Reshaping and transposing in Data Analysis Python - Mini Project: Build & Apply

Reshaping and transposing

📖 Scenario: You work in a small bakery that tracks daily sales of three types of bread. The sales data is stored in a simple table format. You want to reshape this data to better analyze it and then transpose it to switch rows and columns for a different view.

🎯 Goal: Learn how to reshape a data table from wide to long format and then transpose it using Python's pandas library.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Create a variable to hold the list of bread types

Use pandas melt function to reshape the DataFrame

Transpose the reshaped DataFrame

Print the final transposed DataFrame

💡 Why This Matters

🌍 Real World

Reshaping and transposing data is common when preparing sales or survey data for analysis or visualization.

💼 Career

Data analysts and scientists often reshape data to fit the needs of different tools or to better understand patterns.

1

Create the initial sales DataFrame

Import pandas as pd and create a DataFrame called sales with these exact values: columns 'Day', 'Sourdough', 'Baguette', 'Rye' and rows for 'Monday', 'Tuesday', 'Wednesday' with sales numbers 20, 35, 30 for Sourdough; 15, 40, 25 for Baguette; and 10, 20, 15 for Rye.

Data Analysis Python

# Import pandas and create the sales DataFrame
# Your code here

Need a hint?

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

2

Create a list of bread types

Create a variable called bread_types that holds the list ['Sourdough', 'Baguette', 'Rye'].

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday'],
    'Sourdough': [20, 35, 30],
    'Baguette': [15, 40, 25],
    'Rye': [10, 20, 15]
})
# Create the bread_types list
# Your code here

Need a hint?

Just assign the list of bread names to the variable bread_types.

3

Reshape the DataFrame from wide to long format

Use pd.melt on the sales DataFrame to reshape it. Use Day as the id variable, bread_types as the value variables, and name the variable column 'Bread' and the value column 'Sales'. Store the result in a variable called long_sales.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday'],
    'Sourdough': [20, 35, 30],
    'Baguette': [15, 40, 25],
    'Rye': [10, 20, 15]
})
bread_types = ['Sourdough', 'Baguette', 'Rye']
# Use pd.melt to reshape sales into long format
# Your code here

Need a hint?

Use pd.melt with id_vars for the column to keep, value_vars for columns to unpivot, and set var_name and value_name for new column names.

4

Transpose and print the reshaped DataFrame

Create a variable called transposed_sales by transposing long_sales using the .T attribute. Then print transposed_sales.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday'],
    'Sourdough': [20, 35, 30],
    'Baguette': [15, 40, 25],
    'Rye': [10, 20, 15]
})
bread_types = ['Sourdough', 'Baguette', 'Rye']
long_sales = pd.melt(sales, id_vars=['Day'], value_vars=bread_types, var_name='Bread', value_name='Sales')
# Transpose long_sales and print the result
# Your code here

Need a hint?

Use .T to transpose the DataFrame and then print it.