Python Modules & Python Packages
the difference
This article explores Python modules and Python packages, two mechanisms that facilitate modular programming.
Modular programming refers to the process of breaking a large, unwieldy programming task into separate, smaller, more manageable subtasks or modules. Individual modules can then be cobbled together like building blocks to create a larger application.
There are several advantages to modularizing code in a large application:
Simplicity: Rather than focusing on the entire problem at hand, a module typically focuses on one relatively small portion of the problem. If you’re working on a single module, you’ll have a smaller problem domain to wrap your head around. This makes development easier and less error-prone.
Maintainability: Modules are typically designed so that they enforce logical boundaries between different problem domains. If modules are written in a way that minimizes interdependency, there is decreased likelihood that modifications to a single module will have an impact on other parts of the program. (You may even be able to make changes to a module without having any knowledge of the application outside that module.) This makes it more viable for a team of many programmers to work collaboratively on a large application.
Reusability: Functionality defined in a single module can be easily reused (through an appropriately defined interface) by other parts of the application. This eliminates the need to duplicate code.
Scoping: Modules typically define a separate namespace, which helps avoid collisions between identifiers in different areas of a program. (One of the tenets in the Zen of Python is Namespaces are one honking great idea—let’s do more of those!)
Functions, modules and packages are all constructs in Python that promote code modularization.
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Python Modules: Overview
There are actually three different ways to define a module in Python:
A module can be written in Python itself.
A module can be written in C and loaded dynamically at run-time, like the
re
(regular expression) module.A built-in module is intrinsically contained in the interpreter, like the
itertools
module.
A module’s contents are accessed the same way in all three cases: with the import
statement.
Here, the focus will mostly be on modules that are written in Python. The cool thing about modules written in Python is that they are exceedingly straightforward to build. All you need to do is create a file that contains legitimate Python code and then give the file a name with a .py
extension. That’s it! No special syntax or voodoo is necessary.
For example, suppose you have created a file called mod.py
containing the following:
mod.py
Several objects are defined in mod.py
:
s
(a string)a
(a list)foo()
(a function)Foo
(a class)
Assuming mod.py
is in an appropriate location, which you will learn more about shortly, these objects can be accessed by importing the module as follows:>>>
The Module Search Path
Continuing with the above example, let’s take a look at what happens when Python executes the statement:
When the interpreter executes the above import
statement, it searches for mod.py
in a list of directories assembled from the following sources:
The directory from which the input script was run or the current directory if the interpreter is being run interactively
The list of directories contained in the
PYTHONPATH
environment variable, if it is set. (The format forPYTHONPATH
is OS-dependent but should mimic thePATH
environment variable.)An installation-dependent list of directories configured at the time Python is installed
The resulting search path is accessible in the Python variable sys.path
, which is obtained from a module named sys
:>>>
Note: The exact contents of sys.path
are installation-dependent. The above will almost certainly look slightly different on your computer.
Thus, to ensure your module is found, you need to do one of the following:
Put
mod.py
in the directory where the input script is located or the current directory, if interactiveModify the
PYTHONPATH
environment variable to contain the directory wheremod.py
is located before starting the interpreterOr: Put
mod.py
in one of the directories already contained in thePYTHONPATH
variable
Put
mod.py
in one of the installation-dependent directories, which you may or may not have write-access to, depending on the OS
There is actually one additional option: you can put the module file in any directory of your choice and then modify sys.path
at run-time so that it contains that directory. For example, in this case, you could put mod.py
in directory C:\Users\john
and then issue the following statements:>>>
Once a module has been imported, you can determine the location where it was found with the module’s __file__
attribute:>>>
The directory portion of __file__
should be one of the directories in sys.path
.
The import
Statement
import
StatementModule contents are made available to the caller with the import
statement. The import
statement takes many different forms, shown below.
import <module_name>
import <module_name>
The simplest form is the one already shown above:
Note that this does not make the module contents directly accessible to the caller. Each module has its own private symbol table, which serves as the global symbol table for all objects defined in the module. Thus, a module creates a separate namespace, as already noted.
The statement import <module_name>
only places <module_name>
in the caller’s symbol table. The objects that are defined in the module remain in the module’s private symbol table.
From the caller, objects in the module are only accessible when prefixed with <module_name>
via dot notation, as illustrated below.
After the following import
statement, mod
is placed into the local symbol table. Thus, mod
has meaning in the caller’s local context:>>>
But s
and foo
remain in the module’s private symbol table and are not meaningful in the local context:>>>
To be accessed in the local context, names of objects defined in the module must be prefixed by mod
:>>>
Several comma-separated modules may be specified in a single import
statement:
from <module_name> import <name(s)>
from <module_name> import <name(s)>
An alternate form of the import
statement allows individual objects from the module to be imported directly into the caller’s symbol table:
Following execution of the above statement, <name(s)>
can be referenced in the caller’s environment without the <module_name>
prefix:>>>
Because this form of import
places the object names directly into the caller’s symbol table, any objects that already exist with the same name will be overwritten:>>>
It is even possible to indiscriminately import
everything from a module at one fell swoop:
This will place the names of all objects from <module_name>
into the local symbol table, with the exception of any that begin with the underscore (_
) character.
