cleanops 0.1.7

A lightweight data cleaning and inspection toolkit.

CleanOps

A lightweight toolkit for inspecting, cleaning, organizing, and exporting datasets.
Designed for quick data quality checks, automated cleaning, and report generation.

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📌 Project Overview

CleanOps provides a simple workflow for handling messy datasets.
It includes tools for:

• Detecting missing values
• Identifying duplicate rows
• Detecting outliers (IQR-based or statistical)
• Cleaning + automatic fixing
• Organizing dataset columns and rows
• Exporting cleaned data (CSV / Excel / JSON)
• Running complete pipelines combining cleaning, exporting, and reporting

The goal is to make data preprocessing easier and more consistent.

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📦 Installation

Install via pip:

pip install cleanops

Or, if you're installing from source:

pip install .

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🚀 Example Usage

1. Inspecting and Cleaning a Dataset

import pandas as pd
from cleanops import DataCleaner

df = pd.read_csv("test_data_150.csv")

cleaner = DataCleaner(df)

# Diagnose issues
issues = cleaner.diagnose()
print("Issues found:", issues)

# Apply cleaning (missing, duplicates, outliers)
cleaned_df = cleaner.treat()

print(cleaned_df.head())

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2. Exporting Cleaned Data

from cleanops import DataExporter

exporter = DataExporter(cleaned_df)

exporter.to_csv("output.csv")
exporter.to_excel("output.xlsx")
exporter.to_json("output.json")

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3. Generating a Data Report

from cleanops import ReportGenerator

reporter = ReportGenerator(cleaned_df)
report = reporter.report()

print("Report:", report)

reporter.export_report("dataset_report.json")

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4. Running a Full Data Pipeline

from cleanops import DataCleaner, DataOutput, DataPipeline
import pandas as pd

df = pd.read_csv("test_data_150.csv")

cleaner = DataCleaner(df)
output = DataOutput(df)

pipeline = DataPipeline(
    cleaner=cleaner, 
    exporter=output.exporter, 
    reporter=output.reporter
)
pipeline.run()

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📄 License

This project is released under the MIT License.

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