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.