doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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FTP.cwd(pathname)
Set the current directory on the server. | python.library.ftplib#ftplib.FTP.cwd |
FTP.delete(filename)
Remove the file named filename from the server. If successful, returns the text of the response, otherwise raises error_perm on permission errors or error_reply on other errors. | python.library.ftplib#ftplib.FTP.delete |
FTP.dir(argument[, ...])
Produce a directory listing as returned by the LIST command, printing it to standard output. The optional argument is a directory to list (default is the current server directory). Multiple arguments can be used to pass non-standard options to the LIST command. If the last argument is a function, it is used as a callback function as for retrlines(); the default prints to sys.stdout. This method returns None. Note If your server supports the command, mlsd() offers a better API. | python.library.ftplib#ftplib.FTP.dir |
FTP.getwelcome()
Return the welcome message sent by the server in reply to the initial connection. (This message sometimes contains disclaimers or help information that may be relevant to the user.) | python.library.ftplib#ftplib.FTP.getwelcome |
FTP.login(user='anonymous', passwd='', acct='')
Log in as the given user. The passwd and acct parameters are optional and default to the empty string. If no user is specified, it defaults to 'anonymous'. If user is 'anonymous', the default passwd is 'anonymous@'. This function should be called only once for each instance, after a connection has been established; it should not be called at all if a host and user were given when the instance was created. Most FTP commands are only allowed after the client has logged in. The acct parameter supplies “accounting information”; few systems implement this. | python.library.ftplib#ftplib.FTP.login |
FTP.mkd(pathname)
Create a new directory on the server. | python.library.ftplib#ftplib.FTP.mkd |
FTP.mlsd(path="", facts=[])
List a directory in a standardized format by using MLSD command (RFC 3659). If path is omitted the current directory is assumed. facts is a list of strings representing the type of information desired (e.g. ["type", "size", "perm"]). Return a generator object yielding a tuple of two elements for every file found in path. First element is the file name, the second one is a dictionary containing facts about the file name. Content of this dictionary might be limited by the facts argument but server is not guaranteed to return all requested facts. New in version 3.3. | python.library.ftplib#ftplib.FTP.mlsd |
FTP.nlst(argument[, ...])
Return a list of file names as returned by the NLST command. The optional argument is a directory to list (default is the current server directory). Multiple arguments can be used to pass non-standard options to the NLST command. Note If your server supports the command, mlsd() offers a better API. | python.library.ftplib#ftplib.FTP.nlst |
FTP.ntransfercmd(cmd, rest=None)
Like transfercmd(), but returns a tuple of the data connection and the expected size of the data. If the expected size could not be computed, None will be returned as the expected size. cmd and rest means the same thing as in transfercmd(). | python.library.ftplib#ftplib.FTP.ntransfercmd |
FTP.pwd()
Return the pathname of the current directory on the server. | python.library.ftplib#ftplib.FTP.pwd |
FTP.quit()
Send a QUIT command to the server and close the connection. This is the “polite” way to close a connection, but it may raise an exception if the server responds with an error to the QUIT command. This implies a call to the close() method which renders the FTP instance useless for subsequent calls (see below). | python.library.ftplib#ftplib.FTP.quit |
FTP.rename(fromname, toname)
Rename file fromname on the server to toname. | python.library.ftplib#ftplib.FTP.rename |
FTP.retrbinary(cmd, callback, blocksize=8192, rest=None)
Retrieve a file in binary transfer mode. cmd should be an appropriate RETR command: 'RETR filename'. The callback function is called for each block of data received, with a single bytes argument giving the data block. The optional blocksize argument specifies the maximum chunk size to read on the low-level socket object created to do the actual transfer (which will also be the largest size of the data blocks passed to callback). A reasonable default is chosen. rest means the same thing as in the transfercmd() method. | python.library.ftplib#ftplib.FTP.retrbinary |
FTP.retrlines(cmd, callback=None)
Retrieve a file or directory listing in the encoding specified by the encoding parameter at initialization. cmd should be an appropriate RETR command (see retrbinary()) or a command such as LIST or NLST (usually just the string 'LIST'). LIST retrieves a list of files and information about those files. NLST retrieves a list of file names. The callback function is called for each line with a string argument containing the line with the trailing CRLF stripped. The default callback prints the line to sys.stdout. | python.library.ftplib#ftplib.FTP.retrlines |
FTP.rmd(dirname)
Remove the directory named dirname on the server. | python.library.ftplib#ftplib.FTP.rmd |
FTP.sendcmd(cmd)
Send a simple command string to the server and return the response string. Raises an auditing event ftplib.sendcmd with arguments self, cmd. | python.library.ftplib#ftplib.FTP.sendcmd |
FTP.set_debuglevel(level)
Set the instance’s debugging level. This controls the amount of debugging output printed. The default, 0, produces no debugging output. A value of 1 produces a moderate amount of debugging output, generally a single line per request. A value of 2 or higher produces the maximum amount of debugging output, logging each line sent and received on the control connection. | python.library.ftplib#ftplib.FTP.set_debuglevel |
FTP.set_pasv(val)
Enable “passive” mode if val is true, otherwise disable passive mode. Passive mode is on by default. | python.library.ftplib#ftplib.FTP.set_pasv |
FTP.size(filename)
Request the size of the file named filename on the server. On success, the size of the file is returned as an integer, otherwise None is returned. Note that the SIZE command is not standardized, but is supported by many common server implementations. | python.library.ftplib#ftplib.FTP.size |
FTP.storbinary(cmd, fp, blocksize=8192, callback=None, rest=None)
Store a file in binary transfer mode. cmd should be an appropriate STOR command: "STOR filename". fp is a file object (opened in binary mode) which is read until EOF using its read() method in blocks of size blocksize to provide the data to be stored. The blocksize argument defaults to 8192. callback is an optional single parameter callable that is called on each block of data after it is sent. rest means the same thing as in the transfercmd() method. Changed in version 3.2: rest parameter added. | python.library.ftplib#ftplib.FTP.storbinary |
FTP.storlines(cmd, fp, callback=None)
Store a file in line mode. cmd should be an appropriate STOR command (see storbinary()). Lines are read until EOF from the file object fp (opened in binary mode) using its readline() method to provide the data to be stored. callback is an optional single parameter callable that is called on each line after it is sent. | python.library.ftplib#ftplib.FTP.storlines |
FTP.transfercmd(cmd, rest=None)
Initiate a transfer over the data connection. If the transfer is active, send an EPRT or PORT command and the transfer command specified by cmd, and accept the connection. If the server is passive, send an EPSV or PASV command, connect to it, and start the transfer command. Either way, return the socket for the connection. If optional rest is given, a REST command is sent to the server, passing rest as an argument. rest is usually a byte offset into the requested file, telling the server to restart sending the file’s bytes at the requested offset, skipping over the initial bytes. Note however that the transfercmd() method converts rest to a string with the encoding parameter specified at initialization, but no check is performed on the string’s contents. If the server does not recognize the REST command, an error_reply exception will be raised. If this happens, simply call transfercmd() without a rest argument. | python.library.ftplib#ftplib.FTP.transfercmd |
FTP.voidcmd(cmd)
Send a simple command string to the server and handle the response. Return nothing if a response code corresponding to success (codes in the range 200–299) is received. Raise error_reply otherwise. Raises an auditing event ftplib.sendcmd with arguments self, cmd. | python.library.ftplib#ftplib.FTP.voidcmd |
class ftplib.FTP_TLS(host='', user='', passwd='', acct='', keyfile=None, certfile=None, context=None, timeout=None, source_address=None, *, encoding='utf-8')
A FTP subclass which adds TLS support to FTP as described in RFC 4217. Connect as usual to port 21 implicitly securing the FTP control connection before authenticating. Securing the data connection requires the user to explicitly ask for it by calling the prot_p() method. context is a ssl.SSLContext object which allows bundling SSL configuration options, certificates and private keys into a single (potentially long-lived) structure. Please read Security considerations for best practices. keyfile and certfile are a legacy alternative to context – they can point to PEM-formatted private key and certificate chain files (respectively) for the SSL connection. New in version 3.2. Changed in version 3.3: source_address parameter was added. Changed in version 3.4: The class now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI). Deprecated since version 3.6: keyfile and certfile are deprecated in favor of context. Please use ssl.SSLContext.load_cert_chain() instead, or let ssl.create_default_context() select the system’s trusted CA certificates for you. Changed in version 3.9: If the timeout parameter is set to be zero, it will raise a ValueError to prevent the creation of a non-blocking socket. The encoding parameter was added, and the default was changed from Latin-1 to UTF-8 to follow RFC 2640. Here’s a sample session using the FTP_TLS class: >>> ftps = FTP_TLS('ftp.pureftpd.org')
>>> ftps.login()
'230 Anonymous user logged in'
>>> ftps.prot_p()
'200 Data protection level set to "private"'
>>> ftps.nlst()
['6jack', 'OpenBSD', 'antilink', 'blogbench', 'bsdcam', 'clockspeed', 'djbdns-jedi', 'docs', 'eaccelerator-jedi', 'favicon.ico', 'francotone', 'fugu', 'ignore', 'libpuzzle', 'metalog', 'minidentd', 'misc', 'mysql-udf-global-user-variables', 'php-jenkins-hash', 'php-skein-hash', 'php-webdav', 'phpaudit', 'phpbench', 'pincaster', 'ping', 'posto', 'pub', 'public', 'public_keys', 'pure-ftpd', 'qscan', 'qtc', 'sharedance', 'skycache', 'sound', 'tmp', 'ucarp'] | python.library.ftplib#ftplib.FTP_TLS |
FTP_TLS.auth()
Set up a secure control connection by using TLS or SSL, depending on what is specified in the ssl_version attribute. Changed in version 3.4: The method now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI). | python.library.ftplib#ftplib.FTP_TLS.auth |
FTP_TLS.ccc()
Revert control channel back to plaintext. This can be useful to take advantage of firewalls that know how to handle NAT with non-secure FTP without opening fixed ports. New in version 3.3. | python.library.ftplib#ftplib.FTP_TLS.ccc |
FTP_TLS.prot_c()
Set up clear text data connection. | python.library.ftplib#ftplib.FTP_TLS.prot_c |
FTP_TLS.prot_p()
Set up secure data connection. | python.library.ftplib#ftplib.FTP_TLS.prot_p |
FTP_TLS.ssl_version
The SSL version to use (defaults to ssl.PROTOCOL_SSLv23). | python.library.ftplib#ftplib.FTP_TLS.ssl_version |
Built-in Functions The Python interpreter has a number of functions and types built into it that are always available. They are listed here in alphabetical order.
Built-in Functions
abs() delattr() hash() memoryview() set()
all() dict() help() min() setattr()
any() dir() hex() next() slice()
ascii() divmod() id() object() sorted()
bin() enumerate() input() oct() staticmethod()
bool() eval() int() open() str()
breakpoint() exec() isinstance() ord() sum()
bytearray() filter() issubclass() pow() super()
bytes() float() iter() print() tuple()
callable() format() len() property() type()
chr() frozenset() list() range() vars()
classmethod() getattr() locals() repr() zip()
compile() globals() map() reversed() __import__()
complex() hasattr() max() round()
abs(x)
Return the absolute value of a number. The argument may be an integer, a floating point number, or an object implementing __abs__(). If the argument is a complex number, its magnitude is returned.
all(iterable)
Return True if all elements of the iterable are true (or if the iterable is empty). Equivalent to: def all(iterable):
for element in iterable:
if not element:
return False
return True
any(iterable)
Return True if any element of the iterable is true. If the iterable is empty, return False. Equivalent to: def any(iterable):
for element in iterable:
if element:
return True
return False
ascii(object)
As repr(), return a string containing a printable representation of an object, but escape the non-ASCII characters in the string returned by repr() using \x, \u or \U escapes. This generates a string similar to that returned by repr() in Python 2.
bin(x)
Convert an integer number to a binary string prefixed with “0b”. The result is a valid Python expression. If x is not a Python int object, it has to define an __index__() method that returns an integer. Some examples: >>> bin(3)
'0b11'
>>> bin(-10)
'-0b1010'
If prefix “0b” is desired or not, you can use either of the following ways. >>> format(14, '#b'), format(14, 'b')
('0b1110', '1110')
>>> f'{14:#b}', f'{14:b}'
('0b1110', '1110')
See also format() for more information.
class bool([x])
Return a Boolean value, i.e. one of True or False. x is converted using the standard truth testing procedure. If x is false or omitted, this returns False; otherwise it returns True. The bool class is a subclass of int (see Numeric Types — int, float, complex). It cannot be subclassed further. Its only instances are False and True (see Boolean Values). Changed in version 3.7: x is now a positional-only parameter.
breakpoint(*args, **kws)
This function drops you into the debugger at the call site. Specifically, it calls sys.breakpointhook(), passing args and kws straight through. By default, sys.breakpointhook() calls pdb.set_trace() expecting no arguments. In this case, it is purely a convenience function so you don’t have to explicitly import pdb or type as much code to enter the debugger. However, sys.breakpointhook() can be set to some other function and breakpoint() will automatically call that, allowing you to drop into the debugger of choice. Raises an auditing event builtins.breakpoint with argument breakpointhook. New in version 3.7.
class bytearray([source[, encoding[, errors]]])
Return a new array of bytes. The bytearray class is a mutable sequence of integers in the range 0 <= x < 256. It has most of the usual methods of mutable sequences, described in Mutable Sequence Types, as well as most methods that the bytes type has, see Bytes and Bytearray Operations. The optional source parameter can be used to initialize the array in a few different ways: If it is a string, you must also give the encoding (and optionally, errors) parameters; bytearray() then converts the string to bytes using str.encode(). If it is an integer, the array will have that size and will be initialized with null bytes. If it is an object conforming to the buffer interface, a read-only buffer of the object will be used to initialize the bytes array. If it is an iterable, it must be an iterable of integers in the range 0 <= x < 256, which are used as the initial contents of the array. Without an argument, an array of size 0 is created. See also Binary Sequence Types — bytes, bytearray, memoryview and Bytearray Objects.
class bytes([source[, encoding[, errors]]])
Return a new “bytes” object, which is an immutable sequence of integers in the range 0 <= x < 256. bytes is an immutable version of bytearray – it has the same non-mutating methods and the same indexing and slicing behavior. Accordingly, constructor arguments are interpreted as for bytearray(). Bytes objects can also be created with literals, see String and Bytes literals. See also Binary Sequence Types — bytes, bytearray, memoryview, Bytes Objects, and Bytes and Bytearray Operations.
callable(object)
Return True if the object argument appears callable, False if not. If this returns True, it is still possible that a call fails, but if it is False, calling object will never succeed. Note that classes are callable (calling a class returns a new instance); instances are callable if their class has a __call__() method. New in version 3.2: This function was first removed in Python 3.0 and then brought back in Python 3.2.
chr(i)
Return the string representing a character whose Unicode code point is the integer i. For example, chr(97) returns the string 'a', while chr(8364) returns the string '€'. This is the inverse of ord(). The valid range for the argument is from 0 through 1,114,111 (0x10FFFF in base 16). ValueError will be raised if i is outside that range.
@classmethod
Transform a method into a class method. A class method receives the class as implicit first argument, just like an instance method receives the instance. To declare a class method, use this idiom: class C:
@classmethod
def f(cls, arg1, arg2, ...): ...
The @classmethod form is a function decorator – see Function definitions for details. A class method can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class. If a class method is called for a derived class, the derived class object is passed as the implied first argument. Class methods are different than C++ or Java static methods. If you want those, see staticmethod() in this section. For more information on class methods, see The standard type hierarchy. Changed in version 3.9: Class methods can now wrap other descriptors such as property().
compile(source, filename, mode, flags=0, dont_inherit=False, optimize=-1)
Compile the source into a code or AST object. Code objects can be executed by exec() or eval(). source can either be a normal string, a byte string, or an AST object. Refer to the ast module documentation for information on how to work with AST objects. The filename argument should give the file from which the code was read; pass some recognizable value if it wasn’t read from a file ('<string>' is commonly used). The mode argument specifies what kind of code must be compiled; it can be 'exec' if source consists of a sequence of statements, 'eval' if it consists of a single expression, or 'single' if it consists of a single interactive statement (in the latter case, expression statements that evaluate to something other than None will be printed). The optional arguments flags and dont_inherit control which compiler options should be activated and which future features should be allowed. If neither is present (or both are zero) the code is compiled with the same flags that affect the code that is calling compile(). If the flags argument is given and dont_inherit is not (or is zero) then the compiler options and the future statements specified by the flags argument are used in addition to those that would be used anyway. If dont_inherit is a non-zero integer then the flags argument is it – the flags (future features and compiler options) in the surrounding code are ignored. Compiler options and future statements are specified by bits which can be bitwise ORed together to specify multiple options. The bitfield required to specify a given future feature can be found as the compiler_flag attribute on the _Feature instance in the __future__ module. Compiler flags can be found in ast module, with PyCF_ prefix. The argument optimize specifies the optimization level of the compiler; the default value of -1 selects the optimization level of the interpreter as given by -O options. Explicit levels are 0 (no optimization; __debug__ is true), 1 (asserts are removed, __debug__ is false) or 2 (docstrings are removed too). This function raises SyntaxError if the compiled source is invalid, and ValueError if the source contains null bytes. If you want to parse Python code into its AST representation, see ast.parse().
