Abstract

This PEP introduces syntax for lazy imports as an explicit language feature:

lazy import json
lazy from json import dumps

Lazy imports defer the loading and execution of a module until the first time the imported name is used, in contrast to ‘normal’ imports, which eagerly load and execute a module at the point of the import statement.

By allowing developers to mark individual imports as lazy with explicit syntax, Python programs can reduce startup time, memory usage, and unnecessary work. This is particularly beneficial for command-line tools, test suites, and applications with large dependency graphs.

This proposal preserves full backwards compatibility: normal import statements remain unchanged, and lazy imports are enabled only where explicitly requested.

Motivation

The dominant convention in Python code is to place all imports at the module level, typically at the beginning of the file. This avoids repetition, makes import dependencies clear and minimizes runtime overhead by only evaluating an import statement once per module.

A major drawback with this approach is that importing the first module for an execution of Python (the “main” module) often triggers an immediate cascade of imports, and optimistically loads many dependencies that may never be used. The effect is especially costly for command-line tools with multiple subcommands, where even running the command with --help can load dozens of unnecessary modules and take several seconds. This basic example demonstrates what must be loaded just to get helpful feedback to the user on how to run the program at all. Inefficiently, the user incurs this overhead again when they figure out the command they want and invoke the program “for real.”

A somewhat common way to delay imports is to move the imports into functions (inline imports), but this practice requires more work to implement and maintain, and can be subverted by a single inadvertent top-level import. Additionally, it obfuscates the full set of dependencies for a module. Analysis of the Python standard library shows that approximately 17% of all imports outside tests (nearly 3500 total imports across 730 files) are already placed inside functions or methods specifically to defer their execution. This demonstrates that developers are already manually implementing lazy imports in performance-sensitive code, but doing so requires scattering imports throughout the codebase and makes the full dependency graph harder to understand at a glance.

The standard library provides the LazyLoader class to solve some of these inefficiency problems. It permits imports at the module level to work mostly like inline imports do. Many scientific Python libraries have adopted a similar pattern, formalized in SPEC 1. There’s also the third-party lazy_loader package, yet another implementation of lazy imports. Imports used solely for static type checking are another source of potentially unneeded imports, and there are similarly disparate approaches to minimizing the overhead. The various approaches used here to defer or remove eager imports do not cover all potential use-cases for a general lazy import mechanism. There is no clear standard, and there are several drawbacks including runtime overhead in unexpected places, or worse runtime introspection.

This proposal introduces syntax for lazy imports with a design that is local, explicit, controlled, and granular. Each of these qualities is essential to making the feature predictable and safe to use in practice.

The behavior is local: laziness applies only to the specific import marked with the lazy keyword, and it does not cascade recursively into other imports. This ensures that developers can reason about the effect of laziness by looking only at the line of code in front of them, without worrying about whether imported modules will themselves behave differently. A lazy import is an isolated decision each time it is used, not a global shift in semantics.

The semantics are explicit. When a name is imported lazily, the binding is created in the importing module immediately, but the target module is not loaded until the first time the name is accessed. After this point, the binding is indistinguishable from one created by a normal import. This clarity reduces surprises and makes the feature accessible to developers who may not be deeply familiar with Python’s import machinery.

Lazy imports are controlled, in the sense that deferred loading is only triggered by the importing code itself. In the general case, a library will only experience lazy imports if its own authors choose to mark them as such. This avoids shifting responsibility onto downstream users and prevents accidental surprises in library behavior. Since library authors typically manage their own import subgraphs, they retain predictable control over when and how laziness is applied.

The mechanism is also granular. It is introduced through explicit syntax on individual imports, rather than a global flag or implicit setting. This allows developers to adopt it incrementally, starting with the most performance-sensitive areas of a codebase. As this feature is introduced to the community, we want to make the experience of onboarding optional, progressive, and adaptable to the needs of each project.

Lazy imports provide several concrete advantages:

• Command-line tools are often invoked directly by a user, so latency – in particular startup latency – is quite noticeable. These programs are also typically short-lived processes (contrasted with, e.g., a web server). With lazy imports, only the code paths actually reached will import a module. This can reduce startup time by 50-70% in practice, providing a significant improvement to a common user experience and improving Python’s competitiveness in domains where fast startup matters most.
• Type annotations frequently require imports that are never used at runtime. The common workaround is to wrap them in if TYPE_CHECKING: blocks [1]. With lazy imports, annotation-only imports impose no runtime penalty, eliminating the need for such guards and making annotated codebases cleaner.
• Large applications often import thousands of modules, and each module creates function and type objects, incurring memory costs. In long-lived processes, this noticeably raises baseline memory usage. Lazy imports defer these costs until a module is needed, keeping unused subsystems unloaded. Memory savings of 30-40% have been observed in real workloads.

