Working with Lists of Athlete Records
Managing a team of athletes often means keeping track of a lot of data—names, performance metrics, and progress over time. In Python, you can organize this information efficiently by using a list of dictionaries, where each dictionary represents an athlete and their individual stats. This structure makes it easy to store, access, and update each athlete's information, which is especially useful when you need to manage data for an entire team or compare athletes side by side.
1234567891011121314151617181920athletes = [ { "name": "Jordan Lee", "speed": [7.2, 7.5, 7.3], # Weekly speed in mph "endurance": 85, # Endurance score out of 100 "attendance": 3 }, { "name": "Morgan Cruz", "speed": [6.8, 7.1, 7.0], "endurance": 90, "attendance": 2 }, { "name": "Taylor Kim", "speed": [7.4, 7.6, 7.8], "endurance": 88, "attendance": 3 } ]
By organizing your data this way, you can easily loop through the list to access or update each athlete's record. Iterating through the list allows you to perform actions such as printing all athletes' names, calculating averages, or modifying stats based on new results. This approach is scalable, meaning you can add or remove athletes without changing how you process the list.
123for athlete in athletes: avg_speed = sum(athlete["speed"]) / len(athlete["speed"]) print(f"{athlete['name']}: Average weekly speed is {avg_speed:.2f} mph")
1. What Python data structure is best for storing multiple athlete records?
2. How can you access the endurance value for the second athlete in the list?
3. What is the benefit of using a loop to process athlete data?
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Managing a team of athletes often means keeping track of a lot of data—names, performance metrics, and progress over time. In Python, you can organize this information efficiently by using a list of dictionaries, where each dictionary represents an athlete and their individual stats. This structure makes it easy to store, access, and update each athlete's information, which is especially useful when you need to manage data for an entire team or compare athletes side by side.
1234567891011121314151617181920athletes = [ { "name": "Jordan Lee", "speed": [7.2, 7.5, 7.3], # Weekly speed in mph "endurance": 85, # Endurance score out of 100 "attendance": 3 }, { "name": "Morgan Cruz", "speed": [6.8, 7.1, 7.0], "endurance": 90, "attendance": 2 }, { "name": "Taylor Kim", "speed": [7.4, 7.6, 7.8], "endurance": 88, "attendance": 3 } ]
By organizing your data this way, you can easily loop through the list to access or update each athlete's record. Iterating through the list allows you to perform actions such as printing all athletes' names, calculating averages, or modifying stats based on new results. This approach is scalable, meaning you can add or remove athletes without changing how you process the list.
123for athlete in athletes: avg_speed = sum(athlete["speed"]) / len(athlete["speed"]) print(f"{athlete['name']}: Average weekly speed is {avg_speed:.2f} mph")
1. What Python data structure is best for storing multiple athlete records?
2. How can you access the endurance value for the second athlete in the list?
3. What is the benefit of using a loop to process athlete data?
Bedankt voor je feedback!
