melt() for unpivoting in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to unpivot the DataFrame using pandas melt function.

Pandas

import pandas as pd

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

melted = pd.melt(df, id_vars=[1])
print(melted)

Drag options to blanks, or click blank then click option'

A['Name', 'Math']

B['Math', 'Science']

C'Math'

D'Name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using columns to unpivot as id_vars instead of the identifier column.

Passing a list instead of a string for a single id_var.

The id_vars parameter specifies columns to keep fixed. Here, 'Name' is the identifier column.

2fill in blank

medium

Complete the code to specify the variable name for the unpivoted columns.

Pandas

melted = pd.melt(df, id_vars='Name', var_name=[1])
print(melted)

Drag options to blanks, or click blank then click option'

A'variable'

B'subject'

C'value'

D'Name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the default 'variable' name without customizing.

Confusing var_name with value_name.

The var_name parameter sets the name of the new column that holds the original column names.

3fill in blank

hard

Fix the error in the code to correctly name the value column after melting.

Pandas

melted = pd.melt(df, id_vars='Name', value_name=[1])
print(melted)

Drag options to blanks, or click blank then click option'

A'score'

B'subject'

C'Name'

D'variable'

Attempts:

3 left

💡 Hint

Common Mistakes

Using value_name as the original column name.

Confusing value_name with var_name.

The value_name parameter sets the name of the new column that holds the values from the original columns.

4fill in blank

hard

Fill both blanks to create a melted DataFrame with custom variable and value column names.

Pandas

melted = pd.melt(df, id_vars=[1], var_name=[2])
print(melted)

Drag options to blanks, or click blank then click option'

A'Name'

B'subject'

C'score'

D'Math'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the variable and value column names.

Using a subject name as the identifier column.

Keep 'Name' as the identifier and rename the variable column to 'subject' for clarity.

5fill in blank

hard

Fill all three blanks to melt the DataFrame with custom id_vars, var_name, and value_name.

Pandas

melted = pd.melt(df, id_vars=[1], var_name=[2], value_name=[3])
print(melted)

Drag options to blanks, or click blank then click option'

A'Name'

B'score'

C'subject'

D'Math'

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of parameters.

Using original column names as new variable or value names.

Use 'Name' as id_vars, rename variable column to 'subject', and value column to 'score' for clear data representation.