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The Python Standard Library -

The Python Standard Library

Library Reference

While The Python Language Reference describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. It also describes some of the optional components that are commonly included in Python distributions.

Python’s standard library is very extensive, offering a wide range of facilities as indicated by the long table of contents listed below. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming. Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs.

The Python installers for the Windows platform usually include the entire standard library and often also include many additional components. For Unix-like operating systems Python is normally provided as a collection of packages, so it may be necessary to use the packaging tools provided with the operating system to obtain some or all of the optional components.

In addition to the standard library, there is a growing collection of several thousand components (from individual programs and modules to packages and entire application development frameworks), available from the Python Package Index.

Introduction

The “Python library” contains several different kinds of components.

It contains data types that would normally be considered part of the “core” of a language, such as numbers and lists. For these types, the Python language core defines the form of literals and places some constraints on their semantics, but does not fully define the semantics. (On the other hand, the language core does define syntactic properties like the spelling and priorities of operators.)

The library also contains built-in functions and exceptions — objects that can be used by all Python code without the need of an import statement. Some of these are defined by the core language, but many are not essential for the core semantics and are only described here.

The bulk of the library, however, consists of a collection of modules. There are many ways to dissect this collection. Some modules are written in C and built in to the Python interpreter; others are written in Python and imported in source form. Some modules provide interfaces that are highly specific to Python, like printing a stack trace; some provide interfaces that are specific to particular operating systems, such as access to specific hardware; others provide interfaces that are specific to a particular application domain, like the World Wide Web. Some modules are available in all versions and ports of Python; others are only available when the underlying system supports or requires them; yet others are available only when a particular configuration option was chosen at the time when Python was compiled and installed.

This manual is organized “from the inside out:” it first describes the built-in functions, data types and exceptions, and finally the modules, grouped in chapters of related modules.

This means that if you start reading this manual from the start, and skip to the next chapter when you get bored, you will get a reasonable overview of the available modules and application areas that are supported by the Python library. Of course, you don’t have to read it like a novel — you can also browse the table of contents (in front of the manual), or look for a specific function, module or term in the index (in the back). And finally, if you enjoy learning about random subjects, you choose a random page number (see module random) and read a section or two. Regardless of the order in which you read the sections of this manual, it helps to start with chapter Built-in Functions, as the remainder of the manual assumes familiarity with this material.

Let the show begin!

Notes on availability

  • An “Availability: Unix” note means that this function is commonly found on Unix systems. It does not make any claims about its existence on a specific operating system.

  • If not separately noted, all functions that claim “Availability: Unix” are supported on macOS, which builds on a Unix core.

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.

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.

aiter(async_iterable)

Return an asynchronous iterator for an asynchronous iterable. Equivalent to calling x.__aiter__().

Note: Unlike iter()aiter() has no 2-argument variant.

New in version 3.10.

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
awaitable anext(async_iterator[default])

When awaited, return the next item from the given asynchronous iterator, or default if given and the iterator is exhausted.

This is the async variant of the next() builtin, and behaves similarly.

This calls the __anext__() method of async_iterator, returning an awaitable. Awaiting this returns the next value of the iterator. If default is given, it is returned if the iterator is exhausted, otherwise StopAsyncIteration is raised.

New in version 3.10.

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 the 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 Falsex 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 < 256bytes 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, memoryviewBytes 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 an 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().

Changed in version 3.10: Class methods now inherit the method attributes (__module____name____qualname____doc__ and __annotations__) and have a new __wrapped__ attribute.

compile(sourcefilenamemodeflags=0dont_inherit=Falseoptimize=– 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 ('' 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 awaitasync 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 xcomplex(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(objectname)

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 listset, 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(ab)

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(iterablestart=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. 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 eval(). 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 return the current global and local dictionary, respectively, which may be useful to pass around for use by eval() or exec().

If the given source is a string, then leading and trailing spaces and tabs are stripped.

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 nonlocalyield, 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 the 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(functioniterable)

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 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 xfloat(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 iterablefrozenset is a built-in class. See frozenset and Set Types — set, frozenset for documentation about this class.

For other containers see the built-in setlisttuple, and dict classes, as well as the collections module.

getattr(objectname[default])

Return the value of the named attribute of objectname 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.

Note

Since private name mangling happens at compilation time, one must manually mangle a private attribute’s (attributes with two leading underscores) name in order to retrieve it with getattr().

globals()

Return the dictionary implementing the current module namespace. For code within functions, this is set when the function is defined and remains the same regardless of where the function is called.

hasattr(objectname)

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(xbase=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/0B0o/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(objectclassinfo)

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) or a Union Type of multiple types, 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.

Changed in version 3.10: classinfo can be a Union Type.

issubclass(classclassinfo)

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 or a Union Type, in which case return True if class is a subclass of any entry in classinfo. In any other case, a TypeError exception is raised.

Changed in version 3.10: classinfo can be a Union Type.

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 iterable 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 sentinelStopIteration 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(functioniterable)

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*[keydefault])
max(arg1arg2*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(object)

Return a “memory view” object created from the given argument. See Memory Views for more information.

min(iterable*[keydefault])
min(arg1arg2*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 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 an octal string either with the 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(filemode=‘r’buffering=– 1encoding=Noneerrors=Nonenewline=Noneclosefd=Trueopener=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 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, a 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 behavior 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. Note that specifying a buffer size this way applies for binary buffered I/O, but TextIOWrapper (i.e., files opened with mode='r+') would have another buffering. To disable buffering in TextIOWrapper, consider using the write_through flag for io.TextIOWrapper.reconfigure(). 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 low surrogate code units ranging from U+DC80 to U+DCFF. These surrogate code units 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 .

  • '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 (fileflags). 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.RawIOBaseio.FileIO, is returned.

See also the file handling modules, such as fileinputio (where open() is declared), osos.pathtempfile, and shutil.

Raises an auditing event open with arguments filemodeflags.

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, 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:

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(baseexp[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, pow(10, 2) returns 100, but pow(10, -2) returns 0.01. For a negative base of type int or float and a non-integral exponent, a complex result is delivered. For example, pow(-9, 0.5) returns a value close to 3j.

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(*objectssep=‘ ‘end=‘\n’file=sys.stdoutflush=False)

Print objects to the text stream file, separated by sep and followed by endsependfile, 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 Nonesys.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 the 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=Nonefset=Nonefdel=Nonedoc=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 Cc.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 gettersetter, 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 fgetfset, 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(startstop[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 iteratorseq 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 numberround 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 iterableset is a built-in class. See set and Set Types — set, frozenset for documentation about this class.

For other containers see the built-in frozensetlisttuple, and dict classes, as well as the collections module.

setattr(objectnamevalue)

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.

Note

Since private name mangling happens at compilation time, one must manually mangle a private attribute’s (attributes with two leading underscores) name in order to set it with setattr().

class slice(stop)
class slice(startstop[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 startstop, and step which merely return the argument values (or their default). They have no other explicit functionality; however, they are used by NumPy and other third-party packages. 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=Nonereverse=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).

The sort algorithm uses only < comparisons between items. While defining an __lt__() method will suffice for sorting, PEP 8 recommends that all six rich comparisons be implemented. This will help avoid bugs when using the same data with other ordering tools such as max() that rely on a different underlying method. Implementing all six comparisons also helps avoid confusion for mixed type comparisons which can call reflected the __gt__() method.

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()). Moreover, they can be called as regular functions (such as 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:

def regular_function():
    ...

class C:
    method = staticmethod(regular_function)

For more information on static methods, see The standard type hierarchy.

Changed in version 3.10: Static methods now inherit the method attributes (__module____name____qualname____doc__ and __annotations__), have a new __wrapped__ attribute, and are now callable as regular functions.

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.

class 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(namebasesdict**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.

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(*iterablesstrict=False)

Iterate over several iterables in parallel, producing tuples with an item from each one.

Example:

>>>

>>> for item in zip([1, 2, 3], ['sugar', 'spice', 'everything nice']):
...     print(item)
...
(1, 'sugar')
(2, 'spice')
(3, 'everything nice')

More formally: zip() returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument iterables.

Another way to think of zip() is that it turns rows into columns, and columns into rows. This is similar to transposing a matrix.

zip() is lazy: The elements won’t be processed until the iterable is iterated on, e.g. by a for loop or by wrapping in a list.

One thing to consider is that the iterables passed to zip() could have different lengths; sometimes by design, and sometimes because of a bug in the code that prepared these iterables. Python offers three different approaches to dealing with this issue:

  • By default, zip() stops when the shortest iterable is exhausted. It will ignore the remaining items in the longer iterables, cutting off the result to the length of the shortest iterable:

    >>>

    >>> list(zip(range(3), ['fee', 'fi', 'fo', 'fum']))
    [(0, 'fee'), (1, 'fi'), (2, 'fo')]
    
  • zip() is often used in cases where the iterables are assumed to be of equal length. In such cases, it’s recommended to use the strict=True option. Its output is the same as regular zip():

    >>>

    >>> list(zip(('a', 'b', 'c'), (1, 2, 3), strict=True))
    [('a', 1), ('b', 2), ('c', 3)]
    

    Unlike the default behavior, it checks that the lengths of iterables are identical, raising a ValueError if they aren’t:

    >>>

    >>> list(zip(range(3), ['fee', 'fi', 'fo', 'fum'], strict=True))
    Traceback (most recent call last):
      ...
    ValueError: zip() argument 2 is longer than argument 1
    

    Without the strict=True argument, any bug that results in iterables of different lengths will be silenced, possibly manifesting as a hard-to-find bug in another part of the program.

  • Shorter iterables can be padded with a constant value to make all the iterables have the same length. This is done by itertools.zip_longest().

Edge cases: With a single iterable argument, zip() returns an iterator of 1-tuples. With no arguments, it returns an empty iterator.

Tips and tricks:

  • 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, strict=True). 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() in conjunction with the * operator can be used to unzip a list:

    >>>

    >>> x = [1, 2, 3]
    >>> y = [4, 5, 6]
    >>> list(zip(x, y))
    [(1, 4), (2, 5), (3, 6)]
    >>> x2, y2 = zip(*zip(x, y))
    >>> x == list(x2) and y == list(y2)
    True
    

Changed in version 3.10: Added the strict argument.

__import__(nameglobals=Nonelocals=Nonefromlist=()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.

Built-in Constants

A small number of constants live in the built-in namespace. They are:

False

The false value of the bool type. Assignments to False are illegal and raise a SyntaxError.

True

The true value of the bool type. Assignments to True are illegal and raise a SyntaxError.

None

An object frequently used to represent the absence of a value, as when default arguments are not passed to a function. Assignments to None are illegal and raise a SyntaxErrorNone is the sole instance of the NoneType type.

NotImplemented

A special value which should be returned by the binary special methods (e.g. __eq__()__lt__()__add__()__rsub__(), etc.) to indicate that the operation is not implemented with respect to the other type; may be returned by the in-place binary special methods (e.g. __imul__()__iand__(), etc.) for the same purpose. It should not be evaluated in a boolean context. NotImplemented is the sole instance of the types.NotImplementedType type.

Note

When a binary (or in-place) method returns NotImplemented the interpreter will try the reflected operation on the other type (or some other fallback, depending on the operator). If all attempts return NotImplemented, the interpreter will raise an appropriate exception. Incorrectly returning NotImplemented will result in a misleading error message or the NotImplemented value being returned to Python code.

See Implementing the arithmetic operations for examples.

Note

NotImplementedError and NotImplemented are not interchangeable, even though they have similar names and purposes. See NotImplementedError for details on when to use it.

Changed in version 3.9: Evaluating NotImplemented in a boolean context is deprecated. While it currently evaluates as true, it will emit a DeprecationWarning. It will raise a TypeError in a future version of Python.

Ellipsis

The same as the ellipsis literal “...”. Special value used mostly in conjunction with extended slicing syntax for user-defined container data types. Ellipsis is the sole instance of the types.EllipsisType type.

__debug__

This constant is true if Python was not started with an -O option. See also the assert statement.

Note

The names NoneFalseTrue and __debug__ cannot be reassigned (assignments to them, even as an attribute name, raise SyntaxError), so they can be considered “true” constants.

Constants added by the site module

The site module (which is imported automatically during startup, except if the -S command-line option is given) adds several constants to the built-in namespace. They are useful for the interactive interpreter shell and should not be used in programs.

quit(code=None)
exit(code=None)

Objects that when printed, print a message like “Use quit() or Ctrl-D (i.e. EOF) to exit”, and when called, raise SystemExit with the specified exit code.

credits

Objects that when printed or called, print the text of copyright or credits, respectively.

license

Object that when printed, prints the message “Type license() to see the full license text”, and when called, displays the full license text in a pager-like fashion (one screen at a time).

Built-in Types

The following sections describe the standard types that are built into the interpreter.

The principal built-in types are numerics, sequences, mappings, classes, instances and exceptions.

Some collection classes are mutable. The methods that add, subtract, or rearrange their members in place, and don’t return a specific item, never return the collection instance itself but None.

Some operations are supported by several object types; in particular, practically all objects can be compared for equality, tested for truth value, and converted to a string (with the repr() function or the slightly different str() function). The latter function is implicitly used when an object is written by the print() function.

Truth Value Testing

Any object can be tested for truth value, for use in an if or while condition or as operand of the Boolean operations below.

By default, an object is considered true unless its class defines either a __bool__() method that returns False or a __len__() method that returns zero, when called with the object. 1 Here are most of the built-in objects considered false:

 
  • constants defined to be false: None and False.

  • zero of any numeric type: 00.00jDecimal(0)Fraction(0, 1)

  • empty sequences and collections: ''()[]{}set()range(0)

Operations and built-in functions that have a Boolean result always return 0 or False for false and 1 or True for true, unless otherwise stated. (Important exception: the Boolean operations or and and always return one of their operands.)

Boolean Operations — andornot

These are the Boolean operations, ordered by ascending priority:

Operation

Result

Notes

x or y

if x is false, then y, else x

(1)

x and y

if x is false, then x, else y

(2)

not x

if x is false, then True, else False

(3)

Notes:

  1. This is a short-circuit operator, so it only evaluates the second argument if the first one is false.

  2. This is a short-circuit operator, so it only evaluates the second argument if the first one is true.

  3. not has a lower priority than non-Boolean operators, so not a == b is interpreted as not (a == b), and a == not b is a syntax error.

Comparisons

There are eight comparison operations in Python. They all have the same priority (which is higher than that of the Boolean operations). Comparisons can be chained arbitrarily; for example, x < y <= z is equivalent to x < y and y <= z, except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

This table summarizes the comparison operations:

Operation

Meaning

<

strictly less than

<=

less than or equal

>

strictly greater than

>=

greater than or equal

==

equal

!=

not equal

is

object identity

is not

negated object identity

Objects of different types, except different numeric types, never compare equal. The == operator is always defined but for some object types (for example, class objects) is equivalent to is. The <<=> and >= operators are only defined where they make sense; for example, they raise a TypeError exception when one of the arguments is a complex number.

Non-identical instances of a class normally compare as non-equal unless the class defines the __eq__() method.

Instances of a class cannot be ordered with respect to other instances of the same class, or other types of object, unless the class defines enough of the methods __lt__()__le__()__gt__(), and __ge__() (in general, __lt__() and __eq__() are sufficient, if you want the conventional meanings of the comparison operators).

The behavior of the is and is not operators cannot be customized; also they can be applied to any two objects and never raise an exception.

Two more operations with the same syntactic priority, in and not in, are supported by types that are iterable or implement the __contains__() method.

Numeric Types — intfloatcomplex

There are three distinct numeric types: integersfloating point numbers, and complex numbers. In addition, Booleans are a subtype of integers. Integers have unlimited precision. Floating point numbers are usually implemented using double in C; information about the precision and internal representation of floating point numbers for the machine on which your program is running is available in sys.float_info. Complex numbers have a real and imaginary part, which are each a floating point number. To extract these parts from a complex number z, use z.real and z.imag. (The standard library includes the additional numeric types fractions.Fraction, for rationals, and decimal.Decimal, for floating-point numbers with user-definable precision.)