Raises an auditing event compile with arguments source and filename. This event may also be raised by implicit compilation. Note When compiling a string with multi-line code in 'single' or 'eval' mode, input must be terminated by at least one newline character. This is to facilitate detection of incomplete and complete statements in the code module. Warning It is possible to crash the Python interpreter with a sufficiently large/complex string when compiling to an AST object due to stack depth limitations in Python’s AST compiler. Changed in version 3.2: Allowed use of Windows and Mac newlines. Also input in 'exec' mode does not have to end in a newline anymore. Added the optimize parameter. Changed in version 3.5: Previously, TypeError was raised when null bytes were encountered in source. New in version 3.8: ast.PyCF_ALLOW_TOP_LEVEL_AWAIT can now be passed in flags to enable support for top-level await, async for, and async with.
class complex([real[, imag]])
Return a complex number with the value real + imag*1j or convert a string or number to a complex number. If the first parameter is a string, it will be interpreted as a complex number and the function must be called without a second parameter. The second parameter can never be a string. Each argument may be any numeric type (including complex). If imag is omitted, it defaults to zero and the constructor serves as a numeric conversion like int and float. If both arguments are omitted, returns 0j. For a general Python object x, complex(x) delegates to x.__complex__(). If __complex__() is not defined then it falls back to __float__(). If __float__() is not defined then it falls back to __index__(). Note When converting from a string, the string must not contain whitespace around the central + or - operator. For example, complex('1+2j') is fine, but complex('1 + 2j') raises ValueError. The complex type is described in Numeric Types — int, float, complex. Changed in version 3.6: Grouping digits with underscores as in code literals is allowed. Changed in version 3.8: Falls back to __index__() if __complex__() and __float__() are not defined.
delattr(object, name)
This is a relative of setattr(). The arguments are an object and a string. The string must be the name of one of the object’s attributes. The function deletes the named attribute, provided the object allows it. For example, delattr(x, 'foobar') is equivalent to del x.foobar.
class dict(**kwarg)
class dict(mapping, **kwarg)
class dict(iterable, **kwarg)
Create a new dictionary. The dict object is the dictionary class. See dict and Mapping Types — dict for documentation about this class. For other containers see the built-in list, set, and tuple classes, as well as the collections module.
dir([object])
Without arguments, return the list of names in the current local scope. With an argument, attempt to return a list of valid attributes for that object. If the object has a method named __dir__(), this method will be called and must return the list of attributes. This allows objects that implement a custom __getattr__() or __getattribute__() function to customize the way dir() reports their attributes. If the object does not provide __dir__(), the function tries its best to gather information from the object’s __dict__ attribute, if defined, and from its type object. The resulting list is not necessarily complete, and may be inaccurate when the object has a custom __getattr__(). The default dir() mechanism behaves differently with different types of objects, as it attempts to produce the most relevant, rather than complete, information: If the object is a module object, the list contains the names of the module’s attributes. If the object is a type or class object, the list contains the names of its attributes, and recursively of the attributes of its bases. Otherwise, the list contains the object’s attributes’ names, the names of its class’s attributes, and recursively of the attributes of its class’s base classes. The resulting list is sorted alphabetically. For example: >>> import struct
>>> dir() # show the names in the module namespace
['__builtins__', '__name__', 'struct']
>>> dir(struct) # show the names in the struct module
['Struct', '__all__', '__builtins__', '__cached__', '__doc__', '__file__',
'__initializing__', '__loader__', '__name__', '__package__',
'_clearcache', 'calcsize', 'error', 'pack', 'pack_into',
'unpack', 'unpack_from']
>>> class Shape:
... def __dir__(self):
... return ['area', 'perimeter', 'location']
>>> s = Shape()
>>> dir(s)
['area', 'location', 'perimeter']
Note Because dir() is supplied primarily as a convenience for use at an interactive prompt, it tries to supply an interesting set of names more than it tries to supply a rigorously or consistently defined set of names, and its detailed behavior may change across releases. For example, metaclass attributes are not in the result list when the argument is a class.
divmod(a, b)
Take two (non complex) numbers as arguments and return a pair of numbers consisting of their quotient and remainder when using integer division. With mixed operand types, the rules for binary arithmetic operators apply. For integers, the result is the same as (a // b, a % b). For floating point numbers the result is (q, a % b), where q is usually math.floor(a /
b) but may be 1 less than that. In any case q * b + a % b is very close to a, if a % b is non-zero it has the same sign as b, and 0
<= abs(a % b) < abs(b).
enumerate(iterable, start=0)
Return an enumerate object. iterable must be a sequence, an iterator, or some other object which supports iteration. The __next__() method of the iterator returned by enumerate() returns a tuple containing a count (from start which defaults to 0) and the values obtained from iterating over iterable. >>> seasons = ['Spring', 'Summer', 'Fall', 'Winter']
>>> list(enumerate(seasons))
[(0, 'Spring'), (1, 'Summer'), (2, 'Fall'), (3, 'Winter')]
>>> list(enumerate(seasons, start=1))
[(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]
Equivalent to: def enumerate(sequence, start=0):
n = start
for elem in sequence:
yield n, elem
n += 1
eval(expression[, globals[, locals]])
The arguments are a string and optional globals and locals. If provided, globals must be a dictionary. If provided, locals can be any mapping object. The expression argument is parsed and evaluated as a Python expression (technically speaking, a condition list) using the globals and locals dictionaries as global and local namespace. If the globals dictionary is present and does not contain a value for the key __builtins__, a reference to the dictionary of the built-in module builtins is inserted under that key before expression is parsed. This means that expression normally has full access to the standard builtins module and restricted environments are propagated. If the locals dictionary is omitted it defaults to the globals dictionary. If both dictionaries are omitted, the expression is executed with the globals and locals in the environment where eval() is called. Note, eval() does not have access to the nested scopes (non-locals) in the enclosing environment. The return value is the result of the evaluated expression. Syntax errors are reported as exceptions. Example: >>> x = 1
>>> eval('x+1')
2
This function can also be used to execute arbitrary code objects (such as those created by compile()). In this case pass a code object instead of a string. If the code object has been compiled with 'exec' as the mode argument, eval()’s return value will be None. Hints: dynamic execution of statements is supported by the exec() function. The globals() and locals() functions returns the current global and local dictionary, respectively, which may be useful to pass around for use by eval() or exec(). See ast.literal_eval() for a function that can safely evaluate strings with expressions containing only literals.
Raises an auditing event exec with the code object as the argument. Code compilation events may also be raised.
exec(object[, globals[, locals]])
This function supports dynamic execution of Python code. object must be either a string or a code object. If it is a string, the string is parsed as a suite of Python statements which is then executed (unless a syntax error occurs). 1 If it is a code object, it is simply executed. In all cases, the code that’s executed is expected to be valid as file input (see the section “File input” in the Reference Manual). Be aware that the nonlocal, yield, and return statements may not be used outside of function definitions even within the context of code passed to the exec() function. The return value is None. In all cases, if the optional parts are omitted, the code is executed in the current scope. If only globals is provided, it must be a dictionary (and not a subclass of dictionary), which will be used for both the global and the local variables. If globals and locals are given, they are used for the global and local variables, respectively. If provided, locals can be any mapping object. Remember that at module level, globals and locals are the same dictionary. If exec gets two separate objects as globals and locals, the code will be executed as if it were embedded in a class definition. If the globals dictionary does not contain a value for the key __builtins__, a reference to the dictionary of the built-in module builtins is inserted under that key. That way you can control what builtins are available to the executed code by inserting your own __builtins__ dictionary into globals before passing it to exec().
Raises an auditing event exec with the code object as the argument. Code compilation events may also be raised. Note The built-in functions globals() and locals() return the current global and local dictionary, respectively, which may be useful to pass around for use as the second and third argument to exec(). Note The default locals act as described for function locals() below: modifications to the default locals dictionary should not be attempted. Pass an explicit locals dictionary if you need to see effects of the code on locals after function exec() returns.
filter(function, iterable)
Construct an iterator from those elements of iterable for which function returns true. iterable may be either a sequence, a container which supports iteration, or an iterator. If function is None, the identity function is assumed, that is, all elements of iterable that are false are removed. Note that filter(function, iterable) is equivalent to the generator expression (item for item in iterable if function(item)) if function is not None and (item for item in iterable if item) if function is None. See itertools.filterfalse() for the complementary function that returns elements of iterable for which function returns false.
class float([x])
Return a floating point number constructed from a number or string x. If the argument is a string, it should contain a decimal number, optionally preceded by a sign, and optionally embedded in whitespace. The optional sign may be '+' or '-'; a '+' sign has no effect on the value produced. The argument may also be a string representing a NaN (not-a-number), or a positive or negative infinity. More precisely, the input must conform to the following grammar after leading and trailing whitespace characters are removed:
sign ::= "+" | "-"
infinity ::= "Infinity" | "inf"
nan ::= "nan"
numeric_value ::= floatnumber | infinity | nan
numeric_string ::= [sign] numeric_value
Here floatnumber is the form of a Python floating-point literal, described in Floating point literals. Case is not significant, so, for example, “inf”, “Inf”, “INFINITY” and “iNfINity” are all acceptable spellings for positive infinity. Otherwise, if the argument is an integer or a floating point number, a floating point number with the same value (within Python’s floating point precision) is returned. If the argument is outside the range of a Python float, an OverflowError will be raised. For a general Python object x, float(x) delegates to x.__float__(). If __float__() is not defined then it falls back to __index__(). If no argument is given, 0.0 is returned. Examples: >>> float('+1.23')
1.23
>>> float(' -12345\n')
-12345.0
>>> float('1e-003')
0.001
>>> float('+1E6')
1000000.0
>>> float('-Infinity')
-inf
The float type is described in Numeric Types — int, float, complex. Changed in version 3.6: Grouping digits with underscores as in code literals is allowed. Changed in version 3.7: x is now a positional-only parameter. Changed in version 3.8: Falls back to __index__() if __float__() is not defined.
format(value[, format_spec])
Convert a value to a “formatted” representation, as controlled by format_spec. The interpretation of format_spec will depend on the type of the value argument, however there is a standard formatting syntax that is used by most built-in types: Format Specification Mini-Language. The default format_spec is an empty string which usually gives the same effect as calling str(value). A call to format(value, format_spec) is translated to type(value).__format__(value, format_spec) which bypasses the instance dictionary when searching for the value’s __format__() method. A TypeError exception is raised if the method search reaches object and the format_spec is non-empty, or if either the format_spec or the return value are not strings. Changed in version 3.4: object().__format__(format_spec) raises TypeError if format_spec is not an empty string.
class frozenset([iterable])
Return a new frozenset object, optionally with elements taken from iterable. frozenset is a built-in class. See frozenset and Set Types — set, frozenset for documentation about this class. For other containers see the built-in set, list, tuple, and dict classes, as well as the collections module.
getattr(object, name[, default])
Return the value of the named attribute of object. name must be a string. If the string is the name of one of the object’s attributes, the result is the value of that attribute. For example, getattr(x, 'foobar') is equivalent to x.foobar. If the named attribute does not exist, default is returned if provided, otherwise AttributeError is raised.
globals()
Return a dictionary representing the current global symbol table. This is always the dictionary of the current module (inside a function or method, this is the module where it is defined, not the module from which it is called).
hasattr(object, name)
The arguments are an object and a string. The result is True if the string is the name of one of the object’s attributes, False if not. (This is implemented by calling getattr(object, name) and seeing whether it raises an AttributeError or not.)
hash(object)
Return the hash value of the object (if it has one). Hash values are integers. They are used to quickly compare dictionary keys during a dictionary lookup. Numeric values that compare equal have the same hash value (even if they are of different types, as is the case for 1 and 1.0). Note For objects with custom __hash__() methods, note that hash() truncates the return value based on the bit width of the host machine. See __hash__() for details.
help([object])
Invoke the built-in help system. (This function is intended for interactive use.) If no argument is given, the interactive help system starts on the interpreter console. If the argument is a string, then the string is looked up as the name of a module, function, class, method, keyword, or documentation topic, and a help page is printed on the console. If the argument is any other kind of object, a help page on the object is generated. Note that if a slash(/) appears in the parameter list of a function, when invoking help(), it means that the parameters prior to the slash are positional-only. For more info, see the FAQ entry on positional-only parameters. This function is added to the built-in namespace by the site module. Changed in version 3.4: Changes to pydoc and inspect mean that the reported signatures for callables are now more comprehensive and consistent.
hex(x)
Convert an integer number to a lowercase hexadecimal string prefixed with “0x”. If x is not a Python int object, it has to define an __index__() method that returns an integer. Some examples: >>> hex(255)
'0xff'
>>> hex(-42)
'-0x2a'
If you want to convert an integer number to an uppercase or lower hexadecimal string with prefix or not, you can use either of the following ways: >>> '%#x' % 255, '%x' % 255, '%X' % 255
('0xff', 'ff', 'FF')
>>> format(255, '#x'), format(255, 'x'), format(255, 'X')
('0xff', 'ff', 'FF')
>>> f'{255:#x}', f'{255:x}', f'{255:X}'
('0xff', 'ff', 'FF')
See also format() for more information. See also int() for converting a hexadecimal string to an integer using a base of 16. Note To obtain a hexadecimal string representation for a float, use the float.hex() method.
id(object)
Return the “identity” of an object. This is an integer which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id() value. CPython implementation detail: This is the address of the object in memory. Raises an auditing event builtins.id with argument id.
input([prompt])
If the prompt argument is present, it is written to standard output without a trailing newline. The function then reads a line from input, converts it to a string (stripping a trailing newline), and returns that. When EOF is read, EOFError is raised. Example: >>> s = input('--> ')
--> Monty Python's Flying Circus
>>> s
"Monty Python's Flying Circus"
If the readline module was loaded, then input() will use it to provide elaborate line editing and history features.
Raises an auditing event builtins.input with argument prompt before reading input
Raises an auditing event builtins.input/result with the result after successfully reading input.
class int([x])
class int(x, base=10)
Return an integer object constructed from a number or string x, or return 0 if no arguments are given. If x defines __int__(), int(x) returns x.__int__(). If x defines __index__(), it returns x.__index__(). If x defines __trunc__(), it returns x.__trunc__(). For floating point numbers, this truncates towards zero. If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in radix base. Optionally, the literal can be preceded by + or - (with no space in between) and surrounded by whitespace. A base-n literal consists of the digits 0 to n-1, with a to z (or A to Z) having values 10 to 35. The default base is 10. The allowed values are 0 and 2–36. Base-2, -8, and -16 literals can be optionally prefixed with 0b/0B, 0o/0O, or 0x/0X, as with integer literals in code. Base 0 means to interpret exactly as a code literal, so that the actual base is 2, 8, 10, or 16, and so that int('010', 0) is not legal, while int('010') is, as well as int('010', 8). The integer type is described in Numeric Types — int, float, complex. Changed in version 3.4: If base is not an instance of int and the base object has a base.__index__ method, that method is called to obtain an integer for the base. Previous versions used base.__int__ instead of base.__index__. Changed in version 3.6: Grouping digits with underscores as in code literals is allowed. Changed in version 3.7: x is now a positional-only parameter. Changed in version 3.8: Falls back to __index__() if __int__() is not defined.
isinstance(object, classinfo)
Return True if the object argument is an instance of the classinfo argument, or of a (direct, indirect or virtual) subclass thereof. If object is not an object of the given type, the function always returns False. If classinfo is a tuple of type objects (or recursively, other such tuples), return True if object is an instance of any of the types. If classinfo is not a type or tuple of types and such tuples, a TypeError exception is raised.
issubclass(class, classinfo)
Return True if class is a subclass (direct, indirect or virtual) of classinfo. A class is considered a subclass of itself. classinfo may be a tuple of class objects, in which case every entry in classinfo will be checked. In any other case, a TypeError exception is raised.
iter(object[, sentinel])
Return an iterator object. The first argument is interpreted very differently depending on the presence of the second argument. Without a second argument, object must be a collection object which supports the iteration protocol (the __iter__() method), or it must support the sequence protocol (the __getitem__() method with integer arguments starting at 0). If it does not support either of those protocols, TypeError is raised. If the second argument, sentinel, is given, then object must be a callable object. The iterator created in this case will call object with no arguments for each call to its __next__() method; if the value returned is equal to sentinel, StopIteration will be raised, otherwise the value will be returned. See also Iterator Types. One useful application of the second form of iter() is to build a block-reader. For example, reading fixed-width blocks from a binary database file until the end of file is reached: from functools import partial
with open('mydata.db', 'rb') as f:
for block in iter(partial(f.read, 64), b''):
process_block(block)
len(s)
Return the length (the number of items) of an object. The argument may be a sequence (such as a string, bytes, tuple, list, or range) or a collection (such as a dictionary, set, or frozen set). CPython implementation detail: len raises OverflowError on lengths larger than sys.maxsize, such as range(2 ** 100).
class list([iterable])
Rather than being a function, list is actually a mutable sequence type, as documented in Lists and Sequence Types — list, tuple, range.
locals()
Update and return a dictionary representing the current local symbol table. Free variables are returned by locals() when it is called in function blocks, but not in class blocks. Note that at the module level, locals() and globals() are the same dictionary. Note The contents of this dictionary should not be modified; changes may not affect the values of local and free variables used by the interpreter.
map(function, iterable, ...)
