3 Python Packages to Try in 2022

1. Lux — Automated Visualization Suggestions and Generation

Lux takes any DataFrame you pass in and analyzes it for possible visualizations. Then Lux will present the pre-configured visualizations that you can choose from. All you have to do is click the visualization you want and you’re done — no coding required! Lux is great for those who want to access visualizations more quickly in Python. Even for more advanced Python users, getting the syntax for a visualization right can be a time consuming process.

To install lux:

import lux
import pandas as pd

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

Lux will recommend and present a variety of charts, ranging from data exploration to more visual and geographical (if the data is pertinent).

With Lux’s intent feature, you can specify the columns you are interested in and it will recommend charts specific to those columns.

df.intent = ["Column1","Column2"]
df

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

Bokeh — Create Interactive Visualizations in Python

To start using Bokeh:

pip install bokeh

Bokeh is all about graphs that are interactive, allowing the end users to not only receive insights from the visualized data, but also from the ability toggle the data in new orientations.

Here are some example graphs from the Bokeh documentation:

https://demo.bokeh.org/

Here is the full documentation

Here is a demo video from the Data Professor that walks through great graphing packages like Bokeh:

Mito — A Spreadsheet Extension for Jupyter Lab that Generates Code

Mito is an editable spreadsheet that you can call into your Python environment. Each edit you make in the Mitosheet will generate the equivalent Python in the code cell below. Mito is primarily used by two types of people. The first is Python users who want to save time on googling syntax and want to generate their code faster and more visually. The second is spreadsheet users who want to dive into Python analysis, but may not be proficient in coding yet.

Here is a demo video for Mito:

Within Mito, you can:

• Create Visualizations
• Look at Summary Statistics
• Merge Dataset Together
• Generate Pivot Tables
• Edit Specific Cell Values
• Filter and Sort Datasets
• Save and Replay Analyses (macros)
• See a record of your edits
• and more!

from Author

Each edit generates the equivalent Python.

From Author

To install Mito, run these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then open Jupyter Lab and render a Mitosheet:

import mitosheet
mitosheet.sheet()

Here are the full install instructions.

I hope you find these packages helpful. Leave a comment if you get a chance to try any of them!