For example:>>>
This isn’t necessarily recommended in large-scale production code. It’s a bit dangerous because you are entering names into the local symbol table en masse. Unless you know them all well and can be confident there won’t be a conflict, you have a decent chance of overwriting an existing name inadvertently. However, this syntax is quite handy when you are just mucking around with the interactive interpreter, for testing or discovery purposes, because it quickly gives you access to everything a module has to offer without a lot of typing.
from <module_name> import <name> as <alt_name>
from <module_name> import <name> as <alt_name>
It is also possible to import
individual objects but enter them into the local symbol table with alternate names:
This makes it possible to place names directly into the local symbol table but avoid conflicts with previously existing names:>>>
import <module_name> as <alt_name>
import <module_name> as <alt_name>
You can also import an entire module under an alternate name:
>>>
Module contents can be imported from within a function definition. In that case, the import
does not occur until the function is called:>>>
However, Python 3 does not allow the indiscriminate import *
syntax from within a function:>>>
Lastly, a try
statement with an except ImportError
clause can be used to guard against unsuccessful import
attempts:>>>
>>>
The dir()
Function
dir()
FunctionThe built-in function dir()
returns a list of defined names in a namespace. Without arguments, it produces an alphabetically sorted list of names in the current local symbol table:>>>
Note how the first call to dir()
above lists several names that are automatically defined and already in the namespace when the interpreter starts. As new names are defined (qux
, Bar
, x
), they appear on subsequent invocations of dir()
.
This can be useful for identifying what exactly has been added to the namespace by an import statement:>>>
When given an argument that is the name of a module, dir()
lists the names defined in the module:>>>
>>>
Executing a Module as a Script
Any .py
file that contains a module is essentially also a Python script, and there isn’t any reason it can’t be executed like one.
Here again is mod.py
as it was defined above:
mod.py
This can be run as a script:
There are no errors, so it apparently worked. Granted, it’s not very interesting. As it is written, it only defines objects. It doesn’t do anything with them, and it doesn’t generate any output.
Let’s modify the above Python module so it does generate some output when run as a script:
mod.py
Now it should be a little more interesting:
Unfortunately, now it also generates output when imported as a module:>>>
This is probably not what you want. It isn’t usual for a module to generate output when it is imported.
Wouldn’t it be nice if you could distinguish between when the file is loaded as a module and when it is run as a standalone script?
Ask and ye shall receive.
When a .py
file is imported as a module, Python sets the special dunder variable __name__
to the name of the module. However, if a file is run as a standalone script, __name__
is (creatively) set to the string '__main__'
. Using this fact, you can discern which is the case at run-time and alter behavior accordingly:
mod.py
Now, if you run as a script, you get output:
But if you import as a module, you don’t:>>>
Modules are often designed with the capability to run as a standalone script for purposes of testing the functionality that is contained within the module. This is referred to as unit testing. For example, suppose you have created a module fact.py
containing a factorial function, as follows:
fact.py
The file can be treated as a module, and the fact()
function imported:>>>
But it can also be run as a standalone by passing an integer argument on the command-line for testing:
Reloading a Module
For reasons of efficiency, a module is only loaded once per interpreter session. That is fine for function and class definitions, which typically make up the bulk of a module’s contents. But a module can contain executable statements as well, usually for initialization. Be aware that these statements will only be executed the first time a module is imported.
Consider the following file mod.py
:
mod.py
>>>
The print()
statement is not executed on subsequent imports. (For that matter, neither is the assignment statement, but as the final display of the value of mod.a
shows, that doesn’t matter. Once the assignment is made, it sticks.)
If you make a change to a module and need to reload it, you need to either restart the interpreter or use a function called reload()
from module importlib
:>>>
Python Packages
Suppose you have developed a very large application that includes many modules. As the number of modules grows, it becomes difficult to keep track of them all if they are dumped into one location. This is particularly so if they have similar names or functionality. You might wish for a means of grouping and organizing them.
Packages allow for a hierarchical structuring of the module namespace using dot notation. In the same way that modules help avoid collisions between global variable names, packages help avoid collisions between module names.
Here, there is a directory named pkg
that contains two modules, mod1.py
and mod2.py
. The contents of the modules are:
mod1.py
mod2.py
Given this structure, if the pkg
directory resides in a location where it can be found (in one of the directories contained in sys.path
), you can refer to the two modules with dot notation (pkg.mod1
, pkg.mod2
) and import them with the syntax you are already familiar with:
>>>
>>>
>>>
You can import modules with these statements as well:
>>>
You can technically import the package as well:>>>
But this is of little avail. Though this is, strictly speaking, a syntactically correct Python statement, it doesn’t do much of anything useful. In particular, it does not place any of the modules in pkg
into the local namespace:>>>
Package Initialization
If a file named __init__.py
is present in a package directory, it is invoked when the package or a module in the package is imported. This can be used for execution of package initialization code, such as initialization of package-level data.
For example, consider the following __init__.py
file:
__init__.py
Now when the package is imported, the global list A
is initialized:>>>
A module in the package can access the global variable by importing it in turn:
mod1.py
>>>
__init__.py
can also be used to effect automatic importing of modules from a package. For example, earlier you saw that the statement import pkg
only places the name pkg
in the caller’s local symbol table and doesn’t import any modules. But if __init__.py
in the pkg
directory contains the following:
__init__.py
then when you execute import pkg
, modules mod1
and mod2
are imported automatically:>>>
Note: Much of the Python documentation states that an __init__.py
file must be present in the package directory when creating a package. This was once true. It used to be that the very presence of __init__.py
signified to Python that a package was being defined. The file could contain initialization code or even be empty, but it had to be present.