Rationale

The design of this proposal is centered on clarity, predictability, and ease of adoption. Each decision was made to ensure that lazy imports provide tangible benefits without introducing unnecessary complexity into the language or its runtime.

It is also worth noting that while this PEP outlines one specific approach, we list alternate implementation strategies for some of the core aspects and semantics of the proposal. If the community expresses a strong preference for a different technical path that still preserves the same core semantics or there is fundamental disagreement over the specific option, we have included the brainstorming we have already completed in preparation for this proposal as reference.

The choice to introduce a new lazy keyword reflects the need for explicit syntax. Import behavior is too fundamental to be left implicit or hidden behind global flags or environment variables. By marking laziness directly at the import site, the intent is immediately visible to both readers and tools. This avoids surprises, reduces the cognitive burden of reasoning about imports, and keeps lazy import semantics in line with Python’s tradition of explicitness.

Another important decision is to represent lazy imports with proxy objects in the module’s namespace, rather than by modifying dictionary lookup. Earlier approaches experimented with embedding laziness into dictionaries, but this blurred abstractions and risked affecting unrelated parts of the runtime. The dictionary is a fundamental data structure in Python – literally every object is built on top of dicts – and adding hooks to dictionaries would prevent critical optimizations and complicate the entire runtime. The proxy approach is simpler: it behaves like a placeholder until first use, at which point it resolves the import and rebinds the name. From then on, the binding is indistinguishable from a normal import. This makes the mechanism easy to explain and keeps the rest of the interpreter unchanged.

Compatibility for library authors was also a key concern. Many maintainers need a migration path that allows them to support both new and old versions of Python at once. For this reason, the proposal includes the __lazy_modules__ global as a transitional mechanism. A module can declare which imports should be treated as lazy (by listing the module names as strings), and on Python 3.15 or later those imports will become lazy automatically, as if they were imported with the lazy keyword. On earlier versions the declaration is ignored, leaving imports eager. This gives authors a practical bridge until they can rely on the keyword as the canonical syntax.

Finally, the feature is designed to be adopted incrementally. Nothing changes unless a developer explicitly opts in, and adoption can begin with just a few imports in performance-sensitive areas. This mirrors the experience of gradual typing in Python: a mechanism that can be introduced progressively, without forcing projects to commit globally from day one. Notably, the adoption can also be done from the “outside in”, permitting CLI authors to introduce lazy imports and speed up user-facing tools, without requiring changes to every library the tool might use.

Specification

import_name:
    | 'lazy'? 'import' dotted_as_names

import_from:
    | 'lazy'? 'from' ('.' | '...')* dotted_name 'import' import_from_targets
    | 'lazy'? 'from' ('.' | '...')+ 'import' import_from_targets

# SyntaxError: lazy import not allowed inside functions
def foo():
    lazy import json

# SyntaxError: lazy import not allowed inside classes
class Bar:
    lazy import json

# SyntaxError: lazy import not allowed inside try/except blocks
try:
    lazy import json
except ImportError:
    pass

# SyntaxError: lazy import not allowed inside with blocks
with suppress(ImportError):
    lazy import json

# SyntaxError: lazy from ... import * is not allowed
lazy from json import *

import sys

lazy import json

print('json' in sys.modules)  # False - module not loaded yet

# First use triggers loading
result = json.dumps({"hello": "world"})

print('json' in sys.modules)  # True - now loaded

__lazy_modules__ = ["json"]
import json
print('json' in sys.modules)  # False
result = json.dumps({"hello": "world"})
print('json' in sys.modules)  # True

# app.py - has a typo in the import
lazy from json import dumsp  # Typo: should be 'dumps'

print("App started successfully")
print("Processing data...")

# Error occurs here on first use
result = dumsp({"key": "value"})

App started successfully
Processing data...
Traceback (most recent call last):
  File "app.py", line 2, in <module>
    lazy from json import dumsp
ImportError: deferred import of 'json.dumsp' raised an exception during resolution

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "app.py", line 8, in <module>
    result = dumsp({"key": "value"})
             ^^^^^
ImportError: cannot import name 'dumsp' from 'json'. Did you mean: 'dump'?

