Pandas Melt

Performs an unpivot operation (melt) on a pandas DataFrame.

Understanding Melt (Unpivot)

The melt operation converts a DataFrame from wide format (where multiple columns represent separate variables) into long format (where values are stacked under a single column).

Example: Frozen Food Prices Table

Given the following DataFrame:

Frozen Food Prices

Manufacturer                   Pizza        Burger
---------------------------------------------------
Aimee Italian Foods               10            12
Olivia Cuisine                    11             8
Maddie Foods                      12             7

To demonstrate melting a single column, we will only transform the "Pizza" column and temporarily exclude "Burger" from the output. "Burger" is not affected in this step because we are only selecting "Pizza" for melting.

Frozen Food Prices

Manufacturer                   X                Y
---------------------------------------------------
Aimee Italian Foods            Pizza            10
Olivia Cuisine                 Pizza            11
Maddie Foods                   Pizza            12

Here:

Column X is assigned to "Item" (var_name).
Column Y is assigned to "Price" (value_name).
"Pizza" is explicitly specified in value_vars as the column being unpivoted.
"Manufacturer" remains unchanged and is included in id_vars to preserve row identity.

The final transformed DataFrame:

Frozen Food Prices

Manufacturer                   Item            Price
----------------------------------------------------
Aimee Italian Foods            Pizza            10
Olivia Cuisine                 Pizza            11
Maddie Foods                   Pizza            12

Expanding the Melt Operation

If we want to melt both "Pizza" and "Burger" together, we specify both columns in value_vars:

Frozen Food Prices

Manufacturer                   Item           Price
----------------------------------------------------
Aimee Italian Foods            Pizza            10
Olivia Cuisine                 Pizza            11
Maddie Foods                   Pizza            12
Aimee Italian Foods            Burger           12
Olivia Cuisine                 Burger            8
Maddie Foods                   Burger            7

Here, we explicitly define:

"Pizza" and "Burger" in value_vars.
A new "Item" column (var_name), which holds the original column names ("Pizza", "Burger").
A "Price" column, which stores the corresponding values.

To specify both columns in this node, provide:

Pizza,Burger

This node automatically converts the string into a list and passes it to pandas.DataFrame.melt().

Refer to the Pandas documentation for more information.

Limitation

MultiIndex columns are not supported.

Input

Name	Data type
dataframe	DataFrame
id_vars	String
value_vars	String
var_name	String
value_name	String
ignore_index	Boolean

Output

Data type
DataFrame

Category: Transformation

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