Yield from syntax error

6.2.1. Identifiers (Names)

An identifier occurring as an atom is a name. See section Identifiers and keywords
for lexical definition and section Naming and binding for documentation of naming and
binding.

When the name is bound to an object, evaluation of the atom yields that object.
When a name is not bound, an attempt to evaluate it raises a NameError
exception.

Private name mangling: When an identifier that textually occurs in a class
definition begins with two or more underscore characters and does not end in two
or more underscores, it is considered a private name of that class.
Private names are transformed to a longer form before code is generated for
them. The transformation inserts the class name, with leading underscores
removed and a single underscore inserted, in front of the name. For example,
the identifier __spam occurring in a class named Ham will be transformed
to _Ham__spam. This transformation is independent of the syntactical
context in which the identifier is used. If the transformed name is extremely
long (longer than 255 characters), implementation defined truncation may happen.
If the class name consists only of underscores, no transformation is done.

6.2.2. Literals

Python supports string and bytes literals and various numeric literals:

literal ::=  stringliteral | bytesliteral
             | integer | floatnumber | imagnumber

Evaluation of a literal yields an object of the given type (string, bytes,
integer, floating point number, complex number) with the given value. The value
may be approximated in the case of floating point and imaginary (complex)
literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity
is less important than its value. Multiple evaluations of literals with the
same value (either the same occurrence in the program text or a different
occurrence) may obtain the same object or a different object with the same
value.

6.2.3. Parenthesized forms

A parenthesized form is an optional expression list enclosed in parentheses:

parenth_form ::=  "(" [starred_expression] ")"

A parenthesized expression list yields whatever that expression list yields: if
the list contains at least one comma, it yields a tuple; otherwise, it yields
the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are
immutable, the rules for literals apply (i.e., two occurrences of the empty
tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the
comma operator. The exception is the empty tuple, for which parentheses are
required — allowing unparenthesized “nothing” in expressions would cause
ambiguities and allow common typos to pass uncaught.

6.2.4. Displays for lists, sets and dictionaries

For constructing a list, a set or a dictionary Python provides special syntax
called “displays”, each of them in two flavors:

• either the container contents are listed explicitly, or
• they are computed via a set of looping and filtering instructions, called a
  comprehension.

Common syntax elements for comprehensions are:

comprehension ::=  expression comp_for
comp_for      ::=  [ASYNC] "for" target_list "in" or_test [comp_iter]
comp_iter     ::=  comp_for | comp_if
comp_if       ::=  "if" expression_nocond [comp_iter]

The comprehension consists of a single expression followed by at least one
for clause and zero or more for or if clauses.
In this case, the elements of the new container are those that would be produced
by considering each of the for or if clauses a block,
nesting from left to right, and evaluating the expression to produce an element
each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause,
the comprehension is executed in a separate implicitly nested scope. This ensures
that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated
directly in the enclosing scope and then passed as an argument to the implictly
nested scope. Subsequent for clauses and any filter condition in the
leftmost for clause cannot be evaluated in the enclosing scope as
they may depend on the values obtained from the leftmost iterable. For example:
[x*y for x in range(10) for y in range(x, x+10)].

To ensure the comprehension always results in a container of the appropriate
type, yield and yield from expressions are prohibited in the implicitly
nested scope (in Python 3.7, such expressions emit DeprecationWarning
when compiled, in Python 3.8+ they will emit SyntaxError).

Since Python 3.6, in an async def function, an async for
clause may be used to iterate over a asynchronous iterator.
A comprehension in an async def function may consist of either a
for or async for clause following the leading
expression, may contain additional for or async for
clauses, and may also use await expressions.
If a comprehension contains either async for clauses
or await expressions it is called an
asynchronous comprehension. An asynchronous comprehension may
suspend the execution of the coroutine function in which it appears.
See also PEP 530.

New in version 3.6: Asynchronous comprehensions were introduced.

Deprecated since version 3.7: yield and yield from deprecated in the implicitly nested scope.