Numbers are created by numeric literals or as the result of built-in functions and operators. Unadorned integer literals (including hex, octal and binary numbers) yield integers. Numeric literals containing a decimal point or an exponent sign yield floating point numbers. Appending 'j' or 'J' to a numeric literal yields an imaginary number (a complex number with a zero real part) which you can add to an integer or float to get a complex number with real and imaginary parts.

Python fully supports mixed arithmetic: when a binary arithmetic operator has operands of different numeric types, the operand with the “narrower” type is widened to that of the other, where integer is narrower than floating point, which is narrower than complex. A comparison between numbers of different types behaves as though the exact values of those numbers were being compared. 2

The constructors int()float(), and complex() can be used to produce numbers of a specific type.

All numeric types (except complex) support the following operations (for priorities of the operations, see Operator precedence):

Operation

Result

Notes

Full documentation

x + y

sum of x and y

  

x - y

difference of x and y

  

x * y

product of x and y

  

x / y

quotient of x and y

  

x // y

floored quotient of x and y

(1)

 

x % y

remainder of x / y

(2)

 

-x

x negated

  

+x

x unchanged

  

abs(x)

absolute value or magnitude of x

 

abs()

int(x)

x converted to integer

(3)(6)

int()

float(x)

x converted to floating point

(4)(6)

float()

complex(re, im)

a complex number with real part re, imaginary part imim defaults to zero.

(6)

complex()

c.conjugate()

conjugate of the complex number c

  

divmod(x, y)

the pair (x // y, x % y)

(2)

divmod()

pow(x, y)

x to the power y

(5)

pow()

x ** y

x to the power y

(5)

 

Notes:

  1. Also referred to as integer division. The resultant value is a whole integer, though the result’s type is not necessarily int. The result is always rounded towards minus infinity: 1//2 is 0(-1)//2 is -11//(-2) is -1, and (-1)//(-2) is 0.

  2. Not for complex numbers. Instead convert to floats using abs() if appropriate.

  3. Conversion from floating point to integer may round or truncate as in C; see functions math.floor() and math.ceil() for well-defined conversions.

  4. float also accepts the strings “nan” and “inf” with an optional prefix “+” or “-” for Not a Number (NaN) and positive or negative infinity.

  5. Python defines pow(0, 0) and 0 ** 0 to be 1, as is common for programming languages.

  6. The numeric literals accepted include the digits 0 to 9 or any Unicode equivalent (code points with the Nd property).

    See https://www.unicode.org/Public/13.0.0/ucd/extracted/DerivedNumericType.txt for a complete list of code points with the Nd property.

All numbers.Real types (int and float) also include the following operations:

Operation

Result

math.trunc(x)

x truncated to Integral

round(x[, n])

x rounded to n digits, rounding half to even. If n is omitted, it defaults to 0.

math.floor(x)

the greatest Integral <= x

math.ceil(x)

the least Integral >= x

For additional numeric operations see the math and cmath modules.

Bitwise Operations on Integer Types

Bitwise operations only make sense for integers. The result of bitwise operations is calculated as though carried out in two’s complement with an infinite number of sign bits.

The priorities of the binary bitwise operations are all lower than the numeric operations and higher than the comparisons; the unary operation ~ has the same priority as the other unary numeric operations (+ and -).

This table lists the bitwise operations sorted in ascending priority:

Operation

Result

Notes

x | y

bitwise or of x and y

(4)

x ^ y

bitwise exclusive or of x and y

(4)

x & y

bitwise and of x and y

(4)

x << n

x shifted left by n bits

(1)(2)

x >> n

x shifted right by n bits

(1)(3)

~x

the bits of x inverted

 

Notes:

  1. Negative shift counts are illegal and cause a ValueError to be raised.

  2. A left shift by n bits is equivalent to multiplication by pow(2, n).

  3. A right shift by n bits is equivalent to floor division by pow(2, n).

  4. Performing these calculations with at least one extra sign extension bit in a finite two’s complement representation (a working bit-width of 1 + max(x.bit_length(), y.bit_length()) or more) is sufficient to get the same result as if there were an infinite number of sign bits.

Additional Methods on Integer Types

The int type implements the numbers.Integral abstract base class. In addition, it provides a few more methods:

int.bit_length()

Return the number of bits necessary to represent an integer in binary, excluding the sign and leading zeros:

>>>

>>> n = -37
>>> bin(n)
'-0b100101'
>>> n.bit_length()
6

More precisely, if x is nonzero, then x.bit_length() is the unique positive integer k such that 2**(k-1) <= abs(x) < 2**k. Equivalently, when abs(x) is small enough to have a correctly rounded logarithm, then k = 1 + int(log(abs(x), 2)). If x is zero, then x.bit_length() returns 0.

Equivalent to:

def bit_length(self):
    s = bin(self)       # binary representation:  bin(-37) --> '-0b100101'
    s = s.lstrip('-0b') # remove leading zeros and minus sign
    return len(s)       # len('100101') --> 6

New in version 3.1.

int.bit_count()

Return the number of ones in the binary representation of the absolute value of the integer. This is also known as the population count. Example:

>>>

>>> n = 19
>>> bin(n)
'0b10011'
>>> n.bit_count()
3
>>> (-n).bit_count()
3

Equivalent to:

def bit_count(self):
    return bin(self).count("1")

New in version 3.10.

int.to_bytes(lengthbyteorder*signed=False)

Return an array of bytes representing an integer.

>>>

>>> (1024).to_bytes(2, byteorder='big')
b'\x04\x00'
>>> (1024).to_bytes(10, byteorder='big')
b'\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00'
>>> (-1024).to_bytes(10, byteorder='big', signed=True)
b'\xff\xff\xff\xff\xff\xff\xff\xff\xfc\x00'
>>> x = 1000
>>> x.to_bytes((x.bit_length() + 7) // 8, byteorder='little')
b'\xe8\x03'

The integer is represented using length bytes. An OverflowError is raised if the integer is not representable with the given number of bytes.

The byteorder argument determines the byte order used to represent the integer. If byteorder is "big", the most significant byte is at the beginning of the byte array. If byteorder is "little", the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value.

The signed argument determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised. The default value for signed is False.

New in version 3.2.

classmethod int.from_bytes(bytesbyteorder*signed=False)

Return the integer represented by the given array of bytes.

>>>

>>> int.from_bytes(b'\x00\x10', byteorder='big')
16
>>> int.from_bytes(b'\x00\x10', byteorder='little')
4096
>>> int.from_bytes(b'\xfc\x00', byteorder='big', signed=True)
-1024
>>> int.from_bytes(b'\xfc\x00', byteorder='big', signed=False)
64512
>>> int.from_bytes([255, 0, 0], byteorder='big')
16711680

The argument bytes must either be a bytes-like object or an iterable producing bytes.

The byteorder argument determines the byte order used to represent the integer. If byteorder is "big", the most significant byte is at the beginning of the byte array. If byteorder is "little", the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value.

The signed argument indicates whether two’s complement is used to represent the integer.

New in version 3.2.

int.as_integer_ratio()

Return a pair of integers whose ratio is exactly equal to the original integer and with a positive denominator. The integer ratio of integers (whole numbers) is always the integer as the numerator and 1 as the denominator.

New in version 3.8.

Additional Methods on Float

The float type implements the numbers.Real abstract base class. float also has the following additional methods.

float.as_integer_ratio()

Return a pair of integers whose ratio is exactly equal to the original float and with a positive denominator. Raises OverflowError on infinities and a ValueError on NaNs.

float.is_integer()

Return True if the float instance is finite with integral value, and False otherwise:

>>>

>>> (-2.0).is_integer()
True
>>> (3.2).is_integer()
False

Two methods support conversion to and from hexadecimal strings. Since Python’s floats are stored internally as binary numbers, converting a float to or from a decimal string usually involves a small rounding error. In contrast, hexadecimal strings allow exact representation and specification of floating-point numbers. This can be useful when debugging, and in numerical work.

float.hex()

Return a representation of a floating-point number as a hexadecimal string. For finite floating-point numbers, this representation will always include a leading 0x and a trailing p and exponent.

classmethod float.fromhex(s)

Class method to return the float represented by a hexadecimal string s. The string s may have leading and trailing whitespace.

Note that float.hex() is an instance method, while float.fromhex() is a class method.

A hexadecimal string takes the form:

[sign] ['0x'] integer ['.' fraction] ['p' exponent]

where the optional sign may by either + or -integer and fraction are strings of hexadecimal digits, and exponent is a decimal integer with an optional leading sign. Case is not significant, and there must be at least one hexadecimal digit in either the integer or the fraction. This syntax is similar to the syntax specified in section 6.4.4.2 of the C99 standard, and also to the syntax used in Java 1.5 onwards. In particular, the output of float.hex() is usable as a hexadecimal floating-point literal in C or Java code, and hexadecimal strings produced by C’s %a format character or Java’s Double.toHexString are accepted by float.fromhex().

Note that the exponent is written in decimal rather than hexadecimal, and that it gives the power of 2 by which to multiply the coefficient. For example, the hexadecimal string 0x3.a7p10 represents the floating-point number (3 + 10./16 + 7./16**2) * 2.0**10, or 3740.0:

>>>

>>> float.fromhex('0x3.a7p10')
3740.0

Applying the reverse conversion to 3740.0 gives a different hexadecimal string representing the same number:

>>>

>>> float.hex(3740.0)
'0x1.d380000000000p+11'

Hashing of numeric types

For numbers x and y, possibly of different types, it’s a requirement that hash(x) == hash(y) whenever x == y (see the __hash__() method documentation for more details). For ease of implementation and efficiency across a variety of numeric types (including intfloatdecimal.Decimal and fractions.Fraction) Python’s hash for numeric types is based on a single mathematical function that’s defined for any rational number, and hence applies to all instances of int and fractions.Fraction, and all finite instances of float and decimal.Decimal. Essentially, this function is given by reduction modulo P for a fixed prime P. The value of P is made available to Python as the modulus attribute of sys.hash_info.

CPython implementation detail: Currently, the prime used is P = 2**31 - 1 on machines with 32-bit C longs and P = 2**61 - 1 on machines with 64-bit C longs.

Here are the rules in detail:

  • If x = m / n is a nonnegative rational number and n is not divisible by P, define hash(x) as m * invmod(n, P) % P, where invmod(n, P) gives the inverse of n modulo P.

  • If x = m / n is a nonnegative rational number and n is divisible by P (but m is not) then n has no inverse modulo P and the rule above doesn’t apply; in this case define hash(x) to be the constant value sys.hash_info.inf.

  • If x = m / n is a negative rational number define hash(x) as -hash(-x). If the resulting hash is -1, replace it with -2.

  • The particular values sys.hash_info.inf and -sys.hash_info.inf are used as hash values for positive infinity or negative infinity (respectively).

  • For a complex number z, the hash values of the real and imaginary parts are combined by computing hash(z.real) + sys.hash_info.imag * hash(z.imag), reduced modulo 2**sys.hash_info.width so that it lies in range(-2**(sys.hash_info.width - 1), 2**(sys.hash_info.width - 1)). Again, if the result is -1, it’s replaced with -2.

To clarify the above rules, here’s some example Python code, equivalent to the built-in hash, for computing the hash of a rational number, float, or complex:

import sys, math

def hash_fraction(m, n):
    """Compute the hash of a rational number m / n.

    Assumes m and n are integers, with n positive.
    Equivalent to hash(fractions.Fraction(m, n)).

    """
    P = sys.hash_info.modulus
    # Remove common factors of P.  (Unnecessary if m and n already coprime.)
    while m % P == n % P == 0:
        m, n = m // P, n // P

    if n % P == 0:
        hash_value = sys.hash_info.inf
    else:
        # Fermat's Little Theorem: pow(n, P-1, P) is 1, so
        # pow(n, P-2, P) gives the inverse of n modulo P.
        hash_value = (abs(m) % P) * pow(n, P - 2, P) % P
    if m < 0:
        hash_value = -hash_value
    if hash_value == -1:
        hash_value = -2
    return hash_value

def hash_float(x):
    """Compute the hash of a float x."""

    if math.isnan(x):
        return object.__hash__(x)
    elif math.isinf(x):
        return sys.hash_info.inf if x > 0 else -sys.hash_info.inf
    else:
        return hash_fraction(*x.as_integer_ratio())

def hash_complex(z):
    """Compute the hash of a complex number z."""

    hash_value = hash_float(z.real) + sys.hash_info.imag * hash_float(z.imag)
    # do a signed reduction modulo 2**sys.hash_info.width
    M = 2**(sys.hash_info.width - 1)
    hash_value = (hash_value & (M - 1)) - (hash_value & M)
    if hash_value == -1:
        hash_value = -2
    return hash_value

Iterator Types

Python supports a concept of iteration over containers. This is implemented using two distinct methods; these are used to allow user-defined classes to support iteration. Sequences, described below in more detail, always support the iteration methods.

One method needs to be defined for container objects to provide iterable support:

container.__iter__()

Return an iterator object. The object is required to support the iterator protocol described below. If a container supports different types of iteration, additional methods can be provided to specifically request iterators for those iteration types. (An example of an object supporting multiple forms of iteration would be a tree structure which supports both breadth-first and depth-first traversal.) This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API.

The iterator objects themselves are required to support the following two methods, which together form the iterator protocol:

iterator.__iter__()

Return the iterator object itself. This is required to allow both containers and iterators to be used with the for and in statements. This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API.

iterator.__next__()

Return the next item from the iterator. If there are no further items, raise the StopIteration exception. This method corresponds to the tp_iternext slot of the type structure for Python objects in the Python/C API.

Python defines several iterator objects to support iteration over general and specific sequence types, dictionaries, and other more specialized forms. The specific types are not important beyond their implementation of the iterator protocol.

Once an iterator’s __next__() method raises StopIteration, it must continue to do so on subsequent calls. Implementations that do not obey this property are deemed broken.

Generator Types

Python’s generators provide a convenient way to implement the iterator protocol. If a container object’s __iter__() method is implemented as a generator, it will automatically return an iterator object (technically, a generator object) supplying the __iter__() and __next__() methods. More information about generators can be found in the documentation for the yield expression.

Sequence Types — listtuplerange

There are three basic sequence types: lists, tuples, and range objects. Additional sequence types tailored for processing of binary data and text strings are described in dedicated sections.

Common Sequence Operations

The operations in the following table are supported by most sequence types, both mutable and immutable. The collections.abc.Sequence ABC is provided to make it easier to correctly implement these operations on custom sequence types.

This table lists the sequence operations sorted in ascending priority. In the table, s and t are sequences of the same type, nij and k are integers and x is an arbitrary object that meets any type and value restrictions imposed by s.

The in and not in operations have the same priorities as the comparison operations. The + (concatenation) and * (repetition) operations have the same priority as the corresponding numeric operations. 3

Operation

Result

Notes

x in s

True if an item of s is equal to x, else False

(1)

x not in s

False if an item of s is equal to x, else True

(1)

s + t

the concatenation of s and t

(6)(7)

s * n or n * s

equivalent to adding s to itself n times

(2)(7)

s[i]

ith item of s, origin 0

(3)

s[i:j]

slice of s from i to j

(3)(4)

s[i:j:k]

slice of s from i to j with step k

(3)(5)

len(s)

length of s

 

min(s)

smallest item of s

 

max(s)

largest item of s

 

s.index(x[, i[, j]])

index of the first occurrence of x in s (at or after index i and before index j)

(8)

s.count(x)

total number of occurrences of x in s

 

Sequences of the same type also support comparisons. In particular, tuples and lists are compared lexicographically by comparing corresponding elements. This means that to compare equal, every element must compare equal and the two sequences must be of the same type and have the same length. (For full details see Comparisons in the language reference.)

