Core concepts¶
This chapter deepens our understanding of some concepts seen in http://www.andreamarino.it/python/thinkcspy/.
We quote from the official help system.
First,
>Objects are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann’s model of a “stored
program computer”, code is also represented by objects.)
Second, >Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The ‘“is”’ operator compares the identity of two objects; the “id()” function returns an integer representing its identity.
where
id
<function id(obj, /)>
help(id)
Help on built-in function id in module builtins:
id(obj, /)
Return the identity of an object.
This is guaranteed to be unique among simultaneously existing objects.
(CPython uses the object's memory address.)
help(help)
Help on _Helper in module _sitebuiltins object:
class _Helper(builtins.object)
| Define the builtin 'help'.
|
| This is a wrapper around pydoc.help that provides a helpful message
| when 'help' is typed at the Python interactive prompt.
|
| Calling help() at the Python prompt starts an interactive help session.
| Calling help(thing) prints help for the python object 'thing'.
|
| Methods defined here:
|
| __call__(self, *args, **kwds)
| Call self as a function.
|
| __repr__(self)
| Return repr(self).
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
help(object)
Help on class object in module builtins:
class object
| The base class of the class hierarchy.
|
| When called, it accepts no arguments and returns a new featureless
| instance that has no instance attributes and cannot be given any.
|
| Built-in subclasses:
| anext_awaitable
| async_generator
| async_generator_asend
| async_generator_athrow
| ... and 91 other subclasses
|
| Methods defined here:
|
| __delattr__(self, name, /)
| Implement delattr(self, name).
|
| __dir__(self, /)
| Default dir() implementation.
|
| __eq__(self, value, /)
| Return self==value.
|
| __format__(self, format_spec, /)
| Default object formatter.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __init__(self, /, *args, **kwargs)
| Initialize self. See help(type(self)) for accurate signature.
|
| __le__(self, value, /)
| Return self<=value.
|
| __lt__(self, value, /)
| Return self<value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __reduce__(self, /)
| Helper for pickle.
|
| __reduce_ex__(self, protocol, /)
| Helper for pickle.
|
| __repr__(self, /)
| Return repr(self).
|
| __setattr__(self, name, value, /)
| Implement setattr(self, name, value).
|
| __sizeof__(self, /)
| Size of object in memory, in bytes.
|
| __str__(self, /)
| Return str(self).
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __init_subclass__(...) from builtins.type
| This method is called when a class is subclassed.
|
| The default implementation does nothing. It may be
| overridden to extend subclasses.
|
| __subclasshook__(...) from builtins.type
| Abstract classes can override this to customize issubclass().
|
| This is invoked early on by abc.ABCMeta.__subclasscheck__().
| It should return True, False or NotImplemented. If it returns
| NotImplemented, the normal algorithm is used. Otherwise, it
| overrides the normal algorithm (and the outcome is cached).
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __class__ = <class 'type'>
| type(object_or_name, bases, dict)
| type(object) -> the object's type
| type(name, bases, dict) -> a new type
o = object()
o
<object at 0x10d85b310>
id(o)
4521833232
w = object()
o is w
False
o is o
True
type(object)
type
type(type)
type
help(type)
Help on class type in module builtins:
class type(object)
| type(object_or_name, bases, dict)
| type(object) -> the object's type
| type(name, bases, dict) -> a new type
|
| Methods defined here:
|
| __call__(self, /, *args, **kwargs)
| Call self as a function.
|
| __delattr__(self, name, /)
| Implement delattr(self, name).
|
| __dir__(self, /)
| Specialized __dir__ implementation for types.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __init__(self, /, *args, **kwargs)
| Initialize self. See help(type(self)) for accurate signature.
|
| __instancecheck__(self, instance, /)
| Check if an object is an instance.
|
| __or__(self, value, /)
| Return self|value.
|
| __repr__(self, /)
| Return repr(self).
|
| __ror__(self, value, /)
| Return value|self.
|
| __setattr__(self, name, value, /)
| Implement setattr(self, name, value).
|
| __sizeof__(self, /)
| Return memory consumption of the type object.
|
| __subclasscheck__(self, subclass, /)
| Check if a class is a subclass.
|
| __subclasses__(self, /)
| Return a list of immediate subclasses.
|
| mro(self, /)
| Return a type's method resolution order.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __prepare__(...)
| __prepare__() -> dict
| used to create the namespace for the class statement
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs)
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __abstractmethods__
|
| __annotations__
|
| __dict__
|
| __text_signature__
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __base__ = <class 'object'>
| The base class of the class hierarchy.
|
| When called, it accepts no arguments and returns a new featureless
| instance that has no instance attributes and cannot be given any.
|
|
| __bases__ = (<class 'object'>,)
|
| __basicsize__ = 888
|
| __dictoffset__ = 264
|
| __flags__ = 2148031744
|
| __itemsize__ = 40
|
| __mro__ = (<class 'type'>, <class 'object'>)
|
| __weakrefoffset__ = 368
type(3.14)
float
type([1,2,3])
list
type(help)
_sitebuiltins._Helper
type(id)
builtin_function_or_method
type(1)
int
Third, >An object’s type determines the operations that the object supports >(e.g., “does it have a length?”) and also defines the possible values >for objects of that type. The “type()” function returns an object’s >type (which is an object itself). Like its identity, an object’s >type is also unchangeable.
Fourth, >The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable. (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable.
Fifth, >Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed.
Sixth, >Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed.
class A(object):
x = 3
y = 4
a = A()
id(a)
4534062624
type(a)
__main__.A
A().y
L = []
L
[]
L.append(1)
L.append(3.14)
L
[1, 3.14]
3, 3.14, 3 + 5J
(3, 3.14, (3+5j))
t = (1, 3.14)
t
(1, 3.14)
type(t)
tuple
t.index(3.14)
1
tt = (3.14, [])
tt, id(tt)
((3.14, []), 4533921408)
tt[1].append(4)
tt, id(tt)
((3.14, [4]), 4533921408)
type({})
dict
type(set())
set
S = set()
S
set()
S.add(3.14)
S
{3.14}
S.add('hello')
S.add(tuple())
S
{(), 3.14, 'hello'}
S.add(tuple())
S
{(), 3.14, 'hello'}
S.add([])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/var/folders/0k/y3r96gt12zn1mzgztql9m_8m0000gn/T/ipykernel_2745/334196350.py in <module>
----> 1 S.add([])
TypeError: unhashable type: 'list'
L = []
t = (3.14, L)
tt = (1.618, L)
id(L), id(t), id(tt)
(4613187392, 4612579136, 4613347328)
S = { t, tt }
S
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/var/folders/0k/y3r96gt12zn1mzgztql9m_8m0000gn/T/ipykernel_2745/4157842914.py in <module>
----> 1 S = { t, tt }
2 S
TypeError: unhashable type: 'list'
hash(L)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/var/folders/0k/y3r96gt12zn1mzgztql9m_8m0000gn/T/ipykernel_2745/1001898879.py in <module>
----> 1 hash(L)
TypeError: unhashable type: 'list'
{(3.14, ()), (1.618, frozenset())}
{(1.618, frozenset()), (3.14, ())}
D = {}
D[[]] = 3.14
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/var/folders/0k/y3r96gt12zn1mzgztql9m_8m0000gn/T/ipykernel_2745/1357002942.py in <module>
1 D = {}
----> 2 D[[]] = 3.14
TypeError: unhashable type: 'list'
a = 1; b = 1 # "a" and "b" may or may not refer to the same object with the value one,
id(a), id(b) # depending on the implementation
(94869411972864, 94869411972864)
c = []; d = []
id(c), id(d) # "c" and "d" are guaranteed to refer to two different, unique, newly created empty lists.