Starting with Python 3.3, Implicit Namespace Packages were introduced. These allow for the creation of a package without any __init__.py
file. Of course, it can still be present if package initialization is needed. But it is no longer required.
Importing *
From a Package
*
From a PackageThere are now four modules defined in the pkg
directory. Their contents are as shown below:
mod1.py
mod2.py
mod3.py
mod4.py
(Imaginative, aren’t they?)
You have already seen that when import *
is used for a module, all objects from the module are imported into the local symbol table, except those whose names begin with an underscore, as always:>>>
The analogous statement for a package is this:
What does that do?>>>
Hmph. Not much. You might have expected (assuming you had any expectations at all) that Python would dive down into the package directory, find all the modules it could, and import them all. But as you can see, by default that is not what happens.
Instead, Python follows this convention: if the __init__.py
file in the package directory contains a list named __all__
, it is taken to be a list of modules that should be imported when the statement from <package_name> import *
is encountered.
For the present example, suppose you create an __init__.py
in the pkg
directory like this:
pkg/__init__.py
Now from pkg import *
imports all four modules:>>>
Using import *
still isn’t considered terrific form, any more for packages than for modules. But this facility at least gives the creator of the package some control over what happens when import *
is specified. (In fact, it provides the capability to disallow it entirely, simply by declining to define __all__
at all. As you have seen, the default behavior for packages is to import nothing.)
By the way, __all__
can be defined in a module as well and serves the same purpose: to control what is imported with import *
. For example, modify mod1.py
as follows:
pkg/mod1.py
Now an import *
statement from pkg.mod1
will only import what is contained in __all__
:>>>
foo()
(the function) is now defined in the local namespace, but Foo
(the class) is not, because the latter is not in __all__
.
In summary, __all__
is used by both packages and modules to control what is imported when import *
is specified. But the default behavior differs:
For a package, when
__all__
is not defined,import *
does not import anything.For a module, when
__all__
is not defined,import *
imports everything (except—you guessed it—names starting with an underscore).
Subpackages
The four modules (mod1.py
, mod2.py
, mod3.py
and mod4.py
) are defined as previously. But now, instead of being lumped together into the pkg
directory, they are split out into two subpackage directories, sub_pkg1
and sub_pkg2
.
Importing still works the same as shown previously. Syntax is similar, but additional dot notation is used to separate package name from subpackage name:>>>
In addition, a module in one subpackage can reference objects in a sibling subpackage (in the event that the sibling contains some functionality that you need). For example, suppose you want to import and execute function foo()
(defined in module mod1
) from within module mod3
. You can either use an absolute import:
pkg/sub__pkg2/mod3.py
>>>
Or you can use a relative import, where ..
refers to the package one level up. From within mod3.py
, which is in subpackage sub_pkg2
,
..
evaluates to the parent package (pkg
), and..sub_pkg1
evaluates to subpackagesub_pkg1
of the parent package.
pkg/sub__pkg2/mod3.py
>>>
Conclusion
In this tutorial, you covered the following topics:
How to create a Python module
Locations where the Python interpreter searches for a module
How to obtain access to the objects defined in a module with the
import
statementHow to create a module that is executable as a standalone script
How to organize modules into packages and subpackages
How to control package initialization
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This will hopefully allow you to better understand how to gain access to the functionality available in the many third-party and built-in modules available in Python.
Additionally, if you are developing your own application, creating your own modules and packages will help you organize and modularize your code, which makes coding, maintenance, and debugging easier.
If you want to learn more, check out the following documentation at Python.org:
__main__
— Top-level script environment
__main__
— Top-level script environment'__main__'
is the name of the scope in which top-level code executes. A module’s __name__ is set equal to '__main__'
when read from standard input, a script, or from an interactive prompt.
A module can discover whether or not it is running in the main scope by checking its own __name__
, which allows a common idiom for conditionally executing code in a module when it is run as a script or with python -m
but not when it is imported:
For a package, the same effect can be achieved by including a __main__.py
module, the contents of which will be executed when the module is run
. The import system
Python code in one module gains access to the code in another module by the process of importing it. The import
statement is the most common way of invoking the import machinery, but it is not the only way. Functions such as importlib.import_module()
and built-in __import__()
can also be used to invoke the import machinery.
The import
statement combines two operations; it searches for the named module, then it binds the results of that search to a name in the local scope. The search operation of the import
statement is defined as a call to the __import__()
function, with the appropriate arguments. The return value of __import__()
is used to perform the name binding operation of the import
statement. See the import
statement for the exact details of that name binding operation.
A direct call to __import__()
performs only the module search and, if found, the module creation operation. While certain side-effects may occur, such as the importing of parent packages, and the updating of various caches (including sys.modules
), only the import
statement performs a name binding operation.
When an import
statement is executed, the standard builtin __import__()
function is called. Other mechanisms for invoking the import system (such as importlib.import_module()
) may choose to bypass __import__()
and use their own solutions to implement import semantics.
When a module is first imported, Python searches for the module and if found, it creates a module object 1, initializing it. If the named module cannot be found, a ModuleNotFoundError
is raised. Python implements various strategies to search for the named module when the import machinery is invoked. These strategies can be modified and extended by using various hooks described in the sections below.
