile_df()	Full dataset summary	pandas
plot_distributions()	Histogram + boxplot for every numeric column	seaborn
plot_corr()	Correlation heatmap	seaborn
compare_groups()	Automatic t‑test vs Mann‑Whitney	scipy
quick_eda()	Full exploratory pipeline	pipelines
… and many more	See documentation

Documentation

Full documentation, including API reference and examples, is available at https://Tenali-Rama.github.io/onekit.

License

MIT