Frameon

Frameon extends pandas DataFrame with analysis methods while keeping all original functionality intact.

⭐ If you find Frameon useful, please star the GitHub repo! It helps others discover the project and motivates further development.

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✨ Key Features

• 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

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📦 Installation

❗ Recommended: Use a virtual environment to prevent potential conflicts with existing package versions in your system.

Basic Installation

Using pip

pip install frameon

Using poetry

poetry add frameon

Installation with Virtual Environment

Python's built-in venv

# Create virtual environment
python -m venv frameon_env

# Activate it
source frameon_env/bin/activate 

# Install frameon
pip install frameon

Using poetry (manages virtual env automatically)

# Navigate to your project directory
poetry init  # if starting new project
poetry add frameon

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🚀 Quick Start

import pandas as pd
from frameon import FrameOn as fo

# Create or load your DataFrame
df = pd.read_csv('your_data.csv')

# Add Frameon functionality
df = fo(df)

# Explore your data
df.explore.info()           # For entire DataFrame
df['price'].explore.info()  # For individual column

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📚 Documentation

For complete documentation including API reference and more examples, visit:
Frameon Documentation

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🧪 Examples

Data Exploration

Quickly explore your data:

from frameon import load_dataset, FrameOn as fo

titanic = fo(load_dataset('titanic'))
titanic['age'].explore.info()

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Cohort Analysis

Quickly visualize user retention with a cohort heatmap:

from frameon import load_dataset, FrameOn as fo

superstore = fo(load_dataset('superstore'))
fig = superstore.analysis.cohort(
    user_id_col='Customer ID', 
    date_col='Order Date', 
    revenue_col='Sales',
    granularity='quarter',
    include_period0=False,
)
fig.show()

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Statistical Tests

Compare groups using bootstrap:

from frameon import load_dataset, FrameOn as fo

titanic = fo(load_dataset('titanic'))
titanic.stats.bootstrap(
    dv='age',
    between='alive',
    reference_group='no',
    statistic='mean_diff',
    plot=True
)

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🔧 API Overview

Frameon provides methods through these namespaces:

Namespace	Description	DataFrame	Series
.explore	Data exploration and quality checks	✓	✓
.preproc	Data preprocessing and cleaning	✓	✓
.analysis	Advanced analytical methods	✓	✗
.viz	Visualization methods	✓	✗
.stats	Statistical tests and analysis	✓	✗

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⚙️ Built With

Frameon utilizes these foundational libraries:

• pandas - Core data structures
• numpy - Numerical computing
• plotly - Interactive visualization
• scipy - Scientific computing
• statsmodels - Statistical modeling
• pingouin - Statistics
• scikit-learn - Machine learning

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🤝 Contributing

We welcome contributions! Here's how to help:

1. 🐛 Report bugs via GitHub Issues
2. 📥 Submit PRs for new features
3. 📖 Improve documentation

See our Contributing Guidelines for details.

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📜 License

Frameon is licensed under the MIT License.