Metadata-Version: 2.4
Name: pysuricata
Version: 0.0.6
Summary: A lightweight EDA tool inspired by the curious nature of suricates. Built just for fun 🔬.
Author-email: Alvaro Diez <alvarodiez20@gmail.com>
License: MIT
Project-URL: Repository, https://github.com/alvarodiez20/pysuricata
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: <3.14,>=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas~=2.0; python_version < "3.13"
Requires-Dist: pandas<3.0,>=2.2.3; python_version >= "3.13"
Requires-Dist: numpy>=2.1; python_version >= "3.13"
Dynamic: license-file

# `pysuricata`
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

<div align="center">
  <img src="https://raw.githubusercontent.com/alvarodiez20/pysuricata/main/pysuricata/static/images/logo_suricata_transparent.png" alt="pysuricata Logo" width="300">
</div>



A lightweight Python library to generate self-contained HTML reports for exploratory data analysis (EDA).

📖 [Read the documentation](https://alvarodiez20.github.io/pysuricata/)


## Installation

Install `pysuricata` directly from PyPI:

```bash
pip install pysuricata
```

## Why use pysuricata?
- **Instant reports**: Generate clean, self-contained HTML reports directly from pandas DataFrames.
- **No dependencies on heavy frameworks**: Only requires pandas and numpy.
- **Rich insights**: Summaries for numeric, categorical, datetime columns, missing values, duplicates, correlations, and sample rows.
- **Portable**: Reports are standalone HTML (with inline CSS/JS/images) that can be easily shared.
- **Customizable**: Title, sample display, and output path can be tailored to your needs.

## Quick Example

The following example demonstrates how to generate an EDA report using the Iris dataset with Pandas:


```python
import pandas as pd
import pysuricata
from IPython.display import HTML

# Load the Iris dataset directly using Pandas
iris_url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv"
iris_df = pd.read_csv(iris_url)

# Generate the HTML EDA report and save it to a file
html_report = pysuricata.generate_report(iris_df, output_file="iris_report.html")

# Display the report in a Jupyter Notebook
HTML(html_report)
```
