Exploring pandas melt() function [Practical Examples]

Introduction to Pandas melt() function

Pandas melt() function is used to unpivot a DataFrame from wide to long format, optionally leaving identifiers set. A pivot table aggregates the values in a data set. In this tutorial, we’ll learn how to do the opposite: break an aggregated collection of data into an unaggregated one.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Syntax:

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

Parameters

• id_vars - tuple, list, or array, optional. Column(s) to use as identifier variables.
• value_vars - tuple, list, or array, optional. Column(s) to unpivot. If not specified, uses all columns that are not set asid_vars.
• var_name - scalar. Name to use for the ‘variable’ column. If None it usesframe.columns.nameor ‘variable’.
• value_name - scalar, default ‘value’. Name to use for the ‘value’ column.
• col_level - int or str, optional. If columns are a MultiIndex then use this level to melt.
• ignore_index - bool, default True. If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.

---

What we will cover?

We will discuss the following:

• Set one column as identifier variable and one column as value variable
• Set one column as identifier variable and two columns as value variable

---

Create pandas DataFrame with example data

DataFrame is a data structure used to store the data in two dimensional format. It is similar to table that stores the data in rows and columns. Rows represents the records/tuples and columns refers to the attributes.

We can create the DataFrame by using**pandas.DataFrame()**method.

Syntax:

pandas.DataFrame(input_data,columns,index)

Parameters:

It will take mainly three parameters

1. input_data is represents a list of data
2. columnsrepresent the columns names for the data
3. indexrepresent the row numbers/values

We can also create a DataFrame using dictionary by skipping columns and indices.

Example:Python Program to create a dataframe for college data from a dictionary.

# import pandas 
import pandas 

# create dataframe with college data
dataframe = pandas.DataFrame({'Name': {0: 'sravan', 1: 'bobby', 2: 'deepak',3:'prasad'},
                'Subjects': {0: 'PHP', 1: 'PHP', 2: 'dbms',3:'java'},
                'Marks': {0: 89, 1: 90, 2: 93,3:57}})

# display dataframe
print(dataframe)

Output:

     Name Subjects  Marks
0  sravan      PHP     89
1   bobby      PHP     90
2  deepak     dbms     93
3  prasad     java     57

---

Scenario-1 : Set one column as identifier variable and one column as value variable

Here we are defining one variable as identifier and also one variable is value variable.

Example 1:Python program to set Name as identifier variable and Subjects as value variable

# import pandas 
import pandas 

# create dataframe with college data
dataframe = pandas.DataFrame({'Name': {0: 'sravan', 1: 'bobby', 2: 'deepak',3:'prasad'},
                'Subjects': {0: 'PHP', 1: 'PHP', 2: 'dbms',3:'java'},
                'Marks': {0: 89, 1: 90, 2: 93,3:57}})

# here Name is id_vars and Subjects is value_vars
print(pandas.melt(dataframe, id_vars =['Name'], value_vars =['Subjects']))

Output:

     Name  variable value
0  sravan  Subjects   PHP
1   bobby  Subjects   PHP
2  deepak  Subjects  dbms
3  prasad  Subjects  java

Example 2: Python program to set Name as identifier variable and Marks as value variable

# import pandas 
import pandas 

# create dataframe with college data
dataframe = pandas.DataFrame({'Name': {0: 'sravan', 1: 'bobby', 2: 'deepak',3:'prasad'},
                'Subjects': {0: 'PHP', 1: 'PHP', 2: 'dbms',3:'java'},
                'Marks': {0: 89, 1: 90, 2: 93,3:57}})

# here Name is id_vars and Marks is value_vars
print(pandas.melt(dataframe, id_vars =['Name'], value_vars =['Marks']))

Output:

     Name variable  value
0  sravan    Marks     89
1   bobby    Marks     90
2  deepak    Marks     93
3  prasad    Marks     57

---

Scenario-2 : Set one column as identifier variable and two columns as value variable

Here we are defining one variable as identifier and two variables as value variable.

Example :Python program to set Name as identifier variable and Subjects, Marks as value variable

# import pandas 
import pandas 

# create dataframe with college data
dataframe = pandas.DataFrame({'Name': {0: 'sravan', 1: 'bobby', 2: 'deepak',3:'prasad'},
                'Subjects': {0: 'PHP', 1: 'PHP', 2: 'dbms',3:'java'},
                'Marks': {0: 89, 1: 90, 2: 93,3:57}})

# here Name is id_vars and subjects , Marks is value_vars
print(pandas.melt(dataframe, id_vars =['Name'], value_vars =['Subjects','Marks']))

Output:

     Name  variable value
0  sravan  Subjects   PHP
1   bobby  Subjects   PHP
2  deepak  Subjects  dbms
3  prasad  Subjects  java
4  sravan     Marks    89
5   bobby     Marks    90
6  deepak     Marks    93
7  prasad     Marks    57

---

Summary

In this article we learned aboutPandas and how it melts a DataFrame with the melt method. (Melting is the process of converting a wide data set to a narrow one.) The method accepts two primary parameters:

• Theid_vars parameter sets the identifier column, the column for which the wide data set aggregates data.
• Thevalue_varsparameter accepts the column(s) whose values pandas will melt and store in a new column.

---

References

Pandas - melt()