Changed in version 3.3: The import system has been updated to fully implement the second phase of PEP 302. There is no longer any implicit import machinery - the full import system is exposed through sys.meta_path
. In addition, native namespace package support has been implemented (see PEP 420).
5.1. importlib
importlib
The importlib
module provides a rich API for interacting with the import system. For example importlib.import_module()
provides a recommended, simpler API than built-in __import__()
for invoking the import machinery. Refer to the importlib
library documentation for additional detail.
5.2. Packages
Python has only one type of module object, and all modules are of this type, regardless of whether the module is implemented in Python, C, or something else. To help organize modules and provide a naming hierarchy, Python has a concept of packages.
You can think of packages as the directories on a file system and modules as files within directories, but don’t take this analogy too literally since packages and modules need not originate from the file system. For the purposes of this documentation, we’ll use this convenient analogy of directories and files. Like file system directories, packages are organized hierarchically, and packages may themselves contain subpackages, as well as regular modules.
It’s important to keep in mind that all packages are modules, but not all modules are packages. Or put another way, packages are just a special kind of module. Specifically, any module that contains a __path__
attribute is considered a package.
All modules have a name. Subpackage names are separated from their parent package name by a dot, akin to Python’s standard attribute access syntax. Thus you might have a module called sys
and a package called email
, which in turn has a subpackage called email.mime
and a module within that subpackage called email.mime.text
.
5.2.1. Regular packages
Python defines two types of packages, regular packages and namespace packages. Regular packages are traditional packages as they existed in Python 3.2 and earlier. A regular package is typically implemented as a directory containing an __init__.py
file. When a regular package is imported, this __init__.py
file is implicitly executed, and the objects it defines are bound to names in the package’s namespace. The __init__.py
file can contain the same Python code that any other module can contain, and Python will add some additional attributes to the module when it is imported.
For example, the following file system layout defines a top level parent
package with three subpackages:
Importing parent.one
will implicitly execute parent/__init__.py
and parent/one/__init__.py
. Subsequent imports of parent.two
or parent.three
will execute parent/two/__init__.py
and parent/three/__init__.py
respectively.
5.2.2. Namespace packages
A namespace package is a composite of various portions, where each portion contributes a subpackage to the parent package. Portions may reside in different locations on the file system. Portions may also be found in zip files, on the network, or anywhere else that Python searches during import. Namespace packages may or may not correspond directly to objects on the file system; they may be virtual modules that have no concrete representation.
Namespace packages do not use an ordinary list for their __path__
attribute. They instead use a custom iterable type which will automatically perform a new search for package portions on the next import attempt within that package if the path of their parent package (or sys.path
for a top level package) changes.
With namespace packages, there is no parent/__init__.py
file. In fact, there may be multiple parent
directories found during import search, where each one is provided by a different portion. Thus parent/one
may not be physically located next to parent/two
. In this case, Python will create a namespace package for the top-level parent
package whenever it or one of its subpackages is imported.
See also PEP 420 for the namespace package specification.
5.3. Searching
To begin the search, Python needs the fully qualified name of the module (or package, but for the purposes of this discussion, the difference is immaterial) being imported. This name may come from various arguments to the import
statement, or from the parameters to the importlib.import_module()
or __import__()
functions.
This name will be used in various phases of the import search, and it may be the dotted path to a submodule, e.g. foo.bar.baz
. In this case, Python first tries to import foo
, then foo.bar
, and finally foo.bar.baz
. If any of the intermediate imports fail, a ModuleNotFoundError
is raised.
5.3.1. The module cache
The first place checked during import search is sys.modules
. This mapping serves as a cache of all modules that have been previously imported, including the intermediate paths. So if foo.bar.baz
was previously imported, sys.modules
will contain entries for foo
, foo.bar
, and foo.bar.baz
. Each key will have as its value the corresponding module object.
During import, the module name is looked up in sys.modules
and if present, the associated value is the module satisfying the import, and the process completes. However, if the value is None
, then a ModuleNotFoundError
is raised. If the module name is missing, Python will continue searching for the module.
sys.modules
is writable. Deleting a key may not destroy the associated module (as other modules may hold references to it), but it will invalidate the cache entry for the named module, causing Python to search anew for the named module upon its next import. The key can also be assigned to None
, forcing the next import of the module to result in a ModuleNotFoundError
.
Beware though, as if you keep a reference to the module object, invalidate its cache entry in sys.modules
, and then re-import the named module, the two module objects will not be the same. By contrast, importlib.reload()
will reuse the same module object, and simply reinitialise the module contents by rerunning the module’s code.
5.3.2. Finders and loaders
If the named module is not found in sys.modules
, then Python’s import protocol is invoked to find and load the module. This protocol consists of two conceptual objects, finders and loaders. A finder’s job is to determine whether it can find the named module using whatever strategy it knows about. Objects that implement both of these interfaces are referred to as importers - they return themselves when they find that they can load the requested module.
Python includes a number of default finders and importers. The first one knows how to locate built-in modules, and the second knows how to locate frozen modules. A third default finder searches an import path for modules. The import path is a list of locations that may name file system paths or zip files. It can also be extended to search for any locatable resource, such as those identified by URLs.
The import machinery is extensible, so new finders can be added to extend the range and scope of module searching.
Finders do not actually load modules. If they can find the named module, they return a module spec, an encapsulation of the module’s import-related information, which the import machinery then uses when loading the module.
The following sections describe the protocol for finders and loaders in more detail, including how you can create and register new ones to extend the import machinery.
