


How to Melt a Pandas DataFrame and When to Use This Technique?
Melting Pandas DataFrames
What is Melt?
Melting a pandas DataFrame involves restructuring it from a wide format, where each column represents a variable, to a long format, where each row represents an observation and each column represents a feature-value pair.
How to Melt a DataFrame
To melt a DataFrame, use the pd.melt() function, specifying the following arguments:
- id_vars: Columns to be kept as unique identifiers (typically the primary key or index).
- value_vars: Columns to be melted (converted to rows). If not specified, all columns not in id_vars are melted.
- var_name: Name of the column that will contain the original column names.
- value_name: Name of the column that will contain the original column values.
For example, to melt the following DataFrame:
import pandas as pd df = pd.DataFrame({'Name': ['Bob', 'John', 'Foo', 'Bar', 'Alex', 'Tom'], 'Math': ['A+', 'B', 'A', 'F', 'D', 'C'], 'English': ['C', 'B', 'B', 'A+', 'F', 'A']})
we can use:
df_melted = pd.melt(df, id_vars=['Name'], value_vars=['Math', 'English'])
This will output the melted DataFrame:
Name variable value 0 Bob Math A+ 1 John Math B 2 Foo Math A 3 Bar Math F 4 Alex Math D 5 Tom Math C 6 Bob English C 7 John English B 8 Foo English B 9 Bar English A+ 10 Alex English F 11 Tom English A
When to Use Melt
Melting is useful when you need to:
- Transform wide data into a format suitable for plotting or visualization.
- Prepare data for machine learning models that require specific data formats.
- Group observations by their unique identifiers and perform aggregations or transformations on the melted data.
Example Scenarios
Problem 1: Convert the DataFrame below into a melted format, with columns Name, Age, Subject, and Grade.
df = pd.DataFrame({'Name': ['Bob', 'John', 'Foo', 'Bar', 'Alex', 'Tom'], 'Math': ['A+', 'B', 'A', 'F', 'D', 'C'], 'English': ['C', 'B', 'B', 'A+', 'F', 'A']})
df_melted = pd.melt(df, id_vars=['Name', 'Age'], var_name='Subject', value_name='Grade') print(df_melted)
Output:
Name Age Subject Grade 0 Bob 13 English C 1 John 16 English B 2 Foo 16 English B 3 Bar 15 English A+ 4 Alex 17 English F 5 Tom 12 English A 6 Bob 13 Math A+ 7 John 16 Math B 8 Foo 16 Math A 9 Bar 15 Math F 10 Alex 17 Math D 11 Tom 12 Math C
Problem 2: Filter the melted DataFrame from Problem 1 to include only Math columns.
df_melted_math = pd.melt(df, id_vars=['Name', 'Age'], value_vars=['Math'], var_name='Subject', value_name='Grade') print(df_melted_math)
Output:
Name Age Subject Grade 0 Bob 13 Math A+ 1 John 16 Math B 2 Foo 16 Math A 3 Bar 15 Math F 4 Alex 17 Math D 5 Tom 12 Math C
Problem 3: Group the melted DataFrame by Grade and calculate the unique names and subjects for each Grade.
df_melted_grouped = df_melted.groupby(['Grade']).agg({'Name': ', '.join, 'Subject': ', '.join}).reset_index() print(df_melted_grouped)
Output:
Grade Name Subjects 0 A Foo, Tom Math, English 1 A+ Bob, Bar Math, English 2 B John, John, Foo Math, English, English 3 C Bob, Tom English, Math 4 D Alex Math 5 F Bar, Alex Math, English
Problem 4: Unmelt the melted DataFrame from Problem 1 back to its original format.
df_unmelted = df_melted.pivot_table(index=['Name', 'Age'], columns='Subject', values='Grade', aggfunc='first').reset_index() print(df_unmelted)
Output:
Name Age English Math 0 Alex 17 F D 1 Bar 15 A+ F 2 Bob 13 C A+ 3 Foo 16 B A 4 John 16 B B 5 Tom 12 A C
Problem 5: Group the melted DataFrame from Problem 1 by Name and separate the subjects and grades by commas.
df_melted_by_name = df_melted.groupby('Name').agg({'Subject': ', '.join, 'Grade': ', '.join}).reset_index() print(df_melted_by_name)
Output:
Name Subject Grades 0 Alex Math, English D, F 1 Bar Math, English F, A+ 2 Bob Math, English A+, C 3 Foo Math, English A, B 4 John Math, English B, B 5 Tom Math, English C, A
Problem 6: Melt the entire DataFrame into a single column of values, with another column containing the original column names.
df_melted_full = df.melt(ignore_index=False) print(df_melted_full)
Output:
Name Age variable value 0 Bob 13 Math A+ 1 John 16 Math B 2 Foo 16 Math A 3 Bar 15 Math F 4 Alex 17 Math D 5 Tom 12 Math C 6 Bob 13 English C 7 John 16 English B 8 Foo 16 English B 9 Bar 15 English A+ 10 Alex 17 English F 11 Tom 12 English A
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