(140325921500736, 140325921500224)
type(c), type(d)
(list, list)
A full description can be read by evaluating the following,
help('OBJECTS')
Objects, values and types ********************* Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer”, code is also represented by objects.) Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The ‘"is"’ operator compares the identity of two objects; the "id()" function returns an integer representing its identity. CPython implementation detail: For CPython, "id(x)" is the memory address where "x" is stored. An object’s type determines the operations that the object supports (e.g., “does it have a length?”) and also defines the possible values for objects of that type. The "type()" function returns an object’s type (which is an object itself). Like its identity, an object’s type is also unchangeable. [1] The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable. (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable. Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether — it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable. CPython implementation detail: CPython currently uses a reference- counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the "gc" module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (so you should always close files explicitly). Note that the use of the implementation’s tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a ‘"try"…"except"’ statement may keep objects alive. Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a "close()" method. Programs are strongly recommended to explicitly close such objects. The ‘"try"…"finally"’ statement and the ‘"with"’ statement provide convenient ways to do this. Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed. Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after "a = 1; b = 1", "a" and "b" may or may not refer to the same object with the value one, depending on the implementation, but after "c = []; d = []", "c" and "d" are guaranteed to refer to two different, unique, newly created empty lists. (Note that "c = d = []" assigns the same object to both "c" and "d".) Related help topics: TYPES
Values and their types¶
Some playground:
"Hello, World!", type("Hello, World!")
('Hello, World!', str)
_
('Hello, World!', str)
_[1]
str
__
('Hello, World!', str)
type(_)
tuple
type(tuple)
type
tuple_doc = tuple.__doc__
type(tuple_doc)
str
tuple_doc + ' maybe that is actually true'
"Built-in immutable sequence.nnIf no argument is given, the constructor returns an empty tuple.nIf iterable is specified the tuple is initialized from iterable's items.nnIf the argument is a tuple, the return value is the same object. maybe that is actually true"
help(tuple)
Help on class tuple in module builtins:
class tuple(object)
| tuple(iterable=(), /)
|
| Built-in immutable sequence.
|
| If no argument is given, the constructor returns an empty tuple.
| If iterable is specified the tuple is initialized from iterable's items.
|
| If the argument is a tuple, the return value is the same object.
|
| Built-in subclasses:
| asyncgen_hooks
| UnraisableHookArgs
|
| Methods defined here:
|
| __add__(self, value, /)
| Return self+value.
|
| __contains__(self, key, /)
| Return key in self.
|
| __eq__(self, value, /)
| Return self==value.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __getitem__(self, key, /)
| Return self[key].
|
| __getnewargs__(self, /)
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __iter__(self, /)
| Implement iter(self).
|
| __le__(self, value, /)
| Return self<=value.
|
| __len__(self, /)
| Return len(self).
|
| __lt__(self, value, /)
| Return self<value.
|
| __mul__(self, value, /)
| Return self*value.
|
| __ne__(self, value, /)
| Return self!=value.
|
| __repr__(self, /)
| Return repr(self).
|
| __rmul__(self, value, /)
| Return value*self.
|
| count(self, value, /)
| Return number of occurrences of value.
|
| index(self, value, start=0, stop=9223372036854775807, /)
| Return first index of value.
|
| Raises ValueError if the value is not present.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __class_getitem__(...) from builtins.type
| See PEP 585
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
print(tuple.__doc__)
Built-in immutable sequence.
If no argument is given, the constructor returns an empty tuple.
If iterable is specified the tuple is initialized from iterable's items.
If the argument is a tuple, the return value is the same object.
3.2, type(3.2), 3 + 9J, type(3 + 9j)
(3.2, float, (3+9j), complex)
'I''m a string'
'Im a string'
'''"Oh no", she exclaimed, "Ben's bike is broken!"'''
'"Oh no", she exclaimed, "Ben's bike is broken!"'
def a(b):
'''A short summary here.
Some more description here.
>>> a(4)
None
'''
...
def b(c):
'''A short summary here.
Some more description here.
'''
pass
help(a)
Help on function a in module __main__:
a(b)
A short summary here.
Some more description here.
>>> a(4)
None
a.__doc__
'A short summary here.n n Some more description here.n n >>> a(4)n Nonen n '
print(a.__doc__)
A short summary here.
Some more description here.
>>> a(4)
None
assert a(4) == b(3) == None
assert True == False, 'This is quite usual'
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/var/folders/0k/y3r96gt12zn1mzgztql9m_8m0000gn/T/ipykernel_2745/1794376097.py in <module>
----> 1 assert True == False, 'This is quite usual'
AssertionError: This is quite usual
id(None), id(None), id(None)
(94869411793504, 94869411793504, 94869411793504)
((3 < 4) < 10) < 7
True
_
'"Oh no", she exclaimed, "Ben's bike is broken!"'
type(_)
str
help('TYPES')
The standard type hierarchy
***********************
Below is a list of the types that are built into Python. Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types. Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.), although such additions
will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing
‘special attributes.’ These are attributes that provide access to the
implementation and are not intended for general use. Their definition
may change in the future.
None
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name "None". It
is used to signify the absence of a value in many situations, e.g.,
it is returned from functions that don’t explicitly return
anything. Its truth value is false.
NotImplemented
This type has a single value. There is a single object with this
value. This object is accessed through the built-in name
"NotImplemented". Numeric methods and rich comparison methods
should return this value if they do not implement the operation for
the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the
operator.) It should not be evaluated in a boolean context.
See Implementing the arithmetic operations for more details.
Changed in version 3.9: Evaluating "NotImplemented" in a boolean
context is deprecated. While it currently evaluates as true, it
will emit a "DeprecationWarning". It will raise a "TypeError" in a
future version of Python.
Ellipsis
This type has a single value. There is a single object with this
value. This object is accessed through the literal "..." or the
built-in name "Ellipsis". Its truth value is true.
"numbers.Number"
These are created by numeric literals and returned as results by
arithmetic operators and arithmetic built-in functions. Numeric
objects are immutable; once created their value never changes.
Python numbers are of course strongly related to mathematical
numbers, but subject to the limitations of numerical representation
in computers.
Python distinguishes between integers, floating point numbers, and
complex numbers:
"numbers.Integral"
These represent elements from the mathematical set of integers
(positive and negative).
There are two types of integers:
Integers ("int")
These represent numbers in an unlimited range, subject to
available (virtual) memory only. For the purpose of shift
and mask operations, a binary representation is assumed, and
negative numbers are represented in a variant of 2’s
complement which gives the illusion of an infinite string of
sign bits extending to the left.
Booleans ("bool")
These represent the truth values False and True. The two
objects representing the values "False" and "True" are the
only Boolean objects. The Boolean type is a subtype of the
integer type, and Boolean values behave like the values 0 and
1, respectively, in almost all contexts, the exception being
that when converted to a string, the strings ""False"" or
""True"" are returned, respectively.
The rules for integer representation are intended to give the
most meaningful interpretation of shift and mask operations
involving negative integers.
"numbers.Real" ("float")
These represent machine-level double precision floating point
numbers. You are at the mercy of the underlying machine
architecture (and C or Java implementation) for the accepted
range and handling of overflow. Python does not support single-
precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.