Return an iterator that applies function to every item of iterable, yielding the results. If additional iterable arguments are passed, function must take that many arguments and is applied to the items from all iterables in parallel. With multiple iterables, the iterator stops when the shortest iterable is exhausted. For cases where the function inputs are already arranged into argument tuples, see itertools.starmap().
max(iterable, *[, key, default])
max(arg1, arg2, *args[, key])
Return the largest item in an iterable or the largest of two or more arguments. If one positional argument is provided, it should be an iterable. The largest item in the iterable is returned. If two or more positional arguments are provided, the largest of the positional arguments is returned. There are two optional keyword-only arguments. The key argument specifies a one-argument ordering function like that used for list.sort(). The default argument specifies an object to return if the provided iterable is empty. If the iterable is empty and default is not provided, a ValueError is raised. If multiple items are maximal, the function returns the first one encountered. This is consistent with other sort-stability preserving tools such as sorted(iterable, key=keyfunc, reverse=True)[0] and heapq.nlargest(1, iterable, key=keyfunc). New in version 3.4: The default keyword-only argument. Changed in version 3.8: The key can be None.
class memoryview(obj)
Return a “memory view” object created from the given argument. See Memory Views for more information.
min(iterable, *[, key, default])
min(arg1, arg2, *args[, key])
Return the smallest item in an iterable or the smallest of two or more arguments. If one positional argument is provided, it should be an iterable. The smallest item in the iterable is returned. If two or more positional arguments are provided, the smallest of the positional arguments is returned. There are two optional keyword-only arguments. The key argument specifies a one-argument ordering function like that used for list.sort(). The default argument specifies an object to return if the provided iterable is empty. If the iterable is empty and default is not provided, a ValueError is raised. If multiple items are minimal, the function returns the first one encountered. This is consistent with other sort-stability preserving tools such as sorted(iterable, key=keyfunc)[0] and heapq.nsmallest(1,
iterable, key=keyfunc). New in version 3.4: The default keyword-only argument. Changed in version 3.8: The key can be None.
next(iterator[, default])
Retrieve the next item from the iterator by calling its __next__() method. If default is given, it is returned if the iterator is exhausted, otherwise StopIteration is raised.
class object
Return a new featureless object. object is a base for all classes. It has the methods that are common to all instances of Python classes. This function does not accept any arguments. Note object does not have a __dict__, so you can’t assign arbitrary attributes to an instance of the object class.
oct(x)
Convert an integer number to an octal string prefixed with “0o”. The result is a valid Python expression. If x is not a Python int object, it has to define an __index__() method that returns an integer. For example: >>> oct(8)
'0o10'
>>> oct(-56)
'-0o70'
If you want to convert an integer number to octal string either with prefix “0o” or not, you can use either of the following ways. >>> '%#o' % 10, '%o' % 10
('0o12', '12')
>>> format(10, '#o'), format(10, 'o')
('0o12', '12')
>>> f'{10:#o}', f'{10:o}'
('0o12', '12')
See also format() for more information.
open(file, mode='r', buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None)
Open file and return a corresponding file object. If the file cannot be opened, an OSError is raised. See Reading and Writing Files for more examples of how to use this function. file is a path-like object giving the pathname (absolute or relative to the current working directory) of the file to be opened or an integer file descriptor of the file to be wrapped. (If a file descriptor is given, it is closed when the returned I/O object is closed, unless closefd is set to False.) mode is an optional string that specifies the mode in which the file is opened. It defaults to 'r' which means open for reading in text mode. Other common values are 'w' for writing (truncating the file if it already exists), 'x' for exclusive creation and 'a' for appending (which on some Unix systems, means that all writes append to the end of the file regardless of the current seek position). In text mode, if encoding is not specified the encoding used is platform dependent: locale.getpreferredencoding(False) is called to get the current locale encoding. (For reading and writing raw bytes use binary mode and leave encoding unspecified.) The available modes are:
Character Meaning
'r' open for reading (default)
'w' open for writing, truncating the file first
'x' open for exclusive creation, failing if the file already exists
'a' open for writing, appending to the end of the file if it exists
'b' binary mode
't' text mode (default)
'+' open for updating (reading and writing) The default mode is 'r' (open for reading text, synonym of 'rt'). Modes 'w+' and 'w+b' open and truncate the file. Modes 'r+' and 'r+b' open the file with no truncation. As mentioned in the Overview, Python distinguishes between binary and text I/O. Files opened in binary mode (including 'b' in the mode argument) return contents as bytes objects without any decoding. In text mode (the default, or when 't' is included in the mode argument), the contents of the file are returned as str, the bytes having been first decoded using a platform-dependent encoding or using the specified encoding if given. There is an additional mode character permitted, 'U', which no longer has any effect, and is considered deprecated. It previously enabled universal newlines in text mode, which became the default behaviour in Python 3.0. Refer to the documentation of the newline parameter for further details. Note Python doesn’t depend on the underlying operating system’s notion of text files; all the processing is done by Python itself, and is therefore platform-independent. buffering is an optional integer used to set the buffering policy. Pass 0 to switch buffering off (only allowed in binary mode), 1 to select line buffering (only usable in text mode), and an integer > 1 to indicate the size in bytes of a fixed-size chunk buffer. When no buffering argument is given, the default buffering policy works as follows: Binary files are buffered in fixed-size chunks; the size of the buffer is chosen using a heuristic trying to determine the underlying device’s “block size” and falling back on io.DEFAULT_BUFFER_SIZE. On many systems, the buffer will typically be 4096 or 8192 bytes long. “Interactive” text files (files for which isatty() returns True) use line buffering. Other text files use the policy described above for binary files. encoding is the name of the encoding used to decode or encode the file. This should only be used in text mode. The default encoding is platform dependent (whatever locale.getpreferredencoding() returns), but any text encoding supported by Python can be used. See the codecs module for the list of supported encodings. errors is an optional string that specifies how encoding and decoding errors are to be handled—this cannot be used in binary mode. A variety of standard error handlers are available (listed under Error Handlers), though any error handling name that has been registered with codecs.register_error() is also valid. The standard names include:
'strict' to raise a ValueError exception if there is an encoding error. The default value of None has the same effect.
'ignore' ignores errors. Note that ignoring encoding errors can lead to data loss.
'replace' causes a replacement marker (such as '?') to be inserted where there is malformed data.
'surrogateescape' will represent any incorrect bytes as code points in the Unicode Private Use Area ranging from U+DC80 to U+DCFF. These private code points will then be turned back into the same bytes when the surrogateescape error handler is used when writing data. This is useful for processing files in an unknown encoding.
'xmlcharrefreplace' is only supported when writing to a file. Characters not supported by the encoding are replaced with the appropriate XML character reference &#nnn;.
'backslashreplace' replaces malformed data by Python’s backslashed escape sequences.
'namereplace' (also only supported when writing) replaces unsupported characters with \N{...} escape sequences. newline controls how universal newlines mode works (it only applies to text mode). It can be None, '', '\n', '\r', and '\r\n'. It works as follows: When reading input from the stream, if newline is None, universal newlines mode is enabled. Lines in the input can end in '\n', '\r', or '\r\n', and these are translated into '\n' before being returned to the caller. If it is '', universal newlines mode is enabled, but line endings are returned to the caller untranslated. If it has any of the other legal values, input lines are only terminated by the given string, and the line ending is returned to the caller untranslated. When writing output to the stream, if newline is None, any '\n' characters written are translated to the system default line separator, os.linesep. If newline is '' or '\n', no translation takes place. If newline is any of the other legal values, any '\n' characters written are translated to the given string. If closefd is False and a file descriptor rather than a filename was given, the underlying file descriptor will be kept open when the file is closed. If a filename is given closefd must be True (the default) otherwise an error will be raised. A custom opener can be used by passing a callable as opener. The underlying file descriptor for the file object is then obtained by calling opener with (file, flags). opener must return an open file descriptor (passing os.open as opener results in functionality similar to passing None). The newly created file is non-inheritable. The following example uses the dir_fd parameter of the os.open() function to open a file relative to a given directory: >>> import os
>>> dir_fd = os.open('somedir', os.O_RDONLY)
>>> def opener(path, flags):
... return os.open(path, flags, dir_fd=dir_fd)
...
>>> with open('spamspam.txt', 'w', opener=opener) as f:
... print('This will be written to somedir/spamspam.txt', file=f)
...
>>> os.close(dir_fd) # don't leak a file descriptor
The type of file object returned by the open() function depends on the mode. When open() is used to open a file in a text mode ('w', 'r', 'wt', 'rt', etc.), it returns a subclass of io.TextIOBase (specifically io.TextIOWrapper). When used to open a file in a binary mode with buffering, the returned class is a subclass of io.BufferedIOBase. The exact class varies: in read binary mode, it returns an io.BufferedReader; in write binary and append binary modes, it returns an io.BufferedWriter, and in read/write mode, it returns an io.BufferedRandom. When buffering is disabled, the raw stream, a subclass of io.RawIOBase, io.FileIO, is returned. See also the file handling modules, such as, fileinput, io (where open() is declared), os, os.path, tempfile, and shutil. Raises an auditing event open with arguments file, mode, flags. The mode and flags arguments may have been modified or inferred from the original call. Changed in version 3.3: The opener parameter was added. The 'x' mode was added.
IOError used to be raised, it is now an alias of OSError.
FileExistsError is now raised if the file opened in exclusive creation mode ('x') already exists. Changed in version 3.4: The file is now non-inheritable. Deprecated since version 3.4, will be removed in version 3.10: The 'U' mode. Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the function now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale). The 'namereplace' error handler was added. Changed in version 3.6: Support added to accept objects implementing os.PathLike. On Windows, opening a console buffer may return a subclass of io.RawIOBase other than io.FileIO.
ord(c)
Given a string representing one Unicode character, return an integer representing the Unicode code point of that character. For example, ord('a') returns the integer 97 and ord('€') (Euro sign) returns 8364. This is the inverse of chr().
pow(base, exp[, mod])
Return base to the power exp; if mod is present, return base to the power exp, modulo mod (computed more efficiently than pow(base, exp) % mod). The two-argument form pow(base, exp) is equivalent to using the power operator: base**exp. The arguments must have numeric types. With mixed operand types, the coercion rules for binary arithmetic operators apply. For int operands, the result has the same type as the operands (after coercion) unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100, but 10**-2 returns 0.01. For int operands base and exp, if mod is present, mod must also be of integer type and mod must be nonzero. If mod is present and exp is negative, base must be relatively prime to mod. In that case, pow(inv_base, -exp, mod) is returned, where inv_base is an inverse to base modulo mod. Here’s an example of computing an inverse for 38 modulo 97: >>> pow(38, -1, mod=97)
23
>>> 23 * 38 % 97 == 1
True
Changed in version 3.8: For int operands, the three-argument form of pow now allows the second argument to be negative, permitting computation of modular inverses. Changed in version 3.8: Allow keyword arguments. Formerly, only positional arguments were supported.
print(*objects, sep=' ', end='\n', file=sys.stdout, flush=False)
Print objects to the text stream file, separated by sep and followed by end. sep, end, file and flush, if present, must be given as keyword arguments. All non-keyword arguments are converted to strings like str() does and written to the stream, separated by sep and followed by end. Both sep and end must be strings; they can also be None, which means to use the default values. If no objects are given, print() will just write end. The file argument must be an object with a write(string) method; if it is not present or None, sys.stdout will be used. Since printed arguments are converted to text strings, print() cannot be used with binary mode file objects. For these, use file.write(...) instead. Whether output is buffered is usually determined by file, but if the flush keyword argument is true, the stream is forcibly flushed. Changed in version 3.3: Added the flush keyword argument.
class property(fget=None, fset=None, fdel=None, doc=None)
Return a property attribute. fget is a function for getting an attribute value. fset is a function for setting an attribute value. fdel is a function for deleting an attribute value. And doc creates a docstring for the attribute. A typical use is to define a managed attribute x: class C:
def __init__(self):
self._x = None
def getx(self):
return self._x
def setx(self, value):
self._x = value
def delx(self):
del self._x
x = property(getx, setx, delx, "I'm the 'x' property.")
If c is an instance of C, c.x will invoke the getter, c.x = value will invoke the setter and del c.x the deleter. If given, doc will be the docstring of the property attribute. Otherwise, the property will copy fget’s docstring (if it exists). This makes it possible to create read-only properties easily using property() as a decorator: class Parrot:
def __init__(self):
self._voltage = 100000
@property
def voltage(self):
"""Get the current voltage."""
return self._voltage
The @property decorator turns the voltage() method into a “getter” for a read-only attribute with the same name, and it sets the docstring for voltage to “Get the current voltage.” A property object has getter, setter, and deleter methods usable as decorators that create a copy of the property with the corresponding accessor function set to the decorated function. This is best explained with an example: class C:
def __init__(self):
self._x = None
@property
def x(self):
"""I'm the 'x' property."""
return self._x
@x.setter
def x(self, value):
self._x = value
@x.deleter
def x(self):
del self._x
This code is exactly equivalent to the first example. Be sure to give the additional functions the same name as the original property (x in this case.) The returned property object also has the attributes fget, fset, and fdel corresponding to the constructor arguments. Changed in version 3.5: The docstrings of property objects are now writeable.
class range(stop)
class range(start, stop[, step])
Rather than being a function, range is actually an immutable sequence type, as documented in Ranges and Sequence Types — list, tuple, range.
repr(object)
Return a string containing a printable representation of an object. For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval(), otherwise the representation is a string enclosed in angle brackets that contains the name of the type of the object together with additional information often including the name and address of the object. A class can control what this function returns for its instances by defining a __repr__() method.
reversed(seq)
Return a reverse iterator. seq must be an object which has a __reversed__() method or supports the sequence protocol (the __len__() method and the __getitem__() method with integer arguments starting at 0).
round(number[, ndigits])
Return number rounded to ndigits precision after the decimal point. If ndigits is omitted or is None, it returns the nearest integer to its input. For the built-in types supporting round(), values are rounded to the closest multiple of 10 to the power minus ndigits; if two multiples are equally close, rounding is done toward the even choice (so, for example, both round(0.5) and round(-0.5) are 0, and round(1.5) is 2). Any integer value is valid for ndigits (positive, zero, or negative). The return value is an integer if ndigits is omitted or None. Otherwise the return value has the same type as number. For a general Python object number, round delegates to number.__round__. Note The behavior of round() for floats can be surprising: for example, round(2.675, 2) gives 2.67 instead of the expected 2.68. This is not a bug: it’s a result of the fact that most decimal fractions can’t be represented exactly as a float. See Floating Point Arithmetic: Issues and Limitations for more information.
class set([iterable])
Return a new set object, optionally with elements taken from iterable. set is a built-in class. See set and Set Types — set, frozenset for documentation about this class. For other containers see the built-in frozenset, list, tuple, and dict classes, as well as the collections module.
setattr(object, name, value)
This is the counterpart of getattr(). The arguments are an object, a string and an arbitrary value. The string may name an existing attribute or a new attribute. The function assigns the value to the attribute, provided the object allows it. For example, setattr(x, 'foobar', 123) is equivalent to x.foobar = 123.
class slice(stop)
class slice(start, stop[, step])
Return a slice object representing the set of indices specified by range(start, stop, step). The start and step arguments default to None. Slice objects have read-only data attributes start, stop and step which merely return the argument values (or their default). They have no other explicit functionality; however they are used by Numerical Python and other third party extensions. Slice objects are also generated when extended indexing syntax is used. For example: a[start:stop:step] or a[start:stop, i]. See itertools.islice() for an alternate version that returns an iterator.
sorted(iterable, *, key=None, reverse=False)
Return a new sorted list from the items in iterable. Has two optional arguments which must be specified as keyword arguments. key specifies a function of one argument that is used to extract a comparison key from each element in iterable (for example, key=str.lower). The default value is None (compare the elements directly). reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed. Use functools.cmp_to_key() to convert an old-style cmp function to a key function. The built-in sorted() function is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade). For sorting examples and a brief sorting tutorial, see Sorting HOW TO.
@staticmethod
Transform a method into a static method. A static method does not receive an implicit first argument. To declare a static method, use this idiom: class C:
@staticmethod
def f(arg1, arg2, ...): ...
The @staticmethod form is a function decorator – see Function definitions for details. A static method can be called either on the class (such as C.f()) or on an instance (such as C().f()). Static methods in Python are similar to those found in Java or C++. Also see classmethod() for a variant that is useful for creating alternate class constructors. Like all decorators, it is also possible to call staticmethod as a regular function and do something with its result. This is needed in some cases where you need a reference to a function from a class body and you want to avoid the automatic transformation to instance method. For these cases, use this idiom: class C:
builtin_open = staticmethod(open)
For more information on static methods, see The standard type hierarchy.
class str(object='')
class str(object=b'', encoding='utf-8', errors='strict')
Return a str version of object. See str() for details. str is the built-in string class. For general information about strings, see Text Sequence Type — str.
sum(iterable, /, start=0)
Sums start and the items of an iterable from left to right and returns the total. The iterable’s items are normally numbers, and the start value is not allowed to be a string. For some use cases, there are good alternatives to sum(). The preferred, fast way to concatenate a sequence of strings is by calling ''.join(sequence). To add floating point values with extended precision, see math.fsum(). To concatenate a series of iterables, consider using itertools.chain(). Changed in version 3.8: The start parameter can be specified as a keyword argument.
super([type[, object-or-type]])
Return a proxy object that delegates method calls to a parent or sibling class of type. This is useful for accessing inherited methods that have been overridden in a class. The object-or-type determines the method resolution order to be searched. The search starts from the class right after the type. For example, if __mro__ of object-or-type is D -> B -> C -> A -> object and the value of type is B, then super() searches C -> A -> object. The __mro__ attribute of the object-or-type lists the method resolution search order used by both getattr() and super(). The attribute is dynamic and can change whenever the inheritance hierarchy is updated. If the second argument is omitted, the super object returned is unbound. If the second argument is an object, isinstance(obj, type) must be true. If the second argument is a type, issubclass(type2, type) must be true (this is useful for classmethods). There are two typical use cases for super. In a class hierarchy with single inheritance, super can be used to refer to parent classes without naming them explicitly, thus making the code more maintainable. This use closely parallels the use of super in other programming languages. The second use case is to support cooperative multiple inheritance in a dynamic execution environment. This use case is unique to Python and is not found in statically compiled languages or languages that only support single inheritance. This makes it possible to implement “diamond diagrams” where multiple base classes implement the same method. Good design dictates that such implementations have the same calling signature in every case (because the order of calls is determined at runtime, because that order adapts to changes in the class hierarchy, and because that order can include sibling classes that are unknown prior to runtime). For both use cases, a typical superclass call looks like this: class C(B):
def method(self, arg):
super().method(arg) # This does the same thing as:
# super(C, self).method(arg)
In addition to method lookups, super() also works for attribute lookups. One possible use case for this is calling descriptors in a parent or sibling class. Note that super() is implemented as part of the binding process for explicit dotted attribute lookups such as super().__getitem__(name). It does so by implementing its own __getattribute__() method for searching classes in a predictable order that supports cooperative multiple inheritance. Accordingly, super() is undefined for implicit lookups using statements or operators such as super()[name]. Also note that, aside from the zero argument form, super() is not limited to use inside methods. The two argument form specifies the arguments exactly and makes the appropriate references. The zero argument form only works inside a class definition, as the compiler fills in the necessary details to correctly retrieve the class being defined, as well as accessing the current instance for ordinary methods. For practical suggestions on how to design cooperative classes using super(), see guide to using super().
class tuple([iterable])
Rather than being a function, tuple is actually an immutable sequence type, as documented in Tuples and Sequence Types — list, tuple, range.
class type(object)
class type(name, bases, dict, **kwds)
With one argument, return the type of an object. The return value is a type object and generally the same object as returned by object.__class__. The isinstance() built-in function is recommended for testing the type of an object, because it takes subclasses into account. With three arguments, return a new type object. This is essentially a dynamic form of the class statement. The name string is the class name and becomes the __name__ attribute. The bases tuple contains the base classes and becomes the __bases__ attribute; if empty, object, the ultimate base of all classes, is added. The dict dictionary contains attribute and method definitions for the class body; it may be copied or wrapped before becoming the __dict__ attribute. The following two statements create identical type objects: >>> class X:
... a = 1
...
