Free Alternatives to Expensive Data Science Platforms

Tableau costs $70 for a business license and $35 for a personal license. Alteryx Designer costs $5,195 a year for a user. Data Robot — I literally can’t find the price online, but their website says their standard contract length is three years, so let’s assume its expensive.

But I want to be clear that all three of these tools are great tools, and if you or your organization can afford them, they will be worth the money. But many people/orgs to not have this kind of money to spend on their data stack. Here is how you can set up a data stack inside Python for free, in only a few minutes.

  1. Download Python — obvious but important :)
  2. Download Jupyter Lab
  3. Install a Python package for data pre-processing

There are many free packages that are really good at the data prep stage. In this example, we will cover Mito.

Mito is a spreadsheet interface inside Python. You can call a spreadsheet into your Python environment and each edit you make in the spreadsheet will generate the equivalent Python.

Here is a demo video:

To install Mito, use these commands in your terminal:

python -m pip install mitoinstaller
python -m mitoinstaller install

Here are the full install instructions.

Within Mito you can:

  • Pass in Excel files or Data frames
  • Merge
  • Pivot
  • Clean
  • Graph
  • Filter and Sort
  • and much more!

Mito will generate the Python for each edit.

4. Install a package for visualizing your data

Python has many packages for visualizing data, such as Matplotlib, Seaborn, or Plotly — all of which have many great features.

One I want to highlight is called Lux.

You can import Lux using these commands (make sure to install the package first).

import lux
import pandas as pd

Here are the full install instructions.

Lux separates itself from other visualization packages by automatically recommending visualizations so you don’t have to write the code yourself.

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

All you have to do is select your data frame and then select which recommended chart you want to use.

5. Import a package for creating interactive data apps and dashboards

Processing and visualizing your data is important. But you want to get these data processes into production, so that you can deliver value to the end-users.

Streamlit is a package that allows you to create an interactive data app in just a few minutes.

You can pip install streamlit with this command:

pip install streamlit

There are too many features to cover thoroughly so here is the full documentation.

Here is an example of how easy it is to configure your data app using the interactive settings:

https://docs.streamlit.io/en/stable/main_concepts.html#themes

Combining these three example packages, you can see how you can use Mito for processing, Lux for visualizing and Streamlit for app building.

There are many other packages that will help you create a complete data stack in Python and I hope these examples help you start to explore the Python ecosystem of free tools.

Thanks :)

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