The Python Standard Library

The Python 3 standard library is one of the language's greatest strengths. It is extensive and highly optimized, providing efficient functions, objects, and methods for frequently-used programming tasks.

This page covers the standard library provided with Python 3.

Python's standard library is very extensive. The library contains built-in modules (written in C) for access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, and 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 (the ability to run a program on another operating system) by abstracting away platform-specifics into platform-neutral APIs.

The Python installers for the Windows platform usually includes 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.

Built-in functions

The Python interpreter has many functions and types built into it that are always available. They are listed here in alphabetical order.

abs(x)

all(iterable)

def all(iterable): for element in iterable: if not element: return False return True

any(iterable)

def any(iterable): for element in iterable: if element: return True return False

ascii(object)

bin(x)

bool([x])

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

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

callable(object)

chr(i)

classmethod(function)

class C: @classmethod def f(cls, arg1, arg2, ...): ...

compile(source, filename,  mode, flags=0,  dont_inherit=False,  optimize=-1)

complex([real [, imag]])

delattr(object,  name)

dict(**kwarg)dict(mapping,  **kwarg)dict(iterable,  **kwarg)

dir([object])

>>> 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']

divmod(a, b)

enumerate(iterable,  start=0)

>>> 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')]

def enumerate(sequence, start=0): n = start for elem in sequence: yield n, elem n += 1

eval(expression,  globals=None,  locals=None)

>>> x = 1>>> eval('x+1')2

exec(object [, globals [, locals]])

filter(function,  iterable)

float([x])

sign           ::=  "+" | "-"infinity       ::=  "Infinity" | "inf"nan            ::=  "nan"numeric_value  ::=  floatnumber | infinity | nannumeric_string ::=  [sign] numeric_value

>>> float('+1.23')1.23>>> float('   -12345\n')-12345.0>>> float('1e-003')0.001>>> float('+1E6')1000000.0>>> float('-Infinity')-inf

format(value[,  format_spec])

frozenset([iterable])

getattr(object,  name [, default])

globals()

hasattr(object,name)

hash(object)

help([object])

hex(x)

>>> hex(255)'0xff'>>> hex(-42)'-0x2a'

id(object)

input([prompt])

>>> s = input('--> ')  --> Monty Python's Flying Circus>>> s  "Monty Python's Flying Circus"

int(x=0)int(x, base=10)

isinstance(object,  classinfo)

issubclass(class,  classinfo)

iter(object [, sentinel])

with open('mydata.txt') as fp: for line in iter(fp.readline, ''): process_line(line)

len(s)

list([iterable])

locals()

map(function,  iterable, ...)

max(iterable,  *[, key, default])max(arg1, arg2,  *args[, key])

memoryview(obj)

min(iterable, * [, key,  default])min(arg1,  arg2,  *args [, key])

next(iterator [, default])

object()

oct(x)

open(file,  mode='r',  buffering=-1,  encoding=None,  errors=None,  newline=None,  closefd=True,  opener=None)

>>> 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 is written to somedir/spamspam.txt', file=f)
...
>>> os.close(dir_fd)  # don't leak a file descriptor

ord(c)

pow(x,  y [, z])

print(*objects,  sep=' ',  end='\n',  file=sys.stdout,  flush=False)

property(fget=None,  fset=None, fdel=None, doc=None)

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.")

class Parrot: def __init__(self): self._voltage = 100000 @property def voltage(self): """Get the current voltage.""" return self._voltage

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

range(stop)range(start,  stop [, step])

repr(object)

reversed(seq)

round(number[,  ndigits])

set([iterable])

setattr(object,  name,  value)

slice(stop)slice(start,  stop [, step])

sorted(iterable [, key] [, reverse])

staticmethod(function)

class C: @staticmethod def f(arg1, arg2, ...): ...