>>> X = type('X', (), dict(a=1))
See also Type Objects. Keyword arguments provided to the three argument form are passed to the appropriate metaclass machinery (usually __init_subclass__()) in the same way that keywords in a class definition (besides metaclass) would. See also Customizing class creation. Changed in version 3.6: Subclasses of type which don’t override type.__new__ may no longer use the one-argument form to get the type of an object.
vars([object])
Return the __dict__ attribute for a module, class, instance, or any other object with a __dict__ attribute. Objects such as modules and instances have an updateable __dict__ attribute; however, other objects may have write restrictions on their __dict__ attributes (for example, classes use a types.MappingProxyType to prevent direct dictionary updates). Without an argument, vars() acts like locals(). Note, the locals dictionary is only useful for reads since updates to the locals dictionary are ignored. A TypeError exception is raised if an object is specified but it doesn’t have a __dict__ attribute (for example, if its class defines the __slots__ attribute).
zip(*iterables)
Make an iterator that aggregates elements from each of the iterables. Returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables. The iterator stops when the shortest input iterable is exhausted. With a single iterable argument, it returns an iterator of 1-tuples. With no arguments, it returns an empty iterator. Equivalent to: def zip(*iterables):
# zip('ABCD', 'xy') --> Ax By
sentinel = object()
iterators = [iter(it) for it in iterables]
while iterators:
result = []
for it in iterators:
elem = next(it, sentinel)
if elem is sentinel:
return
result.append(elem)
yield tuple(result)
The left-to-right evaluation order of the iterables is guaranteed. This makes possible an idiom for clustering a data series into n-length groups using zip(*[iter(s)]*n). This repeats the same iterator n times so that each output tuple has the result of n calls to the iterator. This has the effect of dividing the input into n-length chunks. zip() should only be used with unequal length inputs when you don’t care about trailing, unmatched values from the longer iterables. If those values are important, use itertools.zip_longest() instead. zip() in conjunction with the * operator can be used to unzip a list: >>> x = [1, 2, 3]
>>> y = [4, 5, 6]
>>> zipped = zip(x, y)
>>> list(zipped)
[(1, 4), (2, 5), (3, 6)]
>>> x2, y2 = zip(*zip(x, y))
>>> x == list(x2) and y == list(y2)
True
__import__(name, globals=None, locals=None, fromlist=(), level=0)
Note This is an advanced function that is not needed in everyday Python programming, unlike importlib.import_module(). This function is invoked by the import statement. It can be replaced (by importing the builtins module and assigning to builtins.__import__) in order to change semantics of the import statement, but doing so is strongly discouraged as it is usually simpler to use import hooks (see PEP 302) to attain the same goals and does not cause issues with code which assumes the default import implementation is in use. Direct use of __import__() is also discouraged in favor of importlib.import_module(). The function imports the module name, potentially using the given globals and locals to determine how to interpret the name in a package context. The fromlist gives the names of objects or submodules that should be imported from the module given by name. The standard implementation does not use its locals argument at all, and uses its globals only to determine the package context of the import statement. level specifies whether to use absolute or relative imports. 0 (the default) means only perform absolute imports. Positive values for level indicate the number of parent directories to search relative to the directory of the module calling __import__() (see PEP 328 for the details). When the name variable is of the form package.module, normally, the top-level package (the name up till the first dot) is returned, not the module named by name. However, when a non-empty fromlist argument is given, the module named by name is returned. For example, the statement import spam results in bytecode resembling the following code: spam = __import__('spam', globals(), locals(), [], 0)
The statement import spam.ham results in this call: spam = __import__('spam.ham', globals(), locals(), [], 0)
Note how __import__() returns the toplevel module here because this is the object that is bound to a name by the import statement. On the other hand, the statement from spam.ham import eggs, sausage as
saus results in _temp = __import__('spam.ham', globals(), locals(), ['eggs', 'sausage'], 0)
eggs = _temp.eggs
saus = _temp.sausage
Here, the spam.ham module is returned from __import__(). From this object, the names to import are retrieved and assigned to their respective names. If you simply want to import a module (potentially within a package) by name, use importlib.import_module(). Changed in version 3.3: Negative values for level are no longer supported (which also changes the default value to 0). Changed in version 3.9: When the command line options -E or -I are being used, the environment variable PYTHONCASEOK is now ignored.
Footnotes
1
Note that the parser only accepts the Unix-style end of line convention. If you are reading the code from a file, make sure to use newline conversion mode to convert Windows or Mac-style newlines. | python.library.functions |
functools — Higher-order functions and operations on callable objects Source code: Lib/functools.py The functools module is for higher-order functions: functions that act on or return other functions. In general, any callable object can be treated as a function for the purposes of this module. The functools module defines the following functions:
@functools.cache(user_function)
Simple lightweight unbounded function cache. Sometimes called “memoize”. Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict old values, this is smaller and faster than lru_cache() with a size limit. For example: @cache
def factorial(n):
return n * factorial(n-1) if n else 1
>>> factorial(10) # no previously cached result, makes 11 recursive calls
3628800
>>> factorial(5) # just looks up cached value result
120
>>> factorial(12) # makes two new recursive calls, the other 10 are cached
479001600
New in version 3.9.
@functools.cached_property(func)
Transform a method of a class into a property whose value is computed once and then cached as a normal attribute for the life of the instance. Similar to property(), with the addition of caching. Useful for expensive computed properties of instances that are otherwise effectively immutable. Example: class DataSet:
def __init__(self, sequence_of_numbers):
self._data = tuple(sequence_of_numbers)
@cached_property
def stdev(self):
return statistics.stdev(self._data)
The mechanics of cached_property() are somewhat different from property(). A regular property blocks attribute writes unless a setter is defined. In contrast, a cached_property allows writes. The cached_property decorator only runs on lookups and only when an attribute of the same name doesn’t exist. When it does run, the cached_property writes to the attribute with the same name. Subsequent attribute reads and writes take precedence over the cached_property method and it works like a normal attribute. The cached value can be cleared by deleting the attribute. This allows the cached_property method to run again. Note, this decorator interferes with the operation of PEP 412 key-sharing dictionaries. This means that instance dictionaries can take more space than usual. Also, this decorator requires that the __dict__ attribute on each instance be a mutable mapping. This means it will not work with some types, such as metaclasses (since the __dict__ attributes on type instances are read-only proxies for the class namespace), and those that specify __slots__ without including __dict__ as one of the defined slots (as such classes don’t provide a __dict__ attribute at all). If a mutable mapping is not available or if space-efficient key sharing is desired, an effect similar to cached_property() can be achieved by a stacking property() on top of cache(): class DataSet:
def __init__(self, sequence_of_numbers):
self._data = sequence_of_numbers
@property
@cache
def stdev(self):
return statistics.stdev(self._data)
New in version 3.8.
functools.cmp_to_key(func)
Transform an old-style comparison function to a key function. Used with tools that accept key functions (such as sorted(), min(), max(), heapq.nlargest(), heapq.nsmallest(), itertools.groupby()). This function is primarily used as a transition tool for programs being converted from Python 2 which supported the use of comparison functions. A comparison function is any callable that accept two arguments, compares them, and returns a negative number for less-than, zero for equality, or a positive number for greater-than. A key function is a callable that accepts one argument and returns another value to be used as the sort key. Example: sorted(iterable, key=cmp_to_key(locale.strcoll)) # locale-aware sort order
For sorting examples and a brief sorting tutorial, see Sorting HOW TO. New in version 3.2.
@functools.lru_cache(user_function)
@functools.lru_cache(maxsize=128, typed=False)
Decorator to wrap a function with a memoizing callable that saves up to the maxsize most recent calls. It can save time when an expensive or I/O bound function is periodically called with the same arguments. Since a dictionary is used to cache results, the positional and keyword arguments to the function must be hashable. Distinct argument patterns may be considered to be distinct calls with separate cache entries. For example, f(a=1, b=2) and f(b=2, a=1) differ in their keyword argument order and may have two separate cache entries. If user_function is specified, it must be a callable. This allows the lru_cache decorator to be applied directly to a user function, leaving the maxsize at its default value of 128: @lru_cache
def count_vowels(sentence):
sentence = sentence.casefold()
return sum(sentence.count(vowel) for vowel in 'aeiou')
If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound. If typed is set to true, function arguments of different types will be cached separately. For example, f(3) and f(3.0) will be treated as distinct calls with distinct results. The wrapped function is instrumented with a cache_parameters() function that returns a new dict showing the values for maxsize and typed. This is for information purposes only. Mutating the values has no effect. To help measure the effectiveness of the cache and tune the maxsize parameter, the wrapped function is instrumented with a cache_info() function that returns a named tuple showing hits, misses, maxsize and currsize. In a multi-threaded environment, the hits and misses are approximate. The decorator also provides a cache_clear() function for clearing or invalidating the cache. The original underlying function is accessible through the __wrapped__ attribute. This is useful for introspection, for bypassing the cache, or for rewrapping the function with a different cache. An LRU (least recently used) cache works best when the most recent calls are the best predictors of upcoming calls (for example, the most popular articles on a news server tend to change each day). The cache’s size limit assures that the cache does not grow without bound on long-running processes such as web servers. In general, the LRU cache should only be used when you want to reuse previously computed values. Accordingly, it doesn’t make sense to cache functions with side-effects, functions that need to create distinct mutable objects on each call, or impure functions such as time() or random(). Example of an LRU cache for static web content: @lru_cache(maxsize=32)
def get_pep(num):
'Retrieve text of a Python Enhancement Proposal'
resource = 'http://www.python.org/dev/peps/pep-%04d/' % num
try:
with urllib.request.urlopen(resource) as s:
return s.read()
except urllib.error.HTTPError:
return 'Not Found'
>>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
... pep = get_pep(n)
... print(n, len(pep))
>>> get_pep.cache_info()
CacheInfo(hits=3, misses=8, maxsize=32, currsize=8)
Example of efficiently computing Fibonacci numbers using a cache to implement a dynamic programming technique: @lru_cache(maxsize=None)
def fib(n):
if n < 2:
return n
return fib(n-1) + fib(n-2)
>>> [fib(n) for n in range(16)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]
>>> fib.cache_info()
CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)
New in version 3.2. Changed in version 3.3: Added the typed option. Changed in version 3.8: Added the user_function option. New in version 3.9: Added the function cache_parameters()
@functools.total_ordering
Given a class defining one or more rich comparison ordering methods, this class decorator supplies the rest. This simplifies the effort involved in specifying all of the possible rich comparison operations: The class must define one of __lt__(), __le__(), __gt__(), or __ge__(). In addition, the class should supply an __eq__() method. For example: @total_ordering
class Student:
def _is_valid_operand(self, other):
return (hasattr(other, "lastname") and
hasattr(other, "firstname"))
def __eq__(self, other):
if not self._is_valid_operand(other):
return NotImplemented
return ((self.lastname.lower(), self.firstname.lower()) ==
(other.lastname.lower(), other.firstname.lower()))
def __lt__(self, other):
if not self._is_valid_operand(other):
return NotImplemented
return ((self.lastname.lower(), self.firstname.lower()) <
(other.lastname.lower(), other.firstname.lower()))
Note While this decorator makes it easy to create well behaved totally ordered types, it does come at the cost of slower execution and more complex stack traces for the derived comparison methods. If performance benchmarking indicates this is a bottleneck for a given application, implementing all six rich comparison methods instead is likely to provide an easy speed boost. New in version 3.2. Changed in version 3.4: Returning NotImplemented from the underlying comparison function for unrecognised types is now supported.
functools.partial(func, /, *args, **keywords)
Return a new partial object which when called will behave like func called with the positional arguments args and keyword arguments keywords. If more arguments are supplied to the call, they are appended to args. If additional keyword arguments are supplied, they extend and override keywords. Roughly equivalent to: def partial(func, /, *args, **keywords):
def newfunc(*fargs, **fkeywords):
newkeywords = {**keywords, **fkeywords}
return func(*args, *fargs, **newkeywords)
newfunc.func = func
newfunc.args = args
newfunc.keywords = keywords
return newfunc
The partial() is used for partial function application which “freezes” some portion of a function’s arguments and/or keywords resulting in a new object with a simplified signature. For example, partial() can be used to create a callable that behaves like the int() function where the base argument defaults to two: >>> from functools import partial
>>> basetwo = partial(int, base=2)
>>> basetwo.__doc__ = 'Convert base 2 string to an int.'
>>> basetwo('10010')
18
class functools.partialmethod(func, /, *args, **keywords)
Return a new partialmethod descriptor which behaves like partial except that it is designed to be used as a method definition rather than being directly callable. func must be a descriptor or a callable (objects which are both, like normal functions, are handled as descriptors). When func is a descriptor (such as a normal Python function, classmethod(), staticmethod(), abstractmethod() or another instance of partialmethod), calls to __get__ are delegated to the underlying descriptor, and an appropriate partial object returned as the result. When func is a non-descriptor callable, an appropriate bound method is created dynamically. This behaves like a normal Python function when used as a method: the self argument will be inserted as the first positional argument, even before the args and keywords supplied to the partialmethod constructor. Example: >>> class Cell:
... def __init__(self):
... self._alive = False
... @property
... def alive(self):
... return self._alive
... def set_state(self, state):
... self._alive = bool(state)
... set_alive = partialmethod(set_state, True)
... set_dead = partialmethod(set_state, False)
...
>>> c = Cell()
>>> c.alive
False
>>> c.set_alive()
>>> c.alive
True
New in version 3.4.
functools.reduce(function, iterable[, initializer])
Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value. For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the iterable. If the optional initializer is present, it is placed before the items of the iterable in the calculation, and serves as a default when the iterable is empty. If initializer is not given and iterable contains only one item, the first item is returned. Roughly equivalent to: def reduce(function, iterable, initializer=None):
it = iter(iterable)
if initializer is None:
value = next(it)
else:
value = initializer
for element in it:
value = function(value, element)
return value
See itertools.accumulate() for an iterator that yields all intermediate values.
@functools.singledispatch
Transform a function into a single-dispatch generic function. To define a generic function, decorate it with the @singledispatch decorator. Note that the dispatch happens on the type of the first argument, create your function accordingly: >>> from functools import singledispatch
>>> @singledispatch
... def fun(arg, verbose=False):
... if verbose:
... print("Let me just say,", end=" ")
... print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. It is a decorator. For functions annotated with types, the decorator will infer the type of the first argument automatically: >>> @fun.register
... def _(arg: int, verbose=False):
... if verbose:
... print("Strength in numbers, eh?", end=" ")
... print(arg)
...
>>> @fun.register
... def _(arg: list, verbose=False):
... if verbose:
... print("Enumerate this:")
... for i, elem in enumerate(arg):
... print(i, elem)
For code which doesn’t use type annotations, the appropriate type argument can be passed explicitly to the decorator itself: >>> @fun.register(complex)
... def _(arg, verbose=False):
... if verbose:
... print("Better than complicated.", end=" ")
... print(arg.real, arg.imag)
...
To enable registering lambdas and pre-existing functions, the register() attribute can be used in a functional form: >>> def nothing(arg, verbose=False):
... print("Nothing.")
...
>>> fun.register(type(None), nothing)
The register() attribute returns the undecorated function which enables decorator stacking, pickling, as well as creating unit tests for each variant independently: >>> @fun.register(float)
... @fun.register(Decimal)
... def fun_num(arg, verbose=False):
... if verbose:
... print("Half of your number:", end=" ")
... print(arg / 2)
...
>>> fun_num is fun
False
When called, the generic function dispatches on the type of the first argument: >>> fun("Hello, world.")
Hello, world.
>>> fun("test.", verbose=True)
Let me just say, test.
>>> fun(42, verbose=True)
Strength in numbers, eh? 42
>>> fun(['spam', 'spam', 'eggs', 'spam'], verbose=True)
Enumerate this:
0 spam
1 spam
2 eggs
3 spam
>>> fun(None)
Nothing.
>>> fun(1.23)
0.615
Where there is no registered implementation for a specific type, its method resolution order is used to find a more generic implementation. The original function decorated with @singledispatch is registered for the base object type, which means it is used if no better implementation is found. If an implementation registered to abstract base class, virtual subclasses will be dispatched to that implementation: >>> from collections.abc import Mapping
>>> @fun.register
... def _(arg: Mapping, verbose=False):
... if verbose:
... print("Keys & Values")
... for key, value in arg.items():
... print(key, "=>", value)
...
>>> fun({"a": "b"})
a => b
To check which implementation will the generic function choose for a given type, use the dispatch() attribute: >>> fun.dispatch(float)
<function fun_num at 0x1035a2840>
>>> fun.dispatch(dict) # note: default implementation
<function fun at 0x103fe0000>
To access all registered implementations, use the read-only registry attribute: >>> fun.registry.keys()
dict_keys([<class 'NoneType'>, <class 'int'>, <class 'object'>,
<class 'decimal.Decimal'>, <class 'list'>,
<class 'float'>])
>>> fun.registry[float]
<function fun_num at 0x1035a2840>
>>> fun.registry[object]
<function fun at 0x103fe0000>
New in version 3.4. Changed in version 3.7: The register() attribute supports using type annotations.
class functools.singledispatchmethod(func)
Transform a method into a single-dispatch generic function. To define a generic method, decorate it with the @singledispatchmethod decorator. Note that the dispatch happens on the type of the first non-self or non-cls argument, create your function accordingly: class Negator:
@singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
def _(self, arg: int):
return -arg
@neg.register
def _(self, arg: bool):
return not arg
@singledispatchmethod supports nesting with other decorators such as @classmethod. Note that to allow for dispatcher.register, singledispatchmethod must be the outer most decorator. Here is the Negator class with the neg methods being class bound: class Negator:
@singledispatchmethod
@classmethod
def neg(cls, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
@classmethod
def _(cls, arg: int):
return -arg
@neg.register
@classmethod
def _(cls, arg: bool):
return not arg
The same pattern can be used for other similar decorators: staticmethod, abstractmethod, and others. New in version 3.8.
functools.update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
Update a wrapper function to look like the wrapped function. The optional arguments are tuples to specify which attributes of the original function are assigned directly to the matching attributes on the wrapper function and which attributes of the wrapper function are updated with the corresponding attributes from the original function. The default values for these arguments are the module level constants WRAPPER_ASSIGNMENTS (which assigns to the wrapper function’s __module__, __name__, __qualname__, __annotations__ and __doc__, the documentation string) and WRAPPER_UPDATES (which updates the wrapper function’s __dict__, i.e. the instance dictionary). To allow access to the original function for introspection and other purposes (e.g. bypassing a caching decorator such as lru_cache()), this function automatically adds a __wrapped__ attribute to the wrapper that refers to the function being wrapped. The main intended use for this function is in decorator functions which wrap the decorated function and return the wrapper. If the wrapper function is not updated, the metadata of the returned function will reflect the wrapper definition rather than the original function definition, which is typically less than helpful. update_wrapper() may be used with callables other than functions. Any attributes named in assigned or updated that are missing from the object being wrapped are ignored (i.e. this function will not attempt to set them on the wrapper function). AttributeError is still raised if the wrapper function itself is missing any attributes named in updated. New in version 3.2: Automatic addition of the __wrapped__ attribute. New in version 3.2: Copying of the __annotations__ attribute by default. Changed in version 3.2: Missing attributes no longer trigger an AttributeError. Changed in version 3.4: The __wrapped__ attribute now always refers to the wrapped function, even if that function defined a __wrapped__ attribute. (see bpo-17482)
@functools.wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
This is a convenience function for invoking update_wrapper() as a function decorator when defining a wrapper function. It is equivalent to partial(update_wrapper, wrapped=wrapped, assigned=assigned, updated=updated). For example: >>> from functools import wraps
>>> def my_decorator(f):
... @wraps(f)
... def wrapper(*args, **kwds):
... print('Calling decorated function')
... return f(*args, **kwds)
... return wrapper
...
