One way to scale features for machine learning and for some visuals is through a process called “min-max normalization” or “min-max scaling”. This process recalculates all the values of your variables so that they fall within a certain range, usually between 0 and 1. You can use this method to make your data more comparable and contribute to a better-performing model or to build a radar chart.

However, this method may not be as effective with outliers, which can pull the minimum and/or maximum values strongly in one direction.

If you’re using Alteryx Designer, there is no particular tool that normalizes the data, however there is an easy way to quickly attain the same result.

First prepare your data and ensure that you have only numeric columns like an example below:

Add the Python tool to open a Jupiter notebook on the side:

Do not worry, you can just use a pre-made code to normalize the data.

First we import the relevant packages:

from ayx import Alteryx from sklearn import preprocessing import pandas

Imported packages will allow us to use pre-made functions to normalize the data.

Bur before that we have to load the data from Alteryx into pandas dataframe using the below line:“#1”)

Add preprocessing function:

scaler = preprocessing.MinMaxScaler()

Apply the fuction to the dataset:

df[df.columns] = scaler.fit_transform(df[df.columns])

Put the resulted dataframe into Alteryx


Run the workflow after adding the text to the Jupiter notebook and then click run in the notebook itself:

Now our data looks like this!

Alteryx Designer does not have a normalization tool, but you can use a pre-made code in a Python tool to normalize data by loading it into a pandas dataframe and applying the MinMaxScaler function from the sklearn package.

Veronika Varaksina
Author: Veronika Varaksina

Meet Veronika, a dynamic and adaptable individual with a diverse background in economics, accounting, finance, and data analytics. Veronika pursued a Bachelor’s degree in Economics and gained valuable experience in financial analysis, budgeting, and forecasting while working for five years in accounting and finance. However, she soon realized her passion for data analytics and decided to pursue a postgraduate degree in Analytics at Victoria University. Throughout her academic journey, Veronika honed her skills in data visualization, statistical modeling, and machine learning. Her expertise earned her a spot in the highly competitive Data School program, where she further continues to expand her skills in data analysis.