Changed in version 3.4: In previous versions of Python, finders returned loaders directly, whereas now they return module specs which contain loaders. Loaders are still used during import but have fewer responsibilities.
5.3.3. Import hooks
The import machinery is designed to be extensible; the primary mechanism for this are the import hooks. There are two types of import hooks: meta hooks and import path hooks.
Meta hooks are called at the start of import processing, before any other import processing has occurred, other than sys.modules
cache look up. This allows meta hooks to override sys.path
processing, frozen modules, or even built-in modules. Meta hooks are registered by adding new finder objects to sys.meta_path
, as described below.
Import path hooks are called as part of sys.path
(or package.__path__
) processing, at the point where their associated path item is encountered. Import path hooks are registered by adding new callables to sys.path_hooks
as described below.
5.3.4. The meta path
When the named module is not found in sys.modules
, Python next searches sys.meta_path
, which contains a list of meta path finder objects. These finders are queried in order to see if they know how to handle the named module. Meta path finders must implement a method called find_spec()
which takes three arguments: a name, an import path, and (optionally) a target module. The meta path finder can use any strategy it wants to determine whether it can handle the named module or not.
If the meta path finder knows how to handle the named module, it returns a spec object. If it cannot handle the named module, it returns None
. If sys.meta_path
processing reaches the end of its list without returning a spec, then a ModuleNotFoundError
is raised. Any other exceptions raised are simply propagated up, aborting the import process.
The find_spec()
method of meta path finders is called with two or three arguments. The first is the fully qualified name of the module being imported, for example foo.bar.baz
. The second argument is the path entries to use for the module search. For top-level modules, the second argument is None
, but for submodules or subpackages, the second argument is the value of the parent package’s __path__
attribute. If the appropriate __path__
attribute cannot be accessed, a ModuleNotFoundError
is raised. The third argument is an existing module object that will be the target of loading later. The import system passes in a target module only during reload.
The meta path may be traversed multiple times for a single import request. For example, assuming none of the modules involved has already been cached, importing foo.bar.baz
will first perform a top level import, calling mpf.find_spec("foo", None, None)
on each meta path finder (mpf
). After foo
has been imported, foo.bar
will be imported by traversing the meta path a second time, calling mpf.find_spec("foo.bar", foo.__path__, None)
. Once foo.bar
has been imported, the final traversal will call mpf.find_spec("foo.bar.baz", foo.bar.__path__, None)
.
Some meta path finders only support top level imports. These importers will always return None
when anything other than None
is passed as the second argument.
Python’s default sys.meta_path
has three meta path finders, one that knows how to import built-in modules, one that knows how to import frozen modules, and one that knows how to import modules from an import path (i.e. the path based finder).
Changed in version 3.4: The find_spec()
method of meta path finders replaced find_module()
, which is now deprecated. While it will continue to work without change, the import machinery will try it only if the finder does not implement find_spec()
.
5.4. Loading
If and when a module spec is found, the import machinery will use it (and the loader it contains) when loading the module. Here is an approximation of what happens during the loading portion of import:
Note the following details:
If there is an existing module object with the given name in
sys.modules
, import will have already returned it.The module will exist in
sys.modules
before the loader executes the module code. This is crucial because the module code may (directly or indirectly) import itself; adding it tosys.modules
beforehand prevents unbounded recursion in the worst case and multiple loading in the best.If loading fails, the failing module – and only the failing module – gets removed from
sys.modules
. Any module already in thesys.modules
cache, and any module that was successfully loaded as a side-effect, must remain in the cache. This contrasts with reloading where even the failing module is left insys.modules
.After the module is created but before execution, the import machinery sets the import-related module attributes (“_init_module_attrs” in the pseudo-code example above), as summarized in a later section.
Module execution is the key moment of loading in which the module’s namespace gets populated. Execution is entirely delegated to the loader, which gets to decide what gets populated and how.
The module created during loading and passed to exec_module() may not be the one returned at the end of import 2.
Changed in version 3.4: The import system has taken over the boilerplate responsibilities of loaders. These were previously performed by the importlib.abc.Loader.load_module()
method.
5.4.1. Loaders
Module loaders provide the critical function of loading: module execution. The import machinery calls the importlib.abc.Loader.exec_module()
method with a single argument, the module object to execute. Any value returned from exec_module()
is ignored.
Loaders must satisfy the following requirements:
If the module is a Python module (as opposed to a built-in module or a dynamically loaded extension), the loader should execute the module’s code in the module’s global name space (
module.__dict__
).If the loader cannot execute the module, it should raise an
ImportError
, although any other exception raised duringexec_module()
will be propagated.
In many cases, the finder and loader can be the same object; in such cases the find_spec()
method would just return a spec with the loader set to self
.
Module loaders may opt in to creating the module object during loading by implementing a create_module()
method. It takes one argument, the module spec, and returns the new module object to use during loading. create_module()
does not need to set any attributes on the module object. If the method returns None
, the import machinery will create the new module itself.
New in version 3.4: The create_module()
method of loaders.
Changed in version 3.4: The load_module()
method was replaced by exec_module()
and the import machinery assumed all the boilerplate responsibilities of loading.
For compatibility with existing loaders, the import machinery will use the load_module()
method of loaders if it exists and the loader does not also implement exec_module()
. However, load_module()
has been deprecated and loaders should implement exec_module()
instead.
