TL;DR: Premature Optimization is Evil. Optimization is Good.

Problems

Low Performance

Solutions

Sort the conditions from faster to slower

Context

Readability is always essential and you should avoid premature optimization.

Non-premature optimization happens when you have actual evidence you can improve your code execution time without much readability penalizations.

Sample Code

Wrong

def is_warm():
    # This is a fast api call to your thermometer
    response = requests.get("https://iot-device-api.example.com/current_temperature")
    temperature_data = response.json()

    return temperature_data.get('temperature', 0) > 25  

def is_weekend():
    # This function checks if today is a weekend based on a slow calendar API call
    response = requests.get("https://calendar-api.example.com/today")
    calendar_data = response.json()

    return calendar_data.get('day_of_week', '').lower() in ['saturday', 'sunday']

def is_sunny():
    # Very slow function to a low performant weather API call
    response = requests.get("https://weather-api.example.com/current")
    weather_data = response.json()

    return weather_data.get('weather', '') == 'sunny'

is_sunny_value = is_sunny()
is_warm_value = is_warm()
is_weekend_value = is_weekend()  

if is_sunny_value and is_warm_value and is_weekend_value:
    # the 3 conditions are always evaluated
    print("Let's go outside!")
else:
    print("Stay at home.")

Right

if is_warm() and is_weekend() and is_sunny():
    # the 3 conditions are evaluated in short circuit 
    # and sorted from fastest to slowest
    # for a fast exit
    print("Let's go outside!")
else:
    print("Stay at home.")

Detection

[X] Semi-Automatic

You can detect slow calls using actual benchmarks.

Do not consider algorithm complexity since sometimes it is unrelated to actual data distribution. (for example, optimizing an array with a few elements).