Google Colab as alternative in Data Analysis Python - Mini Project: Build & Apply

Using Google Colab as an Alternative for Data Analysis

📖 Scenario: You want to analyze sales data but do not have Python installed on your computer. Google Colab is a free online tool that lets you write and run Python code in your browser without installation.In this project, you will create a simple sales data dictionary, set a sales threshold, filter the sales above that threshold, and print the filtered results using Google Colab.

🎯 Goal: Build a small Python program in Google Colab that stores sales data, sets a sales threshold, filters sales above the threshold, and prints the filtered sales.

📋 What You'll Learn

Create a dictionary called sales with exact product names and sales numbers

Create a variable called threshold with a specific sales number

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

Print the filtered dictionary

💡 Why This Matters

🌍 Real World

Filtering sales data helps businesses focus on products that sell well. Google Colab allows you to do this analysis anywhere without installing software.

💼 Career

Data analysts and scientists often use online tools like Google Colab to quickly explore and analyze data before sharing results with teams.

1

Create the sales data dictionary

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

Data Analysis Python

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

Need a hint?

Use curly braces {} to create a dictionary. Separate keys and values with a colon :. Separate items with commas.

2

Set the sales threshold

Create a variable called threshold and set it to 100.

Data Analysis Python

sales = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
# Create the threshold variable and set it to 100
# Your code here

Need a hint?

Use a simple assignment statement to create the variable threshold.

3

Filter sales above the threshold

Use a dictionary comprehension to create a new dictionary called filtered_sales that contains only products from sales with sales greater than threshold. Use for product, amount in sales.items() in your comprehension.

Data Analysis Python

sales = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
threshold = 100
# Create filtered_sales dictionary using dictionary comprehension
# Your code here

Need a hint?

Dictionary comprehension syntax: {key: value for key, value in dict.items() if condition}.

4

Print the filtered sales

Print the filtered_sales dictionary.

Data Analysis Python

sales = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
threshold = 100
filtered_sales = {product: amount for product, amount in sales.items() if amount > threshold}
# Print the filtered_sales dictionary
# Your code here

Need a hint?

Use print(filtered_sales) to display the filtered dictionary.