The load_module()
method must implement all the boilerplate loading functionality described above in addition to executing the module. All the same constraints apply, with some additional clarification:
If there is an existing module object with the given name in
sys.modules
, the loader must use that existing module. (Otherwise,importlib.reload()
will not work correctly.) If the named module does not exist insys.modules
, the loader must create a new module object and add it tosys.modules
.The module must exist in
sys.modules
before the loader executes the module code, to prevent unbounded recursion or multiple loading.If loading fails, the loader must remove any modules it has inserted into
sys.modules
, but it must remove only the failing module(s), and only if the loader itself has loaded the module(s) explicitly.
Changed in version 3.5: A DeprecationWarning
is raised when exec_module()
is defined but create_module()
is not.
Changed in version 3.6: An ImportError
is raised when exec_module()
is defined but create_module()
is not.
5.4.2. Submodules
When a submodule is loaded using any mechanism (e.g. importlib
APIs, the import
or import-from
statements, or built-in __import__()
) a binding is placed in the parent module’s namespace to the submodule object. For example, if package spam
has a submodule foo
, after importing spam.foo
, spam
will have an attribute foo
which is bound to the submodule. Let’s say you have the following directory structure:
and spam/__init__.py
has the following lines in it:
then executing the following puts a name binding to foo
and bar
in the spam
module:>>>
Given Python’s familiar name binding rules this might seem surprising, but it’s actually a fundamental feature of the import system. The invariant holding is that if you have sys.modules['spam']
and sys.modules['spam.foo']
(as you would after the above import), the latter must appear as the foo
attribute of the former.
5.4.3. Module spec
The import machinery uses a variety of information about each module during import, especially before loading. Most of the information is common to all modules. The purpose of a module’s spec is to encapsulate this import-related information on a per-module basis.
Using a spec during import allows state to be transferred between import system components, e.g. between the finder that creates the module spec and the loader that executes it. Most importantly, it allows the import machinery to perform the boilerplate operations of loading, whereas without a module spec the loader had that responsibility.
The module’s spec is exposed as the __spec__
attribute on a module object. See ModuleSpec
for details on the contents of the module spec.
New in version 3.4.
5.4.4. Import-related module attributes
The import machinery fills in these attributes on each module object during loading, based on the module’s spec, before the loader executes the module.__name__
The __name__
attribute must be set to the fully-qualified name of the module. This name is used to uniquely identify the module in the import system.__loader__
The __loader__
attribute must be set to the loader object that the import machinery used when loading the module. This is mostly for introspection, but can be used for additional loader-specific functionality, for example getting data associated with a loader.__package__
The module’s __package__
attribute must be set. Its value must be a string, but it can be the same value as its __name__
. When the module is a package, its __package__
value should be set to its __name__
. When the module is not a package, __package__
should be set to the empty string for top-level modules, or for submodules, to the parent package’s name. See PEP 366 for further details.
This attribute is used instead of __name__
to calculate explicit relative imports for main modules, as defined in PEP 366. It is expected to have the same value as __spec__.parent
.
Changed in version 3.6: The value of __package__
is expected to be the same as __spec__.parent
.__spec__
The __spec__
attribute must be set to the module spec that was used when importing the module. Setting __spec__
appropriately applies equally to modules initialized during interpreter startup. The one exception is __main__
, where __spec__
is set to None in some cases.
When __package__
is not defined, __spec__.parent
is used as a fallback.
New in version 3.4.
Changed in version 3.6: __spec__.parent
is used as a fallback when __package__
is not defined.__path__
If the module is a package (either regular or namespace), the module object’s __path__
attribute must be set. The value must be iterable, but may be empty if __path__
has no further significance. If __path__
is not empty, it must produce strings when iterated over. More details on the semantics of __path__
are given below.
Non-package modules should not have a __path__
attribute.__file____cached__
__file__
is optional. If set, this attribute’s value must be a string. The import system may opt to leave __file__
unset if it has no semantic meaning (e.g. a module loaded from a database).
If __file__
is set, it may also be appropriate to set the __cached__
attribute which is the path to any compiled version of the code (e.g. byte-compiled file). The file does not need to exist to set this attribute; the path can simply point to where the compiled file would exist (see PEP 3147).
It is also appropriate to set __cached__
when __file__
is not set. However, that scenario is quite atypical. Ultimately, the loader is what makes use of __file__
and/or __cached__
. So if a loader can load from a cached module but otherwise does not load from a file, that atypical scenario may be appropriate.
5.4.5. module.__path__
By definition, if a module has a __path__
attribute, it is a package.
A package’s __path__
attribute is used during imports of its subpackages. Within the import machinery, it functions much the same as sys.path
, i.e. providing a list of locations to search for modules during import. However, __path__
is typically much more constrained than sys.path
.
__path__
must be an iterable of strings, but it may be empty. The same rules used for sys.path
also apply to a package’s __path__
, and sys.path_hooks
(described below) are consulted when traversing a package’s __path__
.
A package’s __init__.py
file may set or alter the package’s __path__
attribute, and this was typically the way namespace packages were implemented prior to PEP 420. With the adoption of PEP 420, namespace packages no longer need to supply __init__.py
files containing only __path__
manipulation code; the import machinery automatically sets __path__
correctly for the namespace package.
5.4.6. Module reprs
By default, all modules have a usable repr, however depending on the attributes set above, and in the module’s spec, you can more explicitly control the repr of module objects.
