If you quit from the Python interpreter and enter it again, the definitions you have made (functions and variables) are lost. Therefore, if you want to write a somewhat longer program, you are better off using a text editor to prepare the input for the interpreter and running it with that file as input instead. This is known as creating a script. As your program gets longer, you may want to split it into several files for easier maintenance. You may also want to use a handy function that you’ve written in several programs without copying its definition into each program.
To support this, Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter. Such a file is called a module; definitions from a module can be imported into other modules or into the main module (the collection of variables that you have access to in a script executed at the top level and in calculator mode).
A module is a file containing Python definitions and statements. The file name
is the module name with the suffix
.py appended. Within a module, the
module’s name (as a string) is available as the value of the global variable
__name__. For instance, use your favorite text editor to create a file
fibo.py in the current directory with the following contents:
# Fibonacci numbers module def fib(n): # write Fibonacci series up to n a, b = 0, 1 while a < n: print(a, end=' ') a, b = b, a+b print() def fib2(n): # return Fibonacci series up to n result =  a, b = 0, 1 while a < n: result.append(a) a, b = b, a+b return result
Now enter the Python interpreter and import this module with the following command:
>>> import fibo
This does not enter the names of the functions defined in
fibo directly in
the current symbol table; it only enters the module name
fibo there. Using
the module name you can access the functions:
>>> fibo.fib(1000) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 >>> fibo.fib2(100) [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89] >>> fibo.__name__ 'fibo'
If you intend to use a function often you can assign it to a local name:
>>> fib = fibo.fib >>> fib(500) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
6.1. More on Modules¶
A module can contain executable statements as well as function definitions. These statements are intended to initialize the module. They are executed only the first time the module name is encountered in an import statement. 1 (They are also run if the file is executed as a script.)
Each module has its own private symbol table, which is used as the global symbol
table by all functions defined in the module. Thus, the author of a module can
use global variables in the module without worrying about accidental clashes
with a user’s global variables. On the other hand, if you know what you are
doing you can touch a module’s global variables with the same notation used to
refer to its functions,
Modules can import other modules. It is customary but not required to place all
import statements at the beginning of a module (or script, for that
matter). The imported module names are placed in the importing module’s global
There is a variant of the
import statement that imports names from a
module directly into the importing module’s symbol table. For example:
>>> from fibo import fib, fib2 >>> fib(500) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
This does not introduce the module name from which the imports are taken in the
local symbol table (so in the example,
fibo is not defined).
There is even a variant to import all names that a module defines:
>>> from fibo import * >>> fib(500) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
This imports all names except those beginning with an underscore (
In most cases Python programmers do not use this facility since it introduces
an unknown set of names into the interpreter, possibly hiding some things
you have already defined.
Note that in general the practice of importing
* from a module or package is
frowned upon, since it often causes poorly readable code. However, it is okay to
use it to save typing in interactive sessions.
If the module name is followed by
as, then the name
as is bound directly to the imported module.
>>> import fibo as fib >>> fib.fib(500) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
This is effectively importing the module in the same way that
will do, with the only difference of it being available as
It can also be used when utilising
from with similar effects:
>>> from fibo import fib as fibonacci >>> fibonacci(500) 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
For efficiency reasons, each module is only imported once per interpreter
session. Therefore, if you change your modules, you must restart the
interpreter – or, if it’s just one module you want to test interactively,
6.1.1. Executing modules as scripts¶
When you run a Python module with
python fibo.py <arguments>
the code in the module will be executed, just as if you imported it, but with
__name__ set to
"__main__". That means that by adding this code at
the end of your module:
if __name__ == "__main__": import sys fib(int(sys.argv))
you can make the file usable as a script as well as an importable module, because the code that parses the command line only runs if the module is executed as the “main” file:
$ python fibo.py 50 0 1 1 2 3 5 8 13 21 34
If the module is imported, the code is not run:
>>> import fibo >>>
This is often used either to provide a convenient user interface to a module, or for testing purposes (running the module as a script executes a test suite).
