Pandas DataFrame melt() Method – Be on the Right Side of Change

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

The Pandas library enables access to/from a DataFrame.
The Xarray library works with labeled multi-dimensional arrays and advanced analytics.

To install these libraries, 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.

$ pip install xarray

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

If the installations were 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
import xarray

DataFrame melt()

The melt() method unpivots a DataFrame/Series from the standard wide (horizontal) format to long (vertical).

The syntax for this method is as follows:

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

Parameter	Description
`id_vars`	This parameter is the column(s) to use for the identifier vars. Allowed `dtypes` are: tuple, list, or ndarray.
`value_vars`	The column(s) name(s) to unpivot. If empty, use all columns. Allowed `dtypes` are: tuple, list, or ndarray.
`var_name`	The name(s) for the variable column(s).
`value_name`	The name(s) for the value column(s).
`col_level`	If `MultiIndex`, use this parameter to melt.
`ignore_index`	If `True`, ignore the original index. If `False`, use the original index.

For this example, four (4) students enroll in coding classes. The output will display in various formats using the melt() method.

Code – Example 1

df = pd.DataFrame({'Enrolled': {0: '01/01/2021', 1: '01/02/2021',  2: '01/29/2021', 3: '01/13/2021'},
                   'Student':  {0: 'Micah',      1: 'Philip',   2: 'Jonas',         3: 'Sarah'},
                   'Class':    {0: 'Python',     1: 'PHP',     2: 'JavaScript', 3: 'Java'},
                   'Grade':    {0: 65, 1: 76, 2: 55, 3: 81}})
print(df)

result = pd.melt(df, id_vars =['Student'], value_vars =['Class', 'Grade'])
print(result)

Line [1] creates a DataFrame from a dictionary containing student details and saves it to df.
Line [2] outputs the DataFrame to the terminal.
Line [3] sets the id_vars to Student and the value_vars to Class and Grade. The output saves to result.
Line [4] outputs the result to the terminal.

Output

df

	Enrolled	Student	Class	Grade
0	01/01/2021	Micah	Python	65
1	01/02/2021	Philip	PHP	76
2	01/29/2021	Jonas	JavaScript	55
3	01/13/2021	Sarah	Java	81

result

	Student	variable	value
0	Micah	Class	Python
1	Philip	Class	PHP
2	Jonas	Class	JavaScript
3	Sarah	Class	Java
4	Micah	Grade	65
5	Philip	Grade	76
6	Jonas	Grade	55
7	Sarah	Grade	81

💡 Note: From the code on line [3] and the result, the melt() method unpivots the DataFrame around the Student column (id_vars =['Student']).

For this example, the column labels (shown above) will update. Previously the default column labels were named variable and value (see example 1).

Below the column labels change.

Code – Example 2

df = pd.DataFrame({'Enrolled': {0: '01/01/2021', 1: '01/02/2021',  2: '01/29/2021', 3: '01/13/2021'},
                   'Student':  {0: 'Micah',      1: 'Philip',   2: 'Jonas',         3: 'Sarah'},
                   'Class':    {0: 'Python',     1: 'PHP',     2: 'JavaScript', 3: 'Java'},
                   'Grade':    {0: 65, 1: 76, 2: 55, 3: 81}})

result = pd.melt(df, id_vars =['Student'], value_vars =['Class', 'Grade'],
                 var_name ='All-Students', value_name ='All-Grades')
print(result)

Line [1] creates a DataFrame from a dictionary containing student details and saves it to df.
Line [2] sets the id_vars to Student and the value_vars to Class and Grade. This line also changes the default names variable and value to All-Students and All-Grades. The output saves to result.
Line [3] outputs the result to the terminal.

result

	Student	All Students	All Grades
0	Micah	Class	Python
1	Philip	Class	PHP
2	Jonas	Class	JavaScript
3	Sarah	Class	Java
	Micah	Grade	65
5	Philip	Grade	76
6	Jonas	Grade	55
7	Sarah	Grade	81

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

👈 df.median()

df.merge() 👉

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.