Metadata-Version: 2.4
Name: sagaranalysis
Version: 0.1.1
Summary: A production-ready Python library for one-line data cleaning, EDA, and beautiful HTML reporting.
Author-email: Sagar <sagarmohite4895@gmail.com>
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: seaborn>=0.11.0
Requires-Dist: jinja2>=3.0.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: rich>=12.0.0
Requires-Dist: pyfiglet>=1.0.0
Requires-Dist: rich-gradient>=0.3.0
Requires-Dist: halo>=0.0.31
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Dynamic: license-file

# SagarAnalysis

A production-ready, beginner-friendly Python package designed for one-line data cleaning, one-line Exploratory Data Analysis (EDA), and stunning, glassmorphic HTML reporting.

## Features

- **One-line Cleaning (`clean(df)`)**:
  - Automatically standardizes column names to `snake_case`.
  - Removes duplicate rows.
  - Detects and drops constant/highly missing columns.
  - Imputes missing values intelligently (median/mean/mode/unknown).
  - Handles numerical outliers via clipping.
  - Coerces data types (string to numeric, string to datetime, Yes/No to boolean).
- **One-line EDA & Report (`analysis(df)`)**:
  - Generates a comprehensive overview of the dataset.
  - Performs descriptive statistics and missingness analysis.
  - Discovers high correlations and skewness.
  - Auto-detects the target column and runs target profiling.
- **Stunning HTML Reports (`report.save()`)**:
  - Interactive, modern Glassmorphism design.
  - Supports light/dark theme toggles.
  - Fully responsive layout with embedded Base64 charts.
  - Searchable variables, collapsible details, and clear data insights.

## Installation

```bash
pip install sagaranalysis
```

## Quick Start

```python
import pandas as pd
import sagaranalysis as sa

# Load your dirty dataset
df = pd.read_csv("dirty_data.csv")

# Clean your data in one line
cleaned_df = sa.clean(df)

# Perform automatic EDA and generate insights
report = sa.analysis(cleaned_df)

# View or save the report
report.show()                  # Opens in local browser or renders in Jupyter Notebook
report.save("report.html")     # Saves self-contained report to disk
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