Forward and reversed iterators over mutable sequences access values using an index. That index will continue to march forward (or backward) even if the underlying sequence is mutated. The iterator terminates only when an IndexError or a StopIteration is encountered (or when the index drops below zero).

Notes:

  1. While the in and not in operations are used only for simple containment testing in the general case, some specialised sequences (such as strbytes and bytearray) also use them for subsequence testing:

    >>>

    >>> "gg" in "eggs"
    True
    
  2. Values of n less than 0 are treated as 0 (which yields an empty sequence of the same type as s). Note that items in the sequence s are not copied; they are referenced multiple times. This often haunts new Python programmers; consider:

    >>>

    >>> lists = [[]] * 3
    >>> lists
    [[], [], []]
    >>> lists[0].append(3)
    >>> lists
    [[3], [3], [3]]
    

    What has happened is that [[]] is a one-element list containing an empty list, so all three elements of [[]] * 3 are references to this single empty list. Modifying any of the elements of lists modifies this single list. You can create a list of different lists this way:

    >>>

    >>> lists = [[] for i in range(3)]
    >>> lists[0].append(3)
    >>> lists[1].append(5)
    >>> lists[2].append(7)
    >>> lists
    [[3], [5], [7]]
    

    Further explanation is available in the FAQ entry How do I create a multidimensional list?.

  3. If i or j is negative, the index is relative to the end of sequence slen(s) + i or len(s) + j is substituted. But note that -0 is still 0.

  4. The slice of s from i to j is defined as the sequence of items with index k such that i <= k < j. If i or j is greater than len(s), use len(s). If i is omitted or None, use 0. If j is omitted or None, use len(s). If i is greater than or equal to j, the slice is empty.

  5. The slice of s from i to j with step k is defined as the sequence of items with index x = i + n*k such that 0 <= n < (j-i)/k. In other words, the indices are ii+ki+2*ki+3*k and so on, stopping when j is reached (but never including j). When k is positive, i and j are reduced to len(s) if they are greater. When k is negative, i and j are reduced to len(s) - 1 if they are greater. If i or j are omitted or None, they become “end” values (which end depends on the sign of k). Note, k cannot be zero. If k is None, it is treated like 1.

  6. Concatenating immutable sequences always results in a new object. This means that building up a sequence by repeated concatenation will have a quadratic runtime cost in the total sequence length. To get a linear runtime cost, you must switch to one of the alternatives below:

    • if concatenating str objects, you can build a list and use str.join() at the end or else write to an io.StringIO instance and retrieve its value when complete

    • if concatenating bytes objects, you can similarly use bytes.join() or io.BytesIO, or you can do in-place concatenation with a bytearray object. bytearray objects are mutable and have an efficient overallocation mechanism

    • if concatenating tuple objects, extend a list instead

    • for other types, investigate the relevant class documentation

  7. Some sequence types (such as range) only support item sequences that follow specific patterns, and hence don’t support sequence concatenation or repetition.

  8. index raises ValueError when x is not found in s. Not all implementations support passing the additional arguments i and j. These arguments allow efficient searching of subsections of the sequence. Passing the extra arguments is roughly equivalent to using s[i:j].index(x), only without copying any data and with the returned index being relative to the start of the sequence rather than the start of the slice.

Immutable Sequence Types

The only operation that immutable sequence types generally implement that is not also implemented by mutable sequence types is support for the hash() built-in.

This support allows immutable sequences, such as tuple instances, to be used as dict keys and stored in set and frozenset instances.

Attempting to hash an immutable sequence that contains unhashable values will result in TypeError.

Mutable Sequence Types

The operations in the following table are defined on mutable sequence types. The collections.abc.MutableSequence ABC is provided to make it easier to correctly implement these operations on custom sequence types.

In the table s is an instance of a mutable sequence type, t is any iterable object and x is an arbitrary object that meets any type and value restrictions imposed by s (for example, bytearray only accepts integers that meet the value restriction 0 <= x <= 255).

Operation

Result

Notes

s[i] = x

item i of s is replaced by x

 

s[i:j] = t

slice of s from i to j is replaced by the contents of the iterable t

 

del s[i:j]

same as s[i:j] = []

 

s[i:j:k] = t

the elements of s[i:j:k] are replaced by those of t

(1)

del s[i:j:k]

removes the elements of s[i:j:k] from the list

 

s.append(x)

appends x to the end of the sequence (same as s[len(s):len(s)] = [x])

 

s.clear()

removes all items from s (same as del s[:])

(5)

s.copy()

creates a shallow copy of s (same as s[:])

(5)

s.extend(t) or s += t

extends s with the contents of t (for the most part the same as s[len(s):len(s)] = t)

 

s *= n

updates s with its contents repeated n times

(6)

s.insert(i, x)

inserts x into s at the index given by i (same as s[i:i] = [x])

 

s.pop() or s.pop(i)

retrieves the item at i and also removes it from s

(2)

s.remove(x)

remove the first item from s where s[i] is equal to x

(3)

s.reverse()

reverses the items of s in place

(4)

Notes:

  1. t must have the same length as the slice it is replacing.

  2. The optional argument i defaults to -1, so that by default the last item is removed and returned.

  3. remove() raises ValueError when x is not found in s.

  4. The reverse() method modifies the sequence in place for economy of space when reversing a large sequence. To remind users that it operates by side effect, it does not return the reversed sequence.

  5. clear() and copy() are included for consistency with the interfaces of mutable containers that don’t support slicing operations (such as dict and set). copy() is not part of the collections.abc.MutableSequence ABC, but most concrete mutable sequence classes provide it.

    New in version 3.3: clear() and copy() methods.

  6. The value n is an integer, or an object implementing __index__(). Zero and negative values of n clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for s * n under Common Sequence Operations.

Lists

Lists are mutable sequences, typically used to store collections of homogeneous items (where the precise degree of similarity will vary by application).

class list([iterable])

Lists may be constructed in several ways:

  • Using a pair of square brackets to denote the empty list: []

  • Using square brackets, separating items with commas: [a][a, b, c]

  • Using a list comprehension: [x for x in iterable]

  • Using the type constructor: list() or list(iterable)

The constructor builds a list whose items are the same and in the same order as iterable’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a list, a copy is made and returned, similar to iterable[:]. For example, list('abc') returns ['a', 'b', 'c'] and list( (1, 2, 3) ) returns [1, 2, 3]. If no argument is given, the constructor creates a new empty list, [].

Many other operations also produce lists, including the sorted() built-in.

Lists implement all of the common and mutable sequence operations. Lists also provide the following additional method:

sort(*key=Nonereverse=False)

This method sorts the list in place, using only < comparisons between items. Exceptions are not suppressed – if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state).

sort() accepts two arguments that can only be passed by keyword (keyword-only arguments):

key specifies a function of one argument that is used to extract a comparison key from each list element (for example, key=str.lower). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of None means that list items are sorted directly without calculating a separate key value.

The functools.cmp_to_key() utility is available to convert a 2.x style cmp function to a key function.

reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed.

This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use sorted() to explicitly request a new sorted list instance).

The sort() method 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.

CPython implementation detail: While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises ValueError if it can detect that the list has been mutated during a sort.

Tuples

Tuples are immutable sequences, typically used to store collections of heterogeneous data (such as the 2-tuples produced by the enumerate() built-in). Tuples are also used for cases where an immutable sequence of homogeneous data is needed (such as allowing storage in a set or dict instance).

class tuple([iterable])

Tuples may be constructed in a number of ways:

  • Using a pair of parentheses to denote the empty tuple: ()

  • Using a trailing comma for a singleton tuple: a, or (a,)

  • Separating items with commas: a, b, c or (a, b, c)

  • Using the tuple() built-in: tuple() or tuple(iterable)

The constructor builds a tuple whose items are the same and in the same order as iterable’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a tuple, it is returned unchanged. For example, tuple('abc') returns ('a', 'b', 'c') and tuple( [1, 2, 3] ) returns (1, 2, 3). If no argument is given, the constructor creates a new empty tuple, ().

Note that it is actually the comma which makes a tuple, not the parentheses. The parentheses are optional, except in the empty tuple case, or when they are needed to avoid syntactic ambiguity. For example, f(a, b, c) is a function call with three arguments, while f((a, b, c)) is a function call with a 3-tuple as the sole argument.

Tuples implement all of the common sequence operations.

For heterogeneous collections of data where access by name is clearer than access by index, collections.namedtuple() may be a more appropriate choice than a simple tuple object.

Ranges

The range type represents an immutable sequence of numbers and is commonly used for looping a specific number of times in for loops.

class range(stop)
class range(startstop[step])

The arguments to the range constructor must be integers (either built-in int or any object that implements the __index__() special method). If the step argument is omitted, it defaults to 1. If the start argument is omitted, it defaults to 0. If step is zero, ValueError is raised.

For a positive step, the contents of a range r are determined by the formula r[i] = start + step*i where i >= 0 and r[i] < stop.

For a negative step, the contents of the range are still determined by the formula r[i] = start + step*i, but the constraints are i >= 0 and r[i] > stop.

A range object will be empty if r[0] does not meet the value constraint. Ranges do support negative indices, but these are interpreted as indexing from the end of the sequence determined by the positive indices.

Ranges containing absolute values larger than sys.maxsize are permitted but some features (such as len()) may raise OverflowError.

Range examples:

>>>

>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> list(range(1, 11))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> list(range(0, 30, 5))
[0, 5, 10, 15, 20, 25]
>>> list(range(0, 10, 3))
[0, 3, 6, 9]
>>> list(range(0, -10, -1))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
>>> list(range(0))
[]
>>> list(range(1, 0))
[]

Ranges implement all of the common sequence operations except concatenation and repetition (due to the fact that range objects can only represent sequences that follow a strict pattern and repetition and concatenation will usually violate that pattern).

start

The value of the start parameter (or 0 if the parameter was not supplied)

stop

The value of the stop parameter

step

The value of the step parameter (or 1 if the parameter was not supplied)

The advantage of the range type over a regular list or tuple is that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the startstop and step values, calculating individual items and subranges as needed).

Range objects implement the collections.abc.Sequence ABC, and provide features such as containment tests, element index lookup, slicing and support for negative indices (see Sequence Types — list, tuple, range):

>>>

>>> r = range(0, 20, 2)
>>> r
range(0, 20, 2)
>>> 11 in r
False
>>> 10 in r
True
>>> r.index(10)
5
>>> r[5]
10
>>> r[:5]
range(0, 10, 2)
>>> r[-1]
18

Testing range objects for equality with == and != compares them as sequences. That is, two range objects are considered equal if they represent the same sequence of values. (Note that two range objects that compare equal might have different startstop and step attributes, for example range(0) == range(2, 1, 3) or range(0, 3, 2) == range(0, 4, 2).)

Changed in version 3.2: Implement the Sequence ABC. Support slicing and negative indices. Test int objects for membership in constant time instead of iterating through all items.

Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects based on the sequence of values they define (instead of comparing based on object identity).

New in version 3.3: The startstop and step attributes.

See also

  • The linspace recipe shows how to implement a lazy version of range suitable for floating point applications.

Text Sequence Type — str

Textual data in Python is handled with str objects, or strings. Strings are immutable sequences of Unicode code points. String literals are written in a variety of ways:

  • Single quotes: 'allows embedded "double" quotes'

  • Double quotes: "allows embedded 'single' quotes"

  • Triple quoted: '''Three single quotes'''"""Three double quotes"""

Triple quoted strings may span multiple lines – all associated whitespace will be included in the string literal.

String literals that are part of a single expression and have only whitespace between them will be implicitly converted to a single string literal. That is, ("spam " "eggs") == "spam eggs".

See String and Bytes literals for more about the various forms of string literal, including supported escape sequences, and the r (“raw”) prefix that disables most escape sequence processing.

Strings may also be created from other objects using the str constructor.

Since there is no separate “character” type, indexing a string produces strings of length 1. That is, for a non-empty string ss[0] == s[0:1].

There is also no mutable string type, but str.join() or io.StringIO can be used to efficiently construct strings from multiple fragments.

Changed in version 3.3: For backwards compatibility with the Python 2 series, the u prefix is once again permitted on string literals. It has no effect on the meaning of string literals and cannot be combined with the r prefix.

 

class str(object=)
class str(object=b”encoding=‘utf-8’errors=‘strict’)

Return a string version of object. If object is not provided, returns the empty string. Otherwise, the behavior of str() depends on whether encoding or errors is given, as follows.

If neither encoding nor errors is given, str(object) returns type(object).__str__(object), which is the “informal” or nicely printable string representation of object. For string objects, this is the string itself. If object does not have a __str__() method, then str() falls back to returning repr(object).

If at least one of encoding or errors is given, object should be a bytes-like object (e.g. bytes or bytearray). In this case, if object is a bytes (or bytearray) object, then str(bytes, encoding, errors) is equivalent to bytes.decode(encoding, errors). Otherwise, the bytes object underlying the buffer object is obtained before calling bytes.decode(). See Binary Sequence Types — bytes, bytearray, memoryview and Buffer Protocol for information on buffer objects.

Passing a bytes object to str() without the encoding or errors arguments falls under the first case of returning the informal string representation (see also the -b command-line option to Python). For example:

>>>

>>> str(b'Zoot!')
"b'Zoot!'"

For more information on the str class and its methods, see Text Sequence Type — str and the String Methods section below. To output formatted strings, see the Formatted string literals and Format String Syntax sections. In addition, see the Text Processing Services section.

String Methods

Strings implement all of the common sequence operations, along with the additional methods described below.

Strings also support two styles of string formatting, one providing a large degree of flexibility and customization (see str.format()Format String Syntax and Custom String Formatting) and the other based on C printf style formatting that handles a narrower range of types and is slightly harder to use correctly, but is often faster for the cases it can handle (printf-style String Formatting).

The Text Processing Services section of the standard library covers a number of other modules that provide various text related utilities (including regular expression support in the re module).

str.capitalize()

Return a copy of the string with its first character capitalized and the rest lowercased.

Changed in version 3.8: The first character is now put into titlecase rather than uppercase. This means that characters like digraphs will only have their first letter capitalized, instead of the full character.

str.casefold()

Return a casefolded copy of the string. Casefolded strings may be used for caseless matching.

Casefolding is similar to lowercasing but more aggressive because it is intended to remove all case distinctions in a string. For example, the German lowercase letter 'ß' is equivalent to "ss". Since it is already lowercase, lower() would do nothing to 'ß'casefold() converts it to "ss".

The casefolding algorithm is described in section 3.13 of the Unicode Standard.

New in version 3.3.

str.center(width[fillchar])

Return centered in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).

str.count(sub[start[end]])

Return the number of non-overlapping occurrences of substring sub in the range [startend]. Optional arguments start and end are interpreted as in slice notation.

str.encode(encoding=‘utf-8’errors=‘strict’)

Return an encoded version of the string as a bytes object. Default encoding is 'utf-8'errors may be given to set a different error handling scheme. The default for errors is 'strict', meaning that encoding errors raise a UnicodeError. Other possible values are 'ignore''replace''xmlcharrefreplace''backslashreplace' and any other name registered via codecs.register_error(), see section Error Handlers. For a list of possible encodings, see section Standard Encodings.

By default, the errors argument is not checked for best performances, but only used at the first encoding error. Enable the Python Development Mode, or use a debug build to check errors.

Changed in version 3.1: Support for keyword arguments added.

Changed in version 3.9: The errors is now checked in development mode and in debug mode.

str.endswith(suffix[start[end]])

Return True if the string ends with the specified suffix, otherwise return Falsesuffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

str.expandtabs(tabsize=8)

Return a copy of the string where all tab characters are replaced by one or more spaces, depending on the current column and the given tab size. Tab positions occur every tabsize characters (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the string, the current column is set to zero and the string is examined character by character. If the character is a tab (\t), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the character is a newline (\n) or return (\r), it is copied and the current column is reset to zero. Any other character is copied unchanged and the current column is incremented by one regardless of how the character is represented when printed.

