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How to do Data Science in Seconds

Python is the most powerful programming language for data science. Packages like Pandas make data wrangling and cleaning efficient and automate-able.

Though, the syntax for Pandas can be cumbersome. Much fo the time in the analysis is spent making that the right functions are being used and more so that the correct parameters are being used.

Each of us is a consistent visitor to Google, Github, or Stack Overflow for figuring out the correct syntax for a Python task.

Mito is a Python package that allows the user to stay in Python, but alleviates the concern of needing perfect syntax to make your their analysis run.

Mito calls a visual spreadsheet into the Python environment of the user. You can think of it as Excel that generates the equivalent Python with each edit, so if you make a pivot table in Mito, it will generate the equivalent Python for that pivot table in the code cell below.

Here is a quick demo of the tool:

Here is how to install Mito:

Then open Jupyter Lab and call the Mitosheet

The full instructions can be found in the documentation.

To get data into Mito, all you need to do is select the import Menu and select your Excel or CSV file — you can also pass in a DataFrame directly, when you call the Mitosheet.

https://docs.trymito.io/

In Mito, you can apply filters to the data:

https://docs.trymito.io/

Create Pivot Tables:

from Author

Use Spreadsheet Functions:

https://docs.trymito.io/

Make Plotly charts:

from Author

There is a lot of other functionality not shown above, such as sorting, deduplicating, formatting, adding and deleting columns — just to name a few. Mito is geared towards making data science faster and more accessible. The two common ways that the tool is used are:

  1. Transitioning spreadsheet workflows to Python
  • The ability to import and export Excel files makes it incredibly easy to bring in a spreadsheet that the users is working on, process it with Mito, and then bring the output back to a spreadsheet. Many users find that Excel, while powerful, is too slow for their data size and that Mito allows them to analyze their data more efficiently.

2. Enhancing the workflows of Python users to complete their analyses more quickly

  • As mentioned above, Mito makes it so that the user does not have consistently go to Stack Overflow to look up the correct syntax for their analysis. This means the user can stay in their analysis and focus on the data, not the code.

I hope you enjoyed the article :)

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