autoclean-swapna

Stop rewriting the same 10 lines of pandas cleanup in every project.

autoclean is a lightweight Python library that automates the repetitive, boring parts of data cleaning — column names, duplicates, missing values, outliers, inconsistent formats — so you can get straight to analysis.

Why autoclean?

Every data science project starts the same way:

df.columns = df.columns.str.lower().str.strip().str.replace(" ", "_")
df = df.drop_duplicates()
df["amount"] = df["amount"].str.replace(",", "").astype(float)
df["date"] = pd.to_datetime(df["date"], errors="coerce")
# ...and five more lines you've written a hundred times before

autoclean replaces all of that with:

from autoclean import clean
df = clean(df)

Installation

pip install autoclean-swapna

Quick Start

import pandas as pd
from autoclean import clean

df = pd.read_csv("messy_data.csv")
df = clean(df)

Before:

After clean(df):

Duplicate row dropped, column names normalized, "1,200" converted from text to a real number — automatically.

What clean() does automatically

These three are always safe to run automatically — they never discard meaningful data.

Full function reference

These are opt-in — call them individually when you need judgment-based cleaning that clean() won't do automatically (since they can drop or alter real data).

Full example

from autoclean import fix_column_names, remove_duplicates, fill_missing, remove_outliers, validate_data

df = fix_column_names(df)
df = remove_duplicates(df)
df = fill_missing(df)
df = remove_outliers(df, "amount")

validate_data(df, rules={"age": (0, 120)})

Requirements

• Python 3.8+
• pandas >= 1.5.0

License

MIT © Swapna Choudhary