frameon

🚀 Quick Start

Installation and basic usage

Get started →

getting_started.html

📚 API Reference

Detailed method documentation

Explore API →

api/index.html

🧪 Examples

Practical usage scenarios

View examples →

examples_gallery/index.html

About Frameon

Frameon extends pandas DataFrames and Series with analysis methods while keeping all original functionality intact.

Key principles:

• Seamless integration: Works with existing pandas DataFrames and Series

• Non-intrusive: All pandas methods remain unchanged and fully available

• Modular access: Additional functionality organized in clear namespaces

• Dual-level access: Methods available for both entire DataFrames and individual columns

Method Levels

Frameon provides methods at two levels:

1. DataFrame-level - operate on the entire dataframe:

   df.explore.info()    # Summary for all columns
   df.viz.bar()         # Visualization using multiple columns
   

2. Series-level - work with individual columns:

   df['age'].explore.info()                # Summary for single column
   df['price'].preproc.to_categorical()    # Convert specific column to categorical data
   

Key points:

• Same namespaces (like .explore) provide different methods for DataFrames and Series

• DataFrame methods focus on relationships between columns

• Series methods focus on operations within a single column

Built Upon

Frameon utilizes the following open-source libraries as foundational components:

• pandas - Core data structures

• numpy - Numerical computing

• plotly - Interactive visualization

• scipy - Scientific computing

• pingouin - Statistics

• scikit-learn - Machine learning

• statsmodels - Statistical modeling

• nltk - Text processing

Core Features

• Data exploration: Quick insights and summaries

• Preprocessing: Common data cleaning operations

• Advanced analysis: Statistical tests and cohort analysis

• Visualization: Extended plotting capabilities

Getting Started

1. Install the package:

   pip install frameon
   

2. Wrap your DataFrame:

   from frameon import FrameOn as fo
   df = fo(your_dataframe)
   

3. Start exploring:

   df.explore.info()           # For entire DataFrame
   df['col'].explore.info()    # For individual column
   

Full guide →