>>>

>>> '01\t012\t0123\t01234'.expandtabs()
'01      012     0123    01234'
>>> '01\t012\t0123\t01234'.expandtabs(4)
'01  012 0123    01234'
str.find(sub[start[end]])

Return the lowest index in the string where substring sub is found within the slice s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 if sub is not found.

Note

The find() method should be used only if you need to know the position of sub. To check if sub is a substring or not, use the in operator:

>>>

>>> 'Py' in 'Python'
True
str.format(*args**kwargs)

Perform a string formatting operation. The string on which this method is called can contain literal text or replacement fields delimited by braces {}. Each replacement field contains either the numeric index of a positional argument, or the name of a keyword argument. Returns a copy of the string where each replacement field is replaced with the string value of the corresponding argument.

>>>

>>> "The sum of 1 + 2 is {0}".format(1+2)
'The sum of 1 + 2 is 3'

See Format String Syntax for a description of the various formatting options that can be specified in format strings.

Note

When formatting a number (intfloatcomplexdecimal.Decimal and subclasses) with the n type (ex: '{:n}'.format(1234)), the function temporarily sets the LC_CTYPE locale to the LC_NUMERIC locale to decode decimal_point and thousands_sep fields of localeconv() if they are non-ASCII or longer than 1 byte, and the LC_NUMERIC locale is different than the LC_CTYPE locale. This temporary change affects other threads.

Changed in version 3.7: When formatting a number with the n type, the function sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale in some cases.

str.format_map(mapping)

Similar to str.format(**mapping), except that mapping is used directly and not copied to a dict. This is useful if for example mapping is a dict subclass:

>>>

>>> class Default(dict):
...     def __missing__(self, key):
...         return key
...
>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
'Guido was born in country'

New in version 3.2.

str.index(sub[start[end]])

Like find(), but raise ValueError when the substring is not found.

str.isalnum()

Return True if all characters in the string are alphanumeric and there is at least one character, False otherwise. A character c is alphanumeric if one of the following returns Truec.isalpha()c.isdecimal()c.isdigit(), or c.isnumeric().

str.isalpha()

Return True if all characters in the string are alphabetic and there is at least one character, False otherwise. Alphabetic characters are those characters defined in the Unicode character database as “Letter”, i.e., those with general category property being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is different from the “Alphabetic” property defined in the Unicode Standard.

str.isascii()

Return True if the string is empty or all characters in the string are ASCII, False otherwise. ASCII characters have code points in the range U+0000-U+007F.

New in version 3.7.

str.isdecimal()

Return True if all characters in the string are decimal characters and there is at least one character, False otherwise. Decimal characters are those that can be used to form numbers in base 10, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal character is a character in the Unicode General Category “Nd”.

str.isdigit()

Return True if all characters in the string are digits and there is at least one character, False otherwise. Digits include decimal characters and digits that need special handling, such as the compatibility superscript digits. This covers digits which cannot be used to form numbers in base 10, like the Kharosthi numbers. Formally, a digit is a character that has the property value Numeric_Type=Digit or Numeric_Type=Decimal.

str.isidentifier()

Return True if the string is a valid identifier according to the language definition, section Identifiers and keywords.

Call keyword.iskeyword() to test whether string s is a reserved identifier, such as def and class.

Example:

>>>

>>> from keyword import iskeyword

>>> 'hello'.isidentifier(), iskeyword('hello')
(True, False)
>>> 'def'.isidentifier(), iskeyword('def')
(True, True)
str.islower()

Return True if all cased characters 4 in the string are lowercase and there is at least one cased character, False otherwise.

str.isnumeric()

Return True if all characters in the string are numeric characters, and there is at least one character, False otherwise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION ONE FIFTH. Formally, numeric characters are those with the property value Numeric_Type=Digit, Numeric_Type=Decimal or Numeric_Type=Numeric.

str.isprintable()

Return True if all characters in the string are printable or the string is empty, False otherwise. Nonprintable characters are those characters defined in the Unicode character database as “Other” or “Separator”, excepting the ASCII space (0x20) which is considered printable. (Note that printable characters in this context are those which should not be escaped when repr() is invoked on a string. It has no bearing on the handling of strings written to sys.stdout or sys.stderr.)

str.isspace()

Return True if there are only whitespace characters in the string and there is at least one character, False otherwise.

A character is whitespace if in the Unicode character database (see unicodedata), either its general category is Zs (“Separator, space”), or its bidirectional class is one of WSB, or S.

str.istitle()

Return True if the string is a titlecased string and there is at least one character, for example uppercase characters may only follow uncased characters and lowercase characters only cased ones. Return False otherwise.

str.isupper()

Return True if all cased characters 4 in the string are uppercase and there is at least one cased character, False otherwise.

>>>

>>> 'BANANA'.isupper()
True
>>> 'banana'.isupper()
False
>>> 'baNana'.isupper()
False
>>> ' '.isupper()
False
str.join(iterable)

Return a string which is the concatenation of the strings in iterable. A TypeError will be raised if there are any non-string values in iterable, including bytes objects. The separator between elements is the string providing this method.

str.ljust(width[fillchar])

Return the string left justified in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).

str.lower()

Return a copy of the string with all the cased characters 4 converted to lowercase.

The lowercasing algorithm used is described in section 3.13 of the Unicode Standard.

str.lstrip([chars])

Return a copy of the string with leading characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix; rather, all combinations of its values are stripped:

>>>

>>> '   spacious   '.lstrip()
'spacious   '
>>> 'www.example.com'.lstrip('cmowz.')
'example.com'

See str.removeprefix() for a method that will remove a single prefix string rather than all of a set of characters. For example:

>>>

>>> 'Arthur: three!'.lstrip('Arthur: ')
'ee!'
>>> 'Arthur: three!'.removeprefix('Arthur: ')
'three!'
static str.maketrans(x[y[z]])

This static method returns a translation table usable for str.translate().

If there is only one argument, it must be a dictionary mapping Unicode ordinals (integers) or characters (strings of length 1) to Unicode ordinals, strings (of arbitrary lengths) or None. Character keys will then be converted to ordinals.

If there are two arguments, they must be strings of equal length, and in the resulting dictionary, each character in x will be mapped to the character at the same position in y. If there is a third argument, it must be a string, whose characters will be mapped to None in the result.

str.partition(sep)

Split the string at the first occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing the string itself, followed by two empty strings.

str.removeprefix(prefix/)

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string:

>>>

>>> 'TestHook'.removeprefix('Test')
'Hook'
>>> 'BaseTestCase'.removeprefix('Test')
'BaseTestCase'

New in version 3.9.

str.removesuffix(suffix/)

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string:

>>>

>>> 'MiscTests'.removesuffix('Tests')
'Misc'
>>> 'TmpDirMixin'.removesuffix('Tests')
'TmpDirMixin'

New in version 3.9.

str.replace(oldnew[count])

Return a copy of the string with all occurrences of substring old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.

str.rfind(sub[start[end]])

Return the highest index in the string where substring sub is found, such that sub is contained within s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.

str.rindex(sub[start[end]])

Like rfind() but raises ValueError when the substring sub is not found.

str.rjust(width[fillchar])

Return the string right justified in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).

str.rpartition(sep)

Split the string at the last occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty strings, followed by the string itself.

str.rsplit(sep=Nonemaxsplit=– 1)

Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done, the rightmost ones. If sep is not specified or None, any whitespace string is a separator. Except for splitting from the right, rsplit() behaves like split() which is described in detail below.

str.rstrip([chars])

Return a copy of the string with trailing characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped:

>>>

>>> '   spacious   '.rstrip()
'   spacious'
>>> 'mississippi'.rstrip('ipz')
'mississ'

See str.removesuffix() for a method that will remove a single suffix string rather than all of a set of characters. For example:

>>>

>>> 'Monty Python'.rstrip(' Python')
'M'
>>> 'Monty Python'.removesuffix(' Python')
'Monty'
str.split(sep=Nonemaxsplit=– 1)

Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements). If maxsplit is not specified or -1, then there is no limit on the number of splits (all possible splits are made).

If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings (for example, '1,,2'.split(',') returns ['1', '', '2']). The sep argument may consist of multiple characters (for example, '1<>2<>3'.split('<>') returns ['1', '2', '3']). Splitting an empty string with a specified separator returns [''].

For example:

>>>

>>> '1,2,3'.split(',')
['1', '2', '3']
>>> '1,2,3'.split(',', maxsplit=1)
['1', '2,3']
>>> '1,2,,3,'.split(',')
['1', '2', '', '3', '']

If sep is not specified or is None, a different splitting algorithm is applied: runs of consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the string has leading or trailing whitespace. Consequently, splitting an empty string or a string consisting of just whitespace with a None separator returns [].

For example:

>>>

>>> '1 2 3'.split()
['1', '2', '3']
>>> '1 2 3'.split(maxsplit=1)
['1', '2 3']
>>> '   1   2   3   '.split()
['1', '2', '3']

 

str.splitlines(keepends=False)

Return a list of the lines in the string, breaking at line boundaries. Line breaks are not included in the resulting list unless keepends is given and true.

This method splits on the following line boundaries. In particular, the boundaries are a superset of universal newlines.

Representation

Description

\n

Line Feed

\r

Carriage Return

\r\n

Carriage Return + Line Feed

\v or \x0b

Line Tabulation

\f or \x0c

Form Feed

\x1c

File Separator

\x1d

Group Separator

\x1e

Record Separator

\x85

Next Line (C1 Control Code)

\u2028

Line Separator

\u2029

Paragraph Separator

Changed in version 3.2: \v and \f added to list of line boundaries.

For example:

>>>

>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
['ab c', '', 'de fg', 'kl']
>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
['ab c\n', '\n', 'de fg\r', 'kl\r\n']

Unlike split() when a delimiter string sep is given, this method returns an empty list for the empty string, and a terminal line break does not result in an extra line:

>>>

>>> "".splitlines()
[]
>>> "One line\n".splitlines()
['One line']

For comparison, split('\n') gives:

>>>

>>> ''.split('\n')
['']
>>> 'Two lines\n'.split('\n')
['Two lines', '']
str.startswith(prefix[start[end]])

Return True if string starts with the prefix, otherwise return Falseprefix can also be a tuple of prefixes to look for. With optional start, test string beginning at that position. With optional end, stop comparing string at that position.

str.strip([chars])

Return a copy of the string with the leading and trailing characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped:

>>>

>>> '   spacious   '.strip()
'spacious'
>>> 'www.example.com'.strip('cmowz.')
'example'

The outermost leading and trailing chars argument values are stripped from the string. Characters are removed from the leading end until reaching a string character that is not contained in the set of characters in chars. A similar action takes place on the trailing end. For example:

>>>

>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
>>> comment_string.strip('.#! ')
'Section 3.2.1 Issue #32'
str.swapcase()

Return a copy of the string with uppercase characters converted to lowercase and vice versa. Note that it is not necessarily true that s.swapcase().swapcase() == s.

str.title()

Return a titlecased version of the string where words start with an uppercase character and the remaining characters are lowercase.

For example:

>>>

>>> 'Hello world'.title()
'Hello World'

The algorithm uses a simple language-independent definition of a word as groups of consecutive letters. The definition works in many contexts but it means that apostrophes in contractions and possessives form word boundaries, which may not be the desired result:

>>>

>>> "they're bill's friends from the UK".title()
"They'Re Bill'S Friends From The Uk"

The string.capwords() function does not have this problem, as it splits words on spaces only.

Alternatively, a workaround for apostrophes can be constructed using regular expressions:

>>>

>>> import re
>>> def titlecase(s):
...     return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
...                   lambda mo: mo.group(0).capitalize(),
...                   s)
...
>>> titlecase("they're bill's friends.")
"They're Bill's Friends."
str.translate(table)

Return a copy of the string in which each character has been mapped through the given translation table. The table must be an object that implements indexing via __getitem__(), typically a mapping or sequence. When indexed by a Unicode ordinal (an integer), the table object can do any of the following: return a Unicode ordinal or a string, to map the character to one or more other characters; return None, to delete the character from the return string; or raise a LookupError exception, to map the character to itself.

You can use str.maketrans() to create a translation map from character-to-character mappings in different formats.

See also the codecs module for a more flexible approach to custom character mappings.

str.upper()

Return a copy of the string with all the cased characters 4 converted to uppercase. Note that s.upper().isupper() might be False if s contains uncased characters or if the Unicode category of the resulting character(s) is not “Lu” (Letter, uppercase), but e.g. “Lt” (Letter, titlecase).

The uppercasing algorithm used is described in section 3.13 of the Unicode Standard.

str.zfill(width)

Return a copy of the string left filled with ASCII '0' digits to make a string of length width. A leading sign prefix ('+'/'-') is handled by inserting the padding after the sign character rather than before. The original string is returned if width is less than or equal to len(s).

For example:

>>>

>>> "42".zfill(5)
'00042'
>>> "-42".zfill(5)
'-0042'

printf-style String Formatting

Note

The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). Using the newer formatted string literals, the str.format() interface, or template strings may help avoid these errors. Each of these alternatives provides their own trade-offs and benefits of simplicity, flexibility, and/or extensibility.

String objects have one unique built-in operation: the % operator (modulo). This is also known as the string formatting or interpolation operator. Given format % values (where format is a string), % conversion specifications in format are replaced with zero or more elements of values. The effect is similar to using the sprintf() in the C language.

If format requires a single argument, values may be a single non-tuple object. 5 Otherwise, values must be a tuple with exactly the number of items specified by the format string, or a single mapping object (for example, a dictionary).

A conversion specifier contains two or more characters and has the following components, which must occur in this order:

  1. The '%' character, which marks the start of the specifier.

  2. Mapping key (optional), consisting of a parenthesised sequence of characters (for example, (somename)).

  3. Conversion flags (optional), which affect the result of some conversion types.

  4. Minimum field width (optional). If specified as an '*' (asterisk), the actual width is read from the next element of the tuple in values, and the object to convert comes after the minimum field width and optional precision.

  5. Precision (optional), given as a '.' (dot) followed by the precision. If specified as '*' (an asterisk), the actual precision is read from the next element of the tuple in values, and the value to convert comes after the precision.

  6. Length modifier (optional).

  7. Conversion type.

When the right argument is a dictionary (or other mapping type), then the formats in the string must include a parenthesised mapping key into that dictionary inserted immediately after the '%' character. The mapping key selects the value to be formatted from the mapping. For example:

>>>

>>> print('%(language)s has %(number)03d quote types.' %
...       {'language': "Python", "number": 2})
Python has 002 quote types.

In this case no * specifiers may occur in a format (since they require a sequential parameter list).

The conversion flag characters are:

Flag

Meaning

'#'

The value conversion will use the “alternate form” (where defined below).

'0'

The conversion will be zero padded for numeric values.

'-'

The converted value is left adjusted (overrides the '0' conversion if both are given).

' '

(a space) A blank should be left before a positive number (or empty string) produced by a signed conversion.

'+'

A sign character ('+' or '-') will precede the conversion (overrides a “space” flag).

A length modifier (hl, or L) may be present, but is ignored as it is not necessary for Python – so e.g. %ld is identical to %d.

The conversion types are:

Conversion

Meaning

Notes

'd'

Signed integer decimal.

 

'i'

Signed integer decimal.

 

'o'

Signed octal value.

(1)

'u'

Obsolete type – it is identical to 'd'.

(6)

'x'

Signed hexadecimal (lowercase).

(2)

'X'

Signed hexadecimal (uppercase).

(2)

'e'

Floating point exponential format (lowercase).

(3)

'E'

Floating point exponential format (uppercase).

(3)

'f'

Floating point decimal format.

(3)

'F'

Floating point decimal format.

(3)

'g'

Floating point format. Uses lowercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'G'

Floating point format. Uses uppercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'c'

Single character (accepts integer or single character string).

 

'r'

String (converts any Python object using repr()).

(5)

's'

String (converts any Python object using str()).

(5)

'a'

String (converts any Python object using ascii()).

