Simple statements are comprised within a single logical line. Several simple statements may occur on a single line separated by semicolons. The syntax for simple statements is:
simple_stmt ::= expression_stmt | assert_stmt | assignment_stmt | augmented_assignment_stmt | pass_stmt | del_stmt | print_stmt | return_stmt | yield_stmt | raise_stmt | break_stmt | continue_stmt | import_stmt | global_stmt | exec_stmt
Expression statements are used (mostly interactively) to compute and write a value, or (usually) to call a procedure (a function that returns no meaningful result; in Python, procedures return the value None). Other uses of expression statements are allowed and occasionally useful. The syntax for an expression statement is:
expression_stmt ::= expression_list
An expression statement evaluates the expression list (which may be a single expression).
In interactive mode, if the value is not None, it is converted to a string using the built-in repr() function and the resulting string is written to standard output (see section The print statement) on a line by itself. (Expression statements yielding None are not written, so that procedure calls do not cause any output.)
Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects:
assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression) target_list ::= target ("," target)* [","] target ::= identifier | "(" target_list ")" | "[" target_list "]" | attributeref | subscription | slicing
(See section Primaries for the syntax definitions for the last three symbols.)
An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right.
Assignment is defined recursively depending on the form of the target (list). When a target is part of a mutable object (an attribute reference, subscription or slicing), the mutable object must ultimately perform the assignment and decide about its validity, and may raise an exception if the assignment is unacceptable. The rules observed by various types and the exceptions raised are given with the definition of the object types (see section The standard type hierarchy).
Assignment of an object to a target list is recursively defined as follows.
Assignment of an object to a single target is recursively defined as follows.
If the target is an identifier (name):
The name is rebound if it was already bound. This may cause the reference count for the object previously bound to the name to reach zero, causing the object to be deallocated and its destructor (if it has one) to be called.
If the target is a target list enclosed in parentheses or in square brackets: The object must be an iterable with the same number of items as there are targets in the target list, and its items are assigned, from left to right, to the corresponding targets.
If the target is an attribute reference: The primary expression in the reference is evaluated. It should yield an object with assignable attributes; if this is not the case, TypeError is raised. That object is then asked to assign the assigned object to the given attribute; if it cannot perform the assignment, it raises an exception (usually but not necessarily AttributeError).
Note: If the object is a class instance and the attribute reference occurs on both sides of the assignment operator, the RHS expression, a.x can access either an instance attribute or (if no instance attribute exists) a class attribute. The LHS target a.x is always set as an instance attribute, creating it if necessary. Thus, the two occurrences of a.x do not necessarily refer to the same attribute: if the RHS expression refers to a class attribute, the LHS creates a new instance attribute as the target of the assignment:
class Cls:
x = 3 # class variable
inst = Cls()
inst.x = inst.x + 1 # writes inst.x as 4 leaving Cls.x as 3
This description does not necessarily apply to descriptor attributes, such as properties created with property().
If the target is a subscription: The primary expression in the reference is evaluated. It should yield either a mutable sequence object (such as a list) or a mapping object (such as a dictionary). Next, the subscript expression is evaluated.
If the primary is a mutable sequence object (such as a list), the subscript must yield a plain integer. If it is negative, the sequence’s length is added to it. The resulting value must be a nonnegative integer less than the sequence’s length, and the sequence is asked to assign the assigned object to its item with that index. If the index is out of range, IndexError is raised (assignment to a subscripted sequence cannot add new items to a list).
If the primary is a mapping object (such as a dictionary), the subscript must have a type compatible with the mapping’s key type, and the mapping is then asked to create a key/datum pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed).
If the target is a slicing: The primary expression in the reference is evaluated. It should yield a mutable sequence object (such as a list). The assigned object should be a sequence object of the same type. Next, the lower and upper bound expressions are evaluated, insofar they are present; defaults are zero and the sequence’s length. The bounds should evaluate to (small) integers. If either bound is negative, the sequence’s length is added to it. The resulting bounds are clipped to lie between zero and the sequence’s length, inclusive. Finally, the sequence object is asked to replace the slice with the items of the assigned sequence. The length of the slice may be different from the length of the assigned sequence, thus changing the length of the target sequence, if the object allows it.
CPython implementation detail: In the current implementation, the syntax for targets is taken to be the same as for expressions, and invalid syntax is rejected during the code generation phase, causing less detailed error messages.