6.2.5. List displays

A list display is a possibly empty series of expressions enclosed in square
brackets:

list_display ::=  "[" [starred_list | comprehension] "]"

A list display yields a new list object, the contents being specified by either
a list of expressions or a comprehension. When a comma-separated list of
expressions is supplied, its elements are evaluated from left to right and
placed into the list object in that order. When a comprehension is supplied,
the list is constructed from the elements resulting from the comprehension.

6.2.6. Set displays

A set display is denoted by curly braces and distinguishable from dictionary
displays by the lack of colons separating keys and values:

set_display ::=  "{" (starred_list | comprehension) "}"

A set display yields a new mutable set object, the contents being specified by
either a sequence of expressions or a comprehension. When a comma-separated
list of expressions is supplied, its elements are evaluated from left to right
and added to the set object. When a comprehension is supplied, the set is
constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with {}; this literal constructs an empty
dictionary.

6.2.7. Dictionary displays

A dictionary display is a possibly empty series of key/datum pairs enclosed in
curly braces:

dict_display       ::=  "{" [key_datum_list | dict_comprehension] "}"
key_datum_list     ::=  key_datum ("," key_datum)* [","]
key_datum          ::=  expression ":" expression | "**" or_expr
dict_comprehension ::=  expression ":" expression comp_for

A dictionary display yields a new dictionary object.

If a comma-separated sequence of key/datum pairs is given, they are evaluated
from left to right to define the entries of the dictionary: each key object is
used as a key into the dictionary to store the corresponding datum. This means
that you can specify the same key multiple times in the key/datum list, and the
final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking.
Its operand must be a mapping. Each mapping item is added
to the new dictionary. Later values replace values already set by
earlier key/datum pairs and earlier dictionary unpackings.

New in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448.

A dict comprehension, in contrast to list and set comprehensions, needs two
expressions separated with a colon followed by the usual “for” and “if” clauses.
When the comprehension is run, the resulting key and value elements are inserted
in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section
The standard type hierarchy. (To summarize, the key type should be hashable, which excludes
all mutable objects.) Clashes between duplicate keys are not detected; the last
datum (textually rightmost in the display) stored for a given key value
prevails.

6.2.8. Generator expressions

A generator expression is a compact generator notation in parentheses:

generator_expression ::=  "(" expression comp_for ")"

A generator expression yields a new generator object. Its syntax is the same as
for comprehensions, except that it is enclosed in parentheses instead of
brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the
__next__() method is called for the generator object (in the same
fashion as normal generators). However, the iterable expression in the
leftmost for clause is immediately evaluated, so that an error
produced by it will be emitted at the point where the generator expression
is defined, rather than at the point where the first value is retrieved.
Subsequent for clauses and any filter condition in the leftmost
for clause cannot be evaluated in the enclosing scope as they may
depend on the values obtained from the leftmost iterable. For example:
(x*y for x in range(10) for y in range(x, x+10)).

The parentheses can be omitted on calls with only one argument. See section
Calls for details.

To avoid interfering with the expected operation of the generator expression
itself, yield and yield from expressions are prohibited in the
implicitly defined generator (in Python 3.7, such expressions emit
DeprecationWarning when compiled, in Python 3.8+ they will emit
SyntaxError).

If a generator expression contains either async for
clauses or await expressions it is called an
asynchronous generator expression. An asynchronous generator
expression returns a new asynchronous generator object,
which is an asynchronous iterator (see Asynchronous Iterators).

New in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could
only appear in async def coroutines. Starting
with 3.7, any function can use asynchronous generator expressions.

Deprecated since version 3.7: yield and yield from deprecated in the implicitly nested scope.