6.1.2. The Module Search Path¶
When a module named
spam is imported, the interpreter first searches for
a built-in module with that name. If not found, it then searches for a file
spam.py in a list of directories given by the variable
sys.path is initialized from these locations:
The directory containing the input script (or the current directory when no file is specified).
PYTHONPATH(a list of directory names, with the same syntax as the shell variable
The installation-dependent default.
On file systems which support symlinks, the directory containing the input script is calculated after the symlink is followed. In other words the directory containing the symlink is not added to the module search path.
After initialization, Python programs can modify
directory containing the script being run is placed at the beginning of the
search path, ahead of the standard library path. This means that scripts in that
directory will be loaded instead of modules of the same name in the library
directory. This is an error unless the replacement is intended. See section
Standard Modules for more information.
6.1.3. “Compiled” Python files¶
To speed up loading modules, Python caches the compiled version of each module
__pycache__ directory under the name
where the version encodes the format of the compiled file; it generally contains
the Python version number. For example, in CPython release 3.3 the compiled
version of spam.py would be cached as
naming convention allows compiled modules from different releases and different
versions of Python to coexist.
Python checks the modification date of the source against the compiled version to see if it’s out of date and needs to be recompiled. This is a completely automatic process. Also, the compiled modules are platform-independent, so the same library can be shared among systems with different architectures.
Python does not check the cache in two circumstances. First, it always recompiles and does not store the result for the module that’s loaded directly from the command line. Second, it does not check the cache if there is no source module. To support a non-source (compiled only) distribution, the compiled module must be in the source directory, and there must not be a source module.
Some tips for experts:
You can use the
-OOswitches on the Python command to reduce the size of a compiled module. The
-Oswitch removes assert statements, the
-OOswitch removes both assert statements and __doc__ strings. Since some programs may rely on having these available, you should only use this option if you know what you’re doing. “Optimized” modules have an
opt-tag and are usually smaller. Future releases may change the effects of optimization.
A program doesn’t run any faster when it is read from a
.pycfile than when it is read from a
.pyfile; the only thing that’s faster about
.pycfiles is the speed with which they are loaded.
compileallcan create .pyc files for all modules in a directory.
There is more detail on this process, including a flow chart of the decisions, in PEP 3147.
6.2. Standard Modules¶
Python comes with a library of standard modules, described in a separate
document, the Python Library Reference (“Library Reference” hereafter). Some
modules are built into the interpreter; these provide access to operations that
are not part of the core of the language but are nevertheless built in, either
for efficiency or to provide access to operating system primitives such as
system calls. The set of such modules is a configuration option which also
depends on the underlying platform. For example, the
winreg module is only
provided on Windows systems. One particular module deserves some attention:
sys, which is built into every Python interpreter. The variables
sys.ps2 define the strings used as primary and secondary
>>> import sys >>> sys.ps1 '>>> ' >>> sys.ps2 '... ' >>> sys.ps1 = 'C> ' C> print('Yuck!') Yuck! C>
These two variables are only defined if the interpreter is in interactive mode.
sys.path is a list of strings that determines the interpreter’s
search path for modules. It is initialized to a default path taken from the
PYTHONPATH, or from a built-in default if
PYTHONPATH is not set. You can modify it using standard list
>>> import sys >>> sys.path.append('/ufs/guido/lib/python')
The built-in function
dir() is used to find out which names a module
defines. It returns a sorted list of strings:
>>> import fibo, sys >>> dir(fibo) ['__name__', 'fib', 'fib2'] >>> dir(sys) ['__breakpointhook__', '__displayhook__', '__doc__', '__excepthook__', '__interactivehook__', '__loader__', '__name__', '__package__', '__spec__', '__stderr__', '__stdin__', '__stdout__', '__unraisablehook__', '_clear_type_cache', '_current_frames', '_debugmallocstats', '_framework', '_getframe', '_git', '_home', '_xoptions', 'abiflags', 'addaudithook', 'api_version', 'argv', 'audit', 'base_exec_prefix', 'base_prefix', 'breakpointhook', 'builtin_module_names', 'byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info', 'float_repr_style', 'get_asyncgen_hooks', 'get_coroutine_origin_tracking_depth', 'getallocatedblocks', 'getdefaultencoding', 'getdlopenflags', 'getfilesystemencodeerrors', 'getfilesystemencoding', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info', 'intern', 'is_finalizing', 'last_traceback', 'last_type', 'last_value', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1', 'ps2', 'pycache_prefix', 'set_asyncgen_hooks', 'set_coroutine_origin_tracking_depth', 'setdlopenflags', 'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout', 'thread_info', 'unraisablehook', 'version', 'version_info', 'warnoptions']
dir() lists the names you have defined currently:
>>> a = [1, 2, 3, 4, 5] >>> import fibo >>> fib = fibo.fib >>> dir() ['__builtins__', '__name__', 'a', 'fib', 'fibo', 'sys']
Note that it lists all types of names: variables, modules, functions, etc.