WARNING: Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are ‘safe’ (for example a, b = b, a swaps two variables), overlaps within the collection of assigned-to variables are not safe! For instance, the following program prints [0, 2]:
x = [0, 1]
i = 0
i, x[i] = 1, 2
print x
Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement:
augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression) augtarget ::= identifier | attributeref | subscription | slicing augop ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**=" | ">>=" | "<<=" | "&=" | "^=" | "|="
(See section Primaries for the syntax definitions for the last three symbols.)
An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once.
An augmented assignment expression like x += 1 can be rewritten as x = x + 1 to achieve a similar, but not exactly equal effect. In the augmented version, x is only evaluated once. Also, when possible, the actual operation is performed in-place, meaning that rather than creating a new object and assigning that to the target, the old object is modified instead.
With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible in-place behavior, the binary operation performed by augmented assignment is the same as the normal binary operations.
For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments.
Assert statements are a convenient way to insert debugging assertions into a program:
assert_stmt ::= "assert" expression ["," expression]
The simple form, assert expression, is equivalent to
if __debug__:
if not expression: raise AssertionError
The extended form, assert expression1, expression2, is equivalent to
if __debug__:
if not expression1: raise AssertionError(expression2)
These equivalences assume that __debug__ and AssertionError refer to the built-in variables with those names. In the current implementation, the built-in variable __debug__ is True under normal circumstances, False when optimization is requested (command line option -O). The current code generator emits no code for an assert statement when optimization is requested at compile time. Note that it is unnecessary to include the source code for the expression that failed in the error message; it will be displayed as part of the stack trace.
Assignments to __debug__ are illegal. The value for the built-in variable is determined when the interpreter starts.
pass_stmt ::= "pass"
pass is a null operation — when it is executed, nothing happens. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed, for example:
def f(arg): pass # a function that does nothing (yet)
class C: pass # a class with no methods (yet)
del_stmt ::= "del" target_list
Deletion is recursively defined very similar to the way assignment is defined. Rather than spelling it out in full details, here are some hints.
Deletion of a target list recursively deletes each target, from left to right.
Deletion of a name removes the binding of that name from the local or global namespace, depending on whether the name occurs in a global statement in the same code block. If the name is unbound, a NameError exception will be raised.
It is illegal to delete a name from the local namespace if it occurs as a free variable in a nested block.
Deletion of attribute references, subscriptions and slicings is passed to the primary object involved; deletion of a slicing is in general equivalent to assignment of an empty slice of the right type (but even this is determined by the sliced object).
print_stmt ::= "print" ([expression ("," expression)* [","]] | ">>" expression [("," expression)+ [","]])
print evaluates each expression in turn and writes the resulting object to standard output (see below). If an object is not a string, it is first converted to a string using the rules for string conversions. The (resulting or original) string is then written. A space is written before each object is (converted and) written, unless the output system believes it is positioned at the beginning of a line. This is the case (1) when no characters have yet been written to standard output, (2) when the last character written to standard output is a whitespace character except ' ', or (3) when the last write operation on standard output was not a print statement. (In some cases it may be functional to write an empty string to standard output for this reason.)
Note
Objects which act like file objects but which are not the built-in file objects often do not properly emulate this aspect of the file object’s behavior, so it is best not to rely on this.
A '\n' character is written at the end, unless the print statement ends with a comma. This is the only action if the statement contains just the keyword print.
Standard output is defined as the file object named stdout in the built-in module sys. If no such object exists, or if it does not have a write() method, a RuntimeError exception is raised.
print also has an extended form, defined by the second portion of the syntax described above. This form is sometimes referred to as “print chevron.” In this form, the first expression after the >> must evaluate to a “file-like” object, specifically an object that has a write() method as described above. With this extended form, the subsequent expressions are printed to this file object. If the first expression evaluates to None, then sys.stdout is used as the file for output.
return_stmt ::= "return" [expression_list]
return may only occur syntactically nested in a function definition, not within a nested class definition.
If an expression list is present, it is evaluated, else None is substituted.
return leaves the current function call with the expression list (or None) as return value.
When return passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the function.
In a generator function, the return statement is not allowed to include an expression_list. In that context, a bare return indicates that the generator is done and will cause StopIteration to be raised.
yield_stmt ::= yield_expression
The yield statement is only used when defining a generator function, and is only used in the body of the generator function. Using a yield statement in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.
When a generator function is called, it returns an iterator known as a generator iterator, or more commonly, a generator. The body of the generator function is executed by calling the generator’s next() method repeatedly until it raises an exception.
