melt() for unpivoting in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the melt() function do in pandas?

The melt() function transforms a DataFrame from wide format to long format by unpivoting selected columns into rows.

beginner

In pandas.melt(), what is the role of the id_vars parameter?

id_vars specifies the columns to keep as identifiers (stay fixed) while other columns are unpivoted into rows.

intermediate

How do var_name and value_name parameters affect the output of melt()?

var_name sets the name of the new column that holds the original column names. value_name sets the name of the new column that holds the values from those columns.

beginner

Why would you use melt() in data analysis?

You use melt() to reshape data for easier analysis, especially when you want to convert wide tables into a tidy long format suitable for plotting or grouping.

intermediate

Given a DataFrame with columns ['Name', 'Math', 'Science'], how would melt() transform it?

It would turn the subject columns ('Math', 'Science') into rows under one column (e.g., 'Subject'), with their scores in another column (e.g., 'Score'), keeping 'Name' as the identifier.

What is the default behavior of pandas.melt() if id_vars is not specified?

A An error is raised

B All columns are kept as identifiers

C All columns are unpivoted into rows

D Only the first column is kept as identifier

If id_vars is not set, all columns are unpivoted into two columns: variable and value.

Which parameter in melt() controls the name of the column that holds the original column names?

A value_name

B var_name

C id_vars

D col_name

var_name sets the name of the new column that contains the original column names.

If you want to keep columns 'A' and 'B' fixed and unpivot the rest, how do you specify melt()?

A melt(df, id_vars=['A', 'B'])

B melt(df, var_name=['A', 'B'])

C melt(df, value_name=['A', 'B'])

D melt(df, columns=['A', 'B'])

Use id_vars=['A', 'B'] to keep those columns fixed during unpivoting.

What type of data format does melt() help create?

A Long format

B Hierarchical format

C Nested format

D Wide format

melt() converts data from wide to long format.

Which of these is NOT a typical use case for melt()?

A Preparing data for plotting

B Converting wide tables to long tables

C Tidying data for analysis

D Combining multiple DataFrames vertically

Combining DataFrames vertically is done with functions like concat(), not melt().

Explain how the melt() function works and why it is useful in data analysis.

Think about turning columns into rows to make data easier to analyze.

Describe a real-life example where you would use melt() to reshape your data.

Imagine you have test scores for students in multiple subjects.