dir() does not list the names of built-in functions and variables. If you
want a list of those, they are defined in the standard module
>>> import builtins >>> dir(builtins) ['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException', 'BlockingIOError', 'BrokenPipeError', 'BufferError', 'BytesWarning', 'ChildProcessError', 'ConnectionAbortedError', 'ConnectionError', 'ConnectionRefusedError', 'ConnectionResetError', 'DeprecationWarning', 'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False', 'FileExistsError', 'FileNotFoundError', 'FloatingPointError', 'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError', 'ImportWarning', 'IndentationError', 'IndexError', 'InterruptedError', 'IsADirectoryError', 'KeyError', 'KeyboardInterrupt', 'LookupError', 'MemoryError', 'NameError', 'None', 'NotADirectoryError', 'NotImplemented', 'NotImplementedError', 'OSError', 'OverflowError', 'PendingDeprecationWarning', 'PermissionError', 'ProcessLookupError', 'ReferenceError', 'ResourceWarning', 'RuntimeError', 'RuntimeWarning', 'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', 'TimeoutError', 'True', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning', 'ValueError', 'Warning', 'ZeroDivisionError', '_', '__build_class__', '__debug__', '__doc__', '__import__', '__name__', '__package__', 'abs', 'all', 'any', 'ascii', 'bin', 'bool', 'bytearray', 'bytes', 'callable', 'chr', 'classmethod', 'compile', 'complex', 'copyright', 'credits', 'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'exec', 'exit', 'filter', 'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr', 'hash', 'help', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass', 'iter', 'len', 'license', 'list', 'locals', 'map', 'max', 'memoryview', 'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property', 'quit', 'range', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'vars', 'zip']
Packages are a way of structuring Python’s module namespace by using “dotted
module names”. For example, the module name
A.B designates a submodule
B in a package named
A. Just like the use of modules saves the
authors of different modules from having to worry about each other’s global
variable names, the use of dotted module names saves the authors of multi-module
packages like NumPy or Pillow from having to worry about
each other’s module names.
Suppose you want to design a collection of modules (a “package”) for the uniform
handling of sound files and sound data. There are many different sound file
formats (usually recognized by their extension, for example:
.au), so you may need to create and maintain a growing
collection of modules for the conversion between the various file formats.
There are also many different operations you might want to perform on sound data
(such as mixing, adding echo, applying an equalizer function, creating an
artificial stereo effect), so in addition you will be writing a never-ending
stream of modules to perform these operations. Here’s a possible structure for
your package (expressed in terms of a hierarchical filesystem):
sound/ Top-level package __init__.py Initialize the sound package formats/ Subpackage for file format conversions __init__.py wavread.py wavwrite.py aiffread.py aiffwrite.py auread.py auwrite.py ... effects/ Subpackage for sound effects __init__.py echo.py surround.py reverse.py ... filters/ Subpackage for filters __init__.py equalizer.py vocoder.py karaoke.py ...
When importing the package, Python searches through the directories on
sys.path looking for the package subdirectory.