When a yield statement is executed, the state of the generator is frozen and the value of expression_list is returned to next()‘s caller. By “frozen” we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, and the internal evaluation stack: enough information is saved so that the next time next() is invoked, the function can proceed exactly as if the yield statement were just another external call.
As of Python version 2.5, the yield statement is now allowed in the try clause of a try ... finally 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.
For full details of yield semantics, refer to the Yield expressions section.
Note
In Python 2.2, the yield statement was only allowed when the generators feature has been enabled. This __future__ import statement was used to enable the feature:
from __future__ import generators
raise_stmt ::= "raise" [expression ["," expression ["," expression]]]
If no expressions are present, raise re-raises the last exception that was active in the current scope. If no exception is active in the current scope, a TypeError exception is raised indicating that this is an error (if running under IDLE, a Queue.Empty exception is raised instead).
Otherwise, raise evaluates the expressions to get three objects, using None as the value of omitted expressions. The first two objects are used to determine the type and value of the exception.
If the first object is an instance, the type of the exception is the class of the instance, the instance itself is the value, and the second object must be None.
If the first object is a class, it becomes the type of the exception. The second object is used to determine the exception value: If it is an instance of the class, the instance becomes the exception value. If the second object is a tuple, it is used as the argument list for the class constructor; if it is None, an empty argument list is used, and any other object is treated as a single argument to the constructor. The instance so created by calling the constructor is used as the exception value.
If a third object is present and not None, it must be a traceback object (see section The standard type hierarchy), and it is substituted instead of the current location as the place where the exception occurred. If the third object is present and not a traceback object or None, a TypeError exception is raised. The three-expression form of raise is useful to re-raise an exception transparently in an except clause, but raise with no expressions should be preferred if the exception to be re-raised was the most recently active exception in the current scope.
Additional information on exceptions can be found in section Exceptions, and information about handling exceptions is in section The try statement.
break_stmt ::= "break"
break may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop.
It terminates the nearest enclosing loop, skipping the optional else clause if the loop has one.
If a for loop is terminated by break, the loop control target keeps its current value.
When break passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the loop.
continue_stmt ::= "continue"
continue may only occur syntactically nested in a for or while loop, but not nested in a function or class definition or finally clause within that loop. It continues with the next cycle of the nearest enclosing loop.
When continue passes control out of a try statement with a finally clause, that finally clause is executed before really starting the next loop cycle.
import_stmt ::= "import" module ["as" name] ( "," module ["as" name] )* | "from" relative_module "import" identifier ["as" name] ( "," identifier ["as" name] )* | "from" relative_module "import" "(" identifier ["as" name] ( "," identifier ["as" name] )* [","] ")" | "from" module "import" "*" module ::= (identifier ".")* identifier relative_module ::= "."* module | "."+ name ::= identifier
Import statements are executed in two steps: (1) find a module, and initialize it if necessary; (2) define a name or names in the local namespace (of the scope where the import statement occurs). The statement comes in two forms differing on whether it uses the from keyword. The first form (without from) repeats these steps for each identifier in the list. The form with from performs step (1) once, and then performs step (2) repeatedly.
To understand how step (1) occurs, one must first understand how Python handles hierarchical naming of modules. To help organize modules and provide a hierarchy in naming, Python has a concept of packages. A package can contain other packages and modules while modules cannot contain other modules or packages. From a file system perspective, packages are directories and modules are files. The original specification for packages is still available to read, although minor details have changed since the writing of that document.
Once the name of the module is known (unless otherwise specified, the term “module” will refer to both packages and modules), searching for the module or package can begin. The first place checked is sys.modules, the cache of all modules that have been imported previously. If the module is found there then it is used in step (2) of import.
If the module is not found in the cache, then sys.meta_path is searched (the specification for sys.meta_path can be found in PEP 302). The object is a list of finder objects which are queried in order as to whether they know how to load the module by calling their find_module() method with the name of the module. If the module happens to be contained within a package (as denoted by the existence of a dot in the name), then a second argument to find_module() is given as the value of the __path__ attribute from the parent package (everything up to the last dot in the name of the module being imported). If a finder can find the module it returns a loader (discussed later) or returns None.
If none of the finders on sys.meta_path are able to find the module then some implicitly defined finders are queried. Implementations of Python vary in what implicit meta path finders are defined. The one they all do define, though, is one that handles sys.path_hooks, sys.path_importer_cache, and sys.path.
