The Future of Python Packages is Here

1. Mito

Mito is a spreadsheet interface for Python. It allows you to call a spreadsheet front-end into your Python environment, and each edit you make in the spreadsheet generates the equivalent Python in the code cell below.

Here is a demo video:

To install Mito, use these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install
jupyter lab 

To open the Mitosheet run, run these commands in your notebook:

import mitosheet
mitosheet.sheet()

Here are the full install instructions.

Mito allows you to do full EDA and Data Analysis in a visual environment. With Mito, you can:

• Merge Datasets
• Create Pivot Tables
• Graph and Visualize Data
• Use Spreadsheet Formulas and Functions
• Fill Null Values
• Filter and Sort Datasets
• Look at Summary Statistics
• And more!

Here is the full documentation.

2. Pillow

Pillow is a great Python library for working with images. It allows your Python processor to work with images intelligently. The package allows the user to access the data within the pixels.

To install:

python3 -m pip install --upgrade pip
python3 -m pip install --upgrade Pillow

The functionality covered includes:

• ability to pull statistics out of an image using a histogram method
• image resizing
• image rotating
• connection to the Python Image Library
• reads in varying image formats

Here is the full documentation.

3. Arrow

Working with dates can be one of the most annoying parts of data analysis in Python. Arrow solves this.

Arrow allows for easy manipulation, creation, and management of time values and time stamps.

To install, run:

pip install -U arrow
import arrow

Here is a video tutorial of the package:

Dealing with dates can require many different packages and techniques. One of the great things about Arrow is that it combines all these requirements into one package, unifying your date and time handling.

Within Arrow, you can:

• Handle and unify timezones
• extract dates and times from strings
• create timestamps
• iterate over time
• use Unix time
• manipulate date formats
• and more!

Here is the full documentation.

4. Plotly

Ploty is pushing Python visualization packages into the future. Plotly is the best Python packages for interactive visualizations. You can create UI components to your visualizations where users can change formatting, zoom in on specific pieces of data and more.

Other than their interactive visualizations, Plotly also offers standard graphs used for EDA or statistical analysis.

https://plotly.com/graphing-libraries/

They also allows the users to create visualizations specifically for analyses that involve ML or AI.

https://plotly.com/graphing-libraries/

Here is the Plotly Graphing Package documenation.

I hope you check out these packages. Stay tuned for a part 2 of this blog :)