Metadata-Version: 2.4
Name: pysuricata
Version: 0.0.1
Summary: A lightweight EDA tool inspired by the curious nature of suricates. Built just for fun 🔬.
Author-email: Alvaro Diez <alvarodiez20@gmail.com>
License-Expression: MIT
Project-URL: Repository, https://github.com/alvarodiez20/suricata
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Provides-Extra: notebooks
Requires-Dist: ipykernel; extra == "notebooks"
Provides-Extra: dask
Requires-Dist: dask; extra == "dask"
Provides-Extra: polars
Requires-Dist: polars>=1.26.0; extra == "polars"
Requires-Dist: pyarrow>=19.0.1; extra == "polars"
Dynamic: license-file

# `pysuricata`
[![Build Status](https://github.com/alvarodiez20/pysuricata/workflows/CI/badge.svg)](https://github.com/alvarodiez20/pysuricata/actions)
[![PyPI version](https://badge.fury.io/py/pysuricata.svg)](https://pypi.org/project/pysuricata/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

![pysuricata Logo](pysuricata/static/images/logo.png)

`pysuricata` is a lightweight Python library for exploratory data analysis (EDA) that supports multiple data formats—Pandas, Dask, and Polars DataFrames. It generates self-contained HTML reports featuring summary statistics, missing values, and correlation matrices with a clean, modern design. 


## Installation

Install `pysuricata` directly from PyPI:

```bash
pip install pysuricata
```

## Quick Example

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


```python
import pandas as pd
from pysuricata import generate_report
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 = generate_report(iris_df, output_file="iris_report.html")

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