Melt for wide-to-long reshaping in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to reshape the DataFrame from wide to long format using pandas melt.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Math': [90, 80],
    'Science': [85, 88]
})

long_df = pd.melt(df, id_vars=[1], var_name='Subject', value_name='Score')
print(long_df)

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

A'Name'

B'Math'

C'Science'

D'Score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a subject column like 'Math' or 'Science' as id_vars instead of 'Name'.

Passing the value column name to id_vars.

The id_vars parameter specifies the column(s) to keep fixed. Here, 'Name' is the identifier column, so it should be passed to id_vars.

2fill in blank

medium

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

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'City': ['NY', 'LA'],
    '2019': [100, 200],
    '2020': [110, 210]
})

long_df = pd.melt(df, id_vars='City', var_name=[1], value_name='Population')
print(long_df)

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

A'Year'

B'Population'

C'City'

D'Value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the value column name as var_name.

Using the id_vars column name as var_name.

The var_name parameter sets the name of the new column that holds the original column headers. Here, it should be 'Year' because the columns represent years.

3fill in blank

hard

Fix the error in the code to melt the DataFrame correctly.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Product': ['A', 'B'],
    'Q1': [10, 20],
    'Q2': [15, 25]
})

long_df = pd.melt(df, id_vars='Product', var_name='Quarter', value_name=[1])
print(long_df)

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

A'Quarter'

B'Sales'

C'Product'

D'Q1'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the var_name or id_vars as value_name.

Using a column name that does not represent the values.

The value_name parameter should be the name of the column that will hold the values from the melted columns. Here, it should be 'Sales' to represent the sales numbers.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
lengths = { [1] : [2] for word in words if len(word) > 3 }
print(lengths)

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

Aword

Blen(word)

Cword.upper()

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length as key and word as value.

Using word.upper() instead of word as key.

The dictionary comprehension keys should be the word itself, and the values should be the length of the word using len(word).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 3.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
lengths = { [1] : [2] for word in words if [3] }
print(lengths)

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting words to uppercase for keys.

Using the wrong condition in the if clause.

The keys are uppercase words using word.upper(), the values are their lengths using len(word), and the condition filters words longer than 3 characters.

Practice

1. What does the melt function do in data analysis?

easy

A. It merges two dataframes horizontally.

B. It converts wide data format to long data format by stacking columns.

C. It filters rows based on a condition.

D. It sorts data rows based on column values.

Solution

1. Step 1: Understand data formats

   Wide data has many columns representing variables; long data stacks these variables into fewer columns.

2. Step 2: Role of melt function

   The melt function stacks columns into rows, converting wide data to long format.

3. Final Answer:

   It converts wide data format to long data format by stacking columns. -> Option B

4. Quick Check:

   melt = wide to long [OK]

Hint: Melt stacks columns into rows to reshape wide data [OK]

Common Mistakes:

• Confusing melt with sorting or filtering
• Thinking melt merges dataframes
• Assuming melt changes row order only

2. Which of the following is the correct syntax to melt a dataframe df keeping the column 'ID' fixed?

easy

A. pd.melt(df, id_vars='ID')

B. pd.melt(df, fixed='ID')

C. df.melt(id='ID')

D. pd.melt(df, keep='ID')

Solution

1. Step 1: Identify correct parameter for fixed columns

   The parameter to keep columns fixed during melt is id_vars.

2. Step 2: Check syntax correctness

   Only pd.melt(df, id_vars='ID') uses the correct parameter and function call.

3. Final Answer:

   pd.melt(df, id_vars='ID') -> Option A

4. Quick Check:

   Use id_vars to keep columns fixed [OK]

Hint: Use id_vars to keep columns fixed in melt [OK]

Common Mistakes:

• Using wrong parameter names like fixed or keep
• Calling melt as a dataframe method without pd
• Passing id instead of id_vars

3. Given the dataframe:

import pandas as pd
df = pd.DataFrame({
  'ID': [1, 2],
  'Math': [90, 80],
  'Science': [85, 95]
})

result = pd.melt(df, id_vars='ID', var_name='Subject', value_name='Score')
print(result)

What is the output?

medium

A. A dataframe with columns ID, Subject but no Score column.

B. A dataframe with columns ID, Math, Science unchanged.

C. A dataframe with columns ID, Subject, Score and 4 rows stacking Math and Science scores.

D. An error because var_name and value_name are invalid parameters.

Solution

1. Step 1: Understand melt with id_vars and renaming

   Keeping 'ID' fixed, melt stacks 'Math' and 'Science' into one column named 'Subject' and their values into 'Score'.

2. Step 2: Predict output rows and columns

   There are 2 rows originally and 2 subject columns, so output has 4 rows with columns 'ID', 'Subject', 'Score'.

3. Final Answer:

   A dataframe with columns ID, Subject, Score and 4 rows stacking Math and Science scores. -> Option C

4. Quick Check:

   melt stacks columns into rows with renamed columns [OK]

Hint: Melt stacks columns into rows, increasing row count [OK]

Common Mistakes:

• Expecting original wide format output
• Thinking var_name/value_name cause errors
• Ignoring increase in row count after melt

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'ID':[1,2],'A':[10,20],'B':[30,40]})
result = pd.melt(df, id='ID', var_name='Variable', value_name='Value')
print(result)

medium

A. The parameter 'id' should be 'id_vars' in pd.melt.

B. The dataframe is missing columns to melt.

C. The melt function cannot rename columns.

D. The print statement is incorrect.

Solution

1. Step 1: Check parameter names in melt

   The correct parameter to specify fixed columns is id_vars, not id.

2. Step 2: Identify error cause

   Using id='ID' causes a TypeError because melt does not recognize 'id'.

3. Final Answer:

   The parameter 'id' should be 'id_vars' in pd.melt. -> Option A

4. Quick Check:

   Use id_vars, not id, to fix columns [OK]

Hint: Use id_vars, not id, to keep columns fixed in melt [OK]

Common Mistakes:

• Using 'id' instead of 'id_vars'
• Thinking melt cannot rename columns
• Assuming print causes error

5. You have a dataframe:

import pandas as pd
df = pd.DataFrame({
  'Student': ['Alice', 'Bob'],
  'Math_2023': [88, 92],
  'Science_2023': [95, 85],
  'Math_2024': [90, 94],
  'Science_2024': [93, 89]
})

You want to reshape it to long format with columns: Student, Year, Subject, and Score. Which approach is correct?

hard

A. Use melt with var_name=['Subject', 'Year'] to split automatically.

B. Use melt with id_vars=['Student', 'Year'] directly.

C. Use melt without id_vars and rename columns to 'Year' and 'Subject'.

D. Use melt with id_vars=['Student'], then split the variable column into 'Subject' and 'Year'.

Solution

1. Step 1: Melt keeping 'Student' fixed

   Use id_vars=['Student'] to keep student names fixed and stack the year-subject columns.

2. Step 2: Split the combined variable column

   The variable column contains strings like 'Math_2023'; split this into 'Subject' and 'Year' columns manually after melt.

3. Final Answer:

   Use melt with id_vars=['Student'], then split the variable column into 'Subject' and 'Year'. -> Option D

4. Quick Check:

   Melt then split combined columns for multi-level info [OK]

Hint: Melt first, then split combined variable names into parts [OK]

Common Mistakes:

• Trying to split variable names automatically in melt
• Using id_vars with non-existing columns
• Not splitting combined variable names after melt