Melt for wide-to-long reshaping in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this melt operation?

Given the DataFrame below, what will be the result of melting it with id_vars=['Name'] and var_name='Year'?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    '2019': [85, 90],
    '2020': [88, 92]
})

melted = pd.melt(df, id_vars=['Name'], var_name='Year', value_name='Score')
print(melted)

A [{'Name': 'Alice', 'Year': '2019', 'Score': 85}, {'Name': 'Alice', 'Year': '2020', 'Score': 88}, {'Name': 'Bob', 'Year': '2019', 'Score': 90}, {'Name': 'Bob', 'Year': '2020', 'Score': 92}]

B [{'Name': 'Alice', 'Year': '2019', 'Score': 85}, {'Name': 'Bob', 'Year': '2019', 'Score': 90}, {'Name': 'Alice', 'Year': '2020', 'Score': 88}, {'Name': 'Bob', 'Year': '2020', 'Score': 92}]

C [{'Name': 'Alice', 'Year': '2019', 'Score': 85}, {'Name': 'Bob', 'Year': '2019', 'Score': 90}]

D [{'Name': 'Alice', 'Year': '2019', 'Score': 85}, {'Name': 'Bob', 'Year': '2020', 'Score': 92}]

💡 Hint

Remember that melt stacks columns vertically, keeping id_vars fixed.

Melt transforms columns '2019' and '2020' into rows under 'Year', keeping 'Name' as identifier. The order is by original rows then columns melted.

❓ data_output

intermediate

How many rows after melting?

If you melt a DataFrame with 3 rows and 4 value columns (excluding id_vars), how many rows will the melted DataFrame have?

A 7

B 12

C 3

D 4

💡 Hint

Each original row expands into one row per melted column.

Melt stacks each value column per original row, so total rows = original rows * number of melted columns = 3 * 4 = 12.

🔧 Debug

advanced

Identify the error in this melt code

What error will this code raise?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1,2], 'B': [3,4]})

melted = pd.melt(df, id_vars=['C'])
print(melted)

A ValueError: No var_name specified

B TypeError: id_vars must be a list

C KeyError: 'C'

D No error, prints melted DataFrame

💡 Hint

Check if 'C' exists as a column in df.

The id_vars parameter specifies columns to keep fixed. Since 'C' is not a column, pandas raises KeyError.

🚀 Application

advanced

Using melt to reshape survey data

You have a DataFrame with columns: 'Respondent', 'Q1', 'Q2', 'Q3'. You want a long format with columns: 'Respondent', 'Question', 'Answer'. Which melt call achieves this?

A pd.melt(df, id_vars=['Answer'], var_name='Question', value_name='Respondent')

B pd.melt(df, id_vars=['Respondent'], var_name='Answer', value_name='Question')

C pd.melt(df, id_vars=['Question'], var_name='Respondent', value_name='Answer')

D pd.melt(df, id_vars=['Respondent'], var_name='Question', value_name='Answer')

💡 Hint

id_vars should be the column to keep fixed, var_name the new column for old column names.

To reshape questions into one column 'Question' and answers into 'Answer', keep 'Respondent' fixed as id_vars.

🧠 Conceptual

expert

Why use melt instead of stack for wide-to-long reshaping?

Which statement best explains why melt is preferred over stack for reshaping DataFrames with multiple value columns?

A Melt allows specifying id_vars and custom column names, making it more flexible for complex reshaping.

B Stack only works on Series, not DataFrames, so melt is the only option.

C Stack automatically sorts data alphabetically, which can cause data loss.

D Melt is faster because it uses less memory than stack.

💡 Hint

Think about control over column names and identifiers.

Melt lets you choose which columns to keep fixed and rename the resulting columns, which stack does not easily allow.