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.

Practice

1. What does the pandas.melt() function primarily do to a DataFrame?

easy

A. Filters rows based on a condition.

B. Sorts the DataFrame rows based on column values.

C. Transforms wide format data into long format data by unpivoting columns.

D. Aggregates data by grouping columns.

Solution

1. Step 1: Understand the purpose of melt()

   The melt() function is used to convert wide data (many columns) into long data (fewer columns, more rows).

2. Step 2: Identify the transformation type

   This process is called unpivoting, where columns become rows.

3. Final Answer:

   Transforms wide format data into long format data by unpivoting columns. -> Option C

4. Quick Check:

   melt() = unpivot wide to long [OK]

Hint: melt() turns columns into rows to reshape data [OK]

Common Mistakes:

• Confusing melt() with sorting or filtering functions
• Thinking melt() aggregates data
• Assuming melt() changes row order only

2. Which of the following is the correct syntax to unpivot columns 'A' and 'B' in DataFrame df using melt() while keeping column 'ID' fixed?

easy

A. pd.melt(df, id_vars=['ID'], value_vars=['A', 'B'])

B. pd.melt(df, value_vars=['ID'], id_vars=['A', 'B'])

C. df.melt(id_vars=['A', 'B'], value_vars=['ID'])

D. df.melt(value_vars=['ID'], id_vars=['A', 'B'])

Solution

1. Step 1: Identify correct parameter usage

   id_vars are columns to keep fixed; value_vars are columns to unpivot. Here, 'ID' is fixed, 'A' and 'B' are unpivoted.

2. Step 2: Match syntax with parameters

   pd.melt(df, id_vars=['ID'], value_vars=['A', 'B']) correctly uses pd.melt(df, id_vars=['ID'], value_vars=['A', 'B']). Other options swap or misuse parameters.

3. Final Answer:

   pd.melt(df, id_vars=['ID'], value_vars=['A', 'B']) -> Option A

4. Quick Check:

   id_vars = fixed columns, value_vars = columns to unpivot [OK]

Hint: id_vars = fixed columns; value_vars = columns to unpivot [OK]

Common Mistakes:

• Swapping id_vars and value_vars
• Using DataFrame method without pd prefix incorrectly
• Passing wrong columns to parameters

3. Given the DataFrame df below:

   ID  Jan  Feb
0  1   10   20
1  2   30   40

What is the output of:

pd.melt(df, id_vars=['ID'], var_name='Month', value_name='Sales')

medium

A. DataFrame with columns: Month, Sales only

B. DataFrame with columns: ID, Month, Sales and 4 rows unpivoted from Jan and Feb

C. DataFrame with columns: ID, Jan, Feb unchanged

D. Error due to missing parameters

Solution

1. Step 1: Understand melt() parameters

   id_vars=['ID'] keeps 'ID' fixed. var_name='Month' renames the variable column. value_name='Sales' renames the value column.

2. Step 2: Predict output structure

   Jan and Feb columns become rows under 'Month' with their values under 'Sales'. Result has 4 rows (2 IDs x 2 months).

3. Final Answer:

   DataFrame with columns: ID, Month, Sales and 4 rows unpivoted from Jan and Feb -> Option B

4. Quick Check:

   melt() output = long format with renamed columns [OK]

Hint: melt() creates rows for each value_vars entry per id_vars row [OK]

Common Mistakes:

• Expecting original wide format output
• Ignoring var_name and value_name renaming
• Thinking melt() drops id_vars

4. Consider this code snippet:

df = pd.DataFrame({'ID':[1,2],'A':[10,20],'B':[30,40]})
result = pd.melt(df, id_vars=['A'], value_vars=['ID', 'B'])

What is the main issue with this code?

medium

A. The id_vars column 'A' should be a fixed identifier, but it is a value column here.

B. Syntax error due to wrong parameter names.

C. No issue; code runs correctly and melts as expected.

D. value_vars contains 'ID' which is usually an identifier and should be in id_vars.

Solution

1. Step 1: Analyze id_vars and value_vars roles

   id_vars should be columns to keep fixed (usually identifiers). value_vars are columns to unpivot.

2. Step 2: Check column roles in code

   'ID' is usually an identifier and should be in id_vars, but here it is in value_vars, which is unusual and likely a mistake.

3. Final Answer:

   value_vars contains 'ID' which is usually an identifier and should be in id_vars. -> Option D

4. Quick Check:

   Identifiers belong in id_vars, not value_vars [OK]

Hint: Keep identifiers in id_vars, not in value_vars [OK]

Common Mistakes:

• Swapping id_vars and value_vars
• Assuming melt() ignores column roles
• Thinking code has syntax error

5. You have a DataFrame with columns: ['Date', 'Store', 'Product_A', 'Product_B']. You want to reshape it so each row shows Date, Store, Product (either 'Product_A' or 'Product_B'), and Sales (the value). Which melt() call achieves this?

hard

A. pd.melt(df, id_vars=['Date', 'Store'], var_name='Product', value_name='Sales')

B. pd.melt(df, id_vars=['Product_A', 'Product_B'], var_name='Product', value_name='Sales')

C. pd.melt(df, value_vars=['Date', 'Store'], var_name='Product', value_name='Sales')

D. pd.melt(df, id_vars=['Date'], value_vars=['Store', 'Product_A', 'Product_B'])

Solution

1. Step 1: Identify fixed and unpivot columns

   'Date' and 'Store' are identifiers to keep fixed (id_vars). 'Product_A' and 'Product_B' are value columns to unpivot.

2. Step 2: Use melt() with correct parameters

   Use id_vars=['Date', 'Store'], var_name='Product' to name the unpivoted column, and value_name='Sales' for values.

3. Final Answer:

   pd.melt(df, id_vars=['Date', 'Store'], var_name='Product', value_name='Sales') -> Option A

4. Quick Check:

   Keep identifiers fixed, unpivot product columns [OK]

Hint: Keep identifiers in id_vars; unpivot product columns with var_name [OK]

Common Mistakes:

• Putting product columns in id_vars
• Unpivoting identifier columns
• Not renaming var_name or value_name for clarity