>>> @my_decorator
... def example():
... """Docstring"""
... print('Called example function')
...
>>> example()
Calling decorated function
Called example function
>>> example.__name__
'example'
>>> example.__doc__
'Docstring'
Without the use of this decorator factory, the name of the example function would have been 'wrapper', and the docstring of the original example() would have been lost.
partial Objects partial objects are callable objects created by partial(). They have three read-only attributes:
partial.func
A callable object or function. Calls to the partial object will be forwarded to func with new arguments and keywords.
partial.args
The leftmost positional arguments that will be prepended to the positional arguments provided to a partial object call.
partial.keywords
The keyword arguments that will be supplied when the partial object is called.
partial objects are like function objects in that they are callable, weak referencable, and can have attributes. There are some important differences. For instance, the __name__ and __doc__ attributes are not created automatically. Also, partial objects defined in classes behave like static methods and do not transform into bound methods during instance attribute look-up. | python.library.functools |
@functools.cache(user_function)
Simple lightweight unbounded function cache. Sometimes called “memoize”. Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict old values, this is smaller and faster than lru_cache() with a size limit. For example: @cache
def factorial(n):
return n * factorial(n-1) if n else 1
>>> factorial(10) # no previously cached result, makes 11 recursive calls
3628800
>>> factorial(5) # just looks up cached value result
120
>>> factorial(12) # makes two new recursive calls, the other 10 are cached
479001600
New in version 3.9. | python.library.functools#functools.cache |
@functools.cached_property(func)
Transform a method of a class into a property whose value is computed once and then cached as a normal attribute for the life of the instance. Similar to property(), with the addition of caching. Useful for expensive computed properties of instances that are otherwise effectively immutable. Example: class DataSet:
def __init__(self, sequence_of_numbers):
self._data = tuple(sequence_of_numbers)
@cached_property
def stdev(self):
return statistics.stdev(self._data)
The mechanics of cached_property() are somewhat different from property(). A regular property blocks attribute writes unless a setter is defined. In contrast, a cached_property allows writes. The cached_property decorator only runs on lookups and only when an attribute of the same name doesn’t exist. When it does run, the cached_property writes to the attribute with the same name. Subsequent attribute reads and writes take precedence over the cached_property method and it works like a normal attribute. The cached value can be cleared by deleting the attribute. This allows the cached_property method to run again. Note, this decorator interferes with the operation of PEP 412 key-sharing dictionaries. This means that instance dictionaries can take more space than usual. Also, this decorator requires that the __dict__ attribute on each instance be a mutable mapping. This means it will not work with some types, such as metaclasses (since the __dict__ attributes on type instances are read-only proxies for the class namespace), and those that specify __slots__ without including __dict__ as one of the defined slots (as such classes don’t provide a __dict__ attribute at all). If a mutable mapping is not available or if space-efficient key sharing is desired, an effect similar to cached_property() can be achieved by a stacking property() on top of cache(): class DataSet:
def __init__(self, sequence_of_numbers):
self._data = sequence_of_numbers
@property
@cache
def stdev(self):
return statistics.stdev(self._data)
New in version 3.8. | python.library.functools#functools.cached_property |
functools.cmp_to_key(func)
Transform an old-style comparison function to a key function. Used with tools that accept key functions (such as sorted(), min(), max(), heapq.nlargest(), heapq.nsmallest(), itertools.groupby()). This function is primarily used as a transition tool for programs being converted from Python 2 which supported the use of comparison functions. A comparison function is any callable that accept two arguments, compares them, and returns a negative number for less-than, zero for equality, or a positive number for greater-than. A key function is a callable that accepts one argument and returns another value to be used as the sort key. Example: sorted(iterable, key=cmp_to_key(locale.strcoll)) # locale-aware sort order
For sorting examples and a brief sorting tutorial, see Sorting HOW TO. New in version 3.2. | python.library.functools#functools.cmp_to_key |
@functools.lru_cache(user_function)
@functools.lru_cache(maxsize=128, typed=False)
Decorator to wrap a function with a memoizing callable that saves up to the maxsize most recent calls. It can save time when an expensive or I/O bound function is periodically called with the same arguments. Since a dictionary is used to cache results, the positional and keyword arguments to the function must be hashable. Distinct argument patterns may be considered to be distinct calls with separate cache entries. For example, f(a=1, b=2) and f(b=2, a=1) differ in their keyword argument order and may have two separate cache entries. If user_function is specified, it must be a callable. This allows the lru_cache decorator to be applied directly to a user function, leaving the maxsize at its default value of 128: @lru_cache
def count_vowels(sentence):
sentence = sentence.casefold()
return sum(sentence.count(vowel) for vowel in 'aeiou')
If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound. If typed is set to true, function arguments of different types will be cached separately. For example, f(3) and f(3.0) will be treated as distinct calls with distinct results. The wrapped function is instrumented with a cache_parameters() function that returns a new dict showing the values for maxsize and typed. This is for information purposes only. Mutating the values has no effect. To help measure the effectiveness of the cache and tune the maxsize parameter, the wrapped function is instrumented with a cache_info() function that returns a named tuple showing hits, misses, maxsize and currsize. In a multi-threaded environment, the hits and misses are approximate. The decorator also provides a cache_clear() function for clearing or invalidating the cache. The original underlying function is accessible through the __wrapped__ attribute. This is useful for introspection, for bypassing the cache, or for rewrapping the function with a different cache. An LRU (least recently used) cache works best when the most recent calls are the best predictors of upcoming calls (for example, the most popular articles on a news server tend to change each day). The cache’s size limit assures that the cache does not grow without bound on long-running processes such as web servers. In general, the LRU cache should only be used when you want to reuse previously computed values. Accordingly, it doesn’t make sense to cache functions with side-effects, functions that need to create distinct mutable objects on each call, or impure functions such as time() or random(). Example of an LRU cache for static web content: @lru_cache(maxsize=32)
def get_pep(num):
'Retrieve text of a Python Enhancement Proposal'
resource = 'http://www.python.org/dev/peps/pep-%04d/' % num
try:
with urllib.request.urlopen(resource) as s:
return s.read()
except urllib.error.HTTPError:
return 'Not Found'
>>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
... pep = get_pep(n)
... print(n, len(pep))
>>> get_pep.cache_info()
CacheInfo(hits=3, misses=8, maxsize=32, currsize=8)
Example of efficiently computing Fibonacci numbers using a cache to implement a dynamic programming technique: @lru_cache(maxsize=None)
def fib(n):
if n < 2:
return n
return fib(n-1) + fib(n-2)
>>> [fib(n) for n in range(16)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]
>>> fib.cache_info()
CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)
New in version 3.2. Changed in version 3.3: Added the typed option. Changed in version 3.8: Added the user_function option. New in version 3.9: Added the function cache_parameters() | python.library.functools#functools.lru_cache |
functools.partial(func, /, *args, **keywords)
Return a new partial object which when called will behave like func called with the positional arguments args and keyword arguments keywords. If more arguments are supplied to the call, they are appended to args. If additional keyword arguments are supplied, they extend and override keywords. Roughly equivalent to: def partial(func, /, *args, **keywords):
def newfunc(*fargs, **fkeywords):
newkeywords = {**keywords, **fkeywords}
return func(*args, *fargs, **newkeywords)
newfunc.func = func
newfunc.args = args
newfunc.keywords = keywords
return newfunc
The partial() is used for partial function application which “freezes” some portion of a function’s arguments and/or keywords resulting in a new object with a simplified signature. For example, partial() can be used to create a callable that behaves like the int() function where the base argument defaults to two: >>> from functools import partial
>>> basetwo = partial(int, base=2)
>>> basetwo.__doc__ = 'Convert base 2 string to an int.'
>>> basetwo('10010')
18 | python.library.functools#functools.partial |
partial.args
The leftmost positional arguments that will be prepended to the positional arguments provided to a partial object call. | python.library.functools#functools.partial.args |
partial.func
A callable object or function. Calls to the partial object will be forwarded to func with new arguments and keywords. | python.library.functools#functools.partial.func |
partial.keywords
The keyword arguments that will be supplied when the partial object is called. | python.library.functools#functools.partial.keywords |
class functools.partialmethod(func, /, *args, **keywords)
Return a new partialmethod descriptor which behaves like partial except that it is designed to be used as a method definition rather than being directly callable. func must be a descriptor or a callable (objects which are both, like normal functions, are handled as descriptors). When func is a descriptor (such as a normal Python function, classmethod(), staticmethod(), abstractmethod() or another instance of partialmethod), calls to __get__ are delegated to the underlying descriptor, and an appropriate partial object returned as the result. When func is a non-descriptor callable, an appropriate bound method is created dynamically. This behaves like a normal Python function when used as a method: the self argument will be inserted as the first positional argument, even before the args and keywords supplied to the partialmethod constructor. Example: >>> class Cell:
... def __init__(self):
... self._alive = False
... @property
... def alive(self):
... return self._alive
... def set_state(self, state):
... self._alive = bool(state)
... set_alive = partialmethod(set_state, True)
... set_dead = partialmethod(set_state, False)
...
>>> c = Cell()
>>> c.alive
False
>>> c.set_alive()
>>> c.alive
True
New in version 3.4. | python.library.functools#functools.partialmethod |
functools.reduce(function, iterable[, initializer])
Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value. For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the iterable. If the optional initializer is present, it is placed before the items of the iterable in the calculation, and serves as a default when the iterable is empty. If initializer is not given and iterable contains only one item, the first item is returned. Roughly equivalent to: def reduce(function, iterable, initializer=None):
it = iter(iterable)
if initializer is None:
value = next(it)
else:
value = initializer
for element in it:
value = function(value, element)
return value
See itertools.accumulate() for an iterator that yields all intermediate values. | python.library.functools#functools.reduce |
@functools.singledispatch
Transform a function into a single-dispatch generic function. To define a generic function, decorate it with the @singledispatch decorator. Note that the dispatch happens on the type of the first argument, create your function accordingly: >>> from functools import singledispatch
>>> @singledispatch
... def fun(arg, verbose=False):
... if verbose:
... print("Let me just say,", end=" ")
... print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. It is a decorator. For functions annotated with types, the decorator will infer the type of the first argument automatically: >>> @fun.register
... def _(arg: int, verbose=False):
... if verbose:
... print("Strength in numbers, eh?", end=" ")
... print(arg)
...
>>> @fun.register
... def _(arg: list, verbose=False):
... if verbose:
... print("Enumerate this:")
... for i, elem in enumerate(arg):
... print(i, elem)
For code which doesn’t use type annotations, the appropriate type argument can be passed explicitly to the decorator itself: >>> @fun.register(complex)
... def _(arg, verbose=False):
... if verbose:
... print("Better than complicated.", end=" ")
... print(arg.real, arg.imag)
...
To enable registering lambdas and pre-existing functions, the register() attribute can be used in a functional form: >>> def nothing(arg, verbose=False):
... print("Nothing.")
...
>>> fun.register(type(None), nothing)
The register() attribute returns the undecorated function which enables decorator stacking, pickling, as well as creating unit tests for each variant independently: >>> @fun.register(float)
... @fun.register(Decimal)
... def fun_num(arg, verbose=False):
... if verbose:
... print("Half of your number:", end=" ")
... print(arg / 2)
...
>>> fun_num is fun
False
When called, the generic function dispatches on the type of the first argument: >>> fun("Hello, world.")
Hello, world.
>>> fun("test.", verbose=True)
Let me just say, test.
>>> fun(42, verbose=True)
Strength in numbers, eh? 42
>>> fun(['spam', 'spam', 'eggs', 'spam'], verbose=True)
Enumerate this:
0 spam
1 spam
2 eggs
3 spam
>>> fun(None)
Nothing.
>>> fun(1.23)
0.615
Where there is no registered implementation for a specific type, its method resolution order is used to find a more generic implementation. The original function decorated with @singledispatch is registered for the base object type, which means it is used if no better implementation is found. If an implementation registered to abstract base class, virtual subclasses will be dispatched to that implementation: >>> from collections.abc import Mapping
>>> @fun.register
... def _(arg: Mapping, verbose=False):
... if verbose:
... print("Keys & Values")
... for key, value in arg.items():
... print(key, "=>", value)
...
>>> fun({"a": "b"})
a => b
To check which implementation will the generic function choose for a given type, use the dispatch() attribute: >>> fun.dispatch(float)
<function fun_num at 0x1035a2840>
>>> fun.dispatch(dict) # note: default implementation
<function fun at 0x103fe0000>
To access all registered implementations, use the read-only registry attribute: >>> fun.registry.keys()
dict_keys([<class 'NoneType'>, <class 'int'>, <class 'object'>,
<class 'decimal.Decimal'>, <class 'list'>,
<class 'float'>])
>>> fun.registry[float]
<function fun_num at 0x1035a2840>
>>> fun.registry[object]
<function fun at 0x103fe0000>
New in version 3.4. Changed in version 3.7: The register() attribute supports using type annotations. | python.library.functools#functools.singledispatch |
class functools.singledispatchmethod(func)
Transform a method into a single-dispatch generic function. To define a generic method, decorate it with the @singledispatchmethod decorator. Note that the dispatch happens on the type of the first non-self or non-cls argument, create your function accordingly: class Negator:
@singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
def _(self, arg: int):
return -arg
@neg.register
def _(self, arg: bool):
return not arg
@singledispatchmethod supports nesting with other decorators such as @classmethod. Note that to allow for dispatcher.register, singledispatchmethod must be the outer most decorator. Here is the Negator class with the neg methods being class bound: class Negator:
@singledispatchmethod
@classmethod
def neg(cls, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
@classmethod
def _(cls, arg: int):
return -arg
@neg.register
@classmethod
def _(cls, arg: bool):
return not arg
The same pattern can be used for other similar decorators: staticmethod, abstractmethod, and others. New in version 3.8. | python.library.functools#functools.singledispatchmethod |
@functools.total_ordering
Given a class defining one or more rich comparison ordering methods, this class decorator supplies the rest. This simplifies the effort involved in specifying all of the possible rich comparison operations: The class must define one of __lt__(), __le__(), __gt__(), or __ge__(). In addition, the class should supply an __eq__() method. For example: @total_ordering
class Student:
def _is_valid_operand(self, other):
return (hasattr(other, "lastname") and
hasattr(other, "firstname"))
def __eq__(self, other):
if not self._is_valid_operand(other):
return NotImplemented
return ((self.lastname.lower(), self.firstname.lower()) ==
(other.lastname.lower(), other.firstname.lower()))
def __lt__(self, other):
if not self._is_valid_operand(other):
return NotImplemented
return ((self.lastname.lower(), self.firstname.lower()) <
(other.lastname.lower(), other.firstname.lower()))
Note While this decorator makes it easy to create well behaved totally ordered types, it does come at the cost of slower execution and more complex stack traces for the derived comparison methods. If performance benchmarking indicates this is a bottleneck for a given application, implementing all six rich comparison methods instead is likely to provide an easy speed boost. New in version 3.2. Changed in version 3.4: Returning NotImplemented from the underlying comparison function for unrecognised types is now supported. | python.library.functools#functools.total_ordering |
functools.update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
Update a wrapper function to look like the wrapped function. The optional arguments are tuples to specify which attributes of the original function are assigned directly to the matching attributes on the wrapper function and which attributes of the wrapper function are updated with the corresponding attributes from the original function. The default values for these arguments are the module level constants WRAPPER_ASSIGNMENTS (which assigns to the wrapper function’s __module__, __name__, __qualname__, __annotations__ and __doc__, the documentation string) and WRAPPER_UPDATES (which updates the wrapper function’s __dict__, i.e. the instance dictionary). To allow access to the original function for introspection and other purposes (e.g. bypassing a caching decorator such as lru_cache()), this function automatically adds a __wrapped__ attribute to the wrapper that refers to the function being wrapped. The main intended use for this function is in decorator functions which wrap the decorated function and return the wrapper. If the wrapper function is not updated, the metadata of the returned function will reflect the wrapper definition rather than the original function definition, which is typically less than helpful. update_wrapper() may be used with callables other than functions. Any attributes named in assigned or updated that are missing from the object being wrapped are ignored (i.e. this function will not attempt to set them on the wrapper function). AttributeError is still raised if the wrapper function itself is missing any attributes named in updated. New in version 3.2: Automatic addition of the __wrapped__ attribute. New in version 3.2: Copying of the __annotations__ attribute by default. Changed in version 3.2: Missing attributes no longer trigger an AttributeError. Changed in version 3.4: The __wrapped__ attribute now always refers to the wrapped function, even if that function defined a __wrapped__ attribute. (see bpo-17482) | python.library.functools#functools.update_wrapper |
@functools.wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
This is a convenience function for invoking update_wrapper() as a function decorator when defining a wrapper function. It is equivalent to partial(update_wrapper, wrapped=wrapped, assigned=assigned, updated=updated). For example: >>> from functools import wraps
>>> def my_decorator(f):
... @wraps(f)
... def wrapper(*args, **kwds):
... print('Calling decorated function')
... return f(*args, **kwds)
... return wrapper
...