# my_module.py
import sys
lazy import json

print('json' in sys.modules)  # False - still lazy

# main.py
import sys
import my_module

# Accessing __dict__ from external code DOES reify all lazy imports
d = my_module.__dict__

print('json' in sys.modules)  # True - reified by __dict__ access
print(type(d['json']))  # <class 'module'>

import sys
lazy import json

# Calling globals() does NOT trigger reification
g = globals()

print('json' in sys.modules)  # False - still lazy
print(type(g['json']))  # <class 'lazy_import'>

# Explicitly reify using the get() method
resolved = g['json'].get()

print(type(resolved))  # <class 'module'>
print('json' in sys.modules)  # True - now loaded

Reference Implementation

A reference implementation is available at: https://github.com/LazyImportsCabal/cpython/tree/lazy

lazy import json  # IMPORT_NAME with flag set

IMPORT_NAME              1 (json + lazy)

lazy from json import dumps  # IMPORT_NAME + IMPORT_FROM

IMPORT_NAME              1 (json + lazy)
IMPORT_FROM              1 (dumps)

lazy import json
x = json  # Module-level access

LOAD_NAME                0 (json)

lazy import json

def use_json():
    return json.dumps({})  # Function scope

LOAD_GLOBAL              0 (json)
LOAD_ATTR                2 (dumps)

LOAD_GLOBAL_MODULE       0 (json)
LOAD_ATTR_MODULE         2 (dumps)

import sys

def exclude_side_effect_modules(importer, name, fromlist):
    """
    Filter function to exclude modules with import-time side effects.

    Args:
        importer: Name of the module doing the import
        name: Name of the module being imported
        fromlist: Tuple of names being imported (for 'from' imports), or None

    Returns:
        True to allow lazy import, False to force eager import
    """
    # Modules known to have important import-time side effects
    side_effect_modules = {'legacy_plugin_system', 'metrics_collector'}

    if name in side_effect_modules:
        return False  # Force eager import

    return True  # Allow lazy import

# Install the filter
sys.set_lazy_imports_filter(exclude_side_effect_modules)

# These imports are checked by the filter
lazy import data_processor        # Filter returns True -> stays lazy
lazy import legacy_plugin_system  # Filter returns False -> imported eagerly

print('data_processor' in sys.modules)       # False - still lazy
print('legacy_plugin_system' in sys.modules) # True - loaded eagerly

# First use of data_processor triggers loading
result = data_processor.transform(data)
print('data_processor' in sys.modules)       # True - now loaded

Backwards Compatibility

Lazy imports are opt-in. Existing programs continue to run unchanged unless a project explicitly enables laziness (via lazy syntax, __lazy_modules__, or an interpreter-wide switch).

Security Implications

There are no known security vulnerabilities introduced by lazy imports.

How to Teach This

The new lazy keyword will be documented as part of the language standard.

As this feature is opt-in, new Python users should be able to continue using the language as they are used to. For experienced developers, we expect them to leverage lazy imports for the variety of benefits listed above (decreased latency, decreased memory usage, etc) on a case-by-case basis. Developers interested in the performance of their Python binary will likely leverage profiling to understand the import time overhead in their codebase and mark the necessary imports as lazy. In addition, developers can mark imports that will only be used for type annotations as lazy.

Below is guidance on how to best take advantage of lazy imports and how to avoid incompatibilities:

• When adopting lazy imports, users should be aware that eliding an import until it is used will result in side effects not being executed. In turn, users should be wary of modules that rely on import time side effects. Perhaps the most common reliance on import side effects is the registry pattern, where population of some external registry happens implicitly during the importing of modules, often via decorators but sometimes implemented via metaclasses or __init_subclass__. Instead, registries of objects should be constructed via explicit discovery processes (e.g. a well-known function to call).

  # Problematic: Plugin registers itself on import
  # my_plugin.py
  from plugin_registry import register_plugin
  
  @register_plugin("MyPlugin")
  class MyPlugin:
      pass
  
  # In main code:
  lazy import my_plugin
  # Plugin NOT registered yet - module not loaded!
  
  # Better: Explicit discovery
  # plugin_registry.py
  def discover_plugins():
      from my_plugin import MyPlugin
      register_plugin(MyPlugin)
  
  # In main code:
  plugin_registry.discover_plugins()  # Explicit loading
  

• Always import needed submodules explicitly. It is not enough to rely on a different import to ensure a module has its submodules as attributes. Plainly, unless there is an explicit from . import bar in foo/__init__.py, always use import foo.bar; foo.bar.Baz, not import foo; foo.bar.Baz. The latter only works (unreliably) because the attribute foo.bar is added as a side effect of foo.bar being imported somewhere else.
• Users who are moving imports into functions to improve startup time, should instead consider keeping them where they are but adding the lazy keyword. This allows them to keep dependencies clear and avoid the overhead of repeatedly re-resolving the import but will still speed up the program.

