对象中变量存储形式咨询：列表（list）与属性（attributes）的选择

Great question—this is such a common point of confusion when structuring object data, and your initial observation is really on the mark! Let’s break this down clearly, starting with a quick clarification to avoid a common mix-up.

First: Lists are often values of attributes #

To start, you don’t have to choose between "using a list" and "using an attribute"—lists frequently serve as the value of an object attribute. The real question is: when you have related data points, do you store them as separate, individual attributes, or bundle them into a list (as a single attribute’s value)?

When to use separate attributes (not a list) #

Opt for individual attributes when:

• The data is a unique, discrete feature of the object—there’s no scenario where you’d have multiple versions of it. For example, a Car object’s vin_number (unique identifier) or manufacturer is a single, fixed value that doesn’t need to be a collection.
• Each data point has a distinct, non-interchangeable meaning. A User’s first_name and last_name are both name-related, but they serve different purposes—you wouldn’t bundle these into a list unless you’re storing a full name as a single string.

When to use a list (as an attribute value) #

Use a list when you’re dealing with:

• Ordered, iterable sets of identical-type data that are core to the object’s purpose. Your observation here is spot-on: these are usually the variables you’ll perform calculations, filtering, or bulk operations on. For example:
  • A Playlist’s songs (you’ll sort them, add/remove tracks, calculate total runtime)
  • A Student’s grades (you’ll compute averages, find high/low scores)
  • An Order’s items (you’ll tally totals, filter out out-of-stock products)
• Dynamic, variable-count data. If the number of items can grow or shrink over time (like a BlogPost’s comments or a Course’s enrolled_students), a list is infinitely more practical than creating new attributes like comment_1, comment_2, etc.
• Data that needs batch processing. Lists play nicely with loops, list comprehensions, and built-in functions (sum(), len(), sorted()), making it way easier to work with groups of data than juggling multiple separate attributes.

Refining your initial observation #

Your thought that "lists are often core calculation variables, while attributes are meta-information like type/name" is a fantastic rule of thumb. To add a nuance:

• Meta-information attributes are typically static, descriptive, and rarely need bulk manipulation.
• Even meta-information can use lists if it’s a collection of identical-type details—like a Book’s authors (multiple co-authors) or a Movie’s genres (a film might fit into 2-3 categories). These are still descriptive, but a list is the right structure for multiple related items.

Example to tie it all together #

Here’s a Python class that mixes both patterns:

class CoffeeShop:
    # Discrete, meta-information attributes
    def __init__(self, name, address, opening_hour):
        self.name = name
        self.address = address
        self.opening_hour = opening_hour
        
        # List-based attributes (core business data)
        self.menu_items = []  # Stores drink/food objects for sale
        self.weekly_sales = []  # Stores daily sales totals for calculations

# Usage
my_shop = CoffeeShop("Bean There", "123 Main St", "7:00 AM")
my_shop.menu_items.append({"name": "Latte", "price": 4.50})
my_shop.weekly_sales.append(280.00)
my_shop.weekly_sales.append(320.50)
weekly_average = sum(my_shop.weekly_sales) / len(my_shop.weekly_sales)

内容的提问来源于stack exchange，提问作者Justin Thong