>>> @my_decorator
... def example():
... """Docstring"""
... print('Called example function')
...
>>> example()
Calling decorated function
Called example function
>>> example.__name__
'example'
>>> example.__doc__
'Docstring'
Without the use of this decorator factory, the name of the example function would have been 'wrapper', and the docstring of the original example() would have been lost. | python.library.functools#functools.wraps |
Futures Source code: Lib/asyncio/futures.py, Lib/asyncio/base_futures.py Future objects are used to bridge low-level callback-based code with high-level async/await code. Future Functions
asyncio.isfuture(obj)
Return True if obj is either of: an instance of asyncio.Future, an instance of asyncio.Task, a Future-like object with a _asyncio_future_blocking attribute. New in version 3.5.
asyncio.ensure_future(obj, *, loop=None)
Return:
obj argument as is, if obj is a Future, a Task, or a Future-like object (isfuture() is used for the test.) a Task object wrapping obj, if obj is a coroutine (iscoroutine() is used for the test); in this case the coroutine will be scheduled by ensure_future(). a Task object that would await on obj, if obj is an awaitable (inspect.isawaitable() is used for the test.) If obj is neither of the above a TypeError is raised. Important See also the create_task() function which is the preferred way for creating new Tasks. Changed in version 3.5.1: The function accepts any awaitable object.
asyncio.wrap_future(future, *, loop=None)
Wrap a concurrent.futures.Future object in a asyncio.Future object.
Future Object
class asyncio.Future(*, loop=None)
A Future represents an eventual result of an asynchronous operation. Not thread-safe. Future is an awaitable object. Coroutines can await on Future objects until they either have a result or an exception set, or until they are cancelled. Typically Futures are used to enable low-level callback-based code (e.g. in protocols implemented using asyncio transports) to interoperate with high-level async/await code. The rule of thumb is to never expose Future objects in user-facing APIs, and the recommended way to create a Future object is to call loop.create_future(). This way alternative event loop implementations can inject their own optimized implementations of a Future object. Changed in version 3.7: Added support for the contextvars module.
result()
Return the result of the Future. If the Future is done and has a result set by the set_result() method, the result value is returned. If the Future is done and has an exception set by the set_exception() method, this method raises the exception. If the Future has been cancelled, this method raises a CancelledError exception. If the Future’s result isn’t yet available, this method raises a InvalidStateError exception.
set_result(result)
Mark the Future as done and set its result. Raises a InvalidStateError error if the Future is already done.
set_exception(exception)
Mark the Future as done and set an exception. Raises a InvalidStateError error if the Future is already done.
done()
Return True if the Future is done. A Future is done if it was cancelled or if it has a result or an exception set with set_result() or set_exception() calls.
cancelled()
Return True if the Future was cancelled. The method is usually used to check if a Future is not cancelled before setting a result or an exception for it: if not fut.cancelled():
fut.set_result(42)
add_done_callback(callback, *, context=None)
Add a callback to be run when the Future is done. The callback is called with the Future object as its only argument. If the Future is already done when this method is called, the callback is scheduled with loop.call_soon(). An optional keyword-only context argument allows specifying a custom contextvars.Context for the callback to run in. The current context is used when no context is provided. functools.partial() can be used to pass parameters to the callback, e.g.: # Call 'print("Future:", fut)' when "fut" is done.
fut.add_done_callback(
functools.partial(print, "Future:"))
Changed in version 3.7: The context keyword-only parameter was added. See PEP 567 for more details.
remove_done_callback(callback)
Remove callback from the callbacks list. Returns the number of callbacks removed, which is typically 1, unless a callback was added more than once.
cancel(msg=None)
Cancel the Future and schedule callbacks. If the Future is already done or cancelled, return False. Otherwise, change the Future’s state to cancelled, schedule the callbacks, and return True. Changed in version 3.9: Added the msg parameter.
exception()
Return the exception that was set on this Future. The exception (or None if no exception was set) is returned only if the Future is done. If the Future has been cancelled, this method raises a CancelledError exception. If the Future isn’t done yet, this method raises an InvalidStateError exception.
get_loop()
Return the event loop the Future object is bound to. New in version 3.7.
This example creates a Future object, creates and schedules an asynchronous Task to set result for the Future, and waits until the Future has a result: async def set_after(fut, delay, value):
# Sleep for *delay* seconds.
await asyncio.sleep(delay)
# Set *value* as a result of *fut* Future.
fut.set_result(value)
async def main():
# Get the current event loop.
loop = asyncio.get_running_loop()
# Create a new Future object.
fut = loop.create_future()
# Run "set_after()" coroutine in a parallel Task.
# We are using the low-level "loop.create_task()" API here because
# we already have a reference to the event loop at hand.
# Otherwise we could have just used "asyncio.create_task()".
loop.create_task(
set_after(fut, 1, '... world'))
print('hello ...')
# Wait until *fut* has a result (1 second) and print it.
print(await fut)
asyncio.run(main())
Important The Future object was designed to mimic concurrent.futures.Future. Key differences include: unlike asyncio Futures, concurrent.futures.Future instances cannot be awaited.
asyncio.Future.result() and asyncio.Future.exception() do not accept the timeout argument.
asyncio.Future.result() and asyncio.Future.exception() raise an InvalidStateError exception when the Future is not done. Callbacks registered with asyncio.Future.add_done_callback() are not called immediately. They are scheduled with loop.call_soon() instead. asyncio Future is not compatible with the concurrent.futures.wait() and concurrent.futures.as_completed() functions.
asyncio.Future.cancel() accepts an optional msg argument, but concurrent.futures.cancel() does not. | python.library.asyncio-future |
exception FutureWarning
Base class for warnings about deprecated features when those warnings are intended for end users of applications that are written in Python. | python.library.exceptions#FutureWarning |
gc — Garbage Collector interface This module provides an interface to the optional garbage collector. It provides the ability to disable the collector, tune the collection frequency, and set debugging options. It also provides access to unreachable objects that the collector found but cannot free. Since the collector supplements the reference counting already used in Python, you can disable the collector if you are sure your program does not create reference cycles. Automatic collection can be disabled by calling gc.disable(). To debug a leaking program call gc.set_debug(gc.DEBUG_LEAK). Notice that this includes gc.DEBUG_SAVEALL, causing garbage-collected objects to be saved in gc.garbage for inspection. The gc module provides the following functions:
gc.enable()
Enable automatic garbage collection.
gc.disable()
Disable automatic garbage collection.
gc.isenabled()
Return True if automatic collection is enabled.
gc.collect(generation=2)
With no arguments, run a full collection. The optional argument generation may be an integer specifying which generation to collect (from 0 to 2). A ValueError is raised if the generation number is invalid. The number of unreachable objects found is returned. The free lists maintained for a number of built-in types are cleared whenever a full collection or collection of the highest generation (2) is run. Not all items in some free lists may be freed due to the particular implementation, in particular float.
gc.set_debug(flags)
Set the garbage collection debugging flags. Debugging information will be written to sys.stderr. See below for a list of debugging flags which can be combined using bit operations to control debugging.
gc.get_debug()
Return the debugging flags currently set.
gc.get_objects(generation=None)
Returns a list of all objects tracked by the collector, excluding the list returned. If generation is not None, return only the objects tracked by the collector that are in that generation. Changed in version 3.8: New generation parameter. Raises an auditing event gc.get_objects with argument generation.
gc.get_stats()
Return a list of three per-generation dictionaries containing collection statistics since interpreter start. The number of keys may change in the future, but currently each dictionary will contain the following items:
collections is the number of times this generation was collected;
collected is the total number of objects collected inside this generation;
uncollectable is the total number of objects which were found to be uncollectable (and were therefore moved to the garbage list) inside this generation. New in version 3.4.
gc.set_threshold(threshold0[, threshold1[, threshold2]])
Set the garbage collection thresholds (the collection frequency). Setting threshold0 to zero disables collection. The GC classifies objects into three generations depending on how many collection sweeps they have survived. New objects are placed in the youngest generation (generation 0). If an object survives a collection it is moved into the next older generation. Since generation 2 is the oldest generation, objects in that generation remain there after a collection. In order to decide when to run, the collector keeps track of the number object allocations and deallocations since the last collection. When the number of allocations minus the number of deallocations exceeds threshold0, collection starts. Initially only generation 0 is examined. If generation 0 has been examined more than threshold1 times since generation 1 has been examined, then generation 1 is examined as well. With the third generation, things are a bit more complicated, see Collecting the oldest generation for more information.
gc.get_count()
Return the current collection counts as a tuple of (count0, count1,
count2).
gc.get_threshold()
Return the current collection thresholds as a tuple of (threshold0,
threshold1, threshold2).
gc.get_referrers(*objs)
Return the list of objects that directly refer to any of objs. This function will only locate those containers which support garbage collection; extension types which do refer to other objects but do not support garbage collection will not be found. Note that objects which have already been dereferenced, but which live in cycles and have not yet been collected by the garbage collector can be listed among the resulting referrers. To get only currently live objects, call collect() before calling get_referrers(). Warning Care must be taken when using objects returned by get_referrers() because some of them could still be under construction and hence in a temporarily invalid state. Avoid using get_referrers() for any purpose other than debugging. Raises an auditing event gc.get_referrers with argument objs.
gc.get_referents(*objs)
Return a list of objects directly referred to by any of the arguments. The referents returned are those objects visited by the arguments’ C-level tp_traverse methods (if any), and may not be all objects actually directly reachable. tp_traverse methods are supported only by objects that support garbage collection, and are only required to visit objects that may be involved in a cycle. So, for example, if an integer is directly reachable from an argument, that integer object may or may not appear in the result list. Raises an auditing event gc.get_referents with argument objs.
gc.is_tracked(obj)
Returns True if the object is currently tracked by the garbage collector, False otherwise. As a general rule, instances of atomic types aren’t tracked and instances of non-atomic types (containers, user-defined objects…) are. However, some type-specific optimizations can be present in order to suppress the garbage collector footprint of simple instances (e.g. dicts containing only atomic keys and values): >>> gc.is_tracked(0)
False
>>> gc.is_tracked("a")
False
>>> gc.is_tracked([])
True
>>> gc.is_tracked({})
False
>>> gc.is_tracked({"a": 1})
False
>>> gc.is_tracked({"a": []})
True
New in version 3.1.
gc.is_finalized(obj)
Returns True if the given object has been finalized by the garbage collector, False otherwise. >>> x = None
>>> class Lazarus:
... def __del__(self):
... global x
... x = self
...
>>> lazarus = Lazarus()
>>> gc.is_finalized(lazarus)
False
>>> del lazarus
>>> gc.is_finalized(x)
True
New in version 3.9.
gc.freeze()
Freeze all the objects tracked by gc - move them to a permanent generation and ignore all the future collections. This can be used before a POSIX fork() call to make the gc copy-on-write friendly or to speed up collection. Also collection before a POSIX fork() call may free pages for future allocation which can cause copy-on-write too so it’s advised to disable gc in parent process and freeze before fork and enable gc in child process. New in version 3.7.
gc.unfreeze()
Unfreeze the objects in the permanent generation, put them back into the oldest generation. New in version 3.7.
gc.get_freeze_count()
Return the number of objects in the permanent generation. New in version 3.7.
The following variables are provided for read-only access (you can mutate the values but should not rebind them):
gc.garbage
A list of objects which the collector found to be unreachable but could not be freed (uncollectable objects). Starting with Python 3.4, this list should be empty most of the time, except when using instances of C extension types with a non-NULL tp_del slot. If DEBUG_SAVEALL is set, then all unreachable objects will be added to this list rather than freed. Changed in version 3.2: If this list is non-empty at interpreter shutdown, a ResourceWarning is emitted, which is silent by default. If DEBUG_UNCOLLECTABLE is set, in addition all uncollectable objects are printed. Changed in version 3.4: Following PEP 442, objects with a __del__() method don’t end up in gc.garbage anymore.
gc.callbacks
A list of callbacks that will be invoked by the garbage collector before and after collection. The callbacks will be called with two arguments, phase and info. phase can be one of two values: “start”: The garbage collection is about to start. “stop”: The garbage collection has finished. info is a dict providing more information for the callback. The following keys are currently defined: “generation”: The oldest generation being collected. “collected”: When phase is “stop”, the number of objects successfully collected. “uncollectable”: When phase is “stop”, the number of objects that could not be collected and were put in garbage. Applications can add their own callbacks to this list. The primary use cases are: Gathering statistics about garbage collection, such as how often various generations are collected, and how long the collection takes. Allowing applications to identify and clear their own uncollectable types when they appear in garbage. New in version 3.3.
The following constants are provided for use with set_debug():
gc.DEBUG_STATS
Print statistics during collection. This information can be useful when tuning the collection frequency.
gc.DEBUG_COLLECTABLE
Print information on collectable objects found.
gc.DEBUG_UNCOLLECTABLE
Print information of uncollectable objects found (objects which are not reachable but cannot be freed by the collector). These objects will be added to the garbage list. Changed in version 3.2: Also print the contents of the garbage list at interpreter shutdown, if it isn’t empty.
gc.DEBUG_SAVEALL
When set, all unreachable objects found will be appended to garbage rather than being freed. This can be useful for debugging a leaking program.
gc.DEBUG_LEAK
The debugging flags necessary for the collector to print information about a leaking program (equal to DEBUG_COLLECTABLE | DEBUG_UNCOLLECTABLE |
DEBUG_SAVEALL). | python.library.gc |
gc.callbacks
A list of callbacks that will be invoked by the garbage collector before and after collection. The callbacks will be called with two arguments, phase and info. phase can be one of two values: “start”: The garbage collection is about to start. “stop”: The garbage collection has finished. info is a dict providing more information for the callback. The following keys are currently defined: “generation”: The oldest generation being collected. “collected”: When phase is “stop”, the number of objects successfully collected. “uncollectable”: When phase is “stop”, the number of objects that could not be collected and were put in garbage. Applications can add their own callbacks to this list. The primary use cases are: Gathering statistics about garbage collection, such as how often various generations are collected, and how long the collection takes. Allowing applications to identify and clear their own uncollectable types when they appear in garbage. New in version 3.3. | python.library.gc#gc.callbacks |
gc.collect(generation=2)
With no arguments, run a full collection. The optional argument generation may be an integer specifying which generation to collect (from 0 to 2). A ValueError is raised if the generation number is invalid. The number of unreachable objects found is returned. The free lists maintained for a number of built-in types are cleared whenever a full collection or collection of the highest generation (2) is run. Not all items in some free lists may be freed due to the particular implementation, in particular float. | python.library.gc#gc.collect |
gc.DEBUG_COLLECTABLE
Print information on collectable objects found. | python.library.gc#gc.DEBUG_COLLECTABLE |
gc.DEBUG_LEAK
The debugging flags necessary for the collector to print information about a leaking program (equal to DEBUG_COLLECTABLE | DEBUG_UNCOLLECTABLE |
DEBUG_SAVEALL). | python.library.gc#gc.DEBUG_LEAK |
gc.DEBUG_SAVEALL
When set, all unreachable objects found will be appended to garbage rather than being freed. This can be useful for debugging a leaking program. | python.library.gc#gc.DEBUG_SAVEALL |
gc.DEBUG_STATS
Print statistics during collection. This information can be useful when tuning the collection frequency. | python.library.gc#gc.DEBUG_STATS |
gc.DEBUG_UNCOLLECTABLE
Print information of uncollectable objects found (objects which are not reachable but cannot be freed by the collector). These objects will be added to the garbage list. Changed in version 3.2: Also print the contents of the garbage list at interpreter shutdown, if it isn’t empty. | python.library.gc#gc.DEBUG_UNCOLLECTABLE |
gc.disable()
Disable automatic garbage collection. | python.library.gc#gc.disable |
gc.enable()
Enable automatic garbage collection. | python.library.gc#gc.enable |
gc.freeze()
Freeze all the objects tracked by gc - move them to a permanent generation and ignore all the future collections. This can be used before a POSIX fork() call to make the gc copy-on-write friendly or to speed up collection. Also collection before a POSIX fork() call may free pages for future allocation which can cause copy-on-write too so it’s advised to disable gc in parent process and freeze before fork and enable gc in child process. New in version 3.7. | python.library.gc#gc.freeze |
gc.garbage
A list of objects which the collector found to be unreachable but could not be freed (uncollectable objects). Starting with Python 3.4, this list should be empty most of the time, except when using instances of C extension types with a non-NULL tp_del slot. If DEBUG_SAVEALL is set, then all unreachable objects will be added to this list rather than freed. Changed in version 3.2: If this list is non-empty at interpreter shutdown, a ResourceWarning is emitted, which is silent by default. If DEBUG_UNCOLLECTABLE is set, in addition all uncollectable objects are printed. Changed in version 3.4: Following PEP 442, objects with a __del__() method don’t end up in gc.garbage anymore. | python.library.gc#gc.garbage |
gc.get_count()
Return the current collection counts as a tuple of (count0, count1,
count2). | python.library.gc#gc.get_count |
gc.get_debug()
Return the debugging flags currently set. | python.library.gc#gc.get_debug |
gc.get_freeze_count()
Return the number of objects in the permanent generation. New in version 3.7. | python.library.gc#gc.get_freeze_count |
gc.get_objects(generation=None)
Returns a list of all objects tracked by the collector, excluding the list returned. If generation is not None, return only the objects tracked by the collector that are in that generation. Changed in version 3.8: New generation parameter. Raises an auditing event gc.get_objects with argument generation. | python.library.gc#gc.get_objects |
gc.get_referents(*objs)
Return a list of objects directly referred to by any of the arguments. The referents returned are those objects visited by the arguments’ C-level tp_traverse methods (if any), and may not be all objects actually directly reachable. tp_traverse methods are supported only by objects that support garbage collection, and are only required to visit objects that may be involved in a cycle. So, for example, if an integer is directly reachable from an argument, that integer object may or may not appear in the result list. Raises an auditing event gc.get_referents with argument objs. | python.library.gc#gc.get_referents |
gc.get_referrers(*objs)
Return the list of objects that directly refer to any of objs. This function will only locate those containers which support garbage collection; extension types which do refer to other objects but do not support garbage collection will not be found. Note that objects which have already been dereferenced, but which live in cycles and have not yet been collected by the garbage collector can be listed among the resulting referrers. To get only currently live objects, call collect() before calling get_referrers(). Warning Care must be taken when using objects returned by get_referrers() because some of them could still be under construction and hence in a temporarily invalid state. Avoid using get_referrers() for any purpose other than debugging. Raises an auditing event gc.get_referrers with argument objs. | python.library.gc#gc.get_referrers |
gc.get_stats()
Return a list of three per-generation dictionaries containing collection statistics since interpreter start. The number of keys may change in the future, but currently each dictionary will contain the following items:
collections is the number of times this generation was collected;
collected is the total number of objects collected inside this generation;
uncollectable is the total number of objects which were found to be uncollectable (and were therefore moved to the garbage list) inside this generation. New in version 3.4. | python.library.gc#gc.get_stats |
gc.get_threshold()
Return the current collection thresholds as a tuple of (threshold0,
threshold1, threshold2). | python.library.gc#gc.get_threshold |
gc.isenabled()
Return True if automatic collection is enabled. | python.library.gc#gc.isenabled |
gc.is_finalized(obj)
Returns True if the given object has been finalized by the garbage collector, False otherwise. >>> x = None
>>> class Lazarus:
... def __del__(self):
... global x
... x = self
...