(5)

'%'

No argument is converted, results in a '%' character in the result.

 

Notes:

  1. The alternate form causes a leading octal specifier ('0o') to be inserted before the first digit.

  2. The alternate form causes a leading '0x' or '0X' (depending on whether the 'x' or 'X' format was used) to be inserted before the first digit.

  3. The alternate form causes the result to always contain a decimal point, even if no digits follow it.

    The precision determines the number of digits after the decimal point and defaults to 6.

  4. The alternate form causes the result to always contain a decimal point, and trailing zeroes are not removed as they would otherwise be.

    The precision determines the number of significant digits before and after the decimal point and defaults to 6.

  5. If precision is N, the output is truncated to N characters.

  6. See PEP 237.

Since Python strings have an explicit length, %s conversions do not assume that '\0' is the end of the string.

Changed in version 3.1: %f conversions for numbers whose absolute value is over 1e50 are no longer replaced by %g conversions.

Binary Sequence Types — bytesbytearraymemoryview

The core built-in types for manipulating binary data are bytes and bytearray. They are supported by memoryview which uses the buffer protocol to access the memory of other binary objects without needing to make a copy.

The array module supports efficient storage of basic data types like 32-bit integers and IEEE754 double-precision floating values.

Bytes Objects

Bytes objects are immutable sequences of single bytes. Since many major binary protocols are based on the ASCII text encoding, bytes objects offer several methods that are only valid when working with ASCII compatible data and are closely related to string objects in a variety of other ways.

class bytes([source[encoding[errors]]])

Firstly, the syntax for bytes literals is largely the same as that for string literals, except that a b prefix is added:

  • Single quotes: b'still allows embedded "double" quotes'

  • Double quotes: b"still allows embedded 'single' quotes"

  • Triple quoted: b'''3 single quotes'''b"""3 double quotes"""

Only ASCII characters are permitted in bytes literals (regardless of the declared source code encoding). Any binary values over 127 must be entered into bytes literals using the appropriate escape sequence.

As with string literals, bytes literals may also use a r prefix to disable processing of escape sequences. See String and Bytes literals for more about the various forms of bytes literal, including supported escape sequences.

While bytes literals and representations are based on ASCII text, bytes objects actually behave like immutable sequences of integers, with each value in the sequence restricted such that 0 <= x < 256 (attempts to violate this restriction will trigger ValueError). This is done deliberately to emphasise that while many binary formats include ASCII based elements and can be usefully manipulated with some text-oriented algorithms, this is not generally the case for arbitrary binary data (blindly applying text processing algorithms to binary data formats that are not ASCII compatible will usually lead to data corruption).

In addition to the literal forms, bytes objects can be created in a number of other ways:

  • A zero-filled bytes object of a specified length: bytes(10)

  • From an iterable of integers: bytes(range(20))

  • Copying existing binary data via the buffer protocol: bytes(obj)

Also see the bytes built-in.

Since 2 hexadecimal digits correspond precisely to a single byte, hexadecimal numbers are a commonly used format for describing binary data. Accordingly, the bytes type has an additional class method to read data in that format:

classmethod fromhex(string)

This bytes class method returns a bytes object, decoding the given string object. The string must contain two hexadecimal digits per byte, with ASCII whitespace being ignored.

>>>

>>> bytes.fromhex('2Ef0 F1f2  ')
b'.\xf0\xf1\xf2'

Changed in version 3.7: bytes.fromhex() now skips all ASCII whitespace in the string, not just spaces.

A reverse conversion function exists to transform a bytes object into its hexadecimal representation.

hex([sep[bytes_per_sep]])

Return a string object containing two hexadecimal digits for each byte in the instance.

>>>

>>> b'\xf0\xf1\xf2'.hex()
'f0f1f2'

If you want to make the hex string easier to read, you can specify a single character separator sep parameter to include in the output. By default between each byte. A second optional bytes_per_sep parameter controls the spacing. Positive values calculate the separator position from the right, negative values from the left.

>>>

>>> value = b'\xf0\xf1\xf2'
>>> value.hex('-')
'f0-f1-f2'
>>> value.hex('_', 2)
'f0_f1f2'
>>> b'UUDDLRLRAB'.hex(' ', -4)
'55554444 4c524c52 4142'

New in version 3.5.

Changed in version 3.8: bytes.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

Since bytes objects are sequences of integers (akin to a tuple), for a bytes object bb[0] will be an integer, while b[0:1] will be a bytes object of length 1. (This contrasts with text strings, where both indexing and slicing will produce a string of length 1)

The representation of bytes objects uses the literal format (b'...') since it is often more useful than e.g. bytes([46, 46, 46]). You can always convert a bytes object into a list of integers using list(b).

Note

For Python 2.x users: In the Python 2.x series, a variety of implicit conversions between 8-bit strings (the closest thing 2.x offers to a built-in binary data type) and Unicode strings were permitted. This was a backwards compatibility workaround to account for the fact that Python originally only supported 8-bit text, and Unicode text was a later addition. In Python 3.x, those implicit conversions are gone – conversions between 8-bit binary data and Unicode text must be explicit, and bytes and string objects will always compare unequal.

Bytearray Objects

bytearray objects are a mutable counterpart to bytes objects.

class bytearray([source[encoding[errors]]])

There is no dedicated literal syntax for bytearray objects, instead they are always created by calling the constructor:

  • Creating an empty instance: bytearray()

  • Creating a zero-filled instance with a given length: bytearray(10)

  • From an iterable of integers: bytearray(range(20))

  • Copying existing binary data via the buffer protocol: bytearray(b'Hi!')

As bytearray objects are mutable, they support the mutable sequence operations in addition to the common bytes and bytearray operations described in Bytes and Bytearray Operations.

Also see the bytearray built-in.

Since 2 hexadecimal digits correspond precisely to a single byte, hexadecimal numbers are a commonly used format for describing binary data. Accordingly, the bytearray type has an additional class method to read data in that format:

classmethod fromhex(string)

This bytearray class method returns bytearray object, decoding the given string object. The string must contain two hexadecimal digits per byte, with ASCII whitespace being ignored.

>>>

>>> bytearray.fromhex('2Ef0 F1f2  ')
bytearray(b'.\xf0\xf1\xf2')

Changed in version 3.7: bytearray.fromhex() now skips all ASCII whitespace in the string, not just spaces.

A reverse conversion function exists to transform a bytearray object into its hexadecimal representation.

hex([sep[bytes_per_sep]])

Return a string object containing two hexadecimal digits for each byte in the instance.

>>>

>>> bytearray(b'\xf0\xf1\xf2').hex()
'f0f1f2'

New in version 3.5.

Changed in version 3.8: Similar to bytes.hex()bytearray.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

Since bytearray objects are sequences of integers (akin to a list), for a bytearray object bb[0] will be an integer, while b[0:1] will be a bytearray object of length 1. (This contrasts with text strings, where both indexing and slicing will produce a string of length 1)

The representation of bytearray objects uses the bytes literal format (bytearray(b'...')) since it is often more useful than e.g. bytearray([46, 46, 46]). You can always convert a bytearray object into a list of integers using list(b).

Bytes and Bytearray Operations

Both bytes and bytearray objects support the common sequence operations. They interoperate not just with operands of the same type, but with any bytes-like object. Due to this flexibility, they can be freely mixed in operations without causing errors. However, the return type of the result may depend on the order of operands.

Note

The methods on bytes and bytearray objects don’t accept strings as their arguments, just as the methods on strings don’t accept bytes as their arguments. For example, you have to write:

a = "abc"
b = a.replace("a", "f")

and:

a = b"abc"
b = a.replace(b"a", b"f")

Some bytes and bytearray operations assume the use of ASCII compatible binary formats, and hence should be avoided when working with arbitrary binary data. These restrictions are covered below.

Note

Using these ASCII based operations to manipulate binary data that is not stored in an ASCII based format may lead to data corruption.

The following methods on bytes and bytearray objects can be used with arbitrary binary data.

bytes.count(sub[start[end]])
bytearray.count(sub[start[end]])

Return the number of non-overlapping occurrences of subsequence sub in the range [startend]. Optional arguments start and end are interpreted as in slice notation.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

bytes.removeprefix(prefix/)
bytearray.removeprefix(prefix/)

If the binary data starts with the prefix string, return bytes[len(prefix):]. Otherwise, return a copy of the original binary data:

>>>

>>> b'TestHook'.removeprefix(b'Test')
b'Hook'
>>> b'BaseTestCase'.removeprefix(b'Test')
b'BaseTestCase'

The prefix may be any bytes-like object.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

New in version 3.9.

bytes.removesuffix(suffix/)
bytearray.removesuffix(suffix/)

If the binary data ends with the suffix string and that suffix is not empty, return bytes[:-len(suffix)]. Otherwise, return a copy of the original binary data:

>>>

>>> b'MiscTests'.removesuffix(b'Tests')
b'Misc'
>>> b'TmpDirMixin'.removesuffix(b'Tests')
b'TmpDirMixin'

The suffix may be any bytes-like object.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

New in version 3.9.

bytes.decode(encoding=‘utf-8’errors=‘strict’)
bytearray.decode(encoding=‘utf-8’errors=‘strict’)

Return a string decoded from the given bytes. Default encoding is 'utf-8'errors may be given to set a different error handling scheme. The default for errors is 'strict', meaning that encoding errors raise a UnicodeError. Other possible values are 'ignore''replace' and any other name registered via codecs.register_error(), see section Error Handlers. For a list of possible encodings, see section Standard Encodings.

By default, the errors argument is not checked for best performances, but only used at the first decoding error. Enable the Python Development Mode, or use a debug build to check errors.

Note

Passing the encoding argument to str allows decoding any bytes-like object directly, without needing to make a temporary bytes or bytearray object.

Changed in version 3.1: Added support for keyword arguments.

Changed in version 3.9: The errors is now checked in development mode and in debug mode.

bytes.endswith(suffix[start[end]])
bytearray.endswith(suffix[start[end]])

Return True if the binary data ends with the specified suffix, otherwise return Falsesuffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

The suffix(es) to search for may be any bytes-like object.

bytes.find(sub[start[end]])
bytearray.find(sub[start[end]])

Return the lowest index in the data where the subsequence sub is found, such that sub is contained in the slice s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 if sub is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Note

The find() method should be used only if you need to know the position of sub. To check if sub is a substring or not, use the in operator:

>>>

>>> b'Py' in b'Python'
True

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

bytes.index(sub[start[end]])
bytearray.index(sub[start[end]])

Like find(), but raise ValueError when the subsequence is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

bytes.join(iterable)
bytearray.join(iterable)

Return a bytes or bytearray object which is the concatenation of the binary data sequences in iterable. A TypeError will be raised if there are any values in iterable that are not bytes-like objects, including str objects. The separator between elements is the contents of the bytes or bytearray object providing this method.

static bytes.maketrans(fromto)
static bytearray.maketrans(fromto)

This static method returns a translation table usable for bytes.translate() that will map each character in from into the character at the same position in tofrom and to must both be bytes-like objects and have the same length.

New in version 3.1.

bytes.partition(sep)
bytearray.partition(sep)

Split the sequence at the first occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself or its bytearray copy, and the part after the separator. If the separator is not found, return a 3-tuple containing a copy of the original sequence, followed by two empty bytes or bytearray objects.

The separator to search for may be any bytes-like object.

bytes.replace(oldnew[count])
bytearray.replace(oldnew[count])

Return a copy of the sequence with all occurrences of subsequence old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.

The subsequence to search for and its replacement may be any bytes-like object.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.rfind(sub[start[end]])
bytearray.rfind(sub[start[end]])

Return the highest index in the sequence where the subsequence sub is found, such that sub is contained within s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

bytes.rindex(sub[start[end]])
bytearray.rindex(sub[start[end]])

Like rfind() but raises ValueError when the subsequence sub is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

bytes.rpartition(sep)
bytearray.rpartition(sep)

Split the sequence at the last occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself or its bytearray copy, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty bytes or bytearray objects, followed by a copy of the original sequence.

The separator to search for may be any bytes-like object.

bytes.startswith(prefix[start[end]])
bytearray.startswith(prefix[start[end]])

Return True if the binary data starts with the specified prefix, otherwise return Falseprefix can also be a tuple of prefixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

The prefix(es) to search for may be any bytes-like object.

bytes.translate(table/delete=b”)
bytearray.translate(table/delete=b”)

Return a copy of the bytes or bytearray object where all bytes occurring in the optional argument delete are removed, and the remaining bytes have been mapped through the given translation table, which must be a bytes object of length 256.

You can use the bytes.maketrans() method to create a translation table.

Set the table argument to None for translations that only delete characters:

>>>

>>> b'read this short text'.translate(None, b'aeiou')
b'rd ths shrt txt'

Changed in version 3.6: delete is now supported as a keyword argument.

The following methods on bytes and bytearray objects have default behaviours that assume the use of ASCII compatible binary formats, but can still be used with arbitrary binary data by passing appropriate arguments. Note that all of the bytearray methods in this section do not operate in place, and instead produce new objects.

bytes.center(width[fillbyte])
bytearray.center(width[fillbyte])

Return a copy of the object centered in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.ljust(width[fillbyte])
bytearray.ljust(width[fillbyte])

Return a copy of the object left justified in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.lstrip([chars])
bytearray.lstrip([chars])

Return a copy of the sequence with specified leading bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed – the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a prefix; rather, all combinations of its values are stripped:

>>>

>>> b'   spacious   '.lstrip()
b'spacious   '
>>> b'www.example.com'.lstrip(b'cmowz.')
b'example.com'

The binary sequence of byte values to remove may be any bytes-like object. See removeprefix() for a method that will remove a single prefix string rather than all of a set of characters. For example:

>>>

>>> b'Arthur: three!'.lstrip(b'Arthur: ')
b'ee!'
>>> b'Arthur: three!'.removeprefix(b'Arthur: ')
b'three!'

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.rjust(width[fillbyte])
bytearray.rjust(width[fillbyte])

Return a copy of the object right justified in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.rsplit(sep=Nonemaxsplit=– 1)
bytearray.rsplit(sep=Nonemaxsplit=– 1)

Split the binary sequence into subsequences of the same type, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done, the rightmost ones. If sep is not specified or None, any subsequence consisting solely of ASCII whitespace is a separator. Except for splitting from the right, rsplit() behaves like split() which is described in detail below.

bytes.rstrip([chars])
bytearray.rstrip([chars])

Return a copy of the sequence with specified trailing bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed – the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped:

>>>

>>> b'   spacious   '.rstrip()
b'   spacious'
>>> b'mississippi'.rstrip(b'ipz')
b'mississ'

The binary sequence of byte values to remove may be any bytes-like object. See removesuffix() for a method that will remove a single suffix string rather than all of a set of characters. For example:

>>>

>>> b'Monty Python'.rstrip(b' Python')
b'M'
>>> b'Monty Python'.removesuffix(b' Python')
b'Monty'

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.split(sep=Nonemaxsplit=– 1)
bytearray.split(sep=Nonemaxsplit=– 1)

Split the binary sequence into subsequences of the same type, using sep as the delimiter string. If maxsplit is given and non-negative, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements). If maxsplit is not specified or is -1, then there is no limit on the number of splits (all possible splits are made).

If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty subsequences (for example, b'1,,2'.split(b',') returns [b'1', b'', b'2']). The sep argument may consist of a multibyte sequence (for example, b'1<>2<>3'.split(b'<>') returns [b'1', b'2', b'3']). Splitting an empty sequence with a specified separator returns [b''] or [bytearray(b'')] depending on the type of object being split. The sep argument may be any bytes-like object.

For example:

>>>

>>> b'1,2,3'.split(b',')
[b'1', b'2', b'3']
>>> b'1,2,3'.split(b',', maxsplit=1)
[b'1', b'2,3']
>>> b'1,2,,3,'.split(b',')
[b'1', b'2', b'', b'3', b'']

If sep is not specified or is None, a different splitting algorithm is applied: runs of consecutive ASCII whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the sequence has leading or trailing whitespace. Consequently, splitting an empty sequence or a sequence consisting solely of ASCII whitespace without a specified separator returns [].

