Building fundamentals
Course overview
Pulling data
Transforming data
Exporting data & dashboards
Exporting data &
dashboards
Bonus lessons
Pulling data
Transforming data
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Course overview
Pulling data
Transforming data
Exporting data & dashboards
Bonus lessons
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Transforming data - bonus lesson

Suming by group

If you’ve ever used spreadsheets to create pivot Tables for data analysis, you’ll love Parabola’s grouping steps. These steps simplify the process of aggregating data, allowing you to summarize complex datasets in just a few clicks.

How to use the step

  1. Drag a Sum by group step onto the canvas 
  2. Sum: Select columns with numeric values to sum up.
    • Ex) Revenue and Units Sold 
  3. Group by: Select which columns you’d like to group your aggregations based on.
    • Ex) Month and SKU 

To learn more, check out our support docs. 

Next lesson

Using the Sum by Group Step in Parabola

If you've ever dumped data into a spreadsheet to create a pivot table, you'll love Parabola's grouping steps, which make it incredibly easy to aggregate data with just a few clicks.

Overview

In this video, we’re focusing on the Sum by Group step using our sales data. In this dataset, each order and SKU includes details such as the number of units purchased and the total revenue for that SKU. The goal is to roll up this data and aggregate the units and revenue by SKU.

How to Use the Sum by Group Step

  1. Inspect the Input Data:
    • When you open the Sum by Group step, click on Input 1 to view the unedited data coming into the step.
  2. Select Fields to Sum:
    • In the top section of the step (labeled "Sum"), select the numeric fields you want to aggregate.
    • In this example, we’re summing the Units and Revenue columns.
  3. Define the Grouping Field:
    • In the group section, select the field that will be the primary grouping column in your aggregated dataset.
    • Here, we choose SKU.
    • Click Show Updated Results to see the transformation: the dataset is condensed from eight columns to three, with data aggregated based on each SKU.
  4. Customizing Column Order:
    • You can further customize your view. For instance, if you want to analyze sales by month, you might adjust the grouping:
      • First, set the column order so that Month appears before SKU.
      • Click Show Updated Results again to see your custom view of the data.

Next Steps

After mastering the Sum by Group step, we will explore the Count by Group step to further analyze and summarize your data.

Experiment with these grouping steps to create different custom views and gain valuable insights from your data. Let us know if you have any questions!