  # Before: Inline import (repeated overhead)
  def process_data(data):
      import json  # Re-resolved on every call
      return json.dumps(data)
  
  # After: Lazy import at module level
  lazy import json
  
  def process_data(data):
      return json.dumps(data)  # Loaded once on first call
  

• Avoid using wild card (star) imports, as those are always eager.

FAQ

Q: How does this differ from the rejected PEP 690?

A: PEP 810 takes an explicit, opt-in approach instead of PEP 690’s implicit global approach. The key differences are:

• Explicit syntax: lazy import foo clearly marks which imports are lazy.
• Local scope: Laziness only affects the specific import statement, not cascading to dependencies.
• Simpler implementation: Uses proxy objects instead of modifying core dictionary behavior.

Q: What happens when lazy imports encounter errors?

A: Import errors (ImportError, ModuleNotFoundError, syntax errors) are deferred until first use of the lazy name. This is similar to moving an import into a function. The error will occur with a clear traceback pointing to the first access of the lazy object.

The implementation provides enhanced error reporting through exception chaining. When a lazy import fails during reification, the original exception is preserved and chained, showing both where the import was defined and where it was first used:

Traceback (most recent call last):
  File "test.py", line 1, in <module>
    lazy import broken_module
ImportError: deferred import of 'broken_module' raised an exception during resolution

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "test.py", line 3, in <module>
    broken_module.foo()
    ^^^^^^^^^^^^^
  File "broken_module.py", line 2, in <module>
    1/0
ZeroDivisionError: division by zero

Q: How do lazy imports affect modules with import-time side effects?

A: Side effects are deferred until first use. This is generally desirable for performance, but may require code changes for modules that rely on import-time registration patterns. We recommend:

• Use explicit initialization functions instead of import-time side effects
• Call initialization functions explicitly when needed
• Avoid relying on import order for side effects

Q: Can I use lazy imports with from ... import ... statements?

A: Yes, as long as you don’t use from ... import *. Both lazy import foo and lazy from foo import bar are supported. The bar name will be bound to a lazy object that resolves to foo.bar on first use.

Q: Does lazy from module import Class load the entire module or just the class?

A: It loads the entire module, not just the class. This is because Python’s import system always executes the complete module file – there’s no mechanism to execute only part of a .py file. When you first access Class, Python:

1. Loads and executes the entire module.py file
2. Extracts the Class attribute from the resulting module object
3. Binds Class to the name in your namespace

This is identical to eager from module import Class behavior. The only difference with lazy imports is that steps 1-3 happen on first use instead of at the import statement.

# heavy_module.py
print("Loading heavy_module")  # This ALWAYS runs when module loads

class MyClass:
    pass

class UnusedClass:
    pass  # Also gets defined, even though we don't import it

# app.py
lazy from heavy_module import MyClass

print("Import statement done")  # heavy_module not loaded yet
obj = MyClass()                  # NOW "Loading heavy_module" prints
                                 # (and UnusedClass gets defined too)

Key point: Lazy imports defer when a module loads, not what gets loaded. You cannot selectively load only parts of a module – Python’s import system doesn’t support partial module execution.

Q: What about type annotations and TYPE_CHECKING imports?

A: Lazy imports eliminate the common need for TYPE_CHECKING guards. You can write:

lazy from collections.abc import Sequence, Mapping  # No runtime cost

def process(items: Sequence[str]) -> Mapping[str, int]:
    ...

Instead of:

from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from collections.abc import Sequence, Mapping

def process(items: Sequence[str]) -> Mapping[str, int]:
    ...

Q: What’s the performance overhead of lazy imports?

A: The overhead is minimal:

• Zero overhead after first use thanks to the adaptive interpreter optimizing the slow path away.
• Small one-time cost to create the proxy object.
• Reification (first use) has the same cost as a regular import.
• No ongoing performance penalty unlike importlib.util.LazyLoader.

Benchmarking with the pyperformance suite shows the implementation is performance neutral when lazy imports are not used.