>>> lazarus = Lazarus()
>>> gc.is_finalized(lazarus)
False
>>> del lazarus
>>> gc.is_finalized(x)
True
New in version 3.9. | python.library.gc#gc.is_finalized |
gc.is_tracked(obj)
Returns True if the object is currently tracked by the garbage collector, False otherwise. As a general rule, instances of atomic types aren’t tracked and instances of non-atomic types (containers, user-defined objects…) are. However, some type-specific optimizations can be present in order to suppress the garbage collector footprint of simple instances (e.g. dicts containing only atomic keys and values): >>> gc.is_tracked(0)
False
>>> gc.is_tracked("a")
False
>>> gc.is_tracked([])
True
>>> gc.is_tracked({})
False
>>> gc.is_tracked({"a": 1})
False
>>> gc.is_tracked({"a": []})
True
New in version 3.1. | python.library.gc#gc.is_tracked |
gc.set_debug(flags)
Set the garbage collection debugging flags. Debugging information will be written to sys.stderr. See below for a list of debugging flags which can be combined using bit operations to control debugging. | python.library.gc#gc.set_debug |
gc.set_threshold(threshold0[, threshold1[, threshold2]])
Set the garbage collection thresholds (the collection frequency). Setting threshold0 to zero disables collection. The GC classifies objects into three generations depending on how many collection sweeps they have survived. New objects are placed in the youngest generation (generation 0). If an object survives a collection it is moved into the next older generation. Since generation 2 is the oldest generation, objects in that generation remain there after a collection. In order to decide when to run, the collector keeps track of the number object allocations and deallocations since the last collection. When the number of allocations minus the number of deallocations exceeds threshold0, collection starts. Initially only generation 0 is examined. If generation 0 has been examined more than threshold1 times since generation 1 has been examined, then generation 1 is examined as well. With the third generation, things are a bit more complicated, see Collecting the oldest generation for more information. | python.library.gc#gc.set_threshold |
gc.unfreeze()
Unfreeze the objects in the permanent generation, put them back into the oldest generation. New in version 3.7. | python.library.gc#gc.unfreeze |
exception GeneratorExit
Raised when a generator or coroutine is closed; see generator.close() and coroutine.close(). It directly inherits from BaseException instead of Exception since it is technically not an error. | python.library.exceptions#GeneratorExit |
genericalias.__args__
This attribute is a tuple (possibly of length 1) of generic types passed to the original __class_getitem__() of the generic container: >>> dict[str, list[int]].__args__
(<class 'str'>, list[int]) | python.library.stdtypes#genericalias.__args__ |
genericalias.__origin__
This attribute points at the non-parameterized generic class: >>> list[int].__origin__
<class 'list'> | python.library.stdtypes#genericalias.__origin__ |
genericalias.__parameters__
This attribute is a lazily computed tuple (possibly empty) of unique type variables found in __args__: >>> from typing import TypeVar
>>> T = TypeVar('T')
>>> list[T].__parameters__
(~T,) | python.library.stdtypes#genericalias.__parameters__ |
getattr(object, name[, default])
Return the value of the named attribute of object. name must be a string. If the string is the name of one of the object’s attributes, the result is the value of that attribute. For example, getattr(x, 'foobar') is equivalent to x.foobar. If the named attribute does not exist, default is returned if provided, otherwise AttributeError is raised. | python.library.functions#getattr |
getopt — C-style parser for command line options Source code: Lib/getopt.py Note The getopt module is a parser for command line options whose API is designed to be familiar to users of the C getopt() function. Users who are unfamiliar with the C getopt() function or who would like to write less code and get better help and error messages should consider using the argparse module instead. This module helps scripts to parse the command line arguments in sys.argv. It supports the same conventions as the Unix getopt() function (including the special meanings of arguments of the form ‘-‘ and ‘--‘). Long options similar to those supported by GNU software may be used as well via an optional third argument. This module provides two functions and an exception:
getopt.getopt(args, shortopts, longopts=[])
Parses command line options and parameter list. args is the argument list to be parsed, without the leading reference to the running program. Typically, this means sys.argv[1:]. shortopts is the string of option letters that the script wants to recognize, with options that require an argument followed by a colon (':'; i.e., the same format that Unix getopt() uses). Note Unlike GNU getopt(), after a non-option argument, all further arguments are considered also non-options. This is similar to the way non-GNU Unix systems work. longopts, if specified, must be a list of strings with the names of the long options which should be supported. The leading '--' characters should not be included in the option name. Long options which require an argument should be followed by an equal sign ('='). Optional arguments are not supported. To accept only long options, shortopts should be an empty string. Long options on the command line can be recognized so long as they provide a prefix of the option name that matches exactly one of the accepted options. For example, if longopts is ['foo', 'frob'], the option --fo will match as --foo, but --f will not match uniquely, so GetoptError will be raised. The return value consists of two elements: the first is a list of (option,
value) pairs; the second is the list of program arguments left after the option list was stripped (this is a trailing slice of args). Each option-and-value pair returned has the option as its first element, prefixed with a hyphen for short options (e.g., '-x') or two hyphens for long options (e.g., '--long-option'), and the option argument as its second element, or an empty string if the option has no argument. The options occur in the list in the same order in which they were found, thus allowing multiple occurrences. Long and short options may be mixed.
getopt.gnu_getopt(args, shortopts, longopts=[])
This function works like getopt(), except that GNU style scanning mode is used by default. This means that option and non-option arguments may be intermixed. The getopt() function stops processing options as soon as a non-option argument is encountered. If the first character of the option string is '+', or if the environment variable POSIXLY_CORRECT is set, then option processing stops as soon as a non-option argument is encountered.
exception getopt.GetoptError
This is raised when an unrecognized option is found in the argument list or when an option requiring an argument is given none. The argument to the exception is a string indicating the cause of the error. For long options, an argument given to an option which does not require one will also cause this exception to be raised. The attributes msg and opt give the error message and related option; if there is no specific option to which the exception relates, opt is an empty string.
exception getopt.error
Alias for GetoptError; for backward compatibility.
An example using only Unix style options: >>> import getopt
>>> args = '-a -b -cfoo -d bar a1 a2'.split()
>>> args
['-a', '-b', '-cfoo', '-d', 'bar', 'a1', 'a2']
>>> optlist, args = getopt.getopt(args, 'abc:d:')
>>> optlist
[('-a', ''), ('-b', ''), ('-c', 'foo'), ('-d', 'bar')]
>>> args
['a1', 'a2']
Using long option names is equally easy: >>> s = '--condition=foo --testing --output-file abc.def -x a1 a2'
>>> args = s.split()
>>> args
['--condition=foo', '--testing', '--output-file', 'abc.def', '-x', 'a1', 'a2']
>>> optlist, args = getopt.getopt(args, 'x', [
... 'condition=', 'output-file=', 'testing'])
>>> optlist
[('--condition', 'foo'), ('--testing', ''), ('--output-file', 'abc.def'), ('-x', '')]
>>> args
['a1', 'a2']
In a script, typical usage is something like this: import getopt, sys
def main():
try:
opts, args = getopt.getopt(sys.argv[1:], "ho:v", ["help", "output="])
except getopt.GetoptError as err:
# print help information and exit:
print(err) # will print something like "option -a not recognized"
usage()
sys.exit(2)
output = None
verbose = False
for o, a in opts:
if o == "-v":
verbose = True
elif o in ("-h", "--help"):
usage()
sys.exit()
elif o in ("-o", "--output"):
output = a
else:
assert False, "unhandled option"
# ...
if __name__ == "__main__":
main()
Note that an equivalent command line interface could be produced with less code and more informative help and error messages by using the argparse module: import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-o', '--output')
parser.add_argument('-v', dest='verbose', action='store_true')
args = parser.parse_args()
# ... do something with args.output ...
# ... do something with args.verbose ..
See also
Module argparse
Alternative command line option and argument parsing library. | python.library.getopt |
exception getopt.error
Alias for GetoptError; for backward compatibility. | python.library.getopt#getopt.error |
getopt.getopt(args, shortopts, longopts=[])
Parses command line options and parameter list. args is the argument list to be parsed, without the leading reference to the running program. Typically, this means sys.argv[1:]. shortopts is the string of option letters that the script wants to recognize, with options that require an argument followed by a colon (':'; i.e., the same format that Unix getopt() uses). Note Unlike GNU getopt(), after a non-option argument, all further arguments are considered also non-options. This is similar to the way non-GNU Unix systems work. longopts, if specified, must be a list of strings with the names of the long options which should be supported. The leading '--' characters should not be included in the option name. Long options which require an argument should be followed by an equal sign ('='). Optional arguments are not supported. To accept only long options, shortopts should be an empty string. Long options on the command line can be recognized so long as they provide a prefix of the option name that matches exactly one of the accepted options. For example, if longopts is ['foo', 'frob'], the option --fo will match as --foo, but --f will not match uniquely, so GetoptError will be raised. The return value consists of two elements: the first is a list of (option,
value) pairs; the second is the list of program arguments left after the option list was stripped (this is a trailing slice of args). Each option-and-value pair returned has the option as its first element, prefixed with a hyphen for short options (e.g., '-x') or two hyphens for long options (e.g., '--long-option'), and the option argument as its second element, or an empty string if the option has no argument. The options occur in the list in the same order in which they were found, thus allowing multiple occurrences. Long and short options may be mixed. | python.library.getopt#getopt.getopt |
exception getopt.GetoptError
This is raised when an unrecognized option is found in the argument list or when an option requiring an argument is given none. The argument to the exception is a string indicating the cause of the error. For long options, an argument given to an option which does not require one will also cause this exception to be raised. The attributes msg and opt give the error message and related option; if there is no specific option to which the exception relates, opt is an empty string. | python.library.getopt#getopt.GetoptError |
getopt.gnu_getopt(args, shortopts, longopts=[])
This function works like getopt(), except that GNU style scanning mode is used by default. This means that option and non-option arguments may be intermixed. The getopt() function stops processing options as soon as a non-option argument is encountered. If the first character of the option string is '+', or if the environment variable POSIXLY_CORRECT is set, then option processing stops as soon as a non-option argument is encountered. | python.library.getopt#getopt.gnu_getopt |
getpass — Portable password input Source code: Lib/getpass.py The getpass module provides two functions:
getpass.getpass(prompt='Password: ', stream=None)
Prompt the user for a password without echoing. The user is prompted using the string prompt, which defaults to 'Password: '. On Unix, the prompt is written to the file-like object stream using the replace error handler if needed. stream defaults to the controlling terminal (/dev/tty) or if that is unavailable to sys.stderr (this argument is ignored on Windows). If echo free input is unavailable getpass() falls back to printing a warning message to stream and reading from sys.stdin and issuing a GetPassWarning. Note If you call getpass from within IDLE, the input may be done in the terminal you launched IDLE from rather than the idle window itself.
exception getpass.GetPassWarning
A UserWarning subclass issued when password input may be echoed.
getpass.getuser()
Return the “login name” of the user. This function checks the environment variables LOGNAME, USER, LNAME and USERNAME, in order, and returns the value of the first one which is set to a non-empty string. If none are set, the login name from the password database is returned on systems which support the pwd module, otherwise, an exception is raised. In general, this function should be preferred over os.getlogin(). | python.library.getpass |
getpass.getpass(prompt='Password: ', stream=None)
Prompt the user for a password without echoing. The user is prompted using the string prompt, which defaults to 'Password: '. On Unix, the prompt is written to the file-like object stream using the replace error handler if needed. stream defaults to the controlling terminal (/dev/tty) or if that is unavailable to sys.stderr (this argument is ignored on Windows). If echo free input is unavailable getpass() falls back to printing a warning message to stream and reading from sys.stdin and issuing a GetPassWarning. Note If you call getpass from within IDLE, the input may be done in the terminal you launched IDLE from rather than the idle window itself. | python.library.getpass#getpass.getpass |
exception getpass.GetPassWarning
A UserWarning subclass issued when password input may be echoed. | python.library.getpass#getpass.GetPassWarning |
getpass.getuser()
Return the “login name” of the user. This function checks the environment variables LOGNAME, USER, LNAME and USERNAME, in order, and returns the value of the first one which is set to a non-empty string. If none are set, the login name from the password database is returned on systems which support the pwd module, otherwise, an exception is raised. In general, this function should be preferred over os.getlogin(). | python.library.getpass#getpass.getuser |
gettext — Multilingual internationalization services Source code: Lib/gettext.py The gettext module provides internationalization (I18N) and localization (L10N) services for your Python modules and applications. It supports both the GNU gettext message catalog API and a higher level, class-based API that may be more appropriate for Python files. The interface described below allows you to write your module and application messages in one natural language, and provide a catalog of translated messages for running under different natural languages. Some hints on localizing your Python modules and applications are also given. GNU gettext API The gettext module defines the following API, which is very similar to the GNU gettext API. If you use this API you will affect the translation of your entire application globally. Often this is what you want if your application is monolingual, with the choice of language dependent on the locale of your user. If you are localizing a Python module, or if your application needs to switch languages on the fly, you probably want to use the class-based API instead.
gettext.bindtextdomain(domain, localedir=None)
Bind the domain to the locale directory localedir. More concretely, gettext will look for binary .mo files for the given domain using the path (on Unix): localedir/language/LC_MESSAGES/domain.mo, where language is searched for in the environment variables LANGUAGE, LC_ALL, LC_MESSAGES, and LANG respectively. If localedir is omitted or None, then the current binding for domain is returned. 1
gettext.bind_textdomain_codeset(domain, codeset=None)
Bind the domain to codeset, changing the encoding of byte strings returned by the lgettext(), ldgettext(), lngettext() and ldngettext() functions. If codeset is omitted, then the current binding is returned. Deprecated since version 3.8, will be removed in version 3.10.
gettext.textdomain(domain=None)
Change or query the current global domain. If domain is None, then the current global domain is returned, otherwise the global domain is set to domain, which is returned.
gettext.gettext(message)
Return the localized translation of message, based on the current global domain, language, and locale directory. This function is usually aliased as _() in the local namespace (see examples below).
gettext.dgettext(domain, message)
Like gettext(), but look the message up in the specified domain.
gettext.ngettext(singular, plural, n)
Like gettext(), but consider plural forms. If a translation is found, apply the plural formula to n, and return the resulting message (some languages have more than two plural forms). If no translation is found, return singular if n is 1; return plural otherwise. The Plural formula is taken from the catalog header. It is a C or Python expression that has a free variable n; the expression evaluates to the index of the plural in the catalog. See the GNU gettext documentation for the precise syntax to be used in .po files and the formulas for a variety of languages.
gettext.dngettext(domain, singular, plural, n)
Like ngettext(), but look the message up in the specified domain.
gettext.pgettext(context, message)
gettext.dpgettext(domain, context, message)
gettext.npgettext(context, singular, plural, n)
gettext.dnpgettext(domain, context, singular, plural, n)
Similar to the corresponding functions without the p in the prefix (that is, gettext(), dgettext(), ngettext(), dngettext()), but the translation is restricted to the given message context. New in version 3.8.
gettext.lgettext(message)
gettext.ldgettext(domain, message)
gettext.lngettext(singular, plural, n)
gettext.ldngettext(domain, singular, plural, n)
Equivalent to the corresponding functions without the l prefix (gettext(), dgettext(), ngettext() and dngettext()), but the translation is returned as a byte string encoded in the preferred system encoding if no other encoding was explicitly set with bind_textdomain_codeset(). Warning These functions should be avoided in Python 3, because they return encoded bytes. It’s much better to use alternatives which return Unicode strings instead, since most Python applications will want to manipulate human readable text as strings instead of bytes. Further, it’s possible that you may get unexpected Unicode-related exceptions if there are encoding problems with the translated strings. Deprecated since version 3.8, will be removed in version 3.10.
Note that GNU gettext also defines a dcgettext() method, but this was deemed not useful and so it is currently unimplemented. Here’s an example of typical usage for this API: import gettext
gettext.bindtextdomain('myapplication', '/path/to/my/language/directory')
gettext.textdomain('myapplication')
_ = gettext.gettext
# ...
print(_('This is a translatable string.'))
