Python packages to generate Python Pandas code from visual aids within Jupyter notebook-based setup

Visual aids to generate Python Pandas data wrangling code, can make data analytics coding task more productive.

Multiple Python packages specifically designed to generate Python Pandas code based on visual aids within Jupyter Notebooks, are available for free.

In additional to code generation, some of these packages also enhances the interactivity and exploration of data within Jupyter Notebooks. The landscape of Python packages and tools are evolving. Here are a few packages that can be helpful:

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Visual Python Extension

Visual Python Extension is a widely recognized tool in the Python community. It enhances visual aspects of working with Python, especially within Jupyter Notebook.

It provides support for code generation for pandas and many other popular packages. It also has rich visual interface for data analysis, visualization, statistics and machine learning.

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dtale

It provides easy way to view & analyse pandas data-frame. It allows formatting, visualization and analysis of data. It also allows to export the underlying code.

?Pandas GUI

GUI-based tool for data analysis with Pandas. It provides visual interface for exploring and manipulating data-frames. It can be used to export codes generated during interaction with visual interface. Specifically, it provides visual interface to reshape pandas’ data-frame like pivot, melt, merge etc.

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Mito

It’s like a spreadsheet that can be instantiated inside Jupyter notebook. It provides visual interface to edit Panda’s data-frame like an Excel data file. It generates Python code automatically corresponding to each of the edit made via visual interface.

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Bamboolib

It provides a user interface component for no-code data analysis and transformations within Jupyter notebooks. All typica data wrangling, exploration, and visualization tasks are supported. It generates Python code in the background-based interaction via visual interface. The generated code can be retrieved and extended for more sophisticated tasks.

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These are some of tools and libraries commonly used as visual aids. There are several other tools and extensions that enhance the visual aspects of working with Python, especially within Jupyter Notebooks. Its evolving and getting better.