"numbers.Complex" ("complex")
These represent complex numbers as a pair of machine-level
double precision floating point numbers. The same caveats apply
as for floating point numbers. The real and imaginary parts of a
complex number "z" can be retrieved through the read-only
attributes "z.real" and "z.imag".
Sequences
These represent finite ordered sets indexed by non-negative
numbers. The built-in function "len()" returns the number of items
of a sequence. When the length of a sequence is n, the index set
contains the numbers 0, 1, …, n-1. Item i of sequence a is
selected by "a[i]".
Sequences also support slicing: "a[i:j]" selects all items with
index k such that i "<=" k "<" j. When used as an
expression, a slice is a sequence of the same type. This implies
that the index set is renumbered so that it starts at 0.
Some sequences also support “extended slicing” with a third “step”
parameter: "a[i:j:k]" selects all items of a with index x where
"x = i + n*k", n ">=" "0" and i "<=" x "<" j.
Sequences are distinguished according to their mutability:
Immutable sequences
An object of an immutable sequence type cannot change once it is
created. (If the object contains references to other objects,
these other objects may be mutable and may be changed; however,
the collection of objects directly referenced by an immutable
object cannot change.)
The following types are immutable sequences:
Strings
A string is a sequence of values that represent Unicode code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.
Tuples
The items of a tuple are arbitrary Python objects. Tuples of
two or more items are formed by comma-separated lists of
expressions. A tuple of one item (a ‘singleton’) can be
formed by affixing a comma to an expression (an expression by
itself does not create a tuple, since parentheses must be
usable for grouping of expressions). An empty tuple can be
formed by an empty pair of parentheses.
Bytes
A bytes object is an immutable array. The items are 8-bit
bytes, represented by integers in the range 0 <= x < 256.
Bytes literals (like "b'abc'") and the built-in "bytes()"
constructor can be used to create bytes objects. Also, bytes
objects can be decoded to strings via the "decode()" method.
Mutable sequences
Mutable sequences can be changed after they are created. The
subscription and slicing notations can be used as the target of
assignment and "del" (delete) statements.
There are currently two intrinsic mutable sequence types:
Lists
The items of a list are arbitrary Python objects. Lists are
formed by placing a comma-separated list of expressions in
square brackets. (Note that there are no special cases needed
to form lists of length 0 or 1.)
Byte Arrays
A bytearray object is a mutable array. They are created by
the built-in "bytearray()" constructor. Aside from being
mutable (and hence unhashable), byte arrays otherwise provide
the same interface and functionality as immutable "bytes"
objects.
The extension module "array" provides an additional example of a
mutable sequence type, as does the "collections" module.
Set types
These represent unordered, finite sets of unique, immutable
objects. As such, they cannot be indexed by any subscript. However,
they can be iterated over, and the built-in function "len()"
returns the number of items in a set. Common uses for sets are fast
membership testing, removing duplicates from a sequence, and
computing mathematical operations such as intersection, union,
difference, and symmetric difference.
For set elements, the same immutability rules apply as for
dictionary keys. Note that numeric types obey the normal rules for
numeric comparison: if two numbers compare equal (e.g., "1" and
"1.0"), only one of them can be contained in a set.
There are currently two intrinsic set types:
Sets
These represent a mutable set. They are created by the built-in
"set()" constructor and can be modified afterwards by several
methods, such as "add()".
Frozen sets
These represent an immutable set. They are created by the
built-in "frozenset()" constructor. As a frozenset is immutable
and hashable, it can be used again as an element of another
set, or as a dictionary key.
Mappings
These represent finite sets of objects indexed by arbitrary index
sets. The subscript notation "a[k]" selects the item indexed by "k"
from the mapping "a"; this can be used in expressions and as the
target of assignments or "del" statements. The built-in function
"len()" returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
Dictionaries
These represent finite sets of objects indexed by nearly
arbitrary values. The only types of values not acceptable as
keys are values containing lists or dictionaries or other
mutable types that are compared by value rather than by object
identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant.
Numeric types used for keys obey the normal rules for numeric
comparison: if two numbers compare equal (e.g., "1" and "1.0")
then they can be used interchangeably to index the same
dictionary entry.
Dictionaries preserve insertion order, meaning that keys will be
produced in the same order they were added sequentially over the
dictionary. Replacing an existing key does not change the order,
however removing a key and re-inserting it will add it to the
end instead of keeping its old place.
Dictionaries are mutable; they can be created by the "{...}"
notation (see section Dictionary displays).
The extension modules "dbm.ndbm" and "dbm.gnu" provide
additional examples of mapping types, as does the "collections"
module.
Changed in version 3.7: Dictionaries did not preserve insertion
order in versions of Python before 3.6. In CPython 3.6,
insertion order was preserved, but it was considered an
implementation detail at that time rather than a language
guarantee.
Callable types
These are the types to which the function call operation (see
section Calls) can be applied:
User-defined functions
A user-defined function object is created by a function
definition (see section Function definitions). It should be
called with an argument list containing the same number of items
as the function’s formal parameter list.
Special attributes:
+---------------------------+---------------------------------+-------------+
| Attribute | Meaning | |
|===========================|=================================|=============|
| "__doc__" | The function’s documentation | Writable |
| | string, or "None" if | |
| | unavailable; not inherited by | |
| | subclasses. | |
+---------------------------+---------------------------------+-------------+
| "__name__" | The function’s name. | Writable |
+---------------------------+---------------------------------+-------------+
| "__qualname__" | The function’s qualified | Writable |
| | name. New in version 3.3. | |
+---------------------------+---------------------------------+-------------+
| "__module__" | The name of the module the | Writable |
| | function was defined in, or | |
| | "None" if unavailable. | |
+---------------------------+---------------------------------+-------------+
| "__defaults__" | A tuple containing default | Writable |
| | argument values for those | |
| | arguments that have defaults, | |
| | or "None" if no arguments have | |
| | a default value. | |
+---------------------------+---------------------------------+-------------+
| "__code__" | The code object representing | Writable |
| | the compiled function body. | |
+---------------------------+---------------------------------+-------------+
| "__globals__" | A reference to the dictionary | Read-only |
| | that holds the function’s | |
| | global variables — the global | |
| | namespace of the module in | |
| | which the function was defined. | |
+---------------------------+---------------------------------+-------------+
| "__dict__" | The namespace supporting | Writable |
| | arbitrary function attributes. | |
+---------------------------+---------------------------------+-------------+
| "__closure__" | "None" or a tuple of cells that | Read-only |
| | contain bindings for the | |
| | function’s free variables. See | |
| | below for information on the | |
| | "cell_contents" attribute. | |
+---------------------------+---------------------------------+-------------+
| "__annotations__" | A dict containing annotations | Writable |
| | of parameters. The keys of the | |
| | dict are the parameter names, | |
| | and "'return'" for the return | |
| | annotation, if provided. | |
+---------------------------+---------------------------------+-------------+
| "__kwdefaults__" | A dict containing defaults for | Writable |
| | keyword-only parameters. | |
+---------------------------+---------------------------------+-------------+
Most of the attributes labelled “Writable” check the type of the
assigned value.
Function objects also support getting and setting arbitrary
attributes, which can be used, for example, to attach metadata
to functions. Regular attribute dot-notation is used to get and
set such attributes. Note that the current implementation only
supports function attributes on user-defined functions. Function
attributes on built-in functions may be supported in the
future.