str(object='')str(object=b'',  encoding='utf-8', errors='strict')

sum(iterable [, start])

super([type [, object-or-type]])

class C(B): def method(self, arg): super().method(arg)    # This does the same thing as: # super(C, self).method(arg)

tuple([iterable])

type(object)type(name,  bases,  dict)

>>> class X:...     a = 1...>>> X = type('X', (object,), dict(a=1))

vars([object])

zip(*iterables)

def zip(*iterables): # zip('ABCD', 'xy') --> Ax By sentinel = object() iterators = [iter(it) for it in iterables] while iterators: result = [] for it in iterators: elem = next(it, sentinel) if elem is sentinel: return result.append(elem) yield tuple(result)

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

__import__(name,  globals=None,  locals=None,  fromlist=(), level=0)

spam = __import__('spam', globals(), locals(), [], 0)

spam = __import__('spam.ham', globals(), locals(), [], 0)

_temp = __import__('spam.ham', globals(), locals(), ['eggs', 'sausage'], 0)eggs = _temp.eggssaus = _temp.sausage

Built-in constants

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

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)

copyrightlicensecredits

Built-in types

The following sections describe the standard types built in to 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, 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. The following values are considered false:

• None
• False
• Zero of any numeric type, for example, 0, 0.0, 0j.
• Any empty sequence, for example, '', (), [].
• Any empty mapping, for example, {}.
• Instances of user-defined classes, if the class defines a __bool__() or __len__() method, when that method returns the integer zero or bool value False.

All other values are considered true — so objects of many types are always true.

Operations and built-in functions with 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 — and, or, not

These are the Boolean operations, ordered by ascending priority:

x or y

x and y

not x

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 false). This table summarizes the comparison operations:

Objects of different types, except different numeric types, never compare equal. Furthermore, some types (for example, function objects) support only a degenerate notion of comparison where any two objects of that type are unequal. The <, <=, > and >= operators raises a TypeError exception when comparing a complex number with another built-in numeric type, when the objects are of different types that cannot be compared, or in other cases where there is no defined ordering.

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 only by sequence types (below).

Numeric types — int, float, complex

There are three distinct numeric types: integers, floating point numbers, and complex numbers. Also, 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 additional numeric types, fractions that hold rationals, and decimal that hold 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. Comparisons between numbers of mixed type use the same rule. The constructors int(), float(), and complex() can produce numbers of a specific type.

All numeric types (except complex) support the following operations, sorted by ascending priority (operations in the same box have the same priority; all numeric operations have a higher priority than comparison operations):

Notes:

1. Alternatively 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 -1, 1//(-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).
7. See the official Unicode code points page for a complete list of code points with the Nd property.

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

For additional numeric operations see the math and cmath modules.

Bitwise operations on integer types

Bitwise operations only make sense for integers. Negative numbers are treated as their 2's complement value (this assumes a sufficiently large number of bits that no overflow occurs during the operation).

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 (operations in the same box have the same priority):

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) without overflow check.
3. A right shift by n bits is equivalent to division by pow(2, n) without overflow check

Additional methods on integer types

The int type implements the numbers.Integral abstract base class. Also, it provides:

int.bit_length()

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

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

int.to_bytes(length,  byteorder, *,  signed=False)

>>> (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() // 8) + 1, byteorder='little')b'\xe8\x03'

int.from_bytes(bytes,  byteorder, *,  signed=False)

>>> 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

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()

float.is_integer()

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

float.hex()

float.fromhex(s)

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 int, float, decimal.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. 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, -sys.hash_info.inf and sys.hash_info.nan are used as hash values for positive infinity, negative infinity, or nans (respectively). (All hashable nans have the same hash value.)
• 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.

Whew! That was technical, but that's how it works.

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_ = 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_ = (abs(m) % P) * pow(n, P - 2, P) % P
    if m < 0:
        hash_ = -hash_
    if hash_ == -1:
        hash_ = -2
    return hash_
def hash_float(x):
    """Compute the hash of a float x."""
    if math.isnan(x):
        return sys.hash_info.nan
    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_ = 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_ = (hash_ & (M - 1)) - (hash & M)
    if hash_ == -1:
        hash_ == -2
    return hash_

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 iteration support:

container.__iter__()

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

iterator.__iter__()

iterator.__next__()

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 automatically returns an iterator object (technically, a generator object) supplying the __iter__() and __next__() methods. More information about generators is in the documentation for the yield expression.

Sequence types — list, tuple, range

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 (operations in the same box have the same priority). In the table, s and t are sequences of the same type, n, i, j 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.

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.)

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 str, bytes 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 also that the copies are shallow; nested structures are not copied. 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 (pointers 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]]

3. If i or j is negative, the index is relative to the end of the string: len(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 i, i+k, i+2*k, i+3*k and so on, stopping when j is reached (but never including j). If i or j is greater than len(s), use len(s). 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 has a quadratic runtime cost in the total sequence length. To get a linear runtime cost, you must switch to one of the alternatives below:
   
   
1. If concatenating str objects, you can build a list and use str.join() at the end or else write to a io.StringIO instance and retrieve its value when complete.
2. 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.
3. If concatenating tuple objects, extend a list instead.
4. 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. When supported, the additional arguments to the index method 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 containing unhashable values results 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).

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)

Lists

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

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, [].

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=None,  reverse=None)

Tuples

Tuples are immutable sequences, often 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).

Tuples may be constructed in several 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 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 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.

Ranges may be constructed in two ways:

• range(stop)
• range(start, stop[, 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 is 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 usually violates that pattern).

The advantage of the range type over a regular list or tuple is that a range object always takes the same (small) amount of memory, no matter the size of the range it represents (as it only stores the start, stop 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 start, stop and step attributes, for example range(0) == range(2, 1, 3) or range(0, 3, 2) == range(0, 4, 2).)

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 is included in the string literal.

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

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 s, s[0] == s[0:1].

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

Changed in version 3.3: For backward 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')

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

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(), Syntax and 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 other modules that provide various text related utilities (including regular expression support in the re module).

str.capitalize()

str.casefold()

str.center(width [, fillchar])

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

str.encode(encoding="utf-8",  errors="strict")

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

str.expandtabs(tabsize=8)

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

str.find(sub [, start [, end]])

>>> 'Py' in 'Python'True

str.format(*args,  **kwargs)

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

str.format_map(mapping)

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

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

str.isalnum()

str.isalpha()

str.isdecimal()

str.isdigit()

str.isidentifier()

str.islower()

str.isnumeric()

str.isprintable()

str.isspace()

str.istitle()

str.isupper()

str.join(iterable)

str.ljust(width [, fillchar])

str.lower()

str.lstrip([chars])

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

static str.maketrans(x [, y [, z]])

str.partition(sep)

str.replace(old,  new [, count])

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

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

str.rjust(width [, fillchar])

str.rpartition(sep)

str.rsplit(sep=None,  maxsplit=-1)

str.rstrip([chars])

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

str.split(sep=None,  maxsplit=-1)

str.splitlines([keepends])

str.startswith(prefix [, start [, end]])

str.strip([chars])

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

str.swapcase()

str.title()

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

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

str.translate(map)

str.upper()

str.zfill(width)

printf-style string formatting

Note: The formatting operations described here exhibit a variety of quirks that lead to some common errors (such as failing to display tuples and dictionaries correctly). Using the newer str.format() interface helps avoid these errors, and also provides a generally more powerful, flexible and extensible approach to formatting text.

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. 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:

A length modifier (h, l, 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:

Notes:

1. The alternate form causes a leading zero ('0') to be inserted between left padding and the formatting of the number if the leading character of the result is not already a zero.
2. The alternate form causes a leading '0x' or '0X' (depending on whether the 'x' or 'X' format was used) to be inserted between left padding and the formatting of the number if the leading character of the result is not already a zero.
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: Unifying Long Integers and Integers.

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

Binary sequence types — bytes, bytearray, memoryview

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

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.

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.

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 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 usually leads to data corruption).

In addition to the literal forms, bytes objects can be created in some 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 bytes objects are sequences of integers, for a bytes object b, b[0] is an integer, while b[0:1] is a bytes object of length 1. (This contrasts with text strings, where both indexing and slicing produces 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 backward 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 always compare unequal.

Bytearray objects

Bytearray objects are a mutable counterpart to bytes objects. 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 specified 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.

Bytes and bytearray operations

Both bytes and bytearray objects support the common sequence operations. They interoperate not with operands of the same type, but with any object that supports the buffer protocol. 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.

Due to the common use of ASCII text as the basis for binary protocols, bytes and bytearray objects provide almost all methods found on text strings, with the exceptions of:

• str.encode() (which converts text strings to bytes objects)
• str.format() and str.format_map() (which are used to format text for display to users)
• str.isidentifier(), str.isnumeric(), str.isdecimal(), str.isprintable() (which are used to check various properties of text strings that are not applicable to binary protocols).

All other string methods are supported, although sometimes with slight differences in functionality and semantics (as described below).

Note: The methods on bytes and bytearray objects don't accept strings as their arguments, like methods on strings that 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")

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

The search operations (in, count(), find(), index(), rfind() and rindex()) all accept both integers in the range 0 to 255 (inclusive), and bytes and byte array sequences.

Changed in version 3.3: All the search methods also accept an integer in the range 0 to 255 (inclusive) as their first argument.

Each bytes and bytearray instance provides a decode() convenience method that is the inverse of str.encode():

bytes.decode(encoding="utf-8",  errors="strict")bytearray.decode(encoding="utf-8",  errors="strict")

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

classmethod bytes.fromhex(string)classmethod bytearray.fromhex(string)

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

The maketrans and translate methods differ in semantics from the versions available on strings:

bytes.translate(table[, delete])bytearray.translate(table[, delete])

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

static bytes.maketrans(from,to)static bytearray.maketrans(from,to)

Memory views

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

class memoryview(obj)

>>> v = memoryview(b'abcefg')>>> v[1]98>>> v[-1]103>>> v[1:4]<memory at 0x7f3ddc9f4350>>>> bytes(v[1:4])b'bce'

>>> import array>>> a = array.array('l', [-11111111, 22222222, -33333333, 44444444])>>> a[0]-11111111>>> a[-1]44444444>>> a[2:3].tolist()[-33333333]>>> a[::2].tolist()[-11111111, -33333333]>>> a[::-1].tolist()[44444444, -33333333, 22222222, -11111111]

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

>>> 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

memoryview has several methods:

memoryview.__eq__(exporter)

>>> 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 == bTrue>>> x.tolist() == a.tolist() == y.tolist() == b.tolist()True>>> z = y[::-2]>>> z == cTrue>>> z.tolist() == c.tolist()True

>>> 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 == pointFalse>>> a == bFalse

memoryview.tobytes()

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

memoryview.tolist()

>>> 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]

release()

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

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

cast(format[,  shape])

>>> import array>>> a = array.array('l', [1,2,3])>>> x = memoryview(a)>>> x.format'l'>>> x.itemsize8>>> len(x)3>>> x.nbytes24>>> y = x.cast('B')>>> y.format'B'>>> y.itemsize1>>> len(y)24>>> y.nbytes24

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

>>> 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.itemsize4>>> len(y)2>>> y.nbytes48>>> z = y.cast('b')>>> z.format'b'>>> z.itemsize1>>> len(z)48>>> z.nbytes48

>>> buf = struct.pack("L"*6, *list(range(6)))>>> x = memoryview(buf)>>> y = x.cast('L', shape=[2,3])>>> len(y)2>>> y.nbytes48>>> y.tolist()[[0, 1, 2], [3, 4, 5]]

There are also several readonly attributes available:

memoryview.obj

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

memoryview.nbytes

>>> import array>>> a = array.array('i', [1,2,3,4,5])>>> m = memoryview(a)>>> len(m)5>>> m.nbytes20>>> y = m[::2]>>> len(y)3>>> y.nbytes12>>> len(y.tobytes())12

>>> 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.nbytes96

memoryview.readonly

memoryview.format

memoryview.itemsize

>>> import array, struct>>> m = memoryview(array.array('H', [32000, 32001, 32002]))>>> m.itemsize2>>> m[0]32000>>> struct.calcsize('H') == m.itemsizeTrue

memoryview.ndim

memoryview.shape

memoryview.strides

memoryview.suboffsets

memoryview.c_contiguous

memoryview.f_contiguous

memoryview.contiguous

Set types — set, frozenset

A 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 dict, list, and tuple classes, and the collections module.)

Like other collections, sets support x in set, len(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; therefore, it can 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])

Instances of set and frozenset provide the following operations:

len(s)

x in s

x not in s

isdisjoint(other)

issubset(other)set <= other

set < other

issuperset(other)set >= other

set > other

union(other)set | other ...

difference(other, ...)set - other - ...

symmetric_difference(other)set ^ other

copy()

Note, the non-operator versions of union(), intersection(), difference(), and symmetric_difference(), issubset(), and issuperset() methods accepts 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 the following return False: a<b, a==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(other, ...)set |= other | ...

intersection_update(other, ...)set &= other & ...

difference_update(other, ...)set -= other | ...

symmetric_difference_update(other)set ^= other

add(elem)

remove(elem)

discard(elem)

pop()

clear()

Note, the non-operator versions of the update(), intersection_update(), difference_update(), and symmetric_difference_update() methods 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, the elem set is temporarily mutated during the search and then restored. During the search, the elem set should not be read or mutated since it does not have a meaningful value.

Mapping types — dict

A 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 list, set, 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.)

Dictionaries can be created by placing a comma-separated list of key: value pairs within braces, for example: {'jack': 4098, 'sjoerd': 4127} or {4098: 'jack', 4127: 'sjoerd'}, or by the dict constructor.

class dict(**kwarg)class dict(mapping,  **kwarg)class dict(iterable,  **kwarg)

>>> 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})>>> a == b == c == d == eTrue

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

len(d)

d[key]

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

d[key] = value

del d[key]

key in d

key not in d

iter(d)

clear()

copy()

classmethod fromkeys(seq [, value])

get(key[,  default])

items()

keys()

pop(key [, default])

popitem()

setdefault(key[,  default])

update([other])

values()

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)

iter(dictview)

x in dictview

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 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
>>> list(keys)
['eggs', 'bacon', 'sausage', 'spam']
>>> list(values)
[2, 1, 1, 500]
>>> # view objects are dynamic and reflect dict changes
>>> del dishes['eggs']
>>> del dishes['sausage']
>>> list(keys)
['spam', 'bacon']
>>> # set operations
>>> keys & {'eggs', 'bacon', 'salad'}
{'bacon'}
>>> keys ^ {'sausage', 'juice'}
{'juice', 'sausage', 'bacon', 'spam'}

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__()

contextmanager.__exit__(exc_type,  exc_val,  exc_tb)

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 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 returns 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.

Other built-in types

The interpreter supports 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 actually changes 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'>.

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.

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 adds 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. 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 "<stdin>", line 1, in <module>
AttributeError: 'method' object has no attribute 'whoami'
>>> c.method.__func__.whoami = 'my name is method'
>>> c.method.whoami
'my name is method'

Code objects

Code objects are used by the implementation to represent “pseudo-compiled” executable Python code such as a function body. They differ from function objects because they don’t contain a reference to their global execution environment. Code objects are returned by the built-in compile() function and can be extracted from function objects through their __code__ attribute.

A code object can be executed or evaluated by passing it (instead of a source string) to the exec() or eval() built-in functions.

Type objects represent the various object types. An object's type is accessed by the built-in function type(). There are no special operations on types. The standard module types defines names for all standard built-in types.

Types are written like this: <class 'int'>.

The null object

This object is returned by functions that don’t explicitly return a value. It supports no special operations. There is exactly one null object, named None (a built-in name). type(None)() produces the same singleton.

It is written as None.

The ellipsis object

This object is commonly used by slicing. It supports no special operations. There is exactly one ellipsis object, named Ellipsis (a built-in name). type(Ellipsis)() produces the Ellipsis singleton.

It is written as Ellipsis or ....

The NotImplemented object

This object is returned from comparisons and binary operations when they are asked to operate on types they don’t support. See Comparisons for more information. There is exactly one NotImplemented object. type(NotImplemented)() produces the singleton instance.

It is written as NotImplemented.

Boolean values

Boolean values are the two constant objects False and True. They are used to represent truth values (although other values can also be considered false or true). In numeric contexts (for example when used as the argument to an arithmetic operator), they behave like the integers 0 and 1, respectively. The built-in function bool() can convert any value to a Boolean, if the value can be interpreted as a truth value (see section Truth Value Testing above).

They are written as False and True, respectively.

Internal objects

The standard type hierarchy describes stack frame objects, traceback objects, and slice objects.

Special attributes

The implementation adds a few special read-only attributes to several object types, where they are relevant. Some of these are not reported by the dir() built-in function.

object.__dict__

instance.__class__

class.__bases__

class.__name__

class.__qualname__

class.__mro__

class.mro()

class.__subclasses__()

>>> int.__subclasses__()[<class 'bool'>]

Built-in exceptions

In Python, all exceptions must be instances of a class that derives from BaseException. In a try statement with an except clause that mentions a particular class, that clause also handles any exception classes derived from that class (but not exception classes from which it is derived). Two exception classes that are not related via subclassing are never equivalent, even if they have the same name.

The built-in exceptions listed below can be generated by the interpreter or built-in functions. Except where mentioned, they have an "associated value" indicating the detailed cause of the error. This may be a string or a tuple of several items of information (e.g., an error code and a string explaining the code). The associated value is usually passed as arguments to the exception class's constructor.

User code can raise built-in exceptions. This can test an exception handler or report an error condition like the situation in which the interpreter raises the same exception; but beware that there is nothing to prevent user code from raising an inappropriate error.

The built-in exception classes can be subclassed to define new exceptions; programmers are encouraged to derive new exceptions from the Exception class or one of its subclasses, and not from BaseException. More information on defining exceptions is available in the Python Tutorial under User-defined Exceptions.

When raising (or re-raising) an exception in an except clause __context__ is automatically set to the last exception caught; if the new exception is not handled, the traceback that is eventually displayed includes the originating exception(s) and the final exception.

When raising a new exception (rather than using a bare raise to re-raise the exception currently being handled), the implicit exception context can be supplemented with an explicit cause using from with raise:

raise new_exc from original_exc

The expression following from must be an exception or None. It is set as __cause__ on the raised exception. Setting __cause__ also implicitly sets the __suppress_context__ attribute to True, so that using raise new_exc from None effectively replaces the old exception with the new one for display purposes (e.g., converting KeyError to AttributeError, while leaving the old exception available in __context__ for introspection when debugging.

The default traceback display code shows these chained exceptions in addition to the traceback for the exception itself. An explicitly chained exception in __cause__ is always shown when present. An implicitly chained exception in __context__ is shown only if __cause__ is None and __suppress_context__ is false.

In either case, the exception itself is always shown after any chained exceptions so that the final line of the traceback always shows the last exception that was raised.

Base classes

The following exceptions are used mostly as base classes for other exceptions.

exception BaseException

args

with_traceback(tb)

try: ...except SomeException: tb = sys.exc_info()[2] raise OtherException(...).with_traceback(tb)

exception Exception

exception ArithmeticError

exception BufferError

exception LookupError

Concrete exceptions

The following exceptions are the exceptions that are usually raised.

exception AssertionError

exception AttributeError

exception EOFError

exception FloatingPointError

exception GeneratorExit

exception ImportError

exception IndexError

exception KeyError

exception KeyboardInterrupt

exception MemoryError

exception NameError

exception NotImplementedError

exception OSError

exception OverflowError

exception ReferenceError

exception RuntimeError

exception StopIteration

exception SyntaxError

exception IndentationError

exception TabError

exception SystemError

exception SystemExit

exception TypeError

exception UnboundLocalError

exception UnicodeError

encoding

reason

object

start

end

exception UnicodeEncodeError

exception UnicodeDecodeError

exception UnicodeTranslateError

exception ValueError

exception ZeroDivisionError

The following exceptions are kept for compatibility with previous versions; starting from Python 3.3, they are aliases of OSError.

exception EnvironmentError

exception IOError

exception WindowsError

(WindowsError is only available on Windows)

OS exceptions

The following exceptions are subclasses of OSError, they get raised depending on the system error code.

exception BlockingIOError

characters_written

exception ChildProcessError

exception ConnectionError

exception BrokenPipeError

exception ConnectionAbortedError

exception ConnectionRefusedError

exception ConnectionResetError

exception FileExistsError

exception FileNotFoundError

exception InterruptedError

exception IsADirectoryError

exception NotADirectoryError

exception PermissionError

exception ProcessLookupError

exception TimeoutError

Warnings

The following exceptions are used as warning categories; see the warnings module for more information.

exception Warning

exception UserWarning

exception DeprecationWarning

exception PendingDeprecationWarning

exception SyntaxWarning

exception RuntimeWarning

exception FutureWarning

exception ImportWarning

exception UnicodeWarning

exception BytesWarning

exception ResourceWarning

Exception hierarchy

BaseException
 ├── SystemExit
 ├── KeyboardInterrupt
 ├── GeneratorExit
 └── Exception
      ├── StopIteration
      ├── ArithmeticError
      │    ├── FloatingPointError
      │    ├── OverflowError
      │    └── ZeroDivisionError
      ├── AssertionError
      ├── AttributeError
      ├── BufferError
      ├── EOFError
      ├── ImportError
      ├── LookupError
      │    ├── IndexError
      │    └── KeyError
      ├── MemoryError
      ├── NameError
      │    └── UnboundLocalError
      ├── OSError
      │    ├── BlockingIOError
      │    ├── ChildProcessError
      │    ├── ConnectionError
      │    │    ├── BrokenPipeError
      │    │    ├── ConnectionAbortedError
      │    │    ├── ConnectionRefusedError
      │    │    └── ConnectionResetError
      │    ├── FileExistsError
      │    ├── FileNotFoundError
      │    ├── InterruptedError
      │    ├── IsADirectoryError
      │    ├── NotADirectoryError
      │    ├── PermissionError
      │    ├── ProcessLookupError
      │    └── TimeoutError
      ├── ReferenceError
      ├── RuntimeError
      │    └── NotImplementedError
      ├── SyntaxError
      │    └── IndentationError
      │         └── TabError
      ├── SystemError
      ├── TypeError
      ├── ValueError
      │    └── UnicodeError
      │         ├── UnicodeDecodeError
      │         ├── UnicodeEncodeError
      │         └── UnicodeTranslateError
      └── Warning
           ├── DeprecationWarning
           ├── PendingDeprecationWarning
           ├── RuntimeWarning
           ├── SyntaxWarning
           ├── UserWarning
           ├── FutureWarning
           ├── ImportWarning
           ├── UnicodeWarning
           ├── BytesWarning
           └── ResourceWarning

Additional Python reference

• Python 3 programming language
• Data types in Python
• Python text processing modules
• Python numeric and mathematical modules
• Python binary data services
• Python operating system services
• Functional programming models in Python
• The Python HTML module