6.2.9. Yield expressions

yield_atom       ::=  "(" yield_expression ")"
yield_expression ::=  "yield" [expression_list | "from" expression]

The yield expression is used when defining a generator function
or an asynchronous generator function and
thus can only be used in the body of a function definition. Using a yield
expression in a function’s body causes that function to be a generator,
and using it in an async def function’s body causes that
coroutine function to be an asynchronous generator. For example:

def gen():  # defines a generator function
    yield 123

async def agen(): # defines an asynchronous generator function (PEP 525)
    yield 123

Due to their side effects on the containing scope, yield expressions
are not permitted as part of the implicitly defined scopes used to
implement comprehensions and generator expressions (in Python 3.7, such
expressions emit DeprecationWarning when compiled, in Python 3.8+
they will emit SyntaxError)..

Deprecated since version 3.7: Yield expressions deprecated in the implicitly nested scopes used to
implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator
functions are described separately in section
Asynchronous generator functions.

When a generator function is called, it returns an iterator known as a
generator. That generator then controls the execution of the generator function.
The execution starts when one of the generator’s methods is called. At that
time, the execution proceeds to the first yield expression, where it is
suspended again, returning the value of expression_list to the generator’s
caller. By suspended, we mean that all local state is retained, including the
current bindings of local variables, the instruction pointer, the internal
evaluation stack, and the state of any exception handling. When the execution
is resumed by calling one of the
generator’s methods, the function can proceed exactly as if the yield expression
were just another external call. The value of the yield expression after
resuming depends on the method which resumed the execution. If
__next__() is used (typically via either a for or
the next() builtin) then the result is None. Otherwise, if
send() is used, then the result will be the value passed in to
that method.

All of this makes generator functions quite similar to coroutines; they yield
multiple times, they have more than one entry point and their execution can be
suspended. The only difference is that a generator function cannot control
where the execution should continue after it yields; the control is always
transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the
generator is not resumed before it is
finalized (by reaching a zero reference count or by being garbage collected),
the generator-iterator’s close() method will be called,
allowing any pending finally clauses to execute.

When yield from <expr> is used, it treats the supplied expression as
a subiterator. All values produced by that subiterator are passed directly
to the caller of the current generator’s methods. Any values passed in with
send() and any exceptions passed in with
throw() are passed to the underlying iterator if it has the
appropriate methods. If this is not the case, then send()
will raise AttributeError or TypeError, while
throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value
attribute of the raised StopIteration instance becomes the value of
the yield expression. It can be either set explicitly when raising
StopIteration, or automatically when the sub-iterator is a generator
(by returning a value from the sub-generator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression
on the right hand side of an assignment statement.

>>> def echo(value=None):
...     print("Execution starts when 'next()' is called for the first time.")
...     try:
...         while True:
...             try:
...                 value = (yield value)
...             except Exception as e:
...                 value = e
...     finally:
...         print("Don't forget to clean up when 'close()' is called.")
...
>>> generator = echo(1)
>>> print(next(generator))
Execution starts when 'next()' is called for the first time.
1
>>> print(next(generator))
None
>>> print(generator.send(2))
2
>>> generator.throw(TypeError, "spam")
TypeError('spam',)
>>> generator.close()
Don't forget to clean up when 'close()' is called.

6.3.1. Attribute references

An attribute reference is a primary followed by a period and a name:

attributeref ::=  primary "." identifier

The primary must evaluate to an object of a type that supports attribute
references, which most objects do. This object is then asked to produce the
attribute whose name is the identifier. This production can be customized by
overriding the __getattr__() method. If this attribute is not available,
the exception AttributeError is raised. Otherwise, the type and value of
the object produced is determined by the object. Multiple evaluations of the
same attribute reference may yield different objects.

6.3.2. Subscriptions

A subscription selects an item of a sequence (string, tuple or list) or mapping
(dictionary) object:

subscription ::=  primary "[" expression_list "]"

The primary must evaluate to an object that supports subscription (lists or
dictionaries for example). User-defined objects can support subscription by
defining a __getitem__() method.

For built-in objects, there are two types of objects that support subscription:

If the primary is a mapping, the expression list must evaluate to an object
whose value is one of the keys of the mapping, and the subscription selects the
value in the mapping that corresponds to that key. (The expression list is a
tuple except if it has exactly one item.)

