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 模块 ; 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
appended. Within a module, the module’s name (as a string) is available as the value of the global variable
. For instance, use your favorite text editor to create a file called
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 b < n: print b, a, b = b, a+b def fib2(n): # return Fibonacci series up to n result =  a, b = 0, 1 while b < n: result.append(b) 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
directly in the current symbol table; it only enters the module name
there. Using the module name you can access the functions:
>>> fibo.fib(1000) 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 >>> fibo.fib2(100) [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) 1 1 2 3 5 8 13 21 34 55 89 144 233 377
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
statements at the beginning of a module (or script, for that matter). The imported module names are placed in the importing module’s global symbol table.
There is a variant of the
statement that imports names from a module directly into the importing module’s symbol table. For example:
>>> from fibo import fib, fib2 >>> fib(500) 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,
is not defined).
There is even a variant to import all names that a module defines:
>>> from fibo import * >>> fib(500) 1 1 2 3 5 8 13 21 34 55 89 144 233 377
This imports all names except those beginning with an underscore (
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.
>>> 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
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, use
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 the
. 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 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).
When a module named
is imported, the interpreter first searches for a built-in module with that name. If not found, it then searches for a file named
in a list of directories given by the variable
is initialized from these locations:
the directory containing the input script (or the current directory).
(a list of directory names, with the same syntax as the shell variable
the installation-dependent default.
After initialization, Python programs can modify
. The 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
As an important speed-up of the start-up time for short programs that use a lot of standard modules, if a file called
exists in the directory where
is found, this is assumed to contain an already-“byte-compiled” version of the module
. The modification time of the version of
used to create
is recorded in
file is ignored if these don’t match.
Normally, you don’t need to do anything to create the
is successfully compiled, an attempt is made to write the compiled version to
. It is not an error if this attempt fails; if for any reason the file is not written completely, the resulting
file will be recognized as invalid and thus ignored later. The contents of the
file are platform independent, so a Python module directory can be shared by machines of different architectures.
Some tips for experts:
When the Python interpreter is invoked with the
flag, optimized code is generated and stored in
files. The optimizer currently doesn’t help much; it only removes
files are ignored and
files are compiled to optimized bytecode.
flags to the Python interpreter (
) will cause the bytecode compiler to perform optimizations that could in some rare cases result in malfunctioning programs. Currently only
strings are removed from the bytecode, resulting in more compact
files. Since some programs may rely on having these available, you should only use this option if you know what you’re doing.
A program doesn’t run any faster when it is read from a
file than when it is read from a
file; the only thing that’s faster about
files is the speed with which they are loaded.
When a script is run by giving its name on the command line, the bytecode for the script is never written to a
file. Thus, the startup time of a script may be reduced by moving most of its code to a module and having a small bootstrap script that imports that module. It is also possible to name a
file directly on the command line.
It is possible to have a file called
is used) without a file
for the same module. This can be used to distribute a library of Python code in a form that is moderately hard to reverse engineer.
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
module is only provided on Windows systems. One particular module deserves some attention:
, which is built into every Python interpreter. The variables
define the strings used as primary and secondary prompts:
>>> 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.
is a list of strings that determines the interpreter’s search path for modules. It is initialized to a default path taken from the environment variable
, or from a built-in default if
is not set. You can modify it using standard list operations:
>>> import sys >>> sys.path.append('/ufs/guido/lib/python')
The built-in function
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) ['__displayhook__', '__doc__', '__excepthook__', '__name__', '__package__', '__stderr__', '__stdin__', '__stdout__', '_clear_type_cache', '_current_frames', '_getframe', '_mercurial', 'api_version', 'argv', 'builtin_module_names', 'byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_clear', 'exc_info', 'exc_traceback', 'exc_type', 'exc_value', 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info', 'float_repr_style', 'getcheckinterval', 'getdefaultencoding', 'getdlopenflags', 'getfilesystemencoding', 'getobjects', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'gettotalrefcount', 'gettrace', 'hexversion', 'long_info', 'maxint', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1', 'py3kwarning', 'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit', 'settrace', 'stderr', 'stdin', 'stdout', 'subversion', 'version', 'version_info', 'warnoptions']
lists the names you have defined currently:
>>> a = [1, 2, 3, 4, 5] >>> import fibo >>> fib = fibo.fib >>> dir() ['__builtins__', '__name__', '__package__', 'a', 'fib', 'fibo', 'sys']
Note that it lists all types of names: variables, modules, functions, etc.
