Metadata-Version: 2.4
Name: data_narasak
Version: 0.1.2
Summary: A Python package for data transformation and generating PDF reports with detailed analysis and visualizations.
Author: Kumaran Ravichandran
Author-email: kumaranravi3112003@gmail.com
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: reportlab>=3.6.0
Requires-Dist: openpyxl>=3.0.0
Requires-Dist: seaborn
Requires-Dist: scikit-learn
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Data Analysis & Transformation PDF Report Generator

A powerful Python toolkit for performing data transformations on tabular datasets and generating detailed, well-formatted PDF reports. Designed for data analysts, scientists, and engineers, this tool streamlines data preprocessing, exploratory analysis, and documentation by integrating transformation functions, visualizations, and report generation in one package.

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## 📌 Features

- **Multi-format data loading:** Supports CSV, Excel (`.xlsx`, `.xls`), and JSON files.
- **Flexible data transformations:** Normalization, log transformation, categorical encoding, and more.
- **Comprehensive PDF reports:** Include dataset overview, data types, summary statistics, missing and unique values, correlation matrices, data previews, transformed data insights, and before-after comparisons.
- **Dynamic plot integration:** Optional inclusion of plots like histograms, boxplots, scatterplots, bar charts, and heatmaps.
- **Professional PDF formatting:** Powered by ReportLab with support for pagination, text formatting, and image embedding.
- **Automated cleanup:** Temporary plot images are removed after embedding to keep your workspace tidy.
- **Extensible modular design:** Easy to customize and extend with your own transformations and visualizations.

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## 🚀 Installation

Install dependencies via pip:

```bash
pip install pandas reportlab matplotlib numpy openpyxl