If the primary is a sequence, the expression (list) must evaluate to an integer
or a slice (as discussed in the following section).

The formal syntax makes no special provision for negative indices in
sequences; however, built-in sequences all provide a __getitem__()
method that interprets negative indices by adding the length of the sequence
to the index (so that x[-1] selects the last item of x). The
resulting value must be a nonnegative integer less than the number of items in
the sequence, and the subscription selects the item whose index is that value
(counting from zero). Since the support for negative indices and slicing
occurs in the object’s __getitem__() method, subclasses overriding
this method will need to explicitly add that support.

A string’s items are characters. A character is not a separate data type but a
string of exactly one character.

6.3.3. 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.

6.3.4. Calls

A call calls a callable object (e.g., a function) with a possibly empty
series of arguments:

call                 ::=  primary "(" [argument_list [","] | comprehension] ")"
argument_list        ::=  positional_arguments ["," starred_and_keywords]
                            ["," keywords_arguments]
                          | starred_and_keywords ["," keywords_arguments]
                          | keywords_arguments
positional_arguments ::=  ["*"] expression ("," ["*"] expression)*
starred_and_keywords ::=  ("*" expression | keyword_item)
                          ("," "*" expression | "," keyword_item)*
keywords_arguments   ::=  (keyword_item | "**" expression)
                          ("," keyword_item | "," "**" expression)*
keyword_item         ::=  identifier "=" expression

An optional trailing comma may be present after the positional and keyword arguments
but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in
functions, methods of built-in objects, class objects, methods of class
instances, and all objects having a __call__() method are callable). All
argument expressions are evaluated before the call is attempted. Please refer
to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional
arguments, as follows. First, a list of unfilled slots is created for the
formal parameters. If there are N positional arguments, they are placed in the
first N slots. Next, for each keyword argument, the identifier is used to
determine the corresponding slot (if the identifier is the same as the first
formal parameter name, the first slot is used, and so on). If the slot is
already filled, a TypeError exception is raised. Otherwise, the value of
the argument is placed in the slot, filling it (even if the expression is
None, it fills the slot). When all arguments have been processed, the slots
that are still unfilled are filled with the corresponding default value from the
function definition. (Default values are calculated, once, when the function is
defined; thus, a mutable object such as a list or dictionary used as default
value will be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any unfilled
slots for which no default value is specified, a TypeError exception is
raised. Otherwise, the list of filled slots is used as the argument list for
the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters
do not have names, even if they are ‘named’ for the purpose of documentation,
and which therefore cannot be supplied by keyword. In CPython, this is the
case for functions implemented in C that use PyArg_ParseTuple() to
parse their arguments.

If there are more positional arguments than there are formal parameter slots, a
TypeError exception is raised, unless a formal parameter using the syntax
*identifier is present; in this case, that formal parameter receives a tuple
containing the excess positional arguments (or an empty tuple if there were no
excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a
TypeError exception is raised, unless a formal parameter using the syntax
**identifier is present; in this case, that formal parameter receives a
dictionary containing the excess keyword arguments (using the keywords as keys
and the argument values as corresponding values), or a (new) empty dictionary if
there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must
evaluate to an iterable. Elements from these iterables are
treated as if they were additional positional arguments. For the call
f(x1, x2, *y, x3, x4), if y evaluates to a sequence y1, …, yM,
this is equivalent to a call with M+4 positional arguments x1, x2,
y1, …, yM, x3, x4.

A consequence of this is that although the *expression syntax may appear
after explicit keyword arguments, it is processed before the
keyword arguments (and any **expression arguments – see below). So:

>>> def f(a, b):
...     print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2

It is unusual for both keyword arguments and the *expression syntax to be
used in the same call, so in practice this confusion does not arise.

If the syntax **expression appears in the function call, expression must
evaluate to a mapping, the contents of which are treated as
additional keyword arguments. If a keyword is already present
(as an explicit keyword argument, or from another unpacking),
a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be
used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings,
positional arguments may follow iterable unpackings (*),
and keyword arguments may follow dictionary unpackings (**).
Originally proposed by PEP 448.

