visualpython
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. (github.com)
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
2- Enable the package:
visualpy install
3- Verify the installation:
!visualpy -v
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:
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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
- Import data
Note: for demonstration purposes, I’ve assigned the original data frame to a data variable(like shown in the below image)
- Data head & tail
- Descriptive statistics, infos & value_counts
- Min, Max & count
Conclusion:
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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