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Community repository of bamboolib - the Fastest and Easiest Way to Work with pandas DataFrames 🐼🐍

This is the community repository of bamboolib. There are no source files because bamboolib is closed source. However, you can use bamboolib for free on Open Data. If you have any issues or feature requests, please open an issue.

bamboolib is a python package for easy data exploration & transformation with pandas. You can use it with Jupyter Notebook or JupyterLab. bamboolib adds an interactive UI to your pandas output, which allows you to quickly prepare and visualize your data. All transformations come with full keyboard control, making bamboolib loved both by pandas-savvy users as well as Python novices.

Main features and benefits of bamboolib

90% less working time

  • Focus on what you want to do instead of how
  • No more digging through StackOverflow
  • Use your keyboard or mouse – whatever is fastest

Easy to use

  • Quick onboarding with minimal learning curve
  • Find all features via an intuitive search bar
  • Created by professional Data Scientists

100% compatible and flexible

  • Seamless integration into your existing working environment
  • Work from within Jupyter Notebook or JupyterLab
  • Live code export for reproducibility and full customization

Private, secure, local

  • All your data remains private and secure
  • Use it on your local machine without the need for cloud access
  • Satisfies the requirements of your IT department

🔍Try bamboolib live on Binder

Installation

Install bamboolib for Jupyter Notebook or Jupyter Lab by running the code below in your terminal (or Anaconda Prompt for Windows):

pip install bamboolib

# Jupyter Notebook extensions
python -m bamboolib install_nbextensions

# JupyterLab extensions
python -m bamboolib install_labextensions

After you have installed bamboolib, you can go here to test bamboolib.

Documentation

You find out how to get started along with tutorials and an API reference on our docs.

Further links

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bamboolib - the Fastest and Easiest Way to Work with pandas DataFrames

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  • Jupyter Notebook 66.0%
  • Python 33.7%
  • Shell 0.3%