If the module has a spec (__spec__
), the import machinery will try to generate a repr from it. If that fails or there is no spec, the import system will craft a default repr using whatever information is available on the module. It will try to use the module.__name__
, module.__file__
, and module.__loader__
as input into the repr, with defaults for whatever information is missing.
Here are the exact rules used:
If the module has a
__spec__
attribute, the information in the spec is used to generate the repr. The “name”, “loader”, “origin”, and “has_location” attributes are consulted.If the module has a
__file__
attribute, this is used as part of the module’s repr.If the module has no
__file__
but does have a__loader__
that is notNone
, then the loader’s repr is used as part of the module’s repr.Otherwise, just use the module’s
__name__
in the repr.
Changed in version 3.4: Use of loader.module_repr()
has been deprecated and the module spec is now used by the import machinery to generate a module repr.
For backward compatibility with Python 3.3, the module repr will be generated by calling the loader’s module_repr()
method, if defined, before trying either approach described above. However, the method is deprecated.
5.4.7. Cached bytecode invalidation
Before Python loads cached bytecode from a .pyc
file, it checks whether the cache is up-to-date with the source .py
file. By default, Python does this by storing the source’s last-modified timestamp and size in the cache file when writing it. At runtime, the import system then validates the cache file by checking the stored metadata in the cache file against the source’s metadata.
Python also supports “hash-based” cache files, which store a hash of the source file’s contents rather than its metadata. There are two variants of hash-based .pyc
files: checked and unchecked. For checked hash-based .pyc
files, Python validates the cache file by hashing the source file and comparing the resulting hash with the hash in the cache file. If a checked hash-based cache file is found to be invalid, Python regenerates it and writes a new checked hash-based cache file. For unchecked hash-based .pyc
files, Python simply assumes the cache file is valid if it exists. Hash-based .pyc
files validation behavior may be overridden with the --check-hash-based-pycs
flag.
Changed in version 3.7: Added hash-based .pyc
files. Previously, Python only supported timestamp-based invalidation of bytecode caches.
5.5. The Path Based Finder
As mentioned previously, Python comes with several default meta path finders. One of these, called the path based finder (PathFinder
), searches an import path, which contains a list of path entries. Each path entry names a location to search for modules.
The path based finder itself doesn’t know how to import anything. Instead, it traverses the individual path entries, associating each of them with a path entry finder that knows how to handle that particular kind of path.
The default set of path entry finders implement all the semantics for finding modules on the file system, handling special file types such as Python source code (.py
files), Python byte code (.pyc
files) and shared libraries (e.g. .so
files). When supported by the zipimport
module in the standard library, the default path entry finders also handle loading all of these file types (other than shared libraries) from zipfiles.
Path entries need not be limited to file system locations. They can refer to URLs, database queries, or any other location that can be specified as a string.
The path based finder provides additional hooks and protocols so that you can extend and customize the types of searchable path entries. For example, if you wanted to support path entries as network URLs, you could write a hook that implements HTTP semantics to find modules on the web. This hook (a callable) would return a path entry finder supporting the protocol described below, which was then used to get a loader for the module from the web.
A word of warning: this section and the previous both use the term finder, distinguishing between them by using the terms meta path finder and path entry finder. These two types of finders are very similar, support similar protocols, and function in similar ways during the import process, but it’s important to keep in mind that they are subtly different. In particular, meta path finders operate at the beginning of the import process, as keyed off the sys.meta_path
traversal.
By contrast, path entry finders are in a sense an implementation detail of the path based finder, and in fact, if the path based finder were to be removed from sys.meta_path
, none of the path entry finder semantics would be invoked.
5.5.1. Path entry finders
The path based finder is responsible for finding and loading Python modules and packages whose location is specified with a string path entry. Most path entries name locations in the file system, but they need not be limited to this.
As a meta path finder, the path based finder implements the find_spec()
protocol previously described, however it exposes additional hooks that can be used to customize how modules are found and loaded from the import path.
Three variables are used by the path based finder, sys.path
, sys.path_hooks
and sys.path_importer_cache
. The __path__
attributes on package objects are also used. These provide additional ways that the import machinery can be customized.
sys.path
contains a list of strings providing search locations for modules and packages. It is initialized from the PYTHONPATH
environment variable and various other installation- and implementation-specific defaults. Entries in sys.path
can name directories on the file system, zip files, and potentially other “locations” (see the site
module) that should be searched for modules, such as URLs, or database queries. Only strings and bytes should be present on sys.path
; all other data types are ignored. The encoding of bytes entries is determined by the individual path entry finders.
The path based finder is a meta path finder, so the import machinery begins the import path search by calling the path based finder’s find_spec()
method as described previously. When the path
argument to find_spec()
is given, it will be a list of string paths to traverse - typically a package’s __path__
attribute for an import within that package. If the path
argument is None
, this indicates a top level import and sys.path
is used.
The path based finder iterates over every entry in the search path, and for each of these, looks for an appropriate path entry finder (PathEntryFinder
) for the path entry. Because this can be an expensive operation (e.g. there may be stat() call overheads for this search), the path based finder maintains a cache mapping path entries to path entry finders. This cache is maintained in sys.path_importer_cache
(despite the name, this cache actually stores finder objects rather than being limited to importer objects). In this way, the expensive search for a particular path entry location’s path entry finder need only be done once. User code is free to remove cache entries from sys.path_importer_cache
forcing the path based finder to perform the path entry search again 3.
