Metadata-Version: 2.2
Name: cleanflo
Version: 0.1.0
Summary: A beginner-friendly Python package for easy data cleaning and preprocessing.
Home-page: https://github.com/yourusername/cleanflo
Author: MYNAMPATI SRI RANGANADHA AVIANSH
Author-email: aviinashhreddyy77@gmail.com
Keywords: data cleaning,preprocessing,data science,machine learning
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: nltk
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: requires-dist
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Dynamic: summary

# 🚀 cleanflo: A Lightweight Data Cleaning Library

*cleanflo* is a Python package designed to *automate data cleaning* tasks such as:
- Handling missing values ✅
- Detecting and treating outliers ✅
- Feature scaling (Standardization, Min-Max, Log Scaling) ✅
- Encoding categorical features (One-Hot, Label Encoding) ✅
- Text cleaning (lowercasing, removing special characters, stopwords) ✅

## 📌 1. Installation
To install cleanflo, use:
```sh
pip install cleanflo

## 📌 2. Quick Start
import pandas as pd
from cleanflo import cleanflo_pipeline

# Sample DataFrame
df = pd.DataFrame({
    "Age": [25, 30, None, 40, 50],
    "Salary": [50000, 60000, 70000, None, 90000],
    "Department": ["HR", "Finance", "IT", "IT", "Sales"],
    "Review": ["Great product!", "Worst experience!!!", "Just okay.", "Loved it!", "Nice deal."]
})

# Apply the full cleaning pipeline
df_cleaned = cleanflo_pipeline(
    df,
    handle_missing=True,
    outlier_columns=["Age", "Salary"],
    scale_columns=["Salary"],
    encode_columns=["Department"],
    text_clean_columns=["Review"]
)

print(df_cleaned)
