QuickEDA

Automated Exploratory Data Analysis (EDA) toolkit that delivers statistical insights and publication-ready visualizations with 3 lines of code.

from auto_eda import DataAnalyzer

analyzer = DataAnalyzer(df)
analyzer.explore_all()  # Generates complete report

🔍 Features

• Smart Analysis
  Univariate, bivariate, and multivariate diagnostics
• Visualization Backends
  Switch between Seaborn or Plotly with one parameter
• Statistical Rigor
  Built-in hypothesis testing and model diagnostics
• Zero Configuration
  Works out-of-the-box while allowing customization

⚡ Quick Start

Installation

pip install quickeda

Basic Usage

# Initialize with your dataframe
analyzer = DataAnalyzer(df)

# 1. Univariate stats (all features)
stats = analyzer.univariate_analysis(sort_by="skew")

# 2. Bivariate analysis (against target)
plots = analyzer.bivariate_analysis('price', plot_backend='plotly')

# 3. Check multicollinearity
vif_results = analyzer.multivariate_analysis('price', method='vif')

📊 Visualization Examples

Switch Backends Seamlessly

analyzer.plotter.set_backend('seaborn')  # Default
analyzer.plotter.set_backend('plotly')   # Interactive

Available Plots

Plot Type	Description
.scatter()	Regression plots with stats
.bar_chart()	Group comparisons with ANOVA
.histogram()	Distribution analysis

📈 Advanced Usage

Stepwise Feature Selection

stepwise = analyzer.multivariate_analysis(
    target='price',
    method='stepwise',
    min_features=3
)

Access Raw Statistical Functions

from auto_eda.stats import calculate_vif
vif = calculate_vif(df, target='price')

🤝 Contributing

PRs are welcome! See CONTRIBUTING.md for guidelines.

📜 License

MIT © 2023 Shubham Patel