3 Spreadsheet Tools for Programmers

1. Mito

Mito is a spreadsheet interface for Python. As companies needs to handle larger data sizes more efficiently, normal spreadsheets are becoming more of hassle. This usually manifests in lots of lag and the inability to load your full dataset in the spreadsheet. Mito fixes these problems with Python. Mito calls a familiar spreadsheet into your Python environment, and each edit you make in the spreadsheet generates the equivalent Python in the code cell below.

Mito is great for easily generating Python code and turning slow Excel workflows into faster Python ones.

Here is a demo video from the data professor:

To install Mito:

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.

Mito offer features like:

• Merging
• Filtering
• Sorting
• Adding and Deleting Columns
• Saving and Replaying Analyses (macros)
• Generating Visualizations
• and more

2. Cube

Cube simply posits that the flexibility of spreadsheets is good but their size is bad. Trying to work with more than 100k lines of data in Excel is soul crushing. Instead of shoveling a huge data source into a spreadsheet, why not slap a flexible spreadsheet front-end on top of your huge data source?

You can apply cube to your Salesforce, Quickbooks, NetSuite and other databases and analyze the data like its in a spreadsheet. Cube actually covers your existing portfolio of spreadsheets as well, so you can see all of your dense database data and loose spreadsheet data in the same view.

Here is a still from the demo video on Cube’s website. Don’t look at the cube too long — it plays tricks on your eyes.

Cube allows for data science workflows to take place in a spreadsheet on top of your database, so querying becomes very simple.

3. Layer

Layer aims to place much needed collaboration features on top of Excel. By adding:

• Versioning
• Track Changes
• Granular access sharing
• and more

Layer allows for more sophisticated spreadsheet workflows to take place. A product manager can manage a spreadsheet by giving access of one portion to the marketer, one portion to the engineer, and one portion to finance, without having to worry about overriding and overlapping work.

golayer.io

As the need for analyze large datasets increases, Excel, while it is maybe the finest piece of software ever written, will continue to lack in its ability to handle large datasets well. What each of the three example above share is they they are taking pieces of programming workflows (Python, versioning, data connections) and adding them to a spreadsheet environment. It seems this trend will continue and spreadsheets will continue to blend with programming languages and technical workflows. Spreadsheets are a powerful interface for analyzing data and these tools, amongst other, are allowing them to be used in new workflows that allows technical and non-technical audiences to exist in the same environment.