Melt for wide-to-long reshaping in Data Analysis Python - Mini Project: Build & Apply

Melt for wide-to-long reshaping

📖 Scenario: You work in a small company that tracks monthly sales for different products. The sales data is stored in a table where each product has its own column for each month. This format is hard to analyze because the months are spread across columns.To make the data easier to analyze, you want to reshape it from wide format (many columns for months) to long format (one column for month and one for sales).

🎯 Goal: Learn how to use the pandas.melt() function to reshape a wide table into a long table. You will start with a dictionary of sales data, convert it to a DataFrame, then reshape it so each row shows a product, month, and sales value.

📋 What You'll Learn

Create a pandas DataFrame from a dictionary with product sales for three months

Define the columns to keep fixed during reshaping

Use pandas melt to reshape the DataFrame from wide to long format

Print the reshaped DataFrame

💡 Why This Matters

🌍 Real World

Data scientists often receive data in wide format, which is hard to analyze. Reshaping data to long format helps in plotting, grouping, and applying statistical methods.

💼 Career

Knowing how to reshape data with pandas melt is a key skill for data cleaning and preparation, essential for roles like data analyst, data scientist, and business intelligence specialist.

1

Create the sales data dictionary and DataFrame

Create a dictionary called sales_data with these exact entries: 'Product': ['A', 'B', 'C'], 'Jan': [100, 150, 200], 'Feb': [110, 160, 210], 'Mar': [120, 170, 220]. Then create a pandas DataFrame called df from sales_data.

Data Analysis Python

import pandas as pd
# Create the sales_data dictionary and DataFrame df
# Your code here

Hint

Use pd.DataFrame() to convert the dictionary to a DataFrame.

2

Define the id_vars for melting

Create a list called id_vars containing the column 'Product'. This tells pandas which column to keep fixed when reshaping.

Data Analysis Python

import pandas as pd
sales_data = {'Product': ['A', 'B', 'C'], 'Jan': [100, 150, 200], 'Feb': [110, 160, 210], 'Mar': [120, 170, 220]}
df = pd.DataFrame(sales_data)
# Define id_vars list with 'Product'
# Your code here

Hint

The id_vars list should include columns you want to keep as is.

3

Use pandas melt to reshape the DataFrame

Create a new DataFrame called df_long by using pd.melt() on df, with id_vars=id_vars, var_name='Month', and value_name='Sales'.

Data Analysis Python

import pandas as pd
sales_data = {'Product': ['A', 'B', 'C'], 'Jan': [100, 150, 200], 'Feb': [110, 160, 210], 'Mar': [120, 170, 220]}
df = pd.DataFrame(sales_data)
id_vars = ['Product']
# Use pd.melt to reshape df into df_long
# Your code here

Hint

Use pd.melt() with id_vars, var_name, and value_name parameters.

4

Print the reshaped DataFrame

Print the DataFrame df_long to see the data in long format.

Data Analysis Python

import pandas as pd
sales_data = {'Product': ['A', 'B', 'C'], 'Jan': [100, 150, 200], 'Feb': [110, 160, 210], 'Mar': [120, 170, 220]}
df = pd.DataFrame(sales_data)
id_vars = ['Product']
df_long = pd.melt(df, id_vars=id_vars, var_name='Month', value_name='Sales')
# Print df_long
# Your code here

Hint

Use print(df_long) to display the reshaped DataFrame.

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