For example:

>>>

>>> b'1 2 3'.split()
[b'1', b'2', b'3']
>>> b'1 2 3'.split(maxsplit=1)
[b'1', b'2 3']
>>> b'   1   2   3   '.split()
[b'1', b'2', b'3']
bytes.strip([chars])
bytearray.strip([chars])

Return a copy of the sequence with specified leading and trailing bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed – the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped:

>>>

>>> b'   spacious   '.strip()
b'spacious'
>>> b'www.example.com'.strip(b'cmowz.')
b'example'

The binary sequence of byte values to remove may be any bytes-like object.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

The following methods on bytes and bytearray objects assume the use of ASCII compatible binary formats and should not be applied to arbitrary binary data. Note that all of the bytearray methods in this section do not operate in place, and instead produce new objects.

bytes.capitalize()
bytearray.capitalize()

Return a copy of the sequence with each byte interpreted as an ASCII character, and the first byte capitalized and the rest lowercased. Non-ASCII byte values are passed through unchanged.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.expandtabs(tabsize=8)
bytearray.expandtabs(tabsize=8)

Return a copy of the sequence where all ASCII tab characters are replaced by one or more ASCII spaces, depending on the current column and the given tab size. Tab positions occur every tabsize bytes (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the sequence, the current column is set to zero and the sequence is examined byte by byte. If the byte is an ASCII tab character (b'\t'), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the current byte is an ASCII newline (b'\n') or carriage return (b'\r'), it is copied and the current column is reset to zero. Any other byte value is copied unchanged and the current column is incremented by one regardless of how the byte value is represented when printed:

>>>

>>> b'01\t012\t0123\t01234'.expandtabs()
b'01      012     0123    01234'
>>> b'01\t012\t0123\t01234'.expandtabs(4)
b'01  012 0123    01234'

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.isalnum()
bytearray.isalnum()

Return True if all bytes in the sequence are alphabetical ASCII characters or ASCII decimal digits and the sequence is not empty, False otherwise. Alphabetic ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'. ASCII decimal digits are those byte values in the sequence b'0123456789'.

For example:

>>>

>>> b'ABCabc1'.isalnum()
True
>>> b'ABC abc1'.isalnum()
False
bytes.isalpha()
bytearray.isalpha()

Return True if all bytes in the sequence are alphabetic ASCII characters and the sequence is not empty, False otherwise. Alphabetic ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'.

For example:

>>>

>>> b'ABCabc'.isalpha()
True
>>> b'ABCabc1'.isalpha()
False
bytes.isascii()
bytearray.isascii()

Return True if the sequence is empty or all bytes in the sequence are ASCII, False otherwise. ASCII bytes are in the range 0-0x7F.

New in version 3.7.

bytes.isdigit()
bytearray.isdigit()

Return True if all bytes in the sequence are ASCII decimal digits and the sequence is not empty, False otherwise. ASCII decimal digits are those byte values in the sequence b'0123456789'.

For example:

>>>

>>> b'1234'.isdigit()
True
>>> b'1.23'.isdigit()
False
bytes.islower()
bytearray.islower()

Return True if there is at least one lowercase ASCII character in the sequence and no uppercase ASCII characters, False otherwise.

For example:

>>>

>>> b'hello world'.islower()
True
>>> b'Hello world'.islower()
False

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

bytes.isspace()
bytearray.isspace()

Return True if all bytes in the sequence are ASCII whitespace and the sequence is not empty, False otherwise. ASCII whitespace characters are those byte values in the sequence b' \t\n\r\x0b\f' (space, tab, newline, carriage return, vertical tab, form feed).

bytes.istitle()
bytearray.istitle()

Return True if the sequence is ASCII titlecase and the sequence is not empty, False otherwise. See bytes.title() for more details on the definition of “titlecase”.

For example:

>>>

>>> b'Hello World'.istitle()
True
>>> b'Hello world'.istitle()
False
bytes.isupper()
bytearray.isupper()

Return True if there is at least one uppercase alphabetic ASCII character in the sequence and no lowercase ASCII characters, False otherwise.

For example:

>>>

>>> b'HELLO WORLD'.isupper()
True
>>> b'Hello world'.isupper()
False

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

bytes.lower()
bytearray.lower()

Return a copy of the sequence with all the uppercase ASCII characters converted to their corresponding lowercase counterpart.

For example:

>>>

>>> b'Hello World'.lower()
b'hello world'

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

 

bytes.splitlines(keepends=False)
bytearray.splitlines(keepends=False)

Return a list of the lines in the binary sequence, breaking at ASCII line boundaries. This method uses the universal newlines approach to splitting lines. Line breaks are not included in the resulting list unless keepends is given and true.

For example:

>>>

>>> b'ab c\n\nde fg\rkl\r\n'.splitlines()
[b'ab c', b'', b'de fg', b'kl']
>>> b'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
[b'ab c\n', b'\n', b'de fg\r', b'kl\r\n']

Unlike split() when a delimiter string sep is given, this method returns an empty list for the empty string, and a terminal line break does not result in an extra line:

>>>

>>> b"".split(b'\n'), b"Two lines\n".split(b'\n')
([b''], [b'Two lines', b''])
>>> b"".splitlines(), b"One line\n".splitlines()
([], [b'One line'])
bytes.swapcase()
bytearray.swapcase()

Return a copy of the sequence with all the lowercase ASCII characters converted to their corresponding uppercase counterpart and vice-versa.

For example:

>>>

>>> b'Hello World'.swapcase()
b'hELLO wORLD'

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Unlike str.swapcase(), it is always the case that bin.swapcase().swapcase() == bin for the binary versions. Case conversions are symmetrical in ASCII, even though that is not generally true for arbitrary Unicode code points.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.title()
bytearray.title()

Return a titlecased version of the binary sequence where words start with an uppercase ASCII character and the remaining characters are lowercase. Uncased byte values are left unmodified.

For example:

>>>

>>> b'Hello world'.title()
b'Hello World'

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'. All other byte values are uncased.

The algorithm uses a simple language-independent definition of a word as groups of consecutive letters. The definition works in many contexts but it means that apostrophes in contractions and possessives form word boundaries, which may not be the desired result:

>>>

>>> b"they're bill's friends from the UK".title()
b"They'Re Bill'S Friends From The Uk"

A workaround for apostrophes can be constructed using regular expressions:

>>>

>>> import re
>>> def titlecase(s):
...     return re.sub(rb"[A-Za-z]+('[A-Za-z]+)?",
...                   lambda mo: mo.group(0)[0:1].upper() +
...                              mo.group(0)[1:].lower(),
...                   s)
...
>>> titlecase(b"they're bill's friends.")
b"They're Bill's Friends."

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.upper()
bytearray.upper()

Return a copy of the sequence with all the lowercase ASCII characters converted to their corresponding uppercase counterpart.

For example:

>>>

>>> b'Hello World'.upper()
b'HELLO WORLD'

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

bytes.zfill(width)
bytearray.zfill(width)

Return a copy of the sequence left filled with ASCII b'0' digits to make a sequence of length width. A leading sign prefix (b'+'b'-') is handled by inserting the padding after the sign character rather than before. For bytes objects, the original sequence is returned if width is less than or equal to len(seq).

For example:

>>>

>>> b"42".zfill(5)
b'00042'
>>> b"-42".zfill(5)
b'-0042'

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

printf-style Bytes Formatting

Note

The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). If the value being printed may be a tuple or dictionary, wrap it in a tuple.

Bytes objects (bytes/bytearray) have one unique built-in operation: the % operator (modulo). This is also known as the bytes formatting or interpolation operator. Given format % values (where format is a bytes object), % conversion specifications in format are replaced with zero or more elements of values. The effect is similar to using the sprintf() in the C language.

If format requires a single argument, values may be a single non-tuple object. 5 Otherwise, values must be a tuple with exactly the number of items specified by the format bytes object, or a single mapping object (for example, a dictionary).

A conversion specifier contains two or more characters and has the following components, which must occur in this order:

  1. The '%' character, which marks the start of the specifier.

  2. Mapping key (optional), consisting of a parenthesised sequence of characters (for example, (somename)).

  3. Conversion flags (optional), which affect the result of some conversion types.

  4. Minimum field width (optional). If specified as an '*' (asterisk), the actual width is read from the next element of the tuple in values, and the object to convert comes after the minimum field width and optional precision.

  5. Precision (optional), given as a '.' (dot) followed by the precision. If specified as '*' (an asterisk), the actual precision is read from the next element of the tuple in values, and the value to convert comes after the precision.

  6. Length modifier (optional).

  7. Conversion type.

When the right argument is a dictionary (or other mapping type), then the formats in the bytes object must include a parenthesised mapping key into that dictionary inserted immediately after the '%' character. The mapping key selects the value to be formatted from the mapping. For example:

>>>

>>> print(b'%(language)s has %(number)03d quote types.' %
...       {b'language': b"Python", b"number": 2})
b'Python has 002 quote types.'

In this case no * specifiers may occur in a format (since they require a sequential parameter list).

The conversion flag characters are:

Flag

Meaning

'#'

The value conversion will use the “alternate form” (where defined below).

'0'

The conversion will be zero padded for numeric values.

'-'

The converted value is left adjusted (overrides the '0' conversion if both are given).

' '

(a space) A blank should be left before a positive number (or empty string) produced by a signed conversion.

'+'

A sign character ('+' or '-') will precede the conversion (overrides a “space” flag).

A length modifier (hl, or L) may be present, but is ignored as it is not necessary for Python – so e.g. %ld is identical to %d.

The conversion types are:

Conversion

Meaning

Notes

'd'

Signed integer decimal.

 

'i'

Signed integer decimal.

 

'o'

Signed octal value.

(1)

'u'

Obsolete type – it is identical to 'd'.

(8)

'x'

Signed hexadecimal (lowercase).

(2)

'X'

Signed hexadecimal (uppercase).

(2)

'e'

Floating point exponential format (lowercase).

(3)

'E'

Floating point exponential format (uppercase).

(3)

'f'

Floating point decimal format.

(3)

'F'

Floating point decimal format.

(3)

'g'

Floating point format. Uses lowercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'G'

Floating point format. Uses uppercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'c'

Single byte (accepts integer or single byte objects).

 

'b'

Bytes (any object that follows the buffer protocol or has __bytes__()).

(5)

's'

's' is an alias for 'b' and should only be used for Python2/3 code bases.

(6)

'a'

Bytes (converts any Python object using repr(obj).encode('ascii', 'backslashreplace')).

(5)

'r'

'r' is an alias for 'a' and should only be used for Python2/3 code bases.

(7)

'%'

No argument is converted, results in a '%' character in the result.

 

Notes:

  1. The alternate form causes a leading octal specifier ('0o') to be inserted before the first digit.

  2. The alternate form causes a leading '0x' or '0X' (depending on whether the 'x' or 'X' format was used) to be inserted before the first digit.

  3. The alternate form causes the result to always contain a decimal point, even if no digits follow it.

    The precision determines the number of digits after the decimal point and defaults to 6.

  4. The alternate form causes the result to always contain a decimal point, and trailing zeroes are not removed as they would otherwise be.

    The precision determines the number of significant digits before and after the decimal point and defaults to 6.

  5. If precision is N, the output is truncated to N characters.

  6. b'%s' is deprecated, but will not be removed during the 3.x series.

  7. b'%r' is deprecated, but will not be removed during the 3.x series.

  8. See PEP 237.

Note

The bytearray version of this method does not operate in place – it always produces a new object, even if no changes were made.

See also

PEP 461 – Adding % formatting to bytes and bytearray

New in version 3.5.

Memory Views

memoryview objects allow Python code to access the internal data of an object that supports the buffer protocol without copying.

class memoryview(object)

Create a memoryview that references objectobject must support the buffer protocol. Built-in objects that support the buffer protocol include bytes and bytearray.

memoryview has the notion of an element, which is the atomic memory unit handled by the originating object. For many simple types such as bytes and bytearray, an element is a single byte, but other types such as array.array may have bigger elements.

len(view) is equal to the length of tolist. If view.ndim = 0, the length is 1. If view.ndim = 1, the length is equal to the number of elements in the view. For higher dimensions, the length is equal to the length of the nested list representation of the view. The itemsize attribute will give you the number of bytes in a single element.

memoryview supports slicing and indexing to expose its data. One-dimensional slicing will result in a subview:

>>>

>>> v = memoryview(b'abcefg')
>>> v[1]
98
>>> v[-1]
103
>>> v[1:4]

>>> bytes(v[1:4])
b'bce'

If format is one of the native format specifiers from the struct module, indexing with an integer or a tuple of integers is also supported and returns a single element with the correct type. One-dimensional memoryviews can be indexed with an integer or a one-integer tuple. Multi-dimensional memoryviews can be indexed with tuples of exactly ndim integers where ndim is the number of dimensions. Zero-dimensional memoryviews can be indexed with the empty tuple.

Here is an example with a non-byte format:

>>>

>>> import array
>>> a = array.array('l', [-11111111, 22222222, -33333333, 44444444])
>>> m = memoryview(a)
>>> m[0]
-11111111
>>> m[-1]
44444444
>>> m[::2].tolist()
[-11111111, -33333333]

If the underlying object is writable, the memoryview supports one-dimensional slice assignment. Resizing is not allowed:

>>>

>>> data = bytearray(b'abcefg')
>>> v = memoryview(data)
>>> v.readonly
False
>>> v[0] = ord(b'z')
>>> data
bytearray(b'zbcefg')
>>> v[1:4] = b'123'
>>> data
bytearray(b'z123fg')
>>> v[2:3] = b'spam'
Traceback (most recent call last):
  File "", line 1, in 
ValueError: memoryview assignment: lvalue and rvalue have different structures
>>> v[2:6] = b'spam'
>>> data
bytearray(b'z1spam')

One-dimensional memoryviews of hashable (read-only) types with formats ‘B’, ‘b’ or ‘c’ are also hashable. The hash is defined as hash(m) == hash(m.tobytes()):

>>>

>>> v = memoryview(b'abcefg')
>>> hash(v) == hash(b'abcefg')
True
>>> hash(v[2:4]) == hash(b'ce')
True
>>> hash(v[::-2]) == hash(b'abcefg'[::-2])
True

Changed in version 3.3: One-dimensional memoryviews can now be sliced. One-dimensional memoryviews with formats ‘B’, ‘b’ or ‘c’ are now hashable.

Changed in version 3.4: memoryview is now registered automatically with collections.abc.Sequence

Changed in version 3.5: memoryviews can now be indexed with tuple of integers.

memoryview has several methods:

__eq__(exporter)

A memoryview and a PEP 3118 exporter are equal if their shapes are equivalent and if all corresponding values are equal when the operands’ respective format codes are interpreted using struct syntax.

For the subset of struct format strings currently supported by tolist()v and w are equal if v.tolist() == w.tolist():

>>>

>>> import array
>>> a = array.array('I', [1, 2, 3, 4, 5])
>>> b = array.array('d', [1.0, 2.0, 3.0, 4.0, 5.0])
>>> c = array.array('b', [5, 3, 1])
>>> x = memoryview(a)
>>> y = memoryview(b)
>>> x == a == y == b
True
>>> x.tolist() == a.tolist() == y.tolist() == b.tolist()
True
>>> z = y[::-2]
>>> z == c
True
>>> z.tolist() == c.tolist()
True

If either format string is not supported by the struct module, then the objects will always compare as unequal (even if the format strings and buffer contents are identical):

>>>

>>> from ctypes import BigEndianStructure, c_long
>>> class BEPoint(BigEndianStructure):
...     _fields_ = [("x", c_long), ("y", c_long)]
...
>>> point = BEPoint(100, 200)
>>> a = memoryview(point)
>>> b = memoryview(point)
>>> a == point
False
>>> a == b
False

Note that, as with floating point numbers, v is w does not imply v == w for memoryview objects.

