Why time-based analysis reveals trends in Data Analysis Python - See It in Action

Why time-based analysis reveals trends

📖 Scenario: Imagine you run a small online store. You want to understand how your sales change over time to spot trends and make better decisions.

🎯 Goal: You will create a simple sales record, set a time threshold, filter sales after that time, and then display the filtered sales to see the trend.

📋 What You'll Learn

Create a dictionary with sales dates as keys and sales amounts as values

Create a variable to hold a date threshold

Use a dictionary comprehension to filter sales after the threshold date

Print the filtered sales dictionary

💡 Why This Matters

🌍 Real World

Businesses often analyze sales or user activity over time to spot trends and make decisions.

💼 Career

Data analysts and scientists use time-based filtering to prepare data for trend analysis and forecasting.

1

Create sales data dictionary

Create a dictionary called sales with these exact entries: '2024-01-01': 150, '2024-02-01': 200, '2024-03-01': 250, '2024-04-01': 300, '2024-05-01': 350

Data Analysis Python

# Create the sales dictionary with dates and amounts
# Your code here

Hint

Use curly braces to create a dictionary with date strings as keys and numbers as values.

2

Set the date threshold

Create a variable called threshold_date and set it to the string '2024-03-01'

Data Analysis Python

sales = {
    '2024-01-01': 150,
    '2024-02-01': 200,
    '2024-03-01': 250,
    '2024-04-01': 300,
    '2024-05-01': 350
}
# Set the threshold_date variable
# Your code here

Hint

Assign the string '2024-03-01' to the variable threshold_date.

3

Filter sales after the threshold date

Create a new dictionary called filtered_sales using a dictionary comprehension that includes only the sales from sales where the date is greater than or equal to threshold_date

Data Analysis Python

sales = {
    '2024-01-01': 150,
    '2024-02-01': 200,
    '2024-03-01': 250,
    '2024-04-01': 300,
    '2024-05-01': 350
}
threshold_date = '2024-03-01'
# Create filtered_sales dictionary with dates >= threshold_date
# Your code here

Hint

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

4

Display the filtered sales

Write a print statement to display the filtered_sales dictionary

Data Analysis Python

sales = {
    '2024-01-01': 150,
    '2024-02-01': 200,
    '2024-03-01': 250,
    '2024-04-01': 300,
    '2024-05-01': 350
}
threshold_date = '2024-03-01'
filtered_sales = {date: amount for date, amount in sales.items() if date >= threshold_date}
# Print the filtered_sales dictionary
# Your code here

Hint

Use print(filtered_sales) to show the filtered dictionary.