Python Pandas melt()

Syntax

pandas.melt(frame, 
            id_vars=None,
            value_vars=None, 
            var_name=None, 
            value_name='value', 
            col_level=None, 
            ignore_index=True)

Return Value

The return value for the melt() function is an unpivoted DataFrame.

Background

Direct quote from the Pandas Documentation website:

“This function massages a DataFrame into a format where one or more columns are identifier variables (id_vars). While all other columns are considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, 'variable' and 'value'!”

If the DataFrame contains numerous columns with vast amounts of data, you can restrict columns to a specified amount. Doing this will change the viewport from landscape to portrait: a more manageable solution.

This article delves into each parameter for this function separately.

Preparation

Before any data manipulation can occur, one (1) new library will require installation.

• The Pandas library enables access to/from a DataFrame.

To install this library, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

If the installation was successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

• How to install Pandas on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd

staff = {'FName':  ['Clare', 'Micah', 'Ben', 'Mac', 'Emma'], 
         'EID': [100, 101, 102, 103, 104], 
         'Job': ['Designer I', 'Data Scientist', 'Developer', 'Designer II', 'Manager'],
         'Age': [19, 23, 21, 27, 36]}

The “frame” Parameter

The melt() frame parameter is a DataFrame. The parameter can be one of the following data types or another data type that converts to a DataFrame:

• CSV
• dictionary of lists (used in this article)
• dictionary of tuples, and more

If this parameter is empty, the following output will display:

df = pd.DataFrame()
print(df)

Output

Empty DataFrame
Columns: []
Index: []

If the DataFrame contains the parameter staff, the output will be similar to the table below.

💡 Note: Formatting will vary depending on the IDE used to run the code.

df = pd.DataFrame(staff)
print(df)

Output

The “id_vars” Parameter

The melt() id_vars parameter is not required and can be one of the following data types:

• tuple
• list
• ndarray

These data types pass the column names. These are used as identifier variable(s) and must exist in the DataFrame. This parameter may contain single or multiple column names and must be unique values.

df_id_vars = pd.melt(df, id_vars=['Job'])
print(df_id_vars)

• Line [1] passes a list with one element to the id_vars parameter.
• Line [2] outputs the contents to the terminal.

Output

In this example, the id_vars parameter is a list with one element, Job. The Job element column displays to the right of the index column.

💡Note: These columns show to the right of the default index column in the same order as they appear in the id_vars list.

Looking at the original data structure, you will see that the original column position of Job is three.

staff = {'FName': ['Clare', 'Micah', 'Ben', 'Mac', 'Emma'], 
         'EID':   [100, 101, 102, 103, 104], 
         'Job':   ['Designer I', 'Data Scientist', 'Developer', 'Designer II', 'Manager'],
         'Age':   [19, 23, 21, 27, 36]}

The output displays the Job for each staff member three times. Once for each remaining column:

• FName
• EID
• Age

The value_vars Parameter

The melt() value_vars parameter is not required and maybe one of the following data types:

• tuple
• list
• ndarray

This parameter lists the column(s) to unpivot. If empty, all columns will display.

df_val_vars = pd.melt(df, id_vars=['Job'], value_vars=['EID', 'Age'])
print(df_val_vars)

Output

In this example, the Job list remains set as id_vars (see above).  

The Job for each staff member is displayed twice. Once for each column listed in the value_vars parameter:

• EID
• Age

The var_name Parameter

The melt() var_name is not required and scalar. This name is the name used for the variable column heading. If None, frame.columns.name or the word variable will display.

df_var_name = pd.melt(df, id_vars=['Job'], value_vars=['EID', 'Age'], var_name='EID/Age')
print(df_var_name)

Output

After running this code, the var_name column heading changes to EID/Age.

The value_name Parameter

The melt() value_name parameter is not required and scalar. This name is the name to use for the value column heading. If None the word value is used.

df_val_name = pd.melt(df, id_vars=['Job'], value_vars=['EID', 'Age'], 
                      var_name='EID/Age', value_name='Data')
print(df_val_name)

Output

After running this code, the value_name column changes to Data.

The col_level Parameter

The melt() col_level parameter is not required and can be an integer or string data type. If columns are multi-index, use this level to melt.

df_col_level = df.melt(col_level=0)
print (df_col_level)

Output

In this example, each column name is displayed consecutively with relevant data in the order they appear in the original data structure (see starter code above).

The ignore_index Parameter

The ignore_index parameter is not required and can be True or False (Boolean).

df_ig_index = pd.melt(df, ignore_index=True)
print(df_ig_index)

Output

If True, the original index column does not display. Instead, the output is as follows:

df_ig_index = pd.melt(df, ignore_index=False)
print(df_ig_index)

Output

If False, the original index does display (retained).

Sources:

• https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

Kat McKelvie

At university, I found my love of writing and coding. Both of which I was able to use in my career.

During the past 15 years, I have held a number of positions such as:

In-house Corporate Technical Writer for various software programs such as Navision and Microsoft CRM
Corporate Trainer (staff of 30+)
Programming Instructor
Implementation Specialist for Navision and Microsoft CRM
Senior PHP Coder