If the path entry is not present in the cache, the path based finder iterates over every callable in sys.path_hooks
. Each of the path entry hooks in this list is called with a single argument, the path entry to be searched. This callable may either return a path entry finder that can handle the path entry, or it may raise ImportError
. An ImportError
is used by the path based finder to signal that the hook cannot find a path entry finder for that path entry. The exception is ignored and import path iteration continues. The hook should expect either a string or bytes object; the encoding of bytes objects is up to the hook (e.g. it may be a file system encoding, UTF-8, or something else), and if the hook cannot decode the argument, it should raise ImportError
.
If sys.path_hooks
iteration ends with no path entry finder being returned, then the path based finder’s find_spec()
method will store None
in sys.path_importer_cache
(to indicate that there is no finder for this path entry) and return None
, indicating that this meta path finder could not find the module.
If a path entry finder is returned by one of the path entry hook callables on sys.path_hooks
, then the following protocol is used to ask the finder for a module spec, which is then used when loading the module.
The current working directory – denoted by an empty string – is handled slightly differently from other entries on sys.path
. First, if the current working directory is found to not exist, no value is stored in sys.path_importer_cache
. Second, the value for the current working directory is looked up fresh for each module lookup. Third, the path used for sys.path_importer_cache
and returned by importlib.machinery.PathFinder.find_spec()
will be the actual current working directory and not the empty string.
5.5.2. Path entry finder protocol
In order to support imports of modules and initialized packages and also to contribute portions to namespace packages, path entry finders must implement the find_spec()
method.
find_spec()
takes two arguments: the fully qualified name of the module being imported, and the (optional) target module. find_spec()
returns a fully populated spec for the module. This spec will always have “loader” set (with one exception).
To indicate to the import machinery that the spec represents a namespace portion, the path entry finder sets “submodule_search_locations” to a list containing the portion.
Changed in version 3.4: find_spec()
replaced find_loader()
and find_module()
, both of which are now deprecated, but will be used if find_spec()
is not defined.
Older path entry finders may implement one of these two deprecated methods instead of find_spec()
. The methods are still respected for the sake of backward compatibility. However, if find_spec()
is implemented on the path entry finder, the legacy methods are ignored.
find_loader()
takes one argument, the fully qualified name of the module being imported. find_loader()
returns a 2-tuple where the first item is the loader and the second item is a namespace portion.
For backwards compatibility with other implementations of the import protocol, many path entry finders also support the same, traditional find_module()
method that meta path finders support. However path entry finder find_module()
methods are never called with a path
argument (they are expected to record the appropriate path information from the initial call to the path hook).
The find_module()
method on path entry finders is deprecated, as it does not allow the path entry finder to contribute portions to namespace packages. If both find_loader()
and find_module()
exist on a path entry finder, the import system will always call find_loader()
in preference to find_module()
.
5.6. Replacing the standard import system
The most reliable mechanism for replacing the entire import system is to delete the default contents of sys.meta_path
, replacing them entirely with a custom meta path hook.
If it is acceptable to only alter the behaviour of import statements without affecting other APIs that access the import system, then replacing the builtin __import__()
function may be sufficient. This technique may also be employed at the module level to only alter the behaviour of import statements within that module.
To selectively prevent the import of some modules from a hook early on the meta path (rather than disabling the standard import system entirely), it is sufficient to raise ModuleNotFoundError
directly from find_spec()
instead of returning None
. The latter indicates that the meta path search should continue, while raising an exception terminates it immediately.
5.7. Package Relative Imports
Relative imports use leading dots. A single leading dot indicates a relative import, starting with the current package. Two or more leading dots indicate a relative import to the parent(s) of the current package, one level per dot after the first. For example, given the following package layout:
In either subpackage1/moduleX.py
or subpackage1/__init__.py
, the following are valid relative imports:
Absolute imports may use either the import <>
or from <> import <>
syntax, but relative imports may only use the second form; the reason for this is that:
should expose XXX.YYY.ZZZ
as a usable expression, but .moduleY is not a valid expression.
5.8. Special considerations for __main__
The __main__
module is a special case relative to Python’s import system. As noted elsewhere, the __main__
module is directly initialized at interpreter startup, much like sys
and builtins
. However, unlike those two, it doesn’t strictly qualify as a built-in module. This is because the manner in which __main__
is initialized depends on the flags and other options with which the interpreter is invoked.
5.8.1. __main__.__spec__
Depending on how __main__
is initialized, __main__.__spec__
gets set appropriately or to None
.
When Python is started with the -m
option, __spec__
is set to the module spec of the corresponding module or package. __spec__
is also populated when the __main__
module is loaded as part of executing a directory, zipfile or other sys.path
entry.
In the remaining cases __main__.__spec__
is set to None
, as the code used to populate the __main__
does not correspond directly with an importable module:
interactive prompt
-c
optionrunning from stdin
running directly from a source or bytecode file
Note that __main__.__spec__
is always None
in the last case, even if the file could technically be imported directly as a module instead. Use the -m
switch if valid module metadata is desired in __main__
.
Note also that even when __main__
corresponds with an importable module and __main__.__spec__
is set accordingly, they’re still considered distinct modules. This is due to the fact that blocks guarded by if __name__ == "__main__":
checks only execute when the module is used to populate the __main__
namespace, and not during normal import.
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