Reproducible analysis patterns in Data Analysis Python - Mini Project: Build & Apply

Reproducible Analysis Patterns

📖 Scenario: You are working as a data analyst for a small bakery. You want to analyze daily sales data in a way that you can easily repeat the analysis every day with new data. This means your steps should be clear and easy to follow again and again.

🎯 Goal: Build a simple, reproducible analysis pattern in Python that loads sales data, sets a sales threshold, filters the data for days with high sales, and prints the filtered results.

📋 What You'll Learn

Create a dictionary with daily sales data

Set a sales threshold variable

Filter the sales data using a dictionary comprehension

Print the filtered sales data

💡 Why This Matters

🌍 Real World

Businesses often need to analyze sales or other data repeatedly. Using clear steps and variables makes it easy to update and rerun the analysis with new data.

💼 Career

Data analysts and scientists use reproducible patterns to save time and avoid mistakes when working with data regularly.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called daily_sales with these exact entries: 'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100.

Data Analysis Python

# Create the daily_sales dictionary with exact values
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate each day and sales number with a colon, and separate pairs with commas.

2

CONFIGURATION: Set the sales threshold

Create a variable called sales_threshold and set it to 150.

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
# Set sales_threshold to 150
# Your code here

Hint

Just assign the number 150 to the variable sales_threshold.

3

CORE LOGIC: Filter days with sales above the threshold

Create a new dictionary called high_sales_days using a dictionary comprehension. Include only the days from daily_sales where the sales value is greater than sales_threshold.

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 150
# Create high_sales_days dictionary with days having sales > sales_threshold
# Your code here

Hint

Use a dictionary comprehension with for day, sales in daily_sales.items() and an if condition to filter.

4

OUTPUT: Print the filtered high sales days

Write a print statement to display the high_sales_days dictionary.

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 150
high_sales_days = {day: sales for day, sales in daily_sales.items() if sales > sales_threshold}
# Print the high_sales_days dictionary
# Your code here

Hint

Use print(high_sales_days) to show the filtered dictionary.