__init__.py files are required to make Python treat directories
containing the file as packages. This prevents directories with a common name,
string, unintentionally hiding valid modules that occur later
on the module search path. In the simplest case,
__init__.py can just be
an empty file, but it can also execute initialization code for the package or
__all__ variable, described later.
Users of the package can import individual modules from the package, for example:
This loads the submodule
sound.effects.echo. It must be referenced with
its full name.
sound.effects.echo.echofilter(input, output, delay=0.7, atten=4)
An alternative way of importing the submodule is:
from sound.effects import echo
This also loads the submodule
echo, and makes it available without its
package prefix, so it can be used as follows:
echo.echofilter(input, output, delay=0.7, atten=4)
Yet another variation is to import the desired function or variable directly:
from sound.effects.echo import echofilter
Again, this loads the submodule
echo, but this makes its function
echofilter() directly available:
echofilter(input, output, delay=0.7, atten=4)
Note that when using
from package import item, the item can be either a
submodule (or subpackage) of the package, or some other name defined in the
package, like a function, class or variable. The
import statement first
tests whether the item is defined in the package; if not, it assumes it is a
module and attempts to load it. If it fails to find it, an
exception is raised.
Contrarily, when using syntax like
import item.subitem.subsubitem, each item
except for the last must be a package; the last item can be a module or a
package but can’t be a class or function or variable defined in the previous
6.4.1. Importing * From a Package¶
Now what happens when the user writes
from sound.effects import *? Ideally,
one would hope that this somehow goes out to the filesystem, finds which
submodules are present in the package, and imports them all. This could take a
long time and importing sub-modules might have unwanted side-effects that should
only happen when the sub-module is explicitly imported.
The only solution is for the package author to provide an explicit index of the
import statement uses the following convention: if a package’s
__init__.py code defines a list named
__all__, it is taken to be the
list of module names that should be imported when
from package import * is
encountered. It is up to the package author to keep this list up-to-date when a
new version of the package is released. Package authors may also decide not to
support it, if they don’t see a use for importing * from their package. For
example, the file
sound/effects/__init__.py could contain the following
__all__ = ["echo", "surround", "reverse"]
This would mean that
from sound.effects import * would import the three
named submodules of the
__all__ is not defined, the statement
from sound.effects import *
does not import all submodules from the package
sound.effects into the
current namespace; it only ensures that the package
been imported (possibly running any initialization code in
and then imports whatever names are defined in the package. This includes any
names defined (and submodules explicitly loaded) by
also includes any submodules of the package that were explicitly loaded by
import statements. Consider this code:
import sound.effects.echo import sound.effects.surround from sound.effects import *
In this example, the
surround modules are imported in the
current namespace because they are defined in the
from...import statement is executed. (This also works when
__all__ is defined.)
Although certain modules are designed to export only names that follow certain
patterns when you use
import *, it is still considered bad practice in
Remember, there is nothing wrong with using
from package import
specific_submodule! In fact, this is the recommended notation unless the
importing module needs to use submodules with the same name from different
6.4.2. Intra-package References¶
When packages are structured into subpackages (as with the
in the example), you can use absolute imports to refer to submodules of siblings
packages. For example, if the module
sound.filters.vocoder needs to use
echo module in the
sound.effects package, it can use
sound.effects import echo.
You can also write relative imports, with the
from module import name form
of import statement. These imports use leading dots to indicate the current and
parent packages involved in the relative import. From the
module for example, you might use:
from . import echo from .. import formats from ..filters import equalizer
Note that relative imports are based on the name of the current module. Since
the name of the main module is always
"__main__", modules intended for use
as the main module of a Python application must always use absolute imports.
6.4.3. Packages in Multiple Directories¶
Packages support one more special attribute,
__path__. This is
initialized to be a list containing the name of the directory holding the
__init__.py before the code in that file is executed. This
variable can be modified; doing so affects future searches for modules and
subpackages contained in the package.
While this feature is not often needed, it can be used to extend the set of modules found in a package.
In fact function definitions are also ‘statements’ that are ‘executed’; the execution of a module-level function definition enters the function name in the module’s global symbol table.