The implicit finder searches for the requested module in the “paths” specified in one of two places (“paths” do not have to be file system paths). If the module being imported is supposed to be contained within a package then the second argument passed to find_module(), __path__ on the parent package, is used as the source of paths. If the module is not contained in a package then sys.path is used as the source of paths.
Once the source of paths is chosen it is iterated over to find a finder that can handle that path. The dict at sys.path_importer_cache caches finders for paths and is checked for a finder. If the path does not have a finder cached then sys.path_hooks is searched by calling each object in the list with a single argument of the path, returning a finder or raises ImportError. If a finder is returned then it is cached in sys.path_importer_cache and then used for that path entry. If no finder can be found but the path exists then a value of None is stored in sys.path_importer_cache to signify that an implicit, file-based finder that handles modules stored as individual files should be used for that path. If the path does not exist then a finder which always returns None is placed in the cache for the path.
If no finder can find the module then ImportError is raised. Otherwise some finder returned a loader whose load_module() method is called with the name of the module to load (see PEP 302 for the original definition of loaders). A loader has several responsibilities to perform on a module it loads. First, if the module already exists in sys.modules (a possibility if the loader is called outside of the import machinery) then it is to use that module for initialization and not a new module. But if the module does not exist in sys.modules then it is to be added to that dict before initialization begins. If an error occurs during loading of the module and it was added to sys.modules it is to be removed from the dict. If an error occurs but the module was already in sys.modules it is left in the dict.
The loader must set several attributes on the module. __name__ is to be set to the name of the module. __file__ is to be the “path” to the file unless the module is built-in (and thus listed in sys.builtin_module_names) in which case the attribute is not set. If what is being imported is a package then __path__ is to be set to a list of paths to be searched when looking for modules and packages contained within the package being imported. __package__ is optional but should be set to the name of package that contains the module or package (the empty string is used for module not contained in a package). __loader__ is also optional but should be set to the loader object that is loading the module.
If an error occurs during loading then the loader raises ImportError if some other exception is not already being propagated. Otherwise the loader returns the module that was loaded and initialized.
When step (1) finishes without raising an exception, step (2) can begin.
The first form of import statement binds the module name in the local namespace to the module object, and then goes on to import the next identifier, if any. If the module name is followed by as, the name following as is used as the local name for the module.
The from form does not bind the module name: it goes through the list of identifiers, looks each one of them up in the module found in step (1), and binds the name in the local namespace to the object thus found. As with the first form of import, an alternate local name can be supplied by specifying “as localname”. If a name is not found, ImportError is raised. If the list of identifiers is replaced by a star ('*'), all public names defined in the module are bound in the local namespace of the import statement..
The public names defined by a module are determined by checking the module’s namespace for a variable named __all__; if defined, it must be a sequence of strings which are names defined or imported by that module. The names given in __all__ are all considered public and are required to exist. If __all__ is not defined, the set of public names includes all names found in the module’s namespace which do not begin with an underscore character ('_'). __all__ should contain the entire public API. It is intended to avoid accidentally exporting items that are not part of the API (such as library modules which were imported and used within the module).
The from form with * may only occur in a module scope. If the wild card form of import — import * — is used in a function and the function contains or is a nested block with free variables, the compiler will raise a SyntaxError.
When specifying what module to import you do not have to specify the absolute name of the module. When a module or package is contained within another package it is possible to make a relative import within the same top package without having to mention the package name. By using leading dots in the specified module or package after from you can specify how high to traverse up the current package hierarchy without specifying exact names. One leading dot means the current package where the module making the import exists. Two dots means up one package level. Three dots is up two levels, etc. So if you execute from . import mod from a module in the pkg package then you will end up importing pkg.mod. If you execute from ..subpkg2 import mod from within pkg.subpkg1 you will import pkg.subpkg2.mod. The specification for relative imports is contained within PEP 328.
importlib.import_module() is provided to support applications that determine which modules need to be loaded dynamically.
A future statement is a directive to the compiler that a particular module should be compiled using syntax or semantics that will be available in a specified future release of Python. The future statement is intended to ease migration to future versions of Python that introduce incompatible changes to the language. It allows use of the new features on a per-module basis before the release in which the feature becomes standard.
future_statement ::= "from" "__future__" "import" feature ["as" name]
("," feature ["as" name])*
| "from" "__future__" "import" "(" feature ["as" name]
("," feature ["as" name])* [","] ")"
feature ::= identifier
name ::= identifier
A future statement must appear near the top of the module. The only lines that can appear before a future statement are:
The features recognized by Python 2.6 are unicode_literals, print_function, absolute_import, division, generators, nested_scopes and with_statement. generators, with_statement, nested_scopes are redundant in Python version 2.6 and above because they are always enabled.