A cell object has the attribute "cell_contents". This can be
used to get the value of the cell, as well as set the value.
Additional information about a function’s definition can be
retrieved from its code object; see the description of internal
types below. The "cell" type can be accessed in the "types"
module.
Instance methods
An instance method object combines a class, a class instance and
any callable object (normally a user-defined function).
Special read-only attributes: "__self__" is the class instance
object, "__func__" is the function object; "__doc__" is the
method’s documentation (same as "__func__.__doc__"); "__name__"
is the method name (same as "__func__.__name__"); "__module__"
is the name of the module the method was defined in, or "None"
if unavailable.
Methods also support accessing (but not setting) the arbitrary
function attributes on the underlying function object.
User-defined method objects may be created when getting an
attribute of a class (perhaps via an instance of that class), if
that attribute is a user-defined function object or a class
method object.
When an instance method object is created by retrieving a user-
defined function object from a class via one of its instances,
its "__self__" attribute is the instance, and the method object
is said to be bound. The new method’s "__func__" attribute is
the original function object.
When an instance method object is created by retrieving a class
method object from a class or instance, its "__self__" attribute
is the class itself, and its "__func__" attribute is the
function object underlying the class method.
When an instance method object is called, the underlying
function ("__func__") is called, inserting the class instance
("__self__") in front of the argument list. For instance, when
"C" is a class which contains a definition for a function "f()",
and "x" is an instance of "C", calling "x.f(1)" is equivalent to
calling "C.f(x, 1)".
When an instance method object is derived from a class method
object, the “class instance” stored in "__self__" will actually
be the class itself, so that calling either "x.f(1)" or "C.f(1)"
is equivalent to calling "f(C,1)" where "f" is the underlying
function.
Note that the transformation from function object to instance
method object happens each time the attribute is retrieved from
the instance. In some cases, a fruitful optimization is to
assign the attribute to a local variable and call that local
variable. Also notice that this transformation only happens for
user-defined functions; other callable objects (and all non-
callable objects) are retrieved without transformation. It is
also important to note that user-defined functions which are
attributes of a class instance are not converted to bound
methods; this only happens when the function is an attribute
of the class.
Generator functions
A function or method which uses the "yield" statement (see
section The yield statement) is called a generator function.
Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling
the iterator’s "iterator.__next__()" method will cause the
function to execute until it provides a value using the "yield"
statement. When the function executes a "return" statement or
falls off the end, a "StopIteration" exception is raised and the
iterator will have reached the end of the set of values to be
returned.
Coroutine functions
A function or method which is defined using "async def" is
called a coroutine function. Such a function, when called,
returns a coroutine object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.
Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a asynchronous
generator function. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.
Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an awaitable which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.
Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__name__" is the function’s
name; "__self__" is set to "None" (but see the next item);
"__module__" is the name of the module the function was defined
in or "None" if unavailable.
Built-in methods
This is really a different disguise of a built-in function, this
time containing an object passed to the C function as an
implicit extra argument. An example of a built-in method is
"alist.append()", assuming alist is a list object. In this
case, the special read-only attribute "__self__" is set to the
object denoted by alist.
Classes
Classes are callable. These objects normally act as factories
for new instances of themselves, but variations are possible for
class types that override "__new__()". The arguments of the
call are passed to "__new__()" and, in the typical case, to
"__init__()" to initialize the new instance.
Class Instances
Instances of arbitrary classes can be made callable by defining
a "__call__()" method in their class.
Modules
Modules are a basic organizational unit of Python code, and are
created by the import system as invoked either by the "import"
statement, or by calling functions such as
"importlib.import_module()" and built-in "__import__()". A module
object has a namespace implemented by a dictionary object (this is
the dictionary referenced by the "__globals__" attribute of
functions defined in the module). Attribute references are
translated to lookups in this dictionary, e.g., "m.x" is equivalent
to "m.__dict__["x"]". A module object does not contain the code
object used to initialize the module (since it isn’t needed once
the initialization is done).
Attribute assignment updates the module’s namespace dictionary,
e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".
Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing variable annotations collected during module body
execution; "__file__" is the pathname of the file from which the
module was loaded, if it was loaded from a file. The "__file__"
attribute may be missing for certain types of modules, such as C
modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is
the pathname of the shared library file.
Special read-only attribute: "__dict__" is the module’s namespace
as a dictionary object.
CPython implementation detail: Because of the way CPython
clears module dictionaries, the module dictionary will be cleared
when the module falls out of scope even if the dictionary still has
live references. To avoid this, copy the dictionary or keep the
module around while using its dictionary directly.
Custom classes
Custom class types are typically created by class definitions (see
section Class definitions). A class has a namespace implemented by
a dictionary object. Class attribute references are translated to
lookups in this dictionary, e.g., "C.x" is translated to
"C.__dict__["x"]" (although there are a number of hooks which allow
for other means of locating attributes). When the attribute name is
not found there, the attribute search continues in the base
classes. This search of the base classes uses the C3 method
resolution order which behaves correctly even in the presence of
‘diamond’ inheritance structures where there are multiple
inheritance paths leading back to a common ancestor. Additional
details on the C3 MRO used by Python can be found in the
documentation accompanying the 2.3 release at
https://www.python.org/download/releases/2.3/mro/.
When a class attribute reference (for class "C", say) would yield a
class method object, it is transformed into an instance method
object whose "__self__" attribute is "C". When it would yield a
static method object, it is transformed into the object wrapped by
the static method object. See section Implementing Descriptors for
another way in which attributes retrieved from a class may differ
from those actually contained in its "__dict__".
Class attribute assignments update the class’s dictionary, never
the dictionary of a base class.
A class object can be called (see above) to yield a class instance
(see below).
Special attributes: "__name__" is the class name; "__module__" is
the module name in which the class was defined; "__dict__" is the
dictionary containing the class’s namespace; "__bases__" is a tuple
containing the base classes, in the order of their occurrence in
the base class list; "__doc__" is the class’s documentation string,
or "None" if undefined; "__annotations__" (optional) is a
dictionary containing variable annotations collected during class
body execution.
Class instances
A class instance is created by calling a class object (see above).
A class instance has a namespace implemented as a dictionary which
is the first place in which attribute references are searched.
When an attribute is not found there, and the instance’s class has
an attribute by that name, the search continues with the class
attributes. If a class attribute is found that is a user-defined
function object, it is transformed into an instance method object
whose "__self__" attribute is the instance. Static method and
class method objects are also transformed; see above under
“Classes”. See section Implementing Descriptors for another way in
which attributes of a class retrieved via its instances may differ
from the objects actually stored in the class’s "__dict__". If no
class attribute is found, and the object’s class has a
"__getattr__()" method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s
dictionary, never a class’s dictionary. If the class has a
"__setattr__()" or "__delattr__()" method, this is called instead
of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings
if they have methods with certain special names. See section
Special method names.
Special attributes: "__dict__" is the attribute dictionary;
"__class__" is the instance’s class.
I/O objects (also known as file objects)
A file object represents an open file. Various shortcuts are
available to create file objects: the "open()" built-in function,
and also "os.popen()", "os.fdopen()", and the "makefile()" method
of socket objects (and perhaps by other functions or methods
provided by extension modules).