Changed in version 3.3: Previous versions compared the raw memory disregarding the item format and the logical array structure.

tobytes(order=None)

Return the data in the buffer as a bytestring. This is equivalent to calling the bytes constructor on the memoryview.

>>>

>>> m = memoryview(b"abc")
>>> m.tobytes()
b'abc'
>>> bytes(m)
b'abc'

For non-contiguous arrays the result is equal to the flattened list representation with all elements converted to bytes. tobytes() supports all format strings, including those that are not in struct module syntax.

New in version 3.8: order can be {‘C’, ‘F’, ‘A’}. When order is ‘C’ or ‘F’, the data of the original array is converted to C or Fortran order. For contiguous views, ‘A’ returns an exact copy of the physical memory. In particular, in-memory Fortran order is preserved. For non-contiguous views, the data is converted to C first. order=None is the same as order=’C’.

hex([sep[bytes_per_sep]])

Return a string object containing two hexadecimal digits for each byte in the buffer.

>>>

>>> m = memoryview(b"abc")
>>> m.hex()
'616263'

New in version 3.5.

Changed in version 3.8: Similar to bytes.hex()memoryview.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

tolist()

Return the data in the buffer as a list of elements.

>>>

>>> memoryview(b'abc').tolist()
[97, 98, 99]
>>> import array
>>> a = array.array('d', [1.1, 2.2, 3.3])
>>> m = memoryview(a)
>>> m.tolist()
[1.1, 2.2, 3.3]

Changed in version 3.3: tolist() now supports all single character native formats in struct module syntax as well as multi-dimensional representations.

toreadonly()

Return a readonly version of the memoryview object. The original memoryview object is unchanged.

>>>

>>> m = memoryview(bytearray(b'abc'))
>>> mm = m.toreadonly()
>>> mm.tolist()
[89, 98, 99]
>>> mm[0] = 42
Traceback (most recent call last):
  File "", line 1, in 
TypeError: cannot modify read-only memory
>>> m[0] = 43
>>> mm.tolist()
[43, 98, 99]

New in version 3.8.

release()

Release the underlying buffer exposed by the memoryview object. Many objects take special actions when a view is held on them (for example, a bytearray would temporarily forbid resizing); therefore, calling release() is handy to remove these restrictions (and free any dangling resources) as soon as possible.

After this method has been called, any further operation on the view raises a ValueError (except release() itself which can be called multiple times):

>>>

>>> m = memoryview(b'abc')
>>> m.release()
>>> m[0]
Traceback (most recent call last):
  File "", line 1, in 
ValueError: operation forbidden on released memoryview object

The context management protocol can be used for a similar effect, using the with statement:

>>>

>>> with memoryview(b'abc') as m:
...     m[0]
...
97
>>> m[0]
Traceback (most recent call last):
  File "", line 1, in 
ValueError: operation forbidden on released memoryview object

New in version 3.2.

cast(format[shape])

Cast a memoryview to a new format or shape. shape defaults to [byte_length//new_itemsize], which means that the result view will be one-dimensional. The return value is a new memoryview, but the buffer itself is not copied. Supported casts are 1D -> C-contiguous and C-contiguous -> 1D.

The destination format is restricted to a single element native format in struct syntax. One of the formats must be a byte format (‘B’, ‘b’ or ‘c’). The byte length of the result must be the same as the original length.

Cast 1D/long to 1D/unsigned bytes:

>>>

>>> import array
>>> a = array.array('l', [1,2,3])
>>> x = memoryview(a)
>>> x.format
'l'
>>> x.itemsize
8
>>> len(x)
3
>>> x.nbytes
24
>>> y = x.cast('B')
>>> y.format
'B'
>>> y.itemsize
1
>>> len(y)
24
>>> y.nbytes
24

Cast 1D/unsigned bytes to 1D/char:

>>>

>>> b = bytearray(b'zyz')
>>> x = memoryview(b)
>>> x[0] = b'a'
Traceback (most recent call last):
  File "", line 1, in 
ValueError: memoryview: invalid value for format "B"
>>> y = x.cast('c')
>>> y[0] = b'a'
>>> b
bytearray(b'ayz')

Cast 1D/bytes to 3D/ints to 1D/signed char:

>>>

>>> import struct
>>> buf = struct.pack("i"*12, *list(range(12)))
>>> x = memoryview(buf)
>>> y = x.cast('i', shape=[2,2,3])
>>> y.tolist()
[[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]]]
>>> y.format
'i'
>>> y.itemsize
4
>>> len(y)
2
>>> y.nbytes
48
>>> z = y.cast('b')
>>> z.format
'b'
>>> z.itemsize
1
>>> len(z)
48
>>> z.nbytes
48

Cast 1D/unsigned long to 2D/unsigned long:

>>>

>>> buf = struct.pack("L"*6, *list(range(6)))
>>> x = memoryview(buf)
>>> y = x.cast('L', shape=[2,3])
>>> len(y)
2
>>> y.nbytes
48
>>> y.tolist()
[[0, 1, 2], [3, 4, 5]]

New in version 3.3.

Changed in version 3.5: The source format is no longer restricted when casting to a byte view.

There are also several readonly attributes available:

obj

The underlying object of the memoryview:

>>>

>>> b  = bytearray(b'xyz')
>>> m = memoryview(b)
>>> m.obj is b
True

New in version 3.3.

nbytes

nbytes == product(shape) * itemsize == len(m.tobytes()). This is the amount of space in bytes that the array would use in a contiguous representation. It is not necessarily equal to len(m):

>>>

>>> import array
>>> a = array.array('i', [1,2,3,4,5])
>>> m = memoryview(a)
>>> len(m)
5
>>> m.nbytes
20
>>> y = m[::2]
>>> len(y)
3
>>> y.nbytes
12
>>> len(y.tobytes())
12

Multi-dimensional arrays:

>>>

>>> import struct
>>> buf = struct.pack("d"*12, *[1.5*x for x in range(12)])
>>> x = memoryview(buf)
>>> y = x.cast('d', shape=[3,4])
>>> y.tolist()
[[0.0, 1.5, 3.0, 4.5], [6.0, 7.5, 9.0, 10.5], [12.0, 13.5, 15.0, 16.5]]
>>> len(y)
3
>>> y.nbytes
96

New in version 3.3.

readonly

A bool indicating whether the memory is read only.

format

A string containing the format (in struct module style) for each element in the view. A memoryview can be created from exporters with arbitrary format strings, but some methods (e.g. tolist()) are restricted to native single element formats.

Changed in version 3.3: format 'B' is now handled according to the struct module syntax. This means that memoryview(b'abc')[0] == b'abc'[0] == 97.

itemsize

The size in bytes of each element of the memoryview:

>>>

>>> import array, struct
>>> m = memoryview(array.array('H', [32000, 32001, 32002]))
>>> m.itemsize
2
>>> m[0]
32000
>>> struct.calcsize('H') == m.itemsize
True
ndim

An integer indicating how many dimensions of a multi-dimensional array the memory represents.

shape

A tuple of integers the length of ndim giving the shape of the memory as an N-dimensional array.

Changed in version 3.3: An empty tuple instead of None when ndim = 0.

strides

A tuple of integers the length of ndim giving the size in bytes to access each element for each dimension of the array.

Changed in version 3.3: An empty tuple instead of None when ndim = 0.

suboffsets

Used internally for PIL-style arrays. The value is informational only.

c_contiguous

A bool indicating whether the memory is C-contiguous.

New in version 3.3.

f_contiguous

A bool indicating whether the memory is Fortran contiguous.

New in version 3.3.

contiguous

A bool indicating whether the memory is contiguous.

New in version 3.3.

Set Types — setfrozenset

set object is an unordered collection of distinct hashable objects. Common uses include membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference. (For other containers see the built-in dictlist, and tuple classes, and the collections module.)

Like other collections, sets support x in setlen(set), and for x in set. Being an unordered collection, sets do not record element position or order of insertion. Accordingly, sets do not support indexing, slicing, or other sequence-like behavior.

There are currently two built-in set types, set and frozenset. The set type is mutable — the contents can be changed using methods like add() and remove(). Since it is mutable, it has no hash value and cannot be used as either a dictionary key or as an element of another set. The frozenset type is immutable and hashable — its contents cannot be altered after it is created; it can therefore be used as a dictionary key or as an element of another set.

Non-empty sets (not frozensets) can be created by placing a comma-separated list of elements within braces, for example: {'jack', 'sjoerd'}, in addition to the set constructor.

The constructors for both classes work the same:

class set([iterable])
class frozenset([iterable])

Return a new set or frozenset object whose elements are taken from iterable. The elements of a set must be hashable. To represent sets of sets, the inner sets must be frozenset objects. If iterable is not specified, a new empty set is returned.

Sets can be created by several means:

  • Use a comma-separated list of elements within braces: {'jack', 'sjoerd'}

  • Use a set comprehension: {c for c in 'abracadabra' if c not in 'abc'}

  • Use the type constructor: set()set('foobar')set(['a', 'b', 'foo'])

Instances of set and frozenset provide the following operations:

len(s)

Return the number of elements in set s (cardinality of s).

x in s

Test x for membership in s.

x not in s

Test x for non-membership in s.

isdisjoint(other)

Return True if the set has no elements in common with other. Sets are disjoint if and only if their intersection is the empty set.

issubset(other)
set <= other

Test whether every element in the set is in other.

set < other

Test whether the set is a proper subset of other, that is, set <= other and set != other.

issuperset(other)
set >= other

Test whether every element in other is in the set.

set > other

Test whether the set is a proper superset of other, that is, set >= other and set != other.

union(*others)
set | other | ...

Return a new set with elements from the set and all others.

intersection(*others)
set & other & ...

Return a new set with elements common to the set and all others.

difference(*others)
set - other - ...

Return a new set with elements in the set that are not in the others.

symmetric_difference(other)
set ^ other

Return a new set with elements in either the set or other but not both.

copy()

Return a shallow copy of the set.

Note, the non-operator versions of union()intersection()difference()symmetric_difference()issubset(), and issuperset() methods will accept any iterable as an argument. In contrast, their operator based counterparts require their arguments to be sets. This precludes error-prone constructions like set('abc') & 'cbs' in favor of the more readable set('abc').intersection('cbs').

Both set and frozenset support set to set comparisons. Two sets are equal if and only if every element of each set is contained in the other (each is a subset of the other). A set is less than another set if and only if the first set is a proper subset of the second set (is a subset, but is not equal). A set is greater than another set if and only if the first set is a proper superset of the second set (is a superset, but is not equal).

Instances of set are compared to instances of frozenset based on their members. For example, set('abc') == frozenset('abc') returns True and so does set('abc') in set([frozenset('abc')]).

The subset and equality comparisons do not generalize to a total ordering function. For example, any two nonempty disjoint sets are not equal and are not subsets of each other, so all of the following return Falsea<ba==b, or a>b.

Since sets only define partial ordering (subset relationships), the output of the list.sort() method is undefined for lists of sets.

Set elements, like dictionary keys, must be hashable.

Binary operations that mix set instances with frozenset return the type of the first operand. For example: frozenset('ab') | set('bc') returns an instance of frozenset.

The following table lists operations available for set that do not apply to immutable instances of frozenset:

update(*others)
set |= other | ...

Update the set, adding elements from all others.

intersection_update(*others)
set &= other & ...

Update the set, keeping only elements found in it and all others.

difference_update(*others)
set -= other | ...

Update the set, removing elements found in others.

symmetric_difference_update(other)
set ^= other

Update the set, keeping only elements found in either set, but not in both.

add(elem)

Add element elem to the set.

remove(elem)

Remove element elem from the set. Raises KeyError if elem is not contained in the set.

discard(elem)

Remove element elem from the set if it is present.

pop()

Remove and return an arbitrary element from the set. Raises KeyError if the set is empty.

clear()

Remove all elements from the set.

Note, the non-operator versions of the update()intersection_update()difference_update(), and symmetric_difference_update() methods will accept any iterable as an argument.

Note, the elem argument to the __contains__()remove(), and discard() methods may be a set. To support searching for an equivalent frozenset, a temporary one is created from elem.

Mapping Types — dict

mapping object maps hashable values to arbitrary objects. Mappings are mutable objects. There is currently only one standard mapping type, the dictionary. (For other containers see the built-in listset, and tuple classes, and the collections module.)

A dictionary’s keys are almost arbitrary values. Values that are not hashable, that is, values containing lists, dictionaries or other mutable types (that are compared by value rather than by object identity) may not be used as keys. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (such as 1 and 1.0) then they can be used interchangeably to index the same dictionary entry. (Note however, that since computers store floating-point numbers as approximations it is usually unwise to use them as dictionary keys.)

class dict(**kwargs)
class dict(mapping**kwargs)
class dict(iterable**kwargs)

Return a new dictionary initialized from an optional positional argument and a possibly empty set of keyword arguments.

Dictionaries can be created by several means:

  • Use a comma-separated list of key: value pairs within braces: {'jack': 4098, 'sjoerd': 4127} or {4098: 'jack', 4127: 'sjoerd'}

  • Use a dict comprehension: {}{x: x ** 2 for x in range(10)}

  • Use the type constructor: dict()dict([('foo', 100), ('bar', 200)])dict(foo=100, bar=200)

If no positional argument is given, an empty dictionary is created. If a positional argument is given and it is a mapping object, a dictionary is created with the same key-value pairs as the mapping object. Otherwise, the positional argument must be an iterable object. Each item in the iterable must itself be an iterable with exactly two objects. The first object of each item becomes a key in the new dictionary, and the second object the corresponding value. If a key occurs more than once, the last value for that key becomes the corresponding value in the new dictionary.

If keyword arguments are given, the keyword arguments and their values are added to the dictionary created from the positional argument. If a key being added is already present, the value from the keyword argument replaces the value from the positional argument.

To illustrate, the following examples all return a dictionary equal to {"one": 1, "two": 2, "three": 3}:

>>>

>>> a = dict(one=1, two=2, three=3)
>>> b = {'one': 1, 'two': 2, 'three': 3}
>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
>>> e = dict({'three': 3, 'one': 1, 'two': 2})
>>> f = dict({'one': 1, 'three': 3}, two=2)
>>> a == b == c == d == e == f
True

Providing keyword arguments as in the first example only works for keys that are valid Python identifiers. Otherwise, any valid keys can be used.

These are the operations that dictionaries support (and therefore, custom mapping types should support too):

list(d)

Return a list of all the keys used in the dictionary d.

len(d)

Return the number of items in the dictionary d.

d[key]

Return the item of d with key key. Raises a KeyError if key is not in the map.

If a subclass of dict defines a method __missing__() and key is not present, the d[key] operation calls that method with the key key as argument. The d[key] operation then returns or raises whatever is returned or raised by the __missing__(key) call. No other operations or methods invoke __missing__(). If __missing__() is not defined, KeyError is raised. __missing__() must be a method; it cannot be an instance variable:

>>>

>>> class Counter(dict):
...     def __missing__(self, key):
...         return 0
>>> c = Counter()
>>> c['red']
0
>>> c['red'] += 1
>>> c['red']
1

The example above shows part of the implementation of collections.Counter. A different __missing__ method is used by collections.defaultdict.

d[key] = value

Set d[key] to value.

del d[key]

Remove d[key] from d. Raises a KeyError if key is not in the map.

key in d

Return True if d has a key key, else False.

key not in d

Equivalent to not key in d.

iter(d)

Return an iterator over the keys of the dictionary. This is a shortcut for iter(d.keys()).

clear()

Remove all items from the dictionary.

copy()

Return a shallow copy of the dictionary.

classmethod fromkeys(iterable[value])

Create a new dictionary with keys from iterable and values set to value.

fromkeys() is a class method that returns a new dictionary. value defaults to None. All of the values refer to just a single instance, so it generally doesn’t make sense for value to be a mutable object such as an empty list. To get distinct values, use a dict comprehension instead.

get(key[default])

Return the value for key if key is in the dictionary, else default. If default is not given, it defaults to None, so that this method never raises a KeyError.

items()

Return a new view of the dictionary’s items ((key, value) pairs). See the documentation of view objects.

keys()

Return a new view of the dictionary’s keys. See the documentation of view objects.

pop(key[default])

If key is in the dictionary, remove it and return its value, else return default. If default is not given and key is not in the dictionary, a KeyError is raised.

popitem()

Remove and return a (key, value) pair from the dictionary. Pairs are returned in LIFO order.

popitem() is useful to destructively iterate over a dictionary, as often used in set algorithms. If the dictionary is empty, calling popitem() raises a KeyError.