Q: Can I mix lazy and eager imports of the same module?

A: Yes. If module foo is imported both lazily and eagerly in the same program, the eager import takes precedence and both bindings resolve to the same module object.

Q: How do I migrate existing code to use lazy imports?

A: Migration is incremental:

1. Identify slow-loading modules using profiling tools.
2. Add lazy keyword to imports that aren’t needed immediately.
3. Test that side-effect timing changes don’t break functionality.
4. Use __lazy_modules__ for compatibility with older Python versions.

Q: What about star imports (from module import *)?

A: Wild card (star) imports cannot be lazy - they remain eager. This is because the set of names being imported cannot be determined without loading the module. Using the lazy keyword with star imports will be a syntax error. If lazy imports are globally enabled, star imports will still be eager.

Q: How do lazy imports interact with import hooks and custom loaders?

A: Import hooks and loaders work normally. When a lazy object is first used, the standard import protocol runs, including any custom hooks or loaders that were in place at reification time.

Q: What happens in multi-threaded environments?

A: Lazy import reification is thread-safe. Only one thread will perform the actual import, and the binding is atomically updated. Other threads will see either the lazy proxy or the final resolved object.

Q: Can I force reification of a lazy import without using it?

A: Yes, accessing a module’s __dict__ will reify all lazy objects in that module. Individual lazy objects can be resolved by calling their get() method.

Q: What’s the difference between globals() and mod.__dict__ for lazy imports?

A: Calling globals() returns the module’s dictionary without reifying lazy imports – you’ll see lazy proxy objects when accessing them through the returned dictionary. However, accessing mod.__dict__ from external code reifies all lazy imports in that module first. This design ensures:

# In your module:
lazy import json

g = globals()
print(type(g['json']))  # <class 'lazy_import'> - your problem

# From external code:
import sys
mod = sys.modules['your_module']
d = mod.__dict__
print(type(d['json']))  # <class 'module'> - reified for external access

This distinction means adding lazy imports and calling globals() is your responsibility to manage, while external code accessing mod.__dict__ always sees fully loaded modules.

Q: Why not use importlib.util.LazyLoader instead?

A: LazyLoader has significant limitations:

• Requires verbose setup code for each lazy import.
• Has ongoing performance overhead on every attribute access.
• Doesn’t work well with from ... import statements.
• Less clear and standard than dedicated syntax.

Q: Will this break tools like isort or black?

A: Tools will need updates to recognize the lazy keyword, but the changes should be minimal since the import structure remains the same. The keyword appears at the beginning, making it easy to parse.

Q: How do I know if a library is compatible with lazy imports?

A: Most libraries should work fine with lazy imports. Libraries that might have issues:

• Those with essential import-time side effects (registration, monkey-patching).
• Those that expect specific import ordering.
• Those that modify global state during import.

When in doubt, test lazy imports with your specific use cases.

Q: What happens if I globally enable lazy imports mode and a library doesn’t work correctly?

A: Note: This is an advanced feature. You can use the lazy imports filter to exclude specific modules that are known to have problematic side effects:

import sys

def my_filter(importer, name, fromlist):
    # Don't lazily import modules known to have side effects
    if name in {'problematic_module', 'another_module'}:
        return False  # Import eagerly
    return True  # Allow lazy import

sys.set_lazy_imports_filter(my_filter)

The filter function receives the importer module name, the module being imported, and the fromlist (if using from ... import). Returning False forces an eager import.

Alternatively, set the global mode to "disabled" via -X lazy_imports=disabled to turn off all lazy imports for debugging.

Q: Can I use lazy imports inside functions?

A: No, the lazy keyword is only allowed at module level. For function-level lazy loading, use traditional inline imports or move the import to module level with lazy.

Q: What about forwards compatibility with older Python versions?

A: Use the __lazy_modules__ global for compatibility:

# Works on Python 3.15+ as lazy, eager on older versions
__lazy_modules__ = ['expensive_module', 'expensive_module_2']
import expensive_module
from expensive_module_2 import MyClass

The __lazy_modules__ attribute is a list of module name strings. When an import statement is executed, Python checks if the module name being imported appears in __lazy_modules__. If it does, the import is treated as if it had the lazy keyword (becoming potentially lazy). On Python versions before 3.15 that don’t support lazy imports, the __lazy_modules__ attribute is simply ignored and imports proceed eagerly as normal.

