Wide to long format conversion in Pandas - Mini Project: Build & Apply

Wide to Long Format Conversion

📖 Scenario: You work in a small company that tracks monthly sales data for different products. The sales data is currently stored in a wide format, where each month is a separate column. Your manager wants the data in a long format to analyze trends easily.

🎯 Goal: Convert the sales data from wide format to long format using pandas. You will create a DataFrame, set up the necessary configuration, apply the conversion, and display the final long format data.

📋 What You'll Learn

Create a pandas DataFrame with given sales data in wide format

Create a list of month columns to convert

Use pandas melt function to convert wide format to long format

Print the resulting long format DataFrame

💡 Why This Matters

🌍 Real World

Data often comes in wide format from reports or spreadsheets. Converting it to long format helps in easier analysis and visualization.

💼 Career

Data scientists and analysts frequently reshape data to prepare it for analysis, reporting, and visualization tasks.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact values: columns 'Product', 'Jan', 'Feb', 'Mar' and rows: 'A', 100, 150, 200, 'B', 80, 120, 160, 'C', 90, 110, 130.

Pandas

# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where keys are column names and values are lists of column values.

2

Create a list of month columns

Create a list called months containing the strings 'Jan', 'Feb', and 'Mar' to represent the month columns.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['A', 'B', 'C'],
    'Jan': [100, 80, 90],
    'Feb': [150, 120, 110],
    'Mar': [200, 160, 130]
})

# Your code here

Need a hint?

Simply assign the list of month names to the variable months.

3

Convert wide format to long format

Use the pandas melt function on sales_data to convert it to long format. Store the result in a variable called long_format. Use id_vars='Product', value_vars=months, var_name='Month', and value_name='Sales' as parameters.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['A', 'B', 'C'],
    'Jan': [100, 80, 90],
    'Feb': [150, 120, 110],
    'Mar': [200, 160, 130]
})

months = ['Jan', 'Feb', 'Mar']

# Your code here

Need a hint?

Use pd.melt with the correct parameters to reshape the DataFrame.

4

Display the long format DataFrame

Print the long_format DataFrame to display the converted data.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['A', 'B', 'C'],
    'Jan': [100, 80, 90],
    'Feb': [150, 120, 110],
    'Mar': [200, 160, 130]
})

months = ['Jan', 'Feb', 'Mar']

long_format = pd.melt(sales_data, id_vars='Product', value_vars=months, var_name='Month', value_name='Sales')

# Your code here

Need a hint?

Use print(long_format) to show the DataFrame.