A call always returns some value, possibly None, unless it raises an
exception. How this value is computed depends on the type of the callable
object.

If it is—

a user-defined function:

The code block for the function is executed, passing it the argument list. The
first thing the code block will do is bind the formal parameters to the
arguments; this is described in section Function definitions. When the code block
executes a return statement, this specifies the return value of the
function call.

a built-in function or method:

The result is up to the interpreter; see Built-in Functions for the
descriptions of built-in functions and methods.

a class object:

A new instance of that class is returned.

a class instance method:

The corresponding user-defined function is called, with an argument list that is
one longer than the argument list of the call: the instance becomes the first
argument.

a class instance:

The class must define a __call__() method; the effect is then the same as
if that method was called.

6.10.1. Value comparisons

The operators <, >, ==, >=, <=, and != compare the
values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type
and identity). The value of an object is a rather abstract notion in Python:
For example, there is no canonical access method for an object’s value. Also,
there is no requirement that the value of an object should be constructed in a
particular way, e.g. comprised of all its data attributes. Comparison operators
implement a particular notion of what the value of an object is. One can think
of them as defining the value of an object indirectly, by means of their
comparison implementation.

Because all types are (direct or indirect) subtypes of object, they
inherit the default comparison behavior from object. Types can
customize their comparison behavior by implementing
rich comparison methods like __lt__(), described in
Basic customization.

The default behavior for equality comparison (== and !=) is based on
the identity of the objects. Hence, equality comparison of instances with the
same identity results in equality, and equality comparison of instances with
different identities results in inequality. A motivation for this default
behavior is the desire that all objects should be reflexive (i.e. x is y
implies x == y).

A default order comparison (<, >, <=, and >=) is not provided;
an attempt raises TypeError. A motivation for this default behavior is
the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different
identities are always unequal, may be in contrast to what types will need that
have a sensible definition of object value and value-based equality. Such
types will need to customize their comparison behavior, and in fact, a number
of built-in types have done that.

The following list describes the comparison behavior of the most important
built-in types.

• Numbers of built-in numeric types (Numeric Types — int, float, complex) and of the standard
  library types fractions.Fraction and decimal.Decimal can be
  compared within and across their types, with the restriction that complex
  numbers do not support order comparison. Within the limits of the types
  involved, they compare mathematically (algorithmically) correct without loss
  of precision.

  The not-a-number values float('NaN') and Decimal('NaN')
  are special. They are identical to themselves (x is x is true) but
  are not equal to themselves (x == x is false). Additionally,
  comparing any number to a not-a-number value
  will return False. For example, both 3 < float('NaN') and
  float('NaN') < 3 will return False.

• Binary sequences (instances of bytes or bytearray) can be
  compared within and across their types. They compare lexicographically using
  the numeric values of their elements.

• Strings (instances of str) compare lexicographically using the
  numerical Unicode code points (the result of the built-in function
  ord()) of their characters. [3]

  Strings and binary sequences cannot be directly compared.

• Sequences (instances of tuple, list, or range) can
  be compared only within each of their types, with the restriction that ranges
  do not support order comparison. Equality comparison across these types
  results in inequality, and ordering comparison across these types raises
  TypeError.

  Sequences compare lexicographically using comparison of corresponding
  elements, whereby reflexivity of the elements is enforced.

  In enforcing reflexivity of elements, the comparison of collections assumes
  that for a collection element x, x == x is always true. Based on
  that assumption, element identity is compared first, and element comparison
  is performed only for distinct elements. This approach yields the same
  result as a strict element comparison would, if the compared elements are
  reflexive. For non-reflexive elements, the result is different than for
  strict element comparison, and may be surprising: The non-reflexive
  not-a-number values for example result in the following comparison behavior
  when used in a list:

  >>> nan = float('NaN')
  >>> nan is nan
  True
  >>> nan == nan
  False                 <-- the defined non-reflexive behavior of NaN
  >>> [nan] == [nan]
  True                  <-- list enforces reflexivity and tests identity first
  

