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A clear Python class owns a coherent piece of state and offers the operations needed to maintain or use it. Start by stating that job in one sentence, identify the rules that must always hold, then decide whether the job belongs in a class, a dataclass, a function, or a collaborating object. Python will not enforce private state for you, so a useful public interface and carefully chosen ownership matter more than elaborate getter-and-setter boilerplate.
Start with the class’s responsibility and invariants
Before writing methods, describe the class in terms of the state it owns and the behavior it makes available. For example: “An Order owns its line items and calculates the order total.” That sentence gives the type a coherent center. If the same class also writes records to a database and sends customer messages, those are likely separate responsibilities: a repository can handle persistence, and a notification service can send messages.
Next, write down the invariants: conditions that must remain true for every valid instance. An order might require nonnegative quantities, for example. Keep operations that create, change, or report on its state close enough to the state that those rules can be preserved. A class should expose useful actions, not every internal step callers might use to implement those actions themselves.
This is a design aid, not a mandatory architecture. A small program may not need separate service or repository classes. The test is whether each type has a cohesive purpose and whether changes to one concern force unrelated code to change.
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Choose a class, function, or dataclass based on the job
Use the simplest form that makes the data, behavior, and rules clear. A function is often enough for a stateless operation. A class is useful when related state and behavior need to live together. A dataclass is a convenient class form when the object is mainly named data and generated methods are useful.
| Approach | Best fit | What it provides | Watch for |
|---|---|---|---|
| Function | A focused operation that does not need to retain per-object state. | A direct operation with inputs and a result. | Do not create a class solely to wrap a single stateless function. |
| Regular class | State needs controlled changes, meaningful operations, or a more specific public API. | Explicit control over initialization, behavior, and interface. | Keep the interface focused; attributes are still accessible unless callers follow the convention to treat them as internal. |
| Dataclass | The object primarily represents named values and generated initialization, representation, or comparison methods are useful. | Generated methods for common record-like behavior, with room for natural domain methods. | It does not provide general validation or conversion, and it is not a replacement for every value-object approach. |
A dataclass can still have behavior: add a method when that behavior naturally belongs with the represented data. Prefer a regular class or another representation when you need stricter validation or conversion, tuple or dictionary compatibility, or a more specialized API. PEP 557 describes dataclasses as one option rather than a universal replacement: PEP 557 — Data Classes.
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Keep state per instance and protect meaningful rules
Use instance attributes for values that belong to one object. A mutable class attribute, such as a list, is shared by all instances that use it; putting a list on an instance instead gives each object its own list. The Python tutorial demonstrates this distinction in its discussion of instance and class variables. Class attributes are appropriate for genuinely shared values, such as a constant applying to the class as a whole.
Python does not provide strict data hiding for ordinary attributes. The tutorial puts it plainly: “In fact, nothing in Python makes it possible to enforce data hiding — it is all based upon convention.” Use a leading underscore, as in _items, to signal that an attribute is an implementation detail. When callers must pass through logic to preserve an invariant, provide a meaningful method or property. Avoid getters and setters that merely expose an attribute unchanged; an extra layer is useful when it expresses a policy, validates a change, or keeps the interface stable.
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Use composition for capabilities and inheritance for genuine subtypes
When a type needs an independent capability, consider a collaborator and delegate that work to it. An order can own its line items while a repository handles saving it. This keeps the order’s responsibility distinct from persistence without implying that an order “is a” repository.
Use inheritance when the derived type really is a specialized form of the base type and its behavior remains substitutable wherever the base is expected. Overriding a method is useful when a subtype needs to customize behavior while preserving the base type’s contract. If the relationship is only code reuse, composition may communicate the design more clearly.
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Python supports multiple inheritance, but method lookup follows a method resolution order, and cooperative use of super() requires care. The language tutorial explains multiple inheritance and method resolution. Add multiple base classes only when their interaction is intentional and understandable, rather than just to collect convenient methods.
Make equality and hashing match mutability
If objects compare by their values, decide whether those equality-relevant values can change. A mutable object whose equality is based on changing state is a poor dictionary key or set member: its hash must remain stable while it is stored. Python’s data model advises against defining __hash__ for mutable objects that implement value equality. See the Python 3.13 data model reference for the relationship between equality and hashing.
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For a value-like object that must be a key, design equality-relevant state to be immutable while it is used that way, or choose a stable immutable key instead. Do not add hashing merely because a class defines __eq__; the two behaviors need to agree.
Quick Recap
A practical design checklist
- Can you state what the type owns and does in one sentence?
- Are its invariants clear, and do the public operations preserve them?
- Does each mutable per-object value live on the instance rather than in shared class state?
- Would a function suffice, or is retained state and related behavior genuinely helpful?
- Is a dataclass’s generated record behavior appropriate, or does the type need a more controlled API?
- Does a collaborator represent a separate capability more accurately than inheritance?
- If you define equality or hashing, does the design account for whether the relevant state can change?
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