Mito
Search…
Mito
Mito Docs
Getting Started
Installing Mito
Core Concepts
Feature Reference
Creating a Mitosheet
Importing Data
Graphing
Pivoting/Group By
Filter out Rows
Summary Statistics
Type Changes
Spreadsheet Formulas
Editing Individual Cells
Combining Dataframes
Sort Data
Split Text to Columns
Deleting Columns
Deleting Rows
Column Headers
Deduplicate
Fill NaN Values
Transpose
Unpivot (Melt) a Dataframe
Formatting
Exporting Data
Using the Generated Code
View in Mito Button
Telemetry
Upgrading Mito
How To
Pass a dataframe into Mito
Create a line chart of time series data
Delete Columns with Missing Values
Exploratory Data Analysis
Create a Bar Chart
Split a column on delimiter
Misc
Videos
Release Notes
FAQ
Terms of Service
Privacy Policy
Powered By GitBook
Unpivot (Melt) a Dataframe

Why Unpivot

Unpivoting is useful when you have a dataset in a wide format that you are looking to convert to a long format. Practically, this means that you have columns where you want to maintain their values, but turn them into rows. This is known as a melt in Pandas.
For example, imagine you have a dataset with a product id column, as well as one column per month for a fiscal year: Jan, Feb, etc. The first row in the Jan column contains the quantity of the first product sold in Jan, the second row contains the quantity of the second product sold in Jan, etc.
Unpivoting this dataset would allow you to create a dataframe with three columns: one for the product id, one for the month, and one for the number of that product id sold in that month. This, dataset would make it easier for you to filter down to a specific product and compare the quantity sold across months.

How to Unpivot

Access the Unpivot Taskpane by Clicking Dataframes > Unpivot Dataframe. Then, configure your
  1. 1.
    Select the dataframe to Unpivot.
  2. 2.
    Select the ID Variables. These columns will be present in the unpivoted dataframe. In our example, you would select the product_id column.
  3. 3.
    Select the Values. Each column header will go in the variables column, and the column values will go in the values column. In our example, you would select all the month columns.
​
Feature Reference - Previous
Transpose
Next - Feature Reference
Formatting
Last modified 3mo ago
Copy link
On this page
Why Unpivot
How to Unpivot