Changed in version 3.7: LIFO order is now guaranteed. In prior versions, popitem() would return an arbitrary key/value pair.

reversed(d)

Return a reverse iterator over the keys of the dictionary. This is a shortcut for reversed(d.keys()).

New in version 3.8.

setdefault(key[default])

If key is in the dictionary, return its value. If not, insert key with a value of default and return defaultdefault defaults to None.

update([other])

Update the dictionary with the key/value pairs from other, overwriting existing keys. Return None.

update() accepts either another dictionary object or an iterable of key/value pairs (as tuples or other iterables of length two). If keyword arguments are specified, the dictionary is then updated with those key/value pairs: d.update(red=1, blue=2).

values()

Return a new view of the dictionary’s values. See the documentation of view objects.

An equality comparison between one dict.values() view and another will always return False. This also applies when comparing dict.values() to itself:

>>>

>>> d = {'a': 1}
>>> d.values() == d.values()
False
d | other

Create a new dictionary with the merged keys and values of d and other, which must both be dictionaries. The values of other take priority when d and other share keys.

New in version 3.9.

d |= other

Update the dictionary d with keys and values from other, which may be either a mapping or an iterable of key/value pairs. The values of other take priority when d and other share keys.

New in version 3.9.

Dictionaries compare equal if and only if they have the same (key, value) pairs (regardless of ordering). Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise TypeError.

Dictionaries preserve insertion order. Note that updating a key does not affect the order. Keys added after deletion are inserted at the end.

>>>

>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(d)
['one', 'two', 'three', 'four']
>>> list(d.values())
[1, 2, 3, 4]
>>> d["one"] = 42
>>> d
{'one': 42, 'two': 2, 'three': 3, 'four': 4}
>>> del d["two"]
>>> d["two"] = None
>>> d
{'one': 42, 'three': 3, 'four': 4, 'two': None}

Changed in version 3.7: Dictionary order is guaranteed to be insertion order. This behavior was an implementation detail of CPython from 3.6.

Dictionaries and dictionary views are reversible.

>>>

>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}
>>> list(reversed(d))
['four', 'three', 'two', 'one']
>>> list(reversed(d.values()))
[4, 3, 2, 1]
>>> list(reversed(d.items()))
[('four', 4), ('three', 3), ('two', 2), ('one', 1)]

Changed in version 3.8: Dictionaries are now reversible.

See also

types.MappingProxyType can be used to create a read-only view of a dict.

Dictionary view objects

The objects returned by dict.keys()dict.values() and dict.items() are view objects. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes.

Dictionary views can be iterated over to yield their respective data, and support membership tests:

len(dictview)

Return the number of entries in the dictionary.

iter(dictview)

Return an iterator over the keys, values or items (represented as tuples of (key, value)) in the dictionary.

Keys and values are iterated over in insertion order. This allows the creation of (value, key) pairs using zip()pairs = zip(d.values(), d.keys()). Another way to create the same list is pairs = [(v, k) for (k, v) in d.items()].

Iterating views while adding or deleting entries in the dictionary may raise a RuntimeError or fail to iterate over all entries.

Changed in version 3.7: Dictionary order is guaranteed to be insertion order.

x in dictview

Return True if x is in the underlying dictionary’s keys, values or items (in the latter case, x should be a (key, value) tuple).

reversed(dictview)

Return a reverse iterator over the keys, values or items of the dictionary. The view will be iterated in reverse order of the insertion.

Changed in version 3.8: Dictionary views are now reversible.

dictview.mapping

Return a types.MappingProxyType that wraps the original dictionary to which the view refers.

New in version 3.10.

Keys views are set-like since their entries are unique and hashable. If all values are hashable, so that (key, value) pairs are unique and hashable, then the items view is also set-like. (Values views are not treated as set-like since the entries are generally not unique.) For set-like views, all of the operations defined for the abstract base class collections.abc.Set are available (for example, ==<, or ^).

An example of dictionary view usage:

>>>

>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
>>> keys = dishes.keys()
>>> values = dishes.values()

>>> # iteration
>>> n = 0
>>> for val in values:
...     n += val
>>> print(n)
504

>>> # keys and values are iterated over in the same order (insertion order)
>>> list(keys)
['eggs', 'sausage', 'bacon', 'spam']
>>> list(values)
[2, 1, 1, 500]

>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['bacon', 'spam']

>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}

>>> # get back a read-only proxy for the original dictionary
>>> values.mapping
mappingproxy({'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500})
>>> values.mapping['spam']
500

Context Manager Types

Python’s with statement supports the concept of a runtime context defined by a context manager. This is implemented using a pair of methods that allow user-defined classes to define a runtime context that is entered before the statement body is executed and exited when the statement ends:

contextmanager.__enter__()

Enter the runtime context and return either this object or another object related to the runtime context. The value returned by this method is bound to the identifier in the as clause of with statements using this context manager.

An example of a context manager that returns itself is a file object. File objects return themselves from __enter__() to allow open() to be used as the context expression in a with statement.

An example of a context manager that returns a related object is the one returned by decimal.localcontext(). These managers set the active decimal context to a copy of the original decimal context and then return the copy. This allows changes to be made to the current decimal context in the body of the with statement without affecting code outside the with statement.

contextmanager.__exit__(exc_typeexc_valexc_tb)

Exit the runtime context and return a Boolean flag indicating if any exception that occurred should be suppressed. If an exception occurred while executing the body of the with statement, the arguments contain the exception type, value and traceback information. Otherwise, all three arguments are None.

Returning a true value from this method will cause the with statement to suppress the exception and continue execution with the statement immediately following the with statement. Otherwise the exception continues propagating after this method has finished executing. Exceptions that occur during execution of this method will replace any exception that occurred in the body of the with statement.

The exception passed in should never be reraised explicitly – instead, this method should return a false value to indicate that the method completed successfully and does not want to suppress the raised exception. This allows context management code to easily detect whether or not an __exit__() method has actually failed.

Python defines several context managers to support easy thread synchronisation, prompt closure of files or other objects, and simpler manipulation of the active decimal arithmetic context. The specific types are not treated specially beyond their implementation of the context management protocol. See the contextlib module for some examples.

Python’s generators and the contextlib.contextmanager decorator provide a convenient way to implement these protocols. If a generator function is decorated with the contextlib.contextmanager decorator, it will return a context manager implementing the necessary __enter__() and __exit__() methods, rather than the iterator produced by an undecorated generator function.

Note that there is no specific slot for any of these methods in the type structure for Python objects in the Python/C API. Extension types wanting to define these methods must provide them as a normal Python accessible method. Compared to the overhead of setting up the runtime context, the overhead of a single class dictionary lookup is negligible.

Type Annotation Types — Generic AliasUnion

The core built-in types for type annotations are Generic Alias and Union.

Generic Alias Type

GenericAlias objects are generally created by subscripting a class. They are most often used with container classes, such as list or dict. For example, list[int] is a GenericAlias object created by subscripting the list class with the argument intGenericAlias objects are intended primarily for use with type annotations.

Note

It is generally only possible to subscript a class if the class implements the special method __class_getitem__().

GenericAlias object acts as a proxy for a generic type, implementing parameterized generics.

For a container class, the argument(s) supplied to a subscription of the class may indicate the type(s) of the elements an object contains. For example, set[bytes] can be used in type annotations to signify a set in which all the elements are of type bytes.

For a class which defines __class_getitem__() but is not a container, the argument(s) supplied to a subscription of the class will often indicate the return type(s) of one or more methods defined on an object. For example, regular expressions can be used on both the str data type and the bytes data type:

  • If x = re.search('foo', 'foo')x will be a re.Match object where the return values of x.group(0) and x[0] will both be of type str. We can represent this kind of object in type annotations with the GenericAlias re.Match[str].

  • If y = re.search(b'bar', b'bar'), (note the b for bytes), y will also be an instance of re.Match, but the return values of y.group(0) and y[0] will both be of type bytes. In type annotations, we would represent this variety of re.Match objects with re.Match[bytes].

GenericAlias objects are instances of the class types.GenericAlias, which can also be used to create GenericAlias objects directly.

T[X, Y, ...]

Creates a GenericAlias representing a type T parameterized by types XY, and more depending on the T used. For example, a function expecting a list containing float elements:

def average(values: list[float]) -> float:
    return sum(values) / len(values)

Another example for mapping objects, using a dict, which is a generic type expecting two type parameters representing the key type and the value type. In this example, the function expects a dict with keys of type str and values of type int:

def send_post_request(url: str, body: dict[str, int]) -> None:
    ...

The builtin functions isinstance() and issubclass() do not accept GenericAlias types for their second argument:

>>>

>>> isinstance([1, 2], list[str])
Traceback (most recent call last):
  File "", line 1, in 
TypeError: isinstance() argument 2 cannot be a parameterized generic

The Python runtime does not enforce type annotations. This extends to generic types and their type parameters. When creating a container object from a GenericAlias, the elements in the container are not checked against their type. For example, the following code is discouraged, but will run without errors:

>>>

>>> t = list[str]
>>> t([1, 2, 3])
[1, 2, 3]

Furthermore, parameterized generics erase type parameters during object creation:

>>>

>>> t = list[str]
>>> type(t)
<class 'types.GenericAlias'>

>>> l = t()
>>> type(l)
<class 'list'>

Calling repr() or str() on a generic shows the parameterized type:

>>>

>>> repr(list[int])
'list[int]'

>>> str(list[int])
'list[int]'

The __getitem__() method of generic containers will raise an exception to disallow mistakes like dict[str][str]:

>>>

>>> dict[str][str]
Traceback (most recent call last):
  File "", line 1, in 
TypeError: There are no type variables left in dict[str]

However, such expressions are valid when type variables are used. The index must have as many elements as there are type variable items in the GenericAlias object’s __args__.

>>>

>>> from typing import TypeVar
>>> Y = TypeVar('Y')
>>> dict[str, Y][int]
dict[str, int]

Standard Generic Classes

The following standard library classes support parameterized generics. This list is non-exhaustive.

Special Attributes of GenericAlias objects

All parameterized generics implement special read-only attributes.

genericalias.__origin__

This attribute points at the non-parameterized generic class:

>>>

>>> list[int].__origin__
<class 'list'>
genericalias.__args__

This attribute is a tuple (possibly of length 1) of generic types passed to the original __class_getitem__() of the generic class:

>>>

>>> dict[str, list[int]].__args__
(<class 'str'>, list[int])
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,)

Note

GenericAlias object with typing.ParamSpec parameters may not have correct __parameters__ after substitution because typing.ParamSpec is intended primarily for static type checking.

See also

PEP 484 – Type Hints

Introducing Python’s framework for type annotations.

PEP 585 – Type Hinting Generics In Standard Collections

Introducing the ability to natively parameterize standard-library classes, provided they implement the special class method __class_getitem__().

Genericsuser-defined generics and typing.Generic

Documentation on how to implement generic classes that can be parameterized at runtime and understood by static type-checkers.

New in version 3.9.

Union Type

A union object holds the value of the | (bitwise or) operation on multiple type objects. These types are intended primarily for type annotations. The union type expression enables cleaner type hinting syntax compared to typing.Union.

X | Y | ...

Defines a union object which holds types XY, and so forth. X | Y means either X or Y. It is equivalent to typing.Union[X, Y]. For example, the following function expects an argument of type int or float:

def square(number: int | float) -> int | float:
    return number ** 2
union_object == other

Union objects can be tested for equality with other union objects. Details:

  • Unions of unions are flattened:

    (int | str) | float == int | str | float
    
  • Redundant types are removed:

    int | str | int == int | str
    
  • When comparing unions, the order is ignored:

    int | str == str | int
    
  • It is compatible with typing.Union:

    int | str == typing.Union[int, str]
    
  • Optional types can be spelled as a union with None:

    str | None == typing.Optional[str]
    
isinstance(obj, union_object)
issubclass(obj, union_object)

Calls to isinstance() and issubclass() are also supported with a union object:

>>>

>>> isinstance("", int | str)
True

However, union objects containing parameterized generics cannot be used:

>>>

>>> isinstance(1, int | list[int])
Traceback (most recent call last):
  File "", line 1, in 
TypeError: isinstance() argument 2 cannot contain a parameterized generic

The user-exposed type for the union object can be accessed from types.UnionType and used for isinstance() checks. An object cannot be instantiated from the type:

>>>

>>> import types
>>> isinstance(int | str, types.UnionType)
True
>>> types.UnionType()
Traceback (most recent call last):
  File "", line 1, in 
TypeError: cannot create 'types.UnionType' instances

Note

The __or__() method for type objects was added to support the syntax X | Y. If a metaclass implements __or__(), the Union may override it:

>>>

>>> class M(type):
...     def __or__(self, other):
...         return "Hello"
...
>>> class C(metaclass=M):
...     pass
...
>>> C | int
'Hello'
>>> int | C
int | __main__.C

See also

PEP 604 – PEP proposing the X | Y syntax and the Union type.

New in version 3.10.

Other Built-in Types

The interpreter supports several other kinds of objects. Most of these support only one or two operations.

Modules

The only special operation on a module is attribute access: m.name, where m is a module and name accesses a name defined in m’s symbol table. Module attributes can be assigned to. (Note that the import statement is not, strictly speaking, an operation on a module object; import foo does not require a module object named foo to exist, rather it requires an (external) definition for a module named foo somewhere.)

A special attribute of every module is __dict__. This is the dictionary containing the module’s symbol table. Modifying this dictionary will actually change the module’s symbol table, but direct assignment to the __dict__ attribute is not possible (you can write m.__dict__['a'] = 1, which defines m.a to be 1, but you can’t write m.__dict__ = {}). Modifying __dict__ directly is not recommended.

Modules built into the interpreter are written like this: <module 'sys' (built-in)>. If loaded from a file, they are written as <module 'os' from '/usr/local/lib/pythonX.Y/os.pyc'>.

Classes and Class Instances

See Objects, values and types and Class definitions for these.

Functions

Function objects are created by function definitions. The only operation on a function object is to call it: func(argument-list).

There are really two flavors of function objects: built-in functions and user-defined functions. Both support the same operation (to call the function), but the implementation is different, hence the different object types.

See Function definitions for more information.

Methods

Methods are functions that are called using the attribute notation. There are two flavors: built-in methods (such as append() on lists) and class instance methods. Built-in methods are described with the types that support them.

If you access a method (a function defined in a class namespace) through an instance, you get a special object: a bound method (also called instance method) object. When called, it will add the self argument to the argument list. Bound methods have two special read-only attributes: m.__self__ is the object on which the method operates, and m.__func__ is the function implementing the method. Calling m(arg-1, arg-2, ..., arg-n) is completely equivalent to calling m.__func__(m.__self__, arg-1, arg-2, ..., arg-n).

Like function objects, bound method objects support getting arbitrary attributes. However, since method attributes are actually stored on the underlying function object (meth.__func__), setting method attributes on bound methods is disallowed. Attempting to set an attribute on a method results in an AttributeError being raised. In order to set a method attribute, you need to explicitly set it on the underlying function object:

>>>

>>> class C:
...     def method(self):
...         pass
...
>>> c = C()
>>> c.method.whoami = 'my name is method'  # can't set on the method
Traceback (most recent call last):
  File "", line 1, in 
AttributeError: 'method' object has no attribute 'whoami'
>>> c.