This provides a migration path until you can rely on the lazy keyword. For maximum predictability, it’s recommended to define __lazy_modules__ once, before any imports. But as it is checked on each import, it can be modified between import statements.

Q: How do explicit lazy imports interact with PEP-649/PEP-749

A: If an annotation is not stringified, it is an expression that is evaluated at a later time. It will only be resolved if the annotation is accessed. In the example below, the fake_typing module is only loaded when the user inspects the __annotations__ dictionary. The fake_typing module would also be loaded if the user uses annotationlib.get_annotations() or getattr to access the annotations.

lazy from fake_typing import MyFakeType
def foo(x: MyFakeType):
  pass
print(foo.__annotations__)  # Triggers loading the fake_typing module

Q: How do lazy imports interact with dir(), getattr(), and module introspection?

A: Accessing lazy imports through normal attribute access or getattr() will trigger reification. Calling dir() on a module will reify all lazy imports in that module to ensure the directory listing is complete. This is similar to accessing mod.__dict__.

lazy import json

# Before any access
# json not in sys.modules

# Any of these trigger reification:
dumps_func = json.dumps
dumps_func = getattr(json, 'dumps')
dir(json)
# Now json is in sys.modules

Q: Do lazy imports work with circular imports?

A: Lazy imports don’t automatically solve circular import problems. If two modules have a circular dependency, making the imports lazy might help only if the circular reference isn’t accessed during module initialization. However, if either module accesses the other during import time, you’ll still get an error.

Example that works (deferred access in functions):

# user_model.py
lazy import post_model

class User:
    def get_posts(self):
        # OK - post_model accessed inside function, not during import
        return post_model.Post.get_by_user(self.name)

# post_model.py
lazy import user_model

class Post:
    @staticmethod
    def get_by_user(username):
        return f"Posts by {username}"

This works because neither module accesses the other at module level – the access happens later when get_posts() is called.

Example that fails (access during import):

# module_a.py
lazy import module_b

result = module_b.get_value()  # Error! Accessing during import

def func():
    return "A"

# module_b.py
lazy import module_a

result = module_a.func()  # Circular dependency error here

def get_value():
    return "B"

This fails because module_a tries to access module_b at import time, which then tries to access module_a before it’s fully initialized.

The best practice is still to avoid circular imports in your code design.

Q: Will lazy imports affect the performance of my hot paths?

A: After first use, lazy imports have zero overhead thanks to the adaptive interpreter. The interpreter specializes the bytecode (e.g., LOAD_GLOBAL becomes LOAD_GLOBAL_MODULE) which eliminates the lazy check on subsequent accesses. This means once a lazy import is reified, accessing it is just as fast as a normal import.

lazy import json

def use_json():
    return json.dumps({"test": 1})

# First call triggers reification
use_json()

# After 2-3 calls, bytecode is specialized
use_json()
use_json()

You can observe the specialization using dis.dis(use_json, adaptive=True):

=== Before specialization ===
LOAD_GLOBAL              0 (json)
LOAD_ATTR                2 (dumps)

=== After 3 calls (specialized) ===
LOAD_GLOBAL_MODULE       0 (json)
LOAD_ATTR_MODULE         2 (dumps)

The specialized LOAD_GLOBAL_MODULE and LOAD_ATTR_MODULE instructions are optimized fast paths with no overhead for checking lazy imports.

Q: What about sys.modules? When does a lazy import appear there?

A: A lazily imported module does not appear in sys.modules until it’s reified (first used). Once reified, it appears in sys.modules just like any eager import.

import sys
lazy import json

print('json' in sys.modules)  # False

result = json.dumps({"key": "value"})  # First use

print('json' in sys.modules)  # True

Q: Why you chose ``lazy`` as the keyword name?

A: Not “why”… memorize! :)

Alternate Implementation Ideas

Here are some alternative design decisions that were considered during the development of this PEP. While the current proposal represents what we believe to be the best balance of simplicity, performance, and maintainability, these alternatives offer different trade-offs that may be valuable for implementers to consider or for future refinements.

Rejected Ideas

Acknowledgements

We would like to thank Paul Ganssle, Yury Selivanov, Łukasz Langa, Lysandros Nikolaou, Pradyun Gedam, Mark Shannon, Hana Joo and the Python Google team, the Python team(s) @ Meta, the Python @ HRT team, the Bloomberg Python team, the Scientific Python community, everyone who participated in the initial discussion of PEP 690, and many others who provided valuable feedback and insights that helped shape this PEP.

Footnotes

Copyright

This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive.