Built-in Types¶

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. [1]

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

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 — and, or, not¶

These are the Boolean operations, ordered by ascending priority:

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:

Objects of different types, except different numeric types, never compare equal. Furthermore, some types (for example, file objects) support only a degenerate notion of comparison where any two objects of that type are unequal. The <, <=, > and >= operators will raise a TypeError exception when any operand is a complex number, the objects are of different types that cannot be compared, or 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. In addition, Booleans are a subtype of integers. Integers have unlimited precision. Floating point numbers are implemented using double in C—all bets on their precision are off unless you happen to know the machine you are working with. Complex numbers have a real and imaginary part, which are each implemented using double in C. 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. [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, 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. 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 -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 floor() and ceil() in the math module 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.

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

For additional numeric operations see the math and cmath modules.

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

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

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

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__()¶

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 container. 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.

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.

Sequence Types — str, bytes, bytearray, list, tuple, range¶

There are five sequence types: strings, byte sequences, byte arrays, lists, tuples, and range objects. (For other containers see the built-in dict, list, set, and tuple classes, and the collections module.)

Strings contain Unicode characters. Their literals are written in single or double quotes: 'xyzzy', "frobozz". See String and Bytes literals for more about string literals. In addition to the functionality described here, there are also string-specific methods described in the String Methods section.

Bytes and bytearray objects contain single bytes – the former is immutable while the latter is a mutable sequence. Bytes objects can be constructed the constructor, bytes(), and from literals; use a b prefix with normal string syntax: b'xyzzy'. To construct byte arrays, use the bytearray() function.

Lists are constructed with square brackets, separating items with commas: [a, b, c]. Tuples are constructed by the comma operator (not within square brackets), with or without enclosing parentheses, but an empty tuple must have the enclosing parentheses, such as a, b, c or (). A single item tuple must have a trailing comma, such as (d,).

Objects of type range are created using the range() function. They don’t support slicing, concatenation or repetition, and using in, not in, min() or max() on them is inefficient.

Most sequence types support the following operations. The in and not in operations have the same priorities as the comparison operations. The + and * operations have the same priority as the corresponding numeric operations. [3] Additional methods are provided for Mutable 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 and j are integers:

Sequence types 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.)

Notes:

1. When s is a string object, the in and not in operations act like a substring test.

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. If s and t are both strings, some Python implementations such as CPython can usually perform an in-place optimization for assignments of the form s=s+t or s+=t. When applicable, this optimization makes quadratic run-time much less likely. This optimization is both version and implementation dependent. For performance sensitive code, it is preferable to use the str.join() method which assures consistent linear concatenation performance across versions and implementations.

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

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

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

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 builtin 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.

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.

Instances of set and frozenset provide the following operations:

len(s)

Return the cardinality of set 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 true 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 true superset of other, that is, set >= other and set != other.

union(other, ...)¶

set | other | ...

Return a new set with elements from both sets.

intersection(other, ...)¶

set & other & ...

Return a new set with elements common to both sets.

difference(other, ...)¶

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 new set with a shallow copy of s.

Note, the non-operator versions of union(), intersection(), difference(), and 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 complete ordering function. For example, any two disjoint sets are not equal and are not subsets of each other, so all of 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 | ...

Update the set, adding elements from other.

intersection_update(other, ...)¶

set &= other & ...

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

difference_update(other, ...)¶

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, 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([arg])¶

Return a new dictionary initialized from an optional positional argument or from a set of keyword arguments. If no arguments are given, return a new empty dictionary. If the positional argument arg is a mapping object, return a dictionary mapping the same keys to the same values as does the mapping object. Otherwise the positional argument must be a sequence, a container that supports iteration, or an iterator object. The elements of the argument must each also be of one of those kinds, and each must in turn contain exactly two objects. The first is used as a key in the new dictionary, and the second as the key’s value. If a given key is seen more than once, the last value associated with it is retained in the new dictionary.

If keyword arguments are given, the keywords themselves with their associated values are added as items to the dictionary. If a key is specified both in the positional argument and as a keyword argument, the value associated with the keyword is retained in the dictionary. For example, these all return a dictionary equal to {"one": 2, "two": 3}:

• dict(one=2, two=3)
• dict({'one': 2, 'two': 3})
• dict(zip(('one', 'two'), (2, 3)))
• dict([['two', 3], ['one', 2]])

The first example only works for keys that are valid Python identifiers; the others work with any valid keys.

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

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__(), if the key 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 if the key is not present. 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. For an example, see 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.

clear()¶

Remove all items from the dictionary.

copy()¶

Return a shallow copy of the dictionary.

fromkeys(seq[, value])¶

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

fromkeys() is a class method that returns a new dictionary. value defaults to None.

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 below for documentation of view objects.

keys()¶

Return a new view of the dictionary’s keys. See below for 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 an arbitrary (key, value) pair from the dictionary.

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.

setdefault(key[, default])¶

If key is in the dictionary, return its value. If not, insert key with a value of default and return default. default 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 a tuple or other iterable of length two). If keyword arguments are specified, the dictionary is then is updated with those key/value pairs: d.update(red=1, blue=2).

values()¶

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

>>> 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'}

File Objects¶

File objects are implemented using C’s stdio package and can be created with the built-in open() function. File objects are also returned by some other built-in functions and methods, such as os.popen() and os.fdopen() and the makefile() method of socket objects. Temporary files can be created using the tempfile module, and high-level file operations such as copying, moving, and deleting files and directories can be achieved with the shutil module.

When a file operation fails for an I/O-related reason, the exception IOError is raised. This includes situations where the operation is not defined for some reason, like seek() on a tty device or writing a file opened for reading.

Files have the following methods:

file.close()¶

Close the file. A closed file cannot be read or written any more. Any operation which requires that the file be open will raise a ValueError after the file has been closed. Calling close() more than once is allowed.

You can avoid having to call this method explicitly if you use the with statement. For example, the following code will automatically close f when the with block is exited:

from __future__ import with_statement # This isn't required in Python 2.6

with open("hello.txt") as f:
    for line in f:
        print(line)

In older versions of Python, you would have needed to do this to get the same effect:

f = open("hello.txt")
try:
    for line in f:
        print(line)
finally:
    f.close()

Note

Not all “file-like” types in Python support use as a context manager for the with statement. If your code is intended to work with any file-like object, you can use the function contextlib.closing() instead of using the object directly.

file.flush()¶

Flush the internal buffer, like stdio‘s fflush. This may be a no-op on some file-like objects.

file.fileno()¶

Return the integer “file descriptor” that is used by the underlying implementation to request I/O operations from the operating system. This can be useful for other, lower level interfaces that use file descriptors, such as the fcntl module or os.read() and friends.

Note

File-like objects which do not have a real file descriptor should not provide this method!

file.isatty()¶

Return True if the file is connected to a tty(-like) device, else False.

Note

If a file-like object is not associated with a real file, this method should not be implemented.

file.__next__()¶

A file object is its own iterator, for example iter(f) returns f (unless f is closed). When a file is used as an iterator, typically in a for loop (for example, for line in f: print(line)), the __next__() method is called repeatedly. This method returns the next input line, or raises StopIteration when EOF is hit when the file is open for reading (behavior is undefined when the file is open for writing). In order to make a for loop the most efficient way of looping over the lines of a file (a very common operation), the __next__() method uses a hidden read-ahead buffer. As a consequence of using a read-ahead buffer, combining __next__() with other file methods (like readline()) does not work right. However, using seek() to reposition the file to an absolute position will flush the read-ahead buffer.

file.read([size])¶

Read at most size bytes from the file (less if the read hits EOF before obtaining size bytes). If the size argument is negative or omitted, read all data until EOF is reached. The bytes are returned as a string object. An empty string is returned when EOF is encountered immediately. (For certain files, like ttys, it makes sense to continue reading after an EOF is hit.) Note that this method may call the underlying C function fread more than once in an effort to acquire as close to size bytes as possible. Also note that when in non-blocking mode, less data than was requested may be returned, even if no size parameter was given.

file.readline([size])¶

Read one entire line from the file. A trailing newline character is kept in the string (but may be absent when a file ends with an incomplete line). [6] If the size argument is present and non-negative, it is a maximum byte count (including the trailing newline) and an incomplete line may be returned. An empty string is returned only when EOF is encountered immediately.

Note

Unlike stdio‘s fgets, the returned string contains null characters ('\0') if they occurred in the input.

file.readlines([sizehint])¶

Read until EOF using readline() and return a list containing the lines thus read. If the optional sizehint argument is present, instead of reading up to EOF, whole lines totalling approximately sizehint bytes (possibly after rounding up to an internal buffer size) are read. Objects implementing a file-like interface may choose to ignore sizehint if it cannot be implemented, or cannot be implemented efficiently.

file.seek(offset[, whence])¶

Set the file’s current position, like stdio‘s fseek. The whence argument is optional and defaults to os.SEEK_SET or 0 (absolute file positioning); other values are os.SEEK_CUR or 1 (seek relative to the current position) and os.SEEK_END or 2 (seek relative to the file’s end). There is no return value.

For example, f.seek(2, os.SEEK_CUR) advances the position by two and f.seek(-3, os.SEEK_END) sets the position to the third to last.

Note that if the file is opened for appending (mode 'a' or 'a+'), any seek() operations will be undone at the next write. If the file is only opened for writing in append mode (mode 'a'), this method is essentially a no-op, but it remains useful for files opened in append mode with reading enabled (mode 'a+'). If the file is opened in text mode (without 'b'), only offsets returned by tell() are legal. Use of other offsets causes undefined behavior.

Note that not all file objects are seekable.

file.tell()¶

Return the file’s current position, like stdio‘s ftell.

Note

On Windows, tell() can return illegal values (after an fgets) when reading files with Unix-style line-endings. Use binary mode ('rb') to circumvent this problem.

file.truncate([size])¶

Truncate the file’s size. If the optional size argument is present, the file is truncated to (at most) that size. The size defaults to the current position. The current file position is not changed. Note that if a specified size exceeds the file’s current size, the result is platform-dependent: possibilities include that the file may remain unchanged, increase to the specified size as if zero-filled, or increase to the specified size with undefined new content. Availability: Windows, many Unix variants.

file.write(str)¶

Write a string to the file. Due to buffering, the string may not actually show up in the file until the flush() or close() method is called.

The meaning of the return value is not defined for every file-like object. Some (mostly low-level) file-like objects may return the number of bytes actually written, others return None.

file.writelines(sequence)¶

Write a sequence of strings to the file. The sequence can be any iterable object producing strings, typically a list of strings. There is no return value. (The name is intended to match readlines(); writelines() does not add line separators.)

Files support the iterator protocol. Each iteration returns the same result as file.readline(), and iteration ends when the readline() method returns an empty string.

File objects also offer a number of other interesting attributes. These are not required for file-like objects, but should be implemented if they make sense for the particular object.

file.closed¶

bool indicating the current state of the file object. This is a read-only attribute; the close() method changes the value. It may not be available on all file-like objects.

file.encoding¶

The encoding that this file uses. When strings are written to a file, they will be converted to byte strings using this encoding. In addition, when the file is connected to a terminal, the attribute gives the encoding that the terminal is likely to use (that information might be incorrect if the user has misconfigured the terminal). The attribute is read-only and may not be present on all file-like objects. It may also be None, in which case the file uses the system default encoding for converting strings.

file.errors¶

The Unicode error handler used along with the encoding.

file.mode¶

The I/O mode for the file. If the file was created using the open() built-in function, this will be the value of the mode parameter. This is a read-only attribute and may not be present on all file-like objects.

file.name¶

If the file object was created using open(), the name of the file. Otherwise, some string that indicates the source of the file object, of the form <...>. This is a read-only attribute and may not be present on all file-like objects.

file.newlines¶

If Python was built with the --with-universal-newlines option to configure (the default) this read-only attribute exists, and for files opened in universal newline read mode it keeps track of the types of newlines encountered while reading the file. The values it can take are '\r', '\n', '\r\n', None (unknown, no newlines read yet) or a tuple containing all the newline types seen, to indicate that multiple newline conventions were encountered. For files not opened in universal newline read mode the value of this attribute will be None.

memoryview Types¶

memoryviews allow Python code to access the internal data of an object that supports the buffer protocol without copying. Memory can be interpreted as simple bytes or complex data structures.

class memoryview(obj)¶

Create a memoryview that references obj. obj must support the buffer protocol. Builtin objects that support the buffer protocol include bytes and bytearray.

len(view) returns the total number of bytes in the memoryview, view.

A memoryview supports slicing to expose its data. Taking a single index will return a single byte. Full slicing will result in a subview:

>>> v = memoryview(b'abcefg')
>>> v[1]
b'b'
>>> v[-1]
b'g'
>>> v[1:4]
<memory at 0x77ab28>
>>> bytes(v[1:4])
b'bce'
>>> v[3:-1]
<memory at 0x744f18>
>>> bytes(v[4:-1])

If the object the memory view is over supports changing its data, the memoryview supports slice assignment:

>>> data = bytearray(b'abcefg')
>>> v = memoryview(data)
>>> v.readonly
False
>>> v[0] = 'z'
>>> data
bytearray(b'zbcefg')
>>> v[1:4] = b'123'
>>> data
bytearray(b'a123fg')
>>> v[2] = b'spam'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot modify size of memoryview object

Notice how the size of the memoryview object can not be changed.

memoryview has two methods:

tobytes()¶

Return the data in the buffer as a bytestring.

tolist()¶

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

>>> memoryview(b'abc').tolist()
[97, 98, 99]

There are also several readonly attributes available:

format¶

A string containing the format (in struct module style) for each element in the view. This defaults to 'B', a simple bytestring.

itemsize¶

The size in bytes of each element of the memoryview.

shape¶

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

ndim¶

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

strides¶

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

Context Manager Types¶

Python’s with statement supports the concept of a runtime context defined by a context manager. This is implemented using two separate 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.

The context management protocol consists of a pair of methods that need to be provided for a context manager object to define a runtime context:

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_type, exc_val, exc_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 (such as contextlib.nested) 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.contextfactory decorator provide a convenient way to implement these protocols. If a generator function is decorated with the contextlib.contextfactory 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.

Other Built-in Types¶

The interpreter supports several other kinds of objects. Most of these support only one or two operations.

class C:
    def method(self):
        pass

c = C()
c.method.__func__.whoami = 'my name is c'

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__¶

A dictionary or other mapping object used to store an object’s (writable) attributes.

instance.__class__¶

The class to which a class instance belongs.

class.__bases__¶

The tuple of base classes of a class object. If there are no base classes, this will be an empty tuple.

class.__name__¶

The name of the class or type.

Footnotes