VizDat

Overview 📖

VizDat is a lightweight and efficient visualization library designed to simplify data exploration. It provides a quick function for visualizing data distributions, helping users identify patterns and insights effortlessly.

Features ⚙️

• 🚀 Fast and Simple – Quickly generate visualizations for tabular data.
• 🌟 User-Friendly API – Easily integrates into any workflow.
• ⚡ Lightweight – Minimal dependencies for high performance.

Installation 📦

pip install VizDat

Usage 📊

import VizDat  
import pandas as pd  

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

#Generate histograms for numerical features
VizDat.data_dist(
    data=df, bins=30, exclude=None, include=None, 
    exclude_binary=False, color="skyblue", kde_color="crimson", kde=True
)

Explaination of the function and its parameters 📝

What this function does:

• Plots histograms for all numerical features in a DataFrame in a grid layout.
• Determines the number of rows and columns in the grid dynamically.
• Allows excluding or including specific features.
• Supports Kernel Density Estimate (KDE) overlay.

Parameters explanation:

• data (pd.DataFrame): The input DataFrame (required).
• bins (int): Number of bins for histograms (default: 30).
• exclude (list): Features to exclude from visualization (default: None).
• include (list): Features to specifically include (default: None).
• exclude_binary (bool): Whether to exclude binary features (default: False).
• color (str): Color of the histogram bars (default: "skyblue").
• kde_color (str): Color of the Kernel Density Estimate (KDE) curve (default: "crimson").
• kde (bool): Whether to show the KDE curve over histograms (default: True).

Contribution 🤝

We welcome contributions to improve VizDat! Whether it's fixing bugs, enhancing features, or improving documentation, your help is appreciated.

Please contact me via my email mustafa.farajallah99@gmail.com or through my linked in profile, LinkedIn profile