The objects "sys.stdin", "sys.stdout" and "sys.stderr" are
initialized to file objects corresponding to the interpreter’s
standard input, output and error streams; they are all open in text
mode and therefore follow the interface defined by the
"io.TextIOBase" abstract class.
Internal types
A few types used internally by the interpreter are exposed to the
user. Their definitions may change with future versions of the
interpreter, but they are mentioned here for completeness.
Code objects
Code objects represent byte-compiled executable Python code,
or bytecode. The difference between a code object and a
function object is that the function object contains an explicit
reference to the function’s globals (the module in which it was
defined), while a code object contains no context; also the
default argument values are stored in the function object, not
in the code object (because they represent values calculated at
run-time). Unlike function objects, code objects are immutable
and contain no references (directly or indirectly) to mutable
objects.
Special read-only attributes: "co_name" gives the function name;
"co_argcount" is the total number of positional arguments
(including positional-only arguments and arguments with default
values); "co_posonlyargcount" is the number of positional-only
arguments (including arguments with default values);
"co_kwonlyargcount" is the number of keyword-only arguments
(including arguments with default values); "co_nlocals" is the
number of local variables used by the function (including
arguments); "co_varnames" is a tuple containing the names of the
local variables (starting with the argument names);
"co_cellvars" is a tuple containing the names of local variables
that are referenced by nested functions; "co_freevars" is a
tuple containing the names of free variables; "co_code" is a
string representing the sequence of bytecode instructions;
"co_consts" is a tuple containing the literals used by the
bytecode; "co_names" is a tuple containing the names used by the
bytecode; "co_filename" is the filename from which the code was
compiled; "co_firstlineno" is the first line number of the
function; "co_lnotab" is a string encoding the mapping from
bytecode offsets to line numbers (for details see the source
code of the interpreter); "co_stacksize" is the required stack
size; "co_flags" is an integer encoding a number of flags for
the interpreter.
The following flag bits are defined for "co_flags": bit "0x04"
is set if the function uses the "arguments" syntax to accept an
arbitrary number of positional arguments; bit "0x08" is set if
the function uses the "**keywords" syntax to accept arbitrary
keyword arguments; bit "0x20" is set if the function is a
generator.
Future feature declarations ("from __future__ import division")
also use bits in "co_flags" to indicate whether a code object
was compiled with a particular feature enabled: bit "0x2000" is
set if the function was compiled with future division enabled;
bits "0x10" and "0x1000" were used in earlier versions of
Python.
Other bits in "co_flags" are reserved for internal use.
If a code object represents a function, the first item in
"co_consts" is the documentation string of the function, or
"None" if undefined.
Frame objects
Frame objects represent execution frames. They may occur in
traceback objects (see below), and are also passed to registered
trace functions.
Special read-only attributes: "f_back" is to the previous stack
frame (towards the caller), or "None" if this is the bottom
stack frame; "f_code" is the code object being executed in this
frame; "f_locals" is the dictionary used to look up local
variables; "f_globals" is used for global variables;
"f_builtins" is used for built-in (intrinsic) names; "f_lasti"
gives the precise instruction (this is an index into the
bytecode string of the code object).
Special writable attributes: "f_trace", if not "None", is a
function called for various events during code execution (this
is used by the debugger). Normally an event is triggered for
each new source line - this can be disabled by setting
"f_trace_lines" to "False".
Implementations *may allow per-opcode events to be requested by
setting "f_trace_opcodes" to "True". Note that this may lead to
undefined interpreter behaviour if exceptions raised by the
trace function escape to the function being traced.
"f_lineno" is the current line number of the frame — writing to
this from within a trace function jumps to the given line (only
for the bottom-most frame). A debugger can implement a Jump
command (aka Set Next Statement) by writing to f_lineno.
Frame objects support one method:
frame.clear()
This method clears all references to local variables held by
the frame. Also, if the frame belonged to a generator, the
generator is finalized. This helps break reference cycles
involving frame objects (for example when catching an
exception and storing its traceback for later use).
"RuntimeError" is raised if the frame is currently executing.
New in version 3.4.
Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback object is implicitly created when an exception occurs,
and may also be explicitly created by calling
"types.TracebackType".
For implicitly created tracebacks, when the search for an
exception handler unwinds the execution stack, at each unwound
level a traceback object is inserted in front of the current
traceback. When an exception handler is entered, the stack
trace is made available to the program. (See section The try
statement.) It is accessible as the third item of the tuple
returned by "sys.exc_info()", and as the "__traceback__"
attribute of the caught exception.
When the program contains no suitable handler, the stack trace
is written (nicely formatted) to the standard error stream; if
the interpreter is interactive, it is also made available to the
user as "sys.last_traceback".
For explicitly created tracebacks, it is up to the creator of
the traceback to determine how the "tb_next" attributes should
be linked to form a full stack trace.
Special read-only attributes: "tb_frame" points to the execution
frame of the current level; "tb_lineno" gives the line number
where the exception occurred; "tb_lasti" indicates the precise
instruction. The line number and last instruction in the
traceback may differ from the line number of its frame object if
the exception occurred in a "try" statement with no matching
except clause or with a finally clause.
Special writable attribute: "tb_next" is the next level in the
stack trace (towards the frame where the exception occurred), or
"None" if there is no next level.
Changed in version 3.7: Traceback objects can now be explicitly
instantiated from Python code, and the "tb_next" attribute of
existing instances can be updated.
Slice objects
Slice objects are used to represent slices for "__getitem__()"
methods. They are also created by the built-in "slice()"
function.
Special read-only attributes: "start" is the lower bound; "stop"
is the upper bound; "step" is the step value; each is "None" if
omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument length and
computes information about the slice that the slice object
would describe if applied to a sequence of length items.
It returns a tuple of three integers; respectively these are
the start and stop indices and the step or stride
length of the slice. Missing or out-of-bounds indices are
handled in a manner consistent with regular slices.
Static method objects
Static method objects provide a way of defeating the
transformation of function objects to method objects described
above. A static method object is a wrapper around any other
object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the
object actually returned is the wrapped object, which is not
subject to any further transformation. Static method objects are
not themselves callable, although the objects they wrap usually
are. Static method objects are created by the built-in
"staticmethod()" constructor.
Class method objects
A class method object, like a static method object, is a wrapper
around another object that alters the way in which that object
is retrieved from classes and class instances. The behaviour of
class method objects upon such retrieval is described above,
under “User-defined methods”. Class method objects are created
by the built-in "classmethod()" constructor.
