Impara Tracking Key Metrics with Python | Automating Startup Operations

Understanding and tracking key metrics is crucial for any startup founder aiming to make data-driven decisions. Metrics such as daily active users and retention rate help you measure how well your product is engaging users and whether your growth strategies are working. By monitoring these numbers, you can quickly spot trends, identify problems, and make informed adjustments to your business operations. In this chapter, you will see how Python allows you to efficiently calculate and track these essential metrics, giving you a clear picture of your startup’s health.


              12345678910
            
# Calculate user growth rate from a hardcoded list of daily user counts

daily_users = [100, 120, 150, 180, 210, 250, 300]  # Users for each day

# Calculate growth rate between first and last day
initial_users = daily_users[0]
final_users = daily_users[-1]
growth_rate = (final_users - initial_users) / initial_users * 100

print(f"User growth rate over the period: {growth_rate:.2f}%")

The growth rate formula measures how much your user base has increased over a period. It is calculated as: (final value - initial value) divided by initial value, then multiplied by 100 to get a percentage. Python makes this calculation simple with list operations, allowing you to access the first and last values in your data and perform arithmetic directly. This approach makes it easy to automate metric tracking and quickly see how your user numbers are changing over time.


              123456789101112131415
            
# Calculate retention rate using two lists: signups and returning users

signups = [50, 60, 70, 80, 90]  # Users who signed up each day
returning_users = [20, 25, 30, 40, 50]  # Users who returned the next day

# Calculate daily retention rates
retention_rates = []
for signup, returned in zip(signups, returning_users):
    if signup == 0:
        retention_rates.append(0)
    else:
        retention = returned / signup * 100
        retention_rates.append(retention)

print("Daily retention rates (%):", [round(rate, 2) for rate in retention_rates])

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 1. Capitolo 4

Chieda ad AI

Chieda pure quello che desidera o provi una delle domande suggerite per iniziare la nostra conversazione

Scorri per mostrare il menu


              12345678910
            
# Calculate user growth rate from a hardcoded list of daily user counts

daily_users = [100, 120, 150, 180, 210, 250, 300]  # Users for each day

# Calculate growth rate between first and last day
initial_users = daily_users[0]
final_users = daily_users[-1]
growth_rate = (final_users - initial_users) / initial_users * 100

print(f"User growth rate over the period: {growth_rate:.2f}%")


              123456789101112131415
            
# Calculate retention rate using two lists: signups and returning users

signups = [50, 60, 70, 80, 90]  # Users who signed up each day
returning_users = [20, 25, 30, 40, 50]  # Users who returned the next day

# Calculate daily retention rates
retention_rates = []
for signup, returned in zip(signups, returning_users):
    if signup == 0:
        retention_rates.append(0)
    else:
        retention = returned / signup * 100
        retention_rates.append(retention)

print("Daily retention rates (%):", [round(rate, 2) for rate in retention_rates])

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 1. Capitolo 4

Tracking Key Metrics with Python

1. What metric would you use to measure how many users return after signing up?

2. How can Python help visualize changes in user metrics over time?

3. Why is it important for founders to track metrics regularly?

Tracking Key Metrics with Python

1. What metric would you use to measure how many users return after signing up?

2. How can Python help visualize changes in user metrics over time?

3. Why is it important for founders to track metrics regularly?