>>> import __builtin__ >>> dir(__builtin__) ['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException', 'BufferError', 'BytesWarning', 'DeprecationWarning', 'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False', 'FloatingPointError', 'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError', 'ImportWarning', 'IndentationError', 'IndexError', 'KeyError', 'KeyboardInterrupt', 'LookupError', 'MemoryError', 'NameError', 'None', 'NotImplemented', 'NotImplementedError', 'OSError', 'OverflowError', 'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError', 'RuntimeWarning', 'StandardError', 'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', 'True', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning', 'ValueError', 'Warning', 'ZeroDivisionError', '_', '__debug__', '__doc__', '__import__', '__name__', '__package__', 'abs', 'all', 'any', 'apply', 'basestring', 'bin', 'bool', 'buffer', 'bytearray', 'bytes', 'callable', 'chr', 'classmethod', 'cmp', 'coerce', 'compile', 'complex', 'copyright', 'credits', 'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'execfile', 'exit', 'file', 'filter', 'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr', 'hash', 'help', 'hex', 'id', 'input', 'int', 'intern', 'isinstance', 'issubclass', 'iter', 'len', 'license', 'list', 'locals', 'long', 'map', 'max', 'memoryview', 'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property', 'quit', 'range', 'raw_input', 'reduce', 'reload', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'unichr', 'unicode', 'vars', 'xrange', 'zip']
Packages are a way of structuring Python’s module namespace by using “dotted module names”. For example, the module name
designates a submodule named
in a package named
. 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:
), 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
looking for the package subdirectory.
files are required to make Python treat the directories as containing packages; this is done to prevent directories with a common name, such as
, from unintentionally hiding valid modules that occur later on the module search path. In the simplest case,
can just be an empty file, but it can also execute initialization code for the package or set the
variable, described later.
Users of the package can import individual modules from the package, for example:
This loads the submodule
. 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
, 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
, but this makes its function
echofilter(input, output, delay=0.7, atten=4)
Note that when using
, 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
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
Contrarily, when using syntax like
, 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 item.
Now what happens when the user writes
? 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 package. The
statement uses the following convention: if a package’s
code defines a list named
, it is taken to be the list of module names that should be imported when
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
could contain the following code:
__all__ = ["echo", "surround", "reverse"]
This would mean that
would import the three named submodules of the
is not defined, the statement
import all submodules from the package
into the current namespace; it only ensures that the package
has 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
. It also includes any submodules of the package that were explicitly loaded by previous
statements. Consider this code:
import sound.effects.echo import sound.effects.surround from sound.effects import *
In this example, the
modules are imported in the current namespace because they are defined in the
package when the
statement is executed. (This also works when
Although certain modules are designed to export only names that follow certain patterns when you use
, it is still considered bad practice in production code.
Remember, there is nothing wrong with using
! In fact, this is the recommended notation unless the importing module needs to use submodules with the same name from different packages.
The submodules often need to refer to each other. For example, the
module might use the
module. In fact, such references are so common that the
statement first looks in the containing package before looking in the standard module search path. Thus, the
module can simply use
. If the imported module is not found in the current package (the package of which the current module is a submodule), the
statement looks for a top-level module with the given name.
When packages are structured into subpackages (as with the
package in the example), you can use absolute imports to refer to submodules of siblings packages. For example, if the module
needs to use the
module in the
package, it can use
Starting with Python 2.5, in addition to the implicit relative imports described above, you can write explicit relative imports with the
form of import statement. These explicit relative 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 both explicit and implicit relative imports are based on the name of the current module. Since the name of the main module is always
, modules intended for use as the main module of a Python application should always use absolute imports.
Packages support one more special attribute,
. This is initialized to be a list containing the name of the directory holding the package’s
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.