Class-based API The class-based API of the gettext module gives you more flexibility and greater convenience than the GNU gettext API. It is the recommended way of localizing your Python applications and modules. gettext defines a GNUTranslations class which implements the parsing of GNU .mo format files, and has methods for returning strings. Instances of this class can also install themselves in the built-in namespace as the function _().
gettext.find(domain, localedir=None, languages=None, all=False)
This function implements the standard .mo file search algorithm. It takes a domain, identical to what textdomain() takes. Optional localedir is as in bindtextdomain(). Optional languages is a list of strings, where each string is a language code. If localedir is not given, then the default system locale directory is used. 2 If languages is not given, then the following environment variables are searched: LANGUAGE, LC_ALL, LC_MESSAGES, and LANG. The first one returning a non-empty value is used for the languages variable. The environment variables should contain a colon separated list of languages, which will be split on the colon to produce the expected list of language code strings. find() then expands and normalizes the languages, and then iterates through them, searching for an existing file built of these components: localedir/language/LC_MESSAGES/domain.mo The first such file name that exists is returned by find(). If no such file is found, then None is returned. If all is given, it returns a list of all file names, in the order in which they appear in the languages list or the environment variables.
gettext.translation(domain, localedir=None, languages=None, class_=None, fallback=False, codeset=None)
Return a *Translations instance based on the domain, localedir, and languages, which are first passed to find() to get a list of the associated .mo file paths. Instances with identical .mo file names are cached. The actual class instantiated is class_ if provided, otherwise GNUTranslations. The class’s constructor must take a single file object argument. If provided, codeset will change the charset used to encode translated strings in the lgettext() and lngettext() methods. If multiple files are found, later files are used as fallbacks for earlier ones. To allow setting the fallback, copy.copy() is used to clone each translation object from the cache; the actual instance data is still shared with the cache. If no .mo file is found, this function raises OSError if fallback is false (which is the default), and returns a NullTranslations instance if fallback is true. Changed in version 3.3: IOError used to be raised instead of OSError. Deprecated since version 3.8, will be removed in version 3.10: The codeset parameter.
gettext.install(domain, localedir=None, codeset=None, names=None)
This installs the function _() in Python’s builtins namespace, based on domain, localedir, and codeset which are passed to the function translation(). For the names parameter, please see the description of the translation object’s install() method. As seen below, you usually mark the strings in your application that are candidates for translation, by wrapping them in a call to the _() function, like this: print(_('This string will be translated.'))
For convenience, you want the _() function to be installed in Python’s builtins namespace, so it is easily accessible in all modules of your application. Deprecated since version 3.8, will be removed in version 3.10: The codeset parameter.
The NullTranslations class Translation classes are what actually implement the translation of original source file message strings to translated message strings. The base class used by all translation classes is NullTranslations; this provides the basic interface you can use to write your own specialized translation classes. Here are the methods of NullTranslations:
class gettext.NullTranslations(fp=None)
Takes an optional file object fp, which is ignored by the base class. Initializes “protected” instance variables _info and _charset which are set by derived classes, as well as _fallback, which is set through add_fallback(). It then calls self._parse(fp) if fp is not None.
_parse(fp)
No-op in the base class, this method takes file object fp, and reads the data from the file, initializing its message catalog. If you have an unsupported message catalog file format, you should override this method to parse your format.
add_fallback(fallback)
Add fallback as the fallback object for the current translation object. A translation object should consult the fallback if it cannot provide a translation for a given message.
gettext(message)
If a fallback has been set, forward gettext() to the fallback. Otherwise, return message. Overridden in derived classes.
ngettext(singular, plural, n)
If a fallback has been set, forward ngettext() to the fallback. Otherwise, return singular if n is 1; return plural otherwise. Overridden in derived classes.
pgettext(context, message)
If a fallback has been set, forward pgettext() to the fallback. Otherwise, return the translated message. Overridden in derived classes. New in version 3.8.
npgettext(context, singular, plural, n)
If a fallback has been set, forward npgettext() to the fallback. Otherwise, return the translated message. Overridden in derived classes. New in version 3.8.
lgettext(message)
lngettext(singular, plural, n)
Equivalent to gettext() and ngettext(), but the translation is returned as a byte string encoded in the preferred system encoding if no encoding was explicitly set with set_output_charset(). Overridden in derived classes. Warning These methods should be avoided in Python 3. See the warning for the lgettext() function. Deprecated since version 3.8, will be removed in version 3.10.
info()
Return the “protected” _info variable, a dictionary containing the metadata found in the message catalog file.
charset()
Return the encoding of the message catalog file.
output_charset()
Return the encoding used to return translated messages in lgettext() and lngettext(). Deprecated since version 3.8, will be removed in version 3.10.
set_output_charset(charset)
Change the encoding used to return translated messages. Deprecated since version 3.8, will be removed in version 3.10.
install(names=None)
This method installs gettext() into the built-in namespace, binding it to _. If the names parameter is given, it must be a sequence containing the names of functions you want to install in the builtins namespace in addition to _(). Supported names are 'gettext', 'ngettext', 'pgettext', 'npgettext', 'lgettext', and 'lngettext'. Note that this is only one way, albeit the most convenient way, to make the _() function available to your application. Because it affects the entire application globally, and specifically the built-in namespace, localized modules should never install _(). Instead, they should use this code to make _() available to their module: import gettext
t = gettext.translation('mymodule', ...)
_ = t.gettext
This puts _() only in the module’s global namespace and so only affects calls within this module. Changed in version 3.8: Added 'pgettext' and 'npgettext'.
The GNUTranslations class The gettext module provides one additional class derived from NullTranslations: GNUTranslations. This class overrides _parse() to enable reading GNU gettext format .mo files in both big-endian and little-endian format. GNUTranslations parses optional metadata out of the translation catalog. It is convention with GNU gettext to include metadata as the translation for the empty string. This metadata is in RFC 822-style key: value pairs, and should contain the Project-Id-Version key. If the key Content-Type is found, then the charset property is used to initialize the “protected” _charset instance variable, defaulting to None if not found. If the charset encoding is specified, then all message ids and message strings read from the catalog are converted to Unicode using this encoding, else ASCII is assumed. Since message ids are read as Unicode strings too, all *gettext() methods will assume message ids as Unicode strings, not byte strings. The entire set of key/value pairs are placed into a dictionary and set as the “protected” _info instance variable. If the .mo file’s magic number is invalid, the major version number is unexpected, or if other problems occur while reading the file, instantiating a GNUTranslations class can raise OSError.
class gettext.GNUTranslations
The following methods are overridden from the base class implementation:
gettext(message)
Look up the message id in the catalog and return the corresponding message string, as a Unicode string. If there is no entry in the catalog for the message id, and a fallback has been set, the look up is forwarded to the fallback’s gettext() method. Otherwise, the message id is returned.
ngettext(singular, plural, n)
Do a plural-forms lookup of a message id. singular is used as the message id for purposes of lookup in the catalog, while n is used to determine which plural form to use. The returned message string is a Unicode string. If the message id is not found in the catalog, and a fallback is specified, the request is forwarded to the fallback’s ngettext() method. Otherwise, when n is 1 singular is returned, and plural is returned in all other cases. Here is an example: n = len(os.listdir('.'))
cat = GNUTranslations(somefile)
message = cat.ngettext(
'There is %(num)d file in this directory',
'There are %(num)d files in this directory',
n) % {'num': n}
pgettext(context, message)
Look up the context and message id in the catalog and return the corresponding message string, as a Unicode string. If there is no entry in the catalog for the message id and context, and a fallback has been set, the look up is forwarded to the fallback’s pgettext() method. Otherwise, the message id is returned. New in version 3.8.
npgettext(context, singular, plural, n)
Do a plural-forms lookup of a message id. singular is used as the message id for purposes of lookup in the catalog, while n is used to determine which plural form to use. If the message id for context is not found in the catalog, and a fallback is specified, the request is forwarded to the fallback’s npgettext() method. Otherwise, when n is 1 singular is returned, and plural is returned in all other cases. New in version 3.8.
lgettext(message)
lngettext(singular, plural, n)
Equivalent to gettext() and ngettext(), but the translation is returned as a byte string encoded in the preferred system encoding if no encoding was explicitly set with set_output_charset(). Warning These methods should be avoided in Python 3. See the warning for the lgettext() function. Deprecated since version 3.8, will be removed in version 3.10.
Solaris message catalog support The Solaris operating system defines its own binary .mo file format, but since no documentation can be found on this format, it is not supported at this time. The Catalog constructor GNOME uses a version of the gettext module by James Henstridge, but this version has a slightly different API. Its documented usage was: import gettext
cat = gettext.Catalog(domain, localedir)
_ = cat.gettext
print(_('hello world'))
For compatibility with this older module, the function Catalog() is an alias for the translation() function described above. One difference between this module and Henstridge’s: his catalog objects supported access through a mapping API, but this appears to be unused and so is not currently supported. Internationalizing your programs and modules Internationalization (I18N) refers to the operation by which a program is made aware of multiple languages. Localization (L10N) refers to the adaptation of your program, once internationalized, to the local language and cultural habits. In order to provide multilingual messages for your Python programs, you need to take the following steps: prepare your program or module by specially marking translatable strings run a suite of tools over your marked files to generate raw messages catalogs create language-specific translations of the message catalogs use the gettext module so that message strings are properly translated In order to prepare your code for I18N, you need to look at all the strings in your files. Any string that needs to be translated should be marked by wrapping it in _('...') — that is, a call to the function _(). For example: filename = 'mylog.txt'
message = _('writing a log message')
with open(filename, 'w') as fp:
fp.write(message)
In this example, the string 'writing a log message' is marked as a candidate for translation, while the strings 'mylog.txt' and 'w' are not. There are a few tools to extract the strings meant for translation. The original GNU gettext only supported C or C++ source code but its extended version xgettext scans code written in a number of languages, including Python, to find strings marked as translatable. Babel is a Python internationalization library that includes a pybabel script to extract and compile message catalogs. François Pinard’s program called xpot does a similar job and is available as part of his po-utils package. (Python also includes pure-Python versions of these programs, called pygettext.py and msgfmt.py; some Python distributions will install them for you. pygettext.py is similar to xgettext, but only understands Python source code and cannot handle other programming languages such as C or C++. pygettext.py supports a command-line interface similar to xgettext; for details on its use, run pygettext.py
--help. msgfmt.py is binary compatible with GNU msgfmt. With these two programs, you may not need the GNU gettext package to internationalize your Python applications.) xgettext, pygettext, and similar tools generate .po files that are message catalogs. They are structured human-readable files that contain every marked string in the source code, along with a placeholder for the translated versions of these strings. Copies of these .po files are then handed over to the individual human translators who write translations for every supported natural language. They send back the completed language-specific versions as a <language-name>.po file that’s compiled into a machine-readable .mo binary catalog file using the msgfmt program. The .mo files are used by the gettext module for the actual translation processing at run-time. How you use the gettext module in your code depends on whether you are internationalizing a single module or your entire application. The next two sections will discuss each case. Localizing your module If you are localizing your module, you must take care not to make global changes, e.g. to the built-in namespace. You should not use the GNU gettext API but instead the class-based API. Let’s say your module is called “spam” and the module’s various natural language translation .mo files reside in /usr/share/locale in GNU gettext format. Here’s what you would put at the top of your module: import gettext
t = gettext.translation('spam', '/usr/share/locale')
_ = t.gettext
Localizing your application If you are localizing your application, you can install the _() function globally into the built-in namespace, usually in the main driver file of your application. This will let all your application-specific files just use _('...') without having to explicitly install it in each file. In the simple case then, you need only add the following bit of code to the main driver file of your application: import gettext
gettext.install('myapplication')
If you need to set the locale directory, you can pass it into the install() function: import gettext
gettext.install('myapplication', '/usr/share/locale')
Changing languages on the fly If your program needs to support many languages at the same time, you may want to create multiple translation instances and then switch between them explicitly, like so: import gettext
lang1 = gettext.translation('myapplication', languages=['en'])
lang2 = gettext.translation('myapplication', languages=['fr'])
lang3 = gettext.translation('myapplication', languages=['de'])
# start by using language1
lang1.install()
# ... time goes by, user selects language 2
lang2.install()
# ... more time goes by, user selects language 3
lang3.install()
Deferred translations In most coding situations, strings are translated where they are coded. Occasionally however, you need to mark strings for translation, but defer actual translation until later. A classic example is: animals = ['mollusk',
'albatross',
'rat',
'penguin',
'python', ]
# ...
for a in animals:
print(a)
Here, you want to mark the strings in the animals list as being translatable, but you don’t actually want to translate them until they are printed. Here is one way you can handle this situation: def _(message): return message
animals = [_('mollusk'),
_('albatross'),
_('rat'),
_('penguin'),
_('python'), ]
del _
# ...
for a in animals:
print(_(a))
This works because the dummy definition of _() simply returns the string unchanged. And this dummy definition will temporarily override any definition of _() in the built-in namespace (until the del command). Take care, though if you have a previous definition of _() in the local namespace. Note that the second use of _() will not identify “a” as being translatable to the gettext program, because the parameter is not a string literal. Another way to handle this is with the following example: def N_(message): return message
animals = [N_('mollusk'),
N_('albatross'),
N_('rat'),
N_('penguin'),
N_('python'), ]
# ...
for a in animals:
print(_(a))
In this case, you are marking translatable strings with the function N_(), which won’t conflict with any definition of _(). However, you will need to teach your message extraction program to look for translatable strings marked with N_(). xgettext, pygettext, pybabel extract, and xpot all support this through the use of the -k command-line switch. The choice of N_() here is totally arbitrary; it could have just as easily been MarkThisStringForTranslation(). Acknowledgements The following people contributed code, feedback, design suggestions, previous implementations, and valuable experience to the creation of this module: Peter Funk James Henstridge Juan David Ibáñez Palomar Marc-André Lemburg Martin von Löwis François Pinard Barry Warsaw Gustavo Niemeyer Footnotes
1
The default locale directory is system dependent; for example, on RedHat Linux it is /usr/share/locale, but on Solaris it is /usr/lib/locale. The gettext module does not try to support these system dependent defaults; instead its default is sys.base_prefix/share/locale (see sys.base_prefix). For this reason, it is always best to call bindtextdomain() with an explicit absolute path at the start of your application.
2
See the footnote for bindtextdomain() above. | python.library.gettext |
gettext.bindtextdomain(domain, localedir=None)
Bind the domain to the locale directory localedir. More concretely, gettext will look for binary .mo files for the given domain using the path (on Unix): localedir/language/LC_MESSAGES/domain.mo, where language is searched for in the environment variables LANGUAGE, LC_ALL, LC_MESSAGES, and LANG respectively. If localedir is omitted or None, then the current binding for domain is returned. 1 | python.library.gettext#gettext.bindtextdomain |
gettext.bind_textdomain_codeset(domain, codeset=None)
Bind the domain to codeset, changing the encoding of byte strings returned by the lgettext(), ldgettext(), lngettext() and ldngettext() functions. If codeset is omitted, then the current binding is returned. Deprecated since version 3.8, will be removed in version 3.10. | python.library.gettext#gettext.bind_textdomain_codeset |
gettext.dgettext(domain, message)
Like gettext(), but look the message up in the specified domain. | python.library.gettext#gettext.dgettext |
gettext.dngettext(domain, singular, plural, n)
Like ngettext(), but look the message up in the specified domain. | python.library.gettext#gettext.dngettext |
gettext.dnpgettext(domain, context, singular, plural, n)
Similar to the corresponding functions without the p in the prefix (that is, gettext(), dgettext(), ngettext(), dngettext()), but the translation is restricted to the given message context. New in version 3.8. | python.library.gettext#gettext.dnpgettext |
gettext.dpgettext(domain, context, message) | python.library.gettext#gettext.dpgettext |
gettext.find(domain, localedir=None, languages=None, all=False)
This function implements the standard .mo file search algorithm. It takes a domain, identical to what textdomain() takes. Optional localedir is as in bindtextdomain(). Optional languages is a list of strings, where each string is a language code. If localedir is not given, then the default system locale directory is used. 2 If languages is not given, then the following environment variables are searched: LANGUAGE, LC_ALL, LC_MESSAGES, and LANG. The first one returning a non-empty value is used for the languages variable. The environment variables should contain a colon separated list of languages, which will be split on the colon to produce the expected list of language code strings. find() then expands and normalizes the languages, and then iterates through them, searching for an existing file built of these components: localedir/language/LC_MESSAGES/domain.mo The first such file name that exists is returned by find(). If no such file is found, then None is returned. If all is given, it returns a list of all file names, in the order in which they appear in the languages list or the environment variables. | python.library.gettext#gettext.find |
gettext.gettext(message)
Return the localized translation of message, based on the current global domain, language, and locale directory. This function is usually aliased as _() in the local namespace (see examples below). | python.library.gettext#gettext.gettext |
class gettext.GNUTranslations
The following methods are overridden from the base class implementation:
gettext(message)
Look up the message id in the catalog and return the corresponding message string, as a Unicode string. If there is no entry in the catalog for the message id, and a fallback has been set, the look up is forwarded to the fallback’s gettext() method. Otherwise, the message id is returned.
ngettext(singular, plural, n)
Do a plural-forms lookup of a message id. singular is used as the message id for purposes of lookup in the catalog, while n is used to determine which plural form to use. The returned message string is a Unicode string. If the message id is not found in the catalog, and a fallback is specified, the request is forwarded to the fallback’s ngettext() method. Otherwise, when n is 1 singular is returned, and plural is returned in all other cases. Here is an example: n = len(os.listdir('.'))
cat = GNUTranslations(somefile)
message = cat.ngettext(
'There is %(num)d file in this directory',
'There are %(num)d files in this directory',
n) % {'num': n}
pgettext(context, message)
Look up the context and message id in the catalog and return the corresponding message string, as a Unicode string. If there is no entry in the catalog for the message id and context, and a fallback has been set, the look up is forwarded to the fallback’s pgettext() method. Otherwise, the message id is returned. New in version 3.8.
npgettext(context, singular, plural, n)
Do a plural-forms lookup of a message id. singular is used as the message id for purposes of lookup in the catalog, while n is used to determine which plural form to use. If the message id for context is not found in the catalog, and a fallback is specified, the request is forwarded to the fallback’s npgettext() method. Otherwise, when n is 1 singular is returned, and plural is returned in all other cases. New in version 3.8.
lgettext(message)
lngettext(singular, plural, n)
Equivalent to gettext() and ngettext(), but the translation is returned as a byte string encoded in the preferred system encoding if no encoding was explicitly set with set_output_charset(). Warning These methods should be avoided in Python 3. See the warning for the lgettext() function. Deprecated since version 3.8, will be removed in version 3.10. | python.library.gettext#gettext.GNUTranslations |
gettext(message)
Look up the message id in the catalog and return the corresponding message string, as a Unicode string. If there is no entry in the catalog for the message id, and a fallback has been set, the look up is forwarded to the fallback’s gettext() method. Otherwise, the message id is returned. | python.library.gettext#gettext.GNUTranslations.gettext |
lgettext(message) | python.library.gettext#gettext.GNUTranslations.lgettext |
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