Data analysis workflow (collect, clean, explore, visualize, conclude) in Data Analysis Python - Mini Project: Build & Apply

Data analysis workflow (collect, clean, explore, visualize, conclude)

📖 Scenario: You work as a data analyst for a small online store. You have sales data that needs to be checked and understood before sharing insights with your team.

🎯 Goal: Build a simple data analysis workflow in Python to collect data, clean it, explore basic statistics, visualize sales trends, and draw conclusions.

📋 What You'll Learn

Create a dictionary with sales data including product names and sales numbers

Add a threshold variable to filter low sales

Use a dictionary comprehension to filter products with sales above the threshold

Print the filtered sales data

Use matplotlib to create a bar chart of filtered sales

💡 Why This Matters

🌍 Real World

Data analysts often need to collect raw data, clean it by filtering or correcting, explore it to find patterns, visualize results for easy understanding, and then share conclusions with others.

💼 Career

This workflow is a basic but essential skill for data analysts, business analysts, and anyone working with data to make informed decisions.

1

DATA SETUP: Create sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30.

Data Analysis Python

# Create the sales_data dictionary with product names and sales numbers
# Your code here

Need a hint?

Use curly braces {} to create a dictionary. Separate keys and values with colons.

2

CONFIGURATION: Set sales threshold

Create a variable called sales_threshold and set it to 100.

Data Analysis Python

sales_data = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
# Set the sales_threshold variable to 100
# Your code here

Need a hint?

Just assign the number 100 to the variable sales_threshold.

3

CORE LOGIC: Filter sales data above threshold

Use a dictionary comprehension to create a new dictionary called filtered_sales that includes only products from sales_data with sales greater than sales_threshold.

Data Analysis Python

sales_data = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
sales_threshold = 100
# Create filtered_sales dictionary with products having sales > sales_threshold
# Your code here

Need a hint?

Use {product: sales for product, sales in sales_data.items() if sales > sales_threshold}.

4

OUTPUT: Print filtered sales and visualize

Print the filtered_sales dictionary. Then import matplotlib.pyplot as plt and create a bar chart showing product names on the x-axis and sales numbers on the y-axis using plt.bar(). Finally, call plt.show() to display the chart.

Data Analysis Python

sales_data = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
sales_threshold = 100
filtered_sales = {product: sales for product, sales in sales_data.items() if sales > sales_threshold}
# Print filtered_sales
# Import matplotlib.pyplot as plt
# Create a bar chart of filtered_sales
# Show the plot
# Your code here

Need a hint?

Use print(filtered_sales) and then create a bar chart with plt.bar() using keys and values from filtered_sales.