A future statement is recognized and treated specially at compile time: Changes to the semantics of core constructs are often implemented by generating different code. It may even be the case that a new feature introduces new incompatible syntax (such as a new reserved word), in which case the compiler may need to parse the module differently. Such decisions cannot be pushed off until runtime.
For any given release, the compiler knows which feature names have been defined, and raises a compile-time error if a future statement contains a feature not known to it.
The direct runtime semantics are the same as for any import statement: there is a standard module __future__, described later, and it will be imported in the usual way at the time the future statement is executed.
The interesting runtime semantics depend on the specific feature enabled by the future statement.
Note that there is nothing special about the statement:
import __future__ [as name]
That is not a future statement; it’s an ordinary import statement with no special semantics or syntax restrictions.
Code compiled by an exec statement or calls to the built-in functions compile() and execfile() that occur in a module M containing a future statement will, by default, use the new syntax or semantics associated with the future statement. This can, starting with Python 2.2 be controlled by optional arguments to compile() — see the documentation of that function for details.
A future statement typed at an interactive interpreter prompt will take effect for the rest of the interpreter session. If an interpreter is started with the -i option, is passed a script name to execute, and the script includes a future statement, it will be in effect in the interactive session started after the script is executed.
See also
global_stmt ::= "global" identifier ("," identifier)*
The global statement is a declaration which holds for the entire current code block. It means that the listed identifiers are to be interpreted as globals. It would be impossible to assign to a global variable without global, although free variables may refer to globals without being declared global.
Names listed in a global statement must not be used in the same code block textually preceding that global statement.
Names listed in a global statement must not be defined as formal parameters or in a for loop control target, class definition, function definition, or import statement.
CPython implementation detail: The current implementation does not enforce the latter two restrictions, but programs should not abuse this freedom, as future implementations may enforce them or silently change the meaning of the program.
Programmer’s note: the global is a directive to the parser. It applies only to code parsed at the same time as the global statement. In particular, a global statement contained in an exec statement does not affect the code block containing the exec statement, and code contained in an exec statement is unaffected by global statements in the code containing the exec statement. The same applies to the eval(), execfile() and compile() functions.
exec_stmt ::= "exec" or_expr ["in" expression ["," expression]]
This statement supports dynamic execution of Python code. The first expression should evaluate to either a string, an open file object, a code object, or a tuple. If it is a string, the string is parsed as a suite of Python statements which is then executed (unless a syntax error occurs). [1] If it is an open file, the file is parsed until EOF and executed. If it is a code object, it is simply executed. For the interpretation of a tuple, see below. In all cases, the code that’s executed is expected to be valid as file input (see section File input). Be aware that the return and yield statements may not be used outside of function definitions even within the context of code passed to the exec statement.
In all cases, if the optional parts are omitted, the code is executed in the current scope. If only the first expression after in is specified, it should be a dictionary, which will be used for both the global and the local variables. If two expressions are given, they are used for the global and local variables, respectively. If provided, locals can be any mapping object. Remember that at module level, globals and locals are the same dictionary. If two separate objects are given as globals and locals, the code will be executed as if it were embedded in a class definition.
The first expression may also be a tuple of length 2 or 3. In this case, the optional parts must be omitted. The form exec(expr, globals) is equivalent to exec expr in globals, while the form exec(expr, globals, locals) is equivalent to exec expr in globals, locals. The tuple form of exec provides compatibility with Python 3, where exec is a function rather than a statement.
Changed in version 2.4: Formerly, locals was required to be a dictionary.
As a side effect, an implementation may insert additional keys into the dictionaries given besides those corresponding to variable names set by the executed code. For example, the current implementation may add a reference to the dictionary of the built-in module __builtin__ under the key __builtins__ (!).
Programmer’s hints: dynamic evaluation of expressions is supported by the built-in function eval(). The built-in functions globals() and locals() return the current global and local dictionary, respectively, which may be useful to pass around for use by exec.
Footnotes
| [1] | Note that the parser only accepts the Unix-style end of line convention. If you are reading the code from a file, make sure to use universal newlines mode to convert Windows or Mac-style newlines. |