A-Z of Low Code Python Packages: Part 1

1. Mito

From Author

Mito is a spreadsheet interface for Python. With Mito, you can call an interactive spreadsheet into your Python environment, and each edit you make in the spreadsheet will generate the equivalent Python in the code cell below.

Mito aims to make data science more accessible to all, regardless of Python experience. For intermediate and advanced Python users, Mito allows for quick Python analysis without having to go to Stack Overflow or Google, as all the code is generated for you.

To install Mito, run these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then open Jupyter Lab and call the Mitosheet:

import mitosheet
mitosheet.sheet()

Here are the full install instructions.

One popular Mito feature is the pivot tables:

From author

Mito also allow for interactive charts to be made, as well as their equivalent code:

From author

Mito has features for:

• Merging
• Summary Statistics
• Spreadsheet Functions
• Filtering
• Sorting
• Saving and Replaying analyses (macros)
• Adding and Deleting columns
• Editing specific cells
• and more!

Here is the main Mito website.

2.Pandas Profiling

Pandas Profiling takes the df.describe() function from Pandas and elaborates on the functionality, providing amazing summary information for a dataframe quickly and efficiently.

This package is extremely powerful for EDA, allowing you to visually explore your data without needing to code — but letting you remain in your Python environment.

You can install the package locally with these commands:

import sys
!{sys.executable} -m pip install -U pandas-profiling[notebook]
!jupyter nbextension enable --py widgetsnbextension

Pandas Profiling provides advanced summary statistics and information for a dataset with out having to write very much code at all.

https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/introduction.html

Here it the full description of the Pandas Profiling functionality, as described on the documentation website:

https://pandas-profiling.github.io/pandas-

3. Pivottablejs.

Pivottablejs is an open source JS library that is importable as a Python package.

https://pivottable.js.org/examples/

As you can see above, this package allows the user to analyze their data with advanced pivot tables and visualizations. Pivot tables are a feature that many grow to love in Excel and often find hard to replicate in a Python workflow. The pandas_pivot and pandas_groupby functionality does work for pivoting, but the code can be clunky and hard to work with. Pivoting visually is a much easier process, and this package gives the user that functionality.

To install the package:

pip install pivottablejs

To call a Dataframe into the pivot table UI:

from pivottablejs import pivot_ui
pivot_ui(df)

4. Qgrid

Qgrid is a grid extension for Jupyter, which allows you to call your data into the UI. The main “featured” features are scrolling, filtering and editing specific values. Qgrid is great for quickly manipulating your data.

https://github.com/quantopian/qgrid/blob/master/docs/images/filtering_demo.gif

To install:

pip install qgrid
jupyter nbextension enable --py --sys-prefix qgrid

5. Lux

Lux is the only package in this article that is focused on solely on charting. Instead of giving the user an environment to create their charts, Lux allows the users to pass in their DataFrame and then it will automatically generate and suggest charts that the user can pick from. This is an extremely fast way to get great charts made, especially if you are making very standard charts in a repeated fashion. The actual coding of visualization packages can really slow down a workflow. Lux is a great way to circumvent this.

https://github.com/lux-org/lux

To import Lux:

import lux
import pandas as pd

I hope these packages are helpful for you :)