3 min readJan 27


Visual Python_2.2.8.gif (1742×980) (

It’s time to discover visualpython a GUI for easy dealing with repetitive data science tasks with python in addition to its powerful support for various python data science libraries(pandas, seaborn..etc)

A step-by-step guide:

Try Visual Python if you would like to:

manage big data with minimal coding skills.

help students / business analysts / researchers to overcome learning barriers for Python.

save & reuse repeatedly used codes(snippets).

credit: visualpython/visualpython: GUI-based Python code generator for data science. (

To demonstrate what I’m talking about, I’m going to write down the steps to follow in order to use this extension as well as an example from my jupyter notebook, Enjoy!

1- Install package:

pip install visualpython
author demonstration

2- Enable the package:

visualpy install
author demonstration

3- Verify the installation:

!visualpy -v
author demonstration

4- Activate Visual Python on Jupyter Notebook:

Just reload your jupyter notebook web page and an orange button will appear like shown in the below image:

author demonstration

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5- Let’s start the fun part:

We will be using the famous titanic dataset and performing a basic EDA(Exploratory Data Analysis)

  • Import packages
author demonstration
  • Import data
author demonstration

Note: for demonstration purposes, I’ve assigned the original data frame to a data variable(like shown in the below image)

author demonstration
  • Data head & tail
author demonstration
  • Descriptive statistics, infos & value_counts
author demonstration
  • Min, Max & count
author demonstration


As seen here, the process is easy than you think.

You can now perform exploratory data analysis and play with different methods even if you don’t have a robust python background, enjoy the process!

Thanks for reading.

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