Related help topics: STRINGS, UNICODE, NUMBERS, SEQUENCES, MAPPINGS,
FUNCTIONS, CLASSES, MODULES, FILES, inspect
min, next, object, open, property
(<function min>,
<function next>,
object,
<function io.open(file, mode='r', buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None)>,
property)
True, False, None
(True, False, None)
a
<function __main__.a(b)>
assert a(42) is None
set()
set()
type(set)
type
(lambda x: x + 1)(4)
5
(lambda x: x + 1)
<function __main__.<lambda>(x)>
class A(object):
def __call__(self, b):
return b + 1
a = A()
a(4)
5
type(A)
type
L = list(range(10))
L
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
def inc(a): return a + 1
list(map(inc, L))
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
list(map(print, L))
0
1
2
3
4
5
6
7
8
9
[None, None, None, None, None, None, None, None, None, None]
for i in L:
print(i)
0
1
2
3
4
5
6
7
8
9
LL = []
for i in L:
LL.append(i+1)
LL
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
an_int = 541
my_lambda = lambda x: x + an_int
my_lambda
<function __main__.<lambda>(x)>
my_lambda(1)
542
list(map(my_lambda, L))
[541, 542, 543, 544, 545, 546, 547, 548, 549, 550]
Variables¶
a = 2
b = 3
a, b
(2, 3)
3 + (a := 4)
7
a
4
# the usual way...
c = a
a = b
b = c
b, a = a, b # swap
a, b
(3, 2)
a, (b, *c, d), e, *f = (1, [2, 3, 4, 5, 6], 7, 8, 9) # unpacking of iterables.
a, b, c, d, e, f
(1, 2, [3, 4, 5], 6, 7, [8, 9])
(1, [2, 3, 4, 5, 6], 7, 8, 9)[1][-1]
6
d
6
Operators and operands¶
Built-ins are described at https://docs.python.org/3/library/functions.html#built-in-funcs.
int(1.1)
1
help('INTEGER')
Integer literals
************
Integer literals are described by the following lexical definitions:
integer ::= decinteger | bininteger | octinteger | hexinteger
decinteger ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
bininteger ::= "0" ("b" | "B") (["_"] bindigit)+
octinteger ::= "0" ("o" | "O") (["_"] octdigit)+
hexinteger ::= "0" ("x" | "X") (["_"] hexdigit)+
nonzerodigit ::= "1"..."9"
digit ::= "0"..."9"
bindigit ::= "0" | "1"
octdigit ::= "0"..."7"
hexdigit ::= digit | "a"..."f" | "A"..."F"
There is no limit for the length of integer literals apart from what
can be stored in available memory.
Underscores are ignored for determining the numeric value of the
literal. They can be used to group digits for enhanced readability.
One underscore can occur between digits, and after base specifiers
like "0x".
Note that leading zeros in a non-zero decimal number are not allowed.
This is for disambiguation with C-style octal literals, which Python
used before version 3.0.
Some examples of integer literals:
7 2147483647 0o177 0b100110111
3 79228162514264337593543950336 0o377 0xdeadbeef
100_000_000_000 0b_1110_0101
Changed in version 3.6: Underscores are now allowed for grouping
purposes in literals.
Related help topics: int, range
'banana' * 3
'bananabananabanana'
type(_)
str
help('STRINGS')
String and Bytes literals
*********************
String literals are described by the following lexical definitions:
stringliteral ::= [stringprefix](shortstring | longstring)
stringprefix ::= "r" | "u" | "R" | "U" | "f" | "F"
| "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
shortstring ::= "'" shortstringitem* "'" | '"' shortstringitem* '"'
longstring ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
shortstringitem ::= shortstringchar | stringescapeseq
longstringitem ::= longstringchar | stringescapeseq
shortstringchar ::= <any source character except "" or newline or the quote>
longstringchar ::= <any source character except "">
stringescapeseq ::= "" <any source character>
bytesliteral ::= bytesprefix(shortbytes | longbytes)
bytesprefix ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
shortbytes ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
longbytes ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
shortbytesitem ::= shortbyteschar | bytesescapeseq
longbytesitem ::= longbyteschar | bytesescapeseq
shortbyteschar ::= <any ASCII character except "" or newline or the quote>
longbyteschar ::= <any ASCII character except "">
bytesescapeseq ::= "" <any ASCII character>
One syntactic restriction not indicated by these productions is that
whitespace is not allowed between the "stringprefix" or "bytesprefix"
and the rest of the literal. The source character set is defined by
the encoding declaration; it is UTF-8 if no encoding declaration is
given in the source file; see section Encoding declarations.
In plain English: Both types of literals can be enclosed in matching
single quotes ("'") or double quotes ("""). They can also be enclosed
in matching groups of three single or double quotes (these are
generally referred to as triple-quoted strings). The backslash
("") character is used to escape characters that otherwise have a
special meaning, such as newline, backslash itself, or the quote
character.
Bytes literals are always prefixed with "'b'" or "'B'"; they produce
an instance of the "bytes" type instead of the "str" type. They may
only contain ASCII characters; bytes with a numeric value of 128 or
greater must be expressed with escapes.
Both string and bytes literals may optionally be prefixed with a
letter "'r'" or "'R'"; such strings are called raw strings and treat
backslashes as literal characters. As a result, in string literals,
"'U'" and "'u'" escapes in raw strings are not treated specially.
Given that Python 2.x’s raw unicode literals behave differently than
Python 3.x’s the "'ur'" syntax is not supported.
New in version 3.3: The "'rb'" prefix of raw bytes literals has been
added as a synonym of "'br'".
New in version 3.3: Support for the unicode legacy literal
("u'value'") was reintroduced to simplify the maintenance of dual
Python 2.x and 3.x codebases. See PEP 414 for more information.
A string literal with "'f'" or "'F'" in its prefix is a formatted
string literal; see Formatted string literals. The "'f'" may be
combined with "'r'", but not with "'b'" or "'u'", therefore raw
formatted strings are possible, but formatted bytes literals are not.
In triple-quoted literals, unescaped newlines and quotes are allowed
(and are retained), except that three unescaped quotes in a row
terminate the literal. (A “quote” is the character used to open the
literal, i.e. either "'" or """.)
Unless an "'r'" or "'R'" prefix is present, escape sequences in string
and bytes literals are interpreted according to rules similar to those
used by Standard C. The recognized escape sequences are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "newline" | Backslash and newline ignored | |
+-------------------+-----------------------------------+---------+
| "\" | Backslash ("") | |
+-------------------+-----------------------------------+---------+
| "'" | Single quote ("'") | |
+-------------------+-----------------------------------+---------+
| """ | Double quote (""") | |
+-------------------+-----------------------------------+---------+
| "a" | ASCII Bell (BEL) | |
+-------------------+-----------------------------------+---------+
| "b" | ASCII Backspace (BS) | |
+-------------------+-----------------------------------+---------+
| "f" | ASCII Formfeed (FF) | |
+-------------------+-----------------------------------+---------+
| "n" | ASCII Linefeed (LF) | |
+-------------------+-----------------------------------+---------+
| "r" | ASCII Carriage Return (CR) | |
+-------------------+-----------------------------------+---------+
| "t" | ASCII Horizontal Tab (TAB) | |
+-------------------+-----------------------------------+---------+
| "v" | ASCII Vertical Tab (VT) | |
+-------------------+-----------------------------------+---------+
| "ooo" | Character with octal value ooo | (1,3) |
+-------------------+-----------------------------------+---------+
| "xhh" | Character with hex value hh | (2,3) |
+-------------------+-----------------------------------+---------+
Escape sequences only recognized in string literals are:
+-------------------+-----------------------------------+---------+
| Escape Sequence | Meaning | Notes |
|===================|===================================|=========|
| "N{name}" | Character named name in the | (4) |
| | Unicode database | |
+-------------------+-----------------------------------+---------+
| "uxxxx" | Character with 16-bit hex value | (5) |
| | xxxx | |
+-------------------+-----------------------------------+---------+
| "Uxxxxxxxx" | Character with 32-bit hex value | (6) |
| | xxxxxxxx | |
+-------------------+-----------------------------------+---------+
Notes:
1. As in Standard C, up to three octal digits are accepted.
2. Unlike in Standard C, exactly two hex digits are required.
3. In a bytes literal, hexadecimal and octal escapes denote the byte
with the given value. In a string literal, these escapes denote a
Unicode character with the given value.
