Python Program Design: Analyzing Screen Time Data with Lists

Analyzing Screen Time Data

• 📱 The video demonstrates how to design a Python program to analyze personal screen time usage data.
• 💡 The goal is to understand usage patterns by calculating total active minutes and the number of distinct phone sessions.

Data Structure and Preparation

• 📊 Screen time data is formatted as a list, where each element represents a minute and records the app used or an empty string for idle time.
• 📁 It's good practice to store data in a separate file and import it into the program module to avoid clutter.

Counting Active Minutes

• ⏱️ A function is created to count total active minutes, excluding idle time (represented by empty strings).
• 🚫 The continue statement is used to skip idle minutes during iteration.
• 🧪 Test cases with smaller datasets are used to verify the function's accuracy, including scenarios with all idle, some active, and all active minutes.

Excluding Specific Apps

• ⚙️ The count_minutes function is enhanced to accept a list of apps to exclude from the count.
• ✅ This allows for focusing on personal screen time by excluding work-related apps.

Counting Screen Time Sessions

• 🤳 A function is developed to count distinct phone sessions, defined as periods of app usage separated by idle time.
• ➡️ A new session is counted when the current element is an app name and the previous element was an empty string.
• 🚀 Initializing a previous_activity variable to an empty string correctly handles sessions starting at the beginning of the data.

Calculating Average Session Length

• 📈 The average session length is calculated by dividing total active minutes by the number of sessions.
• ⚠️ A check for zero sessions is implemented to prevent a division-by-zero error, returning zero in such cases.
• 🎯 The analysis can be expanded by excluding work apps from session calculations or comparing data across multiple days.