  Lexicographical comparison between built-in collections works as follows:

  • For two collections to compare equal, they must be of the same type, have
    the same length, and each pair of corresponding elements must compare
    equal (for example, [1,2] == (1,2) is false because the type is not the
    same).
  • Collections that support order comparison are ordered the same as their
    first unequal elements (for example, [1,2,x] <= [1,2,y] has the same
    value as x <= y). If a corresponding element does not exist, the
    shorter collection is ordered first (for example, [1,2] < [1,2,3] is
    true).

• Mappings (instances of dict) compare equal if and only if they have
  equal (key, value) pairs. Equality comparison of the keys and values
  enforces reflexivity.

  Order comparisons (<, >, <=, and >=) raise TypeError.

• Sets (instances of set or frozenset) can be compared within
  and across their types.

  They define order
  comparison operators to mean subset and superset tests. Those relations do
  not define total orderings (for example, the two sets {1,2} and {2,3}
  are not equal, nor subsets of one another, nor supersets of one
  another). Accordingly, sets are not appropriate arguments for functions
  which depend on total ordering (for example, min(), max(), and
  sorted() produce undefined results given a list of sets as inputs).

  Comparison of sets enforces reflexivity of its elements.

• Most other built-in types have no comparison methods implemented, so they
  inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow
some consistency rules, if possible:

• Equality comparison should be reflexive.
  In other words, identical objects should compare equal:

  x is y implies x == y

• Comparison should be symmetric.
  In other words, the following expressions should have the same result:

  x == y and y == x

  x != y and y != x

  x < y and y > x

  x <= y and y >= x

• Comparison should be transitive.
  The following (non-exhaustive) examples illustrate that:

  x > y and y > z implies x > z

  x < y and y <= z implies x < z

• Inverse comparison should result in the boolean negation.
  In other words, the following expressions should have the same result:

  x == y and not x != y

  x < y and not x >= y (for total ordering)

  x > y and not x <= y (for total ordering)

  The last two expressions apply to totally ordered collections (e.g. to
  sequences, but not to sets or mappings). See also the
  total_ordering() decorator.

• The hash() result should be consistent with equality.
  Objects that are equal should either have the same hash value,
  or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number
values are an example for not following these rules.

6.10.2. Membership test operations

The operators in and not in test for membership. x in
s evaluates to True if x is a member of s, and False otherwise.
x not in s returns the negation of x in s. All built-in sequences and
set types support this as well as dictionary, for which in tests
whether the dictionary has a given key. For container types such as list, tuple,
set, frozenset, dict, or collections.deque, the expression x in y is equivalent
to any(x is e or x == e for e in y).

For the string and bytes types, x in y is True if and only if x is a
substring of y. An equivalent test is y.find(x) != -1. Empty strings are
always considered to be a substring of any other string, so "" in "abc" will
return True.

For user-defined classes which define the __contains__() method, x in
y returns True if y.__contains__(x) returns a true value, and
False otherwise.

For user-defined classes which do not define __contains__() but do define
__iter__(), x in y is True if some value z with x == z is
produced while iterating over y. If an exception is raised during the
iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines
__getitem__(), x in y is True if and only if there is a non-negative
integer index i such that x == y[i], and all lower integer indices do not
raise IndexError exception. (If any other exception is raised, it is as
if in raised that exception).

The operator not in is defined to have the inverse true value of
in.

6.10.3. Identity comparisons

The operators is and is not test for object identity: x
is y is true if and only if x and y are the same object. Object identity
is determined using the id() function. x is not y yields the inverse
truth value. [4]

def <lambda>(arguments):
    return expression

expr1, expr2, expr3, expr4
(expr1, expr2, expr3, expr4)
{expr1: expr2, expr3: expr4}
expr1 + expr2 * (expr3 - expr4)
expr1(expr2, expr3, *expr4, **expr5)
expr3, expr4 = expr1, expr2