4. Changed in version 3.3: Support for name aliases [1] has been
added.
5. Exactly four hex digits are required.
6. Any Unicode character can be encoded this way. Exactly eight hex
digits are required.
Unlike Standard C, all unrecognized escape sequences are left in the
string unchanged, i.e., the backslash is left in the result. (This
behavior is useful when debugging: if an escape sequence is mistyped,
the resulting output is more easily recognized as broken.) It is also
important to note that the escape sequences only recognized in string
literals fall into the category of unrecognized escapes for bytes
literals.
Changed in version 3.6: Unrecognized escape sequences produce a
"DeprecationWarning". In a future Python version they will be a
"SyntaxWarning" and eventually a "SyntaxError".
Even in a raw literal, quotes can be escaped with a backslash, but the
backslash remains in the result; for example, "r"""" is a valid
string literal consisting of two characters: a backslash and a double
quote; "r""" is not a valid string literal (even a raw string cannot
end in an odd number of backslashes). Specifically, a raw literal
cannot end in a single backslash (since the backslash would escape
the following quote character). Note also that a single backslash
followed by a newline is interpreted as those two characters as part
of the literal, not as a line continuation.
Related help topics: str, UNICODE, SEQUENCES, STRINGMETHODS, FORMATTING,
TYPES
''.join(reversed('prova')) # `string`s are collections too
'avorp'
a = 1; b = 2 # string interpolation
'{} < {} is actually true.'.format(a, b)
'1 < 2 is actually true.'
f'{a} < {b} is actually true.'
'1 < 2 is actually true.'
total_secs = 43943
hours = total_secs // 3600
secs_still_remaining = total_secs % 3600
minutes = secs_still_remaining // 60
secs_finally_remaining = secs_still_remaining % 60
"{}h {}' {}''".format(hours, minutes, secs_finally_remaining)
"12h 12' 23''"
operators are described here https://docs.python.org/3/library/operator.html?highlight=operator.
help('OPERATORS')
Operator precedence *************** The following table summarizes the operator precedence in Python, from lowest precedence (least binding) to highest precedence (most binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation, which groups from right to left). Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section. +-------------------------------------------------+---------------------------------------+ | Operator | Description | |=================================================|=======================================| | ":=" | Assignment expression | +-------------------------------------------------+---------------------------------------+ | "lambda" | Lambda expression | +-------------------------------------------------+---------------------------------------+ | "if" – "else" | Conditional expression | +-------------------------------------------------+---------------------------------------+ | "or" | Boolean OR | +-------------------------------------------------+---------------------------------------+ | "and" | Boolean AND | +-------------------------------------------------+---------------------------------------+ | "not" "x" | Boolean NOT | +-------------------------------------------------+---------------------------------------+ | "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership | | ">=", "!=", "==" | tests and identity tests | +-------------------------------------------------+---------------------------------------+ | "|" | Bitwise OR | +-------------------------------------------------+---------------------------------------+ | "^" | Bitwise XOR | +-------------------------------------------------+---------------------------------------+ | "&" | Bitwise AND | +-------------------------------------------------+---------------------------------------+ | "<<", ">>" | Shifts | +-------------------------------------------------+---------------------------------------+ | "+", "-" | Addition and subtraction | +-------------------------------------------------+---------------------------------------+ | "*", "@", "/", "//", "%" | Multiplication, matrix | | | multiplication, division, floor | | | division, remainder [5] | +-------------------------------------------------+---------------------------------------+ | "+x", "-x", "~x" | Positive, negative, bitwise NOT | +-------------------------------------------------+---------------------------------------+ | "**" | Exponentiation [6] | +-------------------------------------------------+---------------------------------------+ | "await" "x" | Await expression | +-------------------------------------------------+---------------------------------------+ | "x[index]", "x[index:index]", | Subscription, slicing, call, | | "x(arguments...)", "x.attribute" | attribute reference | +-------------------------------------------------+---------------------------------------+ | "(expressions...)", "[expressions...]", "{key: | Binding or parenthesized expression, | | value...}", "{expressions...}" | list display, dictionary display, set | | | display | +-------------------------------------------------+---------------------------------------+ -[ Footnotes ]- [1] While "abs(x%y) < abs(y)" is true mathematically, for floats it may not be true numerically due to roundoff. For example, and assuming a platform on which a Python float is an IEEE 754 double- precision number, in order that "-1e-100 % 1e100" have the same sign as "1e100", the computed result is "-1e-100 + 1e100", which is numerically exactly equal to "1e100". The function "math.fmod()" returns a result whose sign matches the sign of the first argument instead, and so returns "-1e-100" in this case. Which approach is more appropriate depends on the application. [2] If x is very close to an exact integer multiple of y, it’s possible for "x//y" to be one larger than "(x-x%y)//y" due to rounding. In such cases, Python returns the latter result, in order to preserve that "divmod(x,y)[0] * y + x % y" be very close to "x". [3] The Unicode standard distinguishes between code points (e.g. U+0041) and abstract characters (e.g. “LATIN CAPITAL LETTER A”). While most abstract characters in Unicode are only represented using one code point, there is a number of abstract characters that can in addition be represented using a sequence of more than one code point. For example, the abstract character “LATIN CAPITAL LETTER C WITH CEDILLA” can be represented as a single precomposed character at code position U+00C7, or as a sequence of a base character at code position U+0043 (LATIN CAPITAL LETTER C), followed by a combining character at code position U+0327 (COMBINING CEDILLA). The comparison operators on strings compare at the level of Unicode code points. This may be counter-intuitive to humans. For example, ""u00C7" == "u0043u0327"" is "False", even though both strings represent the same abstract character “LATIN CAPITAL LETTER C WITH CEDILLA”. To compare strings at the level of abstract characters (that is, in a way intuitive to humans), use "unicodedata.normalize()". [4] Due to automatic garbage-collection, free lists, and the dynamic nature of descriptors, you may notice seemingly unusual behaviour in certain uses of the "is" operator, like those involving comparisons between instance methods, or constants. Check their documentation for more info. [5] The "%" operator is also used for string formatting; the same precedence applies. [6] The power operator "**" binds less tightly than an arithmetic or bitwise unary operator on its right, that is, "2**-1" is "0.5". Related help topics: lambda, or, and, not, in, is, BOOLEAN, COMPARISON, BITWISE, SHIFTING, BINARY, FORMATTING, POWER, UNARY, ATTRIBUTES, SUBSCRIPTS, SLICINGS, CALLS, TUPLES, LISTS, DICTIONARIES
Slices¶
'hello'[1:-2]
'el'
s = slice(5, 11, 2)
s.start, s.stop, s.step
(5, 11, 2)
'hello'[slice(1, 3)]
'el'
help('SLICINGS')
Slicings
****
A slicing selects a range of items in a sequence object (e.g., a
string, tuple or list). Slicings may be used as expressions or as
targets in assignment or "del" statements. The syntax for a slicing:
slicing ::= primary "[" slice_list "]"
slice_list ::= slice_item ("," slice_item)* [","]
slice_item ::= expression | proper_slice
proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]
lower_bound ::= expression
upper_bound ::= expression
stride ::= expression
There is ambiguity in the formal syntax here: anything that looks like
an expression list also looks like a slice list, so any subscription
can be interpreted as a slicing. Rather than further complicating the
syntax, this is disambiguated by defining that in this case the
interpretation as a subscription takes priority over the
interpretation as a slicing (this is the case if the slice list
contains no proper slice).
The semantics for a slicing are as follows. The primary is indexed
(using the same "__getitem__()" method as normal subscription) with a
key that is constructed from the slice list, as follows. If the slice
list contains at least one comma, the key is a tuple containing the
conversion of the slice items; otherwise, the conversion of the lone
slice item is the key. The conversion of a slice item that is an
expression is that expression. The conversion of a proper slice is a
slice object (see section The standard type hierarchy) whose "start",
"stop" and "step" attributes are the values of the expressions given
as lower bound, upper bound and stride, respectively, substituting
"None" for missing expressions.
Related help topics: SEQUENCEMETHODS
ranges¶
range
range
type(range)
type
r = range(2, 20)
r
range(2, 20)
len(r)
18
id(_)
4389733408
print(range.__doc__)
range(stop) -> range object
range(start, stop[, step]) -> range object
Return an object that produces a sequence of integers from start (inclusive)
to stop (exclusive) by step. range(i, j) produces i, i+1, i+2, ..., j-1.
start defaults to 0, and stop is omitted! range(4) produces 0, 1, 2, 3.
These are exactly the valid indices for a list of 4 elements.
When step is given, it specifies the increment (or decrement).
range(1_000_000_000_000_000)
range(0, 1000000000000000)
range(1_000000000_000_000)
range(0, 1000000000000000)
type(_)
range
list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
list()
[]
[]
[]
set()
set()
ranges = [range(10), range(10, 20)]
list(map(list, ranges))
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]]
my_map = map(list, ranges)
In Haskell, we can define the naturals using the following definition:
nats = [1..]
type(my_map)
map
list(my_map)
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]]
L = range(20000000000000000000000000000000000000)
L
range(0, 20000000000000000000000000000000000000)
M = map(lambda x: x % 2, L)
M
<map at 0x1054d7d90>
next(M)
0
next(M)
1
next(M)
0
next(M)
1
def one_two_three():
yield 1
yield 2
yield 3
one_two_three
<function __main__.one_two_three()>
OTT = one_two_three()
id(OTT)
4391761872
type(OTT)
generator
next(OTT)
1
next(OTT)
2
next(OTT)
3
next(OTT)
---------------------------------------------------------------------------
StopIteration Traceback (most recent call last)
/var/folders/0k/y3r96gt12zn1mzgztql9m_8m0000gn/T/ipykernel_1055/2932888578.py in <module>
----> 1 next(OTT)
StopIteration:
for i in one_two_three():
print(i)
1
2
3
M = one_two_three()
while True:
try:
i = next(M)
print(i)
except StopIteration:
break
1
2
3
L = range(10)
L
range(0, 10)
def f(x):
print('From f')
return x + 1
M = map(f, L)
type(M)
map
def g(x):
print('From g')
return x + 2
N = map(g, M)
type(N)
map
def h(x):
print('From h')
return x + 3
O = map(h, N)
type(O)
map
next(O)
From f
From g
From h
6
next(O)
From f
From g
From h
7
list(O)
From f
From g
From h
From f
From g
From h
From f
From g
From h
From f
From g
From h
From f
From g
From h
From f
From g
From h
From f
From g
From h
From f
From g
From h
[8, 9, 10, 11, 12, 13, 14, 15]
next()
---------------------------------------------------------------------------
StopIteration Traceback (most recent call last)
/var/folders/0k/y3r96gt12zn1mzgztql9m_8m0000gn/T/ipykernel_1055/3089814063.py in <module>
----> 1 next(O)
StopIteration:
M = [1, 2, 3, 4]
G = (x + 1 for x in range(100))
H = [x + 1 for x in M]
H
[2, 3, 4, 5]
list(G)
[2, 3, 4, 5]
list(map(lambda x: x+1, M))
[2, 3, 4, 5]
lambdas¶
def add(a, b):
return a + b
type(add)
function
callable(add)
True
add(3, 4)
7
help('FUNCTIONS')
Functions ***** Function objects are created by function definitions. The only operation on a function object is to call it: "func(argument-list)". There are really two flavors of function objects: built-in functions and user-defined functions. Both support the same operation (to call the function), but the implementation is different, hence the different object types. See Function definitions for more information. Related help topics: def, TYPES
add_l = lambda a, b: a + b # is a lambda expression
type(add_l)
function
callable(add_l)
True
help('lambda')
Lambdas
***
lambda_expr ::= "lambda" [parameter_list] ":" expression
lambda_expr_nocond ::= "lambda" [parameter_list] ":" expression_nocond
Lambda expressions (sometimes called lambda forms) are used to create
anonymous functions. The expression "lambda parameters: expression"
yields a function object. The unnamed object behaves like a function
object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists.
Note that functions created with lambda expressions cannot contain
statements or annotations.
Related help topics: FUNCTIONS
assert add(1, 2) == add_l(1, 2)
yields¶
L = range(5)
L
range(0, 5)
M = map(lambda i: i + 1, L)
M
<map at 0x7fb7c6626880>
type(_)
map
next(M)
1
help(next)
Help on built-in function next in module builtins:
next(...)
next(iterator[, default])
Return the next item from the iterator. If default is given and the iterator
is exhausted, it is returned instead of raising StopIteration.
next(M)
2
next(M)
3
next(M)
4
next(M)
5
next(M)
---------------------------------------------------------------------------
StopIteration Traceback (most recent call last)
<ipython-input-10-0666361e9047> in <module>
----> 1 next(M)
StopIteration:
help(StopIterationx)
INTERMEZZO
1/0
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
<ipython-input-11-9e1622b385b6> in <module>
----> 1 1/0
ZeroDivisionError: division by zero
try:
1/0
except ZeroDivisionError:
pass
\[\mathcal{B} = \lbrace x\in\mathbb{R} : \sqrt{x + 1} > 4 \rbrace\]
N = ( i for i in range(1, 10) ) # another way to build a generator
N
<generator object <genexpr> at 0x7fb7c687c120>
type(N)
generator
list(N)
[1, 2, 3, 4, 5, 6, 7, 8, 9]
def saturate(g, L=[]):
while True:
try:
L.append(next(g))
except StopIteration:
break
return L
saturate(N, [0])
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Help about help¶
help()
Welcome to Python 3.8's help utility!
If this is your first time using Python, you should definitely check out
the tutorial on the Internet at https://docs.python.org/3.8/tutorial/.
Enter the name of any module, keyword, or topic to get help on writing
Python programs and using Python modules. To quit this help utility and
return to the interpreter, just type "quit".
To get a list of available modules, keywords, symbols, or topics, type
"modules", "keywords", "symbols", or "topics". Each module also comes
with a one-line summary of what it does; to list the modules whose name
or summary contain a given string such as "spam", type "modules spam".
help> map
Help on class map in module builtins:
class map(object)
| map(func, *iterables) --> map object
|
| Make an iterator that computes the function using arguments from
| each of the iterables. Stops when the shortest iterable is exhausted.
|
| Methods defined here:
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __iter__(self, /)
| Implement iter(self).
|
| __next__(self, /)
| Implement next(self).
|
| __reduce__(...)
| Return state information for pickling.
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
You are now leaving help and returning to the Python interpreter.
If you want to ask for help on a particular object directly from the
interpreter, you can type "help(object)". Executing "help('string')"
has the same effect as typing a particular string at the help> prompt.