keyword assignment statement

• Python »
• 3.12.2 Documentation »
• The Python Language Reference »
• 7. Simple statements
• Theme Auto Light Dark |

7. Simple statements ¶

A simple statement is comprised within a single logical line. Several simple statements may occur on a single line separated by semicolons. The syntax for simple statements is:

7.1. Expression statements ¶

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:

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 on a line by itself (except if the result is None , so that procedure calls do not cause any output.)

7.2. Assignment statements ¶

Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects:

(See section Primaries for the syntax definitions for attributeref , subscription , and slicing .)

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, optionally enclosed in parentheses or square brackets, is recursively defined as follows.

If the target list is a single target with no trailing comma, optionally in parentheses, the object is assigned to that target.

If the target list contains one target prefixed with an asterisk, called a “starred” target: The object must be an iterable with at least as many items as there are targets in the target list, minus one. The first items of the iterable are assigned, from left to right, to the targets before the starred target. The final items of the iterable are assigned to the targets after the starred target. A list of the remaining items in the iterable is then assigned to the starred target (the list can be empty).

Else: The object must be an iterable with the same number of items as there are targets in the target list, and the items are assigned, from left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as follows.

If the target is an identifier (name):

If the name does not occur in a global or nonlocal statement in the current code block: the name is bound to the object in the current local namespace.

Otherwise: the name is bound to the object in the global namespace or the outer namespace determined by nonlocal , respectively.

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 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 right-hand side expression, a.x can access either an instance attribute or (if no instance attribute exists) a class attribute. The left-hand side 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 right-hand side expression refers to a class attribute, the left-hand side creates a new instance attribute as the target of the assignment:

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 an 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/value 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).

For user-defined objects, the __setitem__() method is called with appropriate arguments.

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 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 target sequence 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.

Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are ‘simultaneous’ (for example a, b = b, a swaps two variables), overlaps within the collection of assigned-to variables occur left-to-right, sometimes resulting in confusion. For instance, the following program prints [0, 2] :

The specification for the *target feature.

7.2.1. Augmented assignment statements ¶

Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement:

(See section Primaries for the syntax definitions of 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.

Unlike normal assignments, augmented assignments evaluate the left-hand side before evaluating the right-hand side. For example, a[i] += f(x) first looks-up a[i] , then it evaluates f(x) and performs the addition, and lastly, it writes the result back to a[i] .

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.

7.2.2. Annotated assignment statements ¶

Annotation assignment is the combination, in a single statement, of a variable or attribute annotation and an optional assignment statement:

The difference from normal Assignment statements is that only a single target is allowed.

For simple names as assignment targets, if in class or module scope, the annotations are evaluated and stored in a special class or module attribute __annotations__ that is a dictionary mapping from variable names (mangled if private) to evaluated annotations. This attribute is writable and is automatically created at the start of class or module body execution, if annotations are found statically.

For expressions as assignment targets, the annotations are evaluated if in class or module scope, but not stored.

If a name is annotated in a function scope, then this name is local for that scope. Annotations are never evaluated and stored in function scopes.

If the right hand side is present, an annotated assignment performs the actual assignment before evaluating annotations (where applicable). If the right hand side is not present for an expression target, then the interpreter evaluates the target except for the last __setitem__() or __setattr__() call.

The proposal that added syntax for annotating the types of variables (including class variables and instance variables), instead of expressing them through comments.

The proposal that added the typing module to provide a standard syntax for type annotations that can be used in static analysis tools and IDEs.

Changed in version 3.8: Now annotated assignments allow the same expressions in the right hand side as regular assignments. Previously, some expressions (like un-parenthesized tuple expressions) caused a syntax error.

7.3. The assert statement ¶

Assert statements are a convenient way to insert debugging assertions into a program:

The simple form, assert expression , is equivalent to

The extended form, assert expression1, expression2 , is equivalent to

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.

7.4. The pass statement ¶

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:

7.5. The del statement ¶

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.

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).

Changed in version 3.2: Previously it was illegal to delete a name from the local namespace if it occurs as a free variable in a nested block.

7.6. The return statement ¶

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 indicates that the generator is done and will cause StopIteration to be raised. The returned value (if any) is used as an argument to construct StopIteration and becomes the StopIteration.value attribute.

In an asynchronous generator function, an empty return statement indicates that the asynchronous generator is done and will cause StopAsyncIteration to be raised. A non-empty return statement is a syntax error in an asynchronous generator function.

7.7. The yield statement ¶

A yield statement is semantically equivalent to a yield expression . The yield statement can be used to omit the parentheses that would otherwise be required in the equivalent yield expression statement. For example, the yield statements

are equivalent to the yield expression statements

Yield expressions and statements are only used when defining a generator function, and are only used in the body of the generator function. Using yield in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.

For full details of yield semantics, refer to the Yield expressions section.

7.8. The raise statement ¶

If no expressions are present, raise re-raises the exception that is currently being handled, which is also known as the active exception . If there isn’t currently an active exception, a RuntimeError exception is raised indicating that this is an error.

Otherwise, raise evaluates the first expression as the exception object. It must be either a subclass or an instance of BaseException . If it is a class, the exception instance will be obtained when needed by instantiating the class with no arguments.

The type of the exception is the exception instance’s class, the value is the instance itself.

A traceback object is normally created automatically when an exception is raised and attached to it as the __traceback__ attribute. You can create an exception and set your own traceback in one step using the with_traceback() exception method (which returns the same exception instance, with its traceback set to its argument), like so:

The from clause is used for exception chaining: if given, the second expression must be another exception class or instance. If the second expression is an exception instance, it will be attached to the raised exception as the __cause__ attribute (which is writable). If the expression is an exception class, the class will be instantiated and the resulting exception instance will be attached to the raised exception as the __cause__ attribute. If the raised exception is not handled, both exceptions will be printed:

A similar mechanism works implicitly if a new exception is raised when an exception is already being handled. An exception may be handled when an except or finally clause, or a with statement, is used. The previous exception is then attached as the new exception’s __context__ attribute:

Exception chaining can be explicitly suppressed by specifying None in the from clause:

Additional information on exceptions can be found in section Exceptions , and information about handling exceptions is in section The try statement .

Changed in version 3.3: None is now permitted as Y in raise X from Y .

Added the __suppress_context__ attribute to suppress automatic display of the exception context.

Changed in version 3.11: If the traceback of the active exception is modified in an except clause, a subsequent raise statement re-raises the exception with the modified traceback. Previously, the exception was re-raised with the traceback it had when it was caught.

7.9. The break statement ¶

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.

7.10. The continue statement ¶

continue may only occur syntactically nested in a for or while loop, but not nested in a function or class definition 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.

7.11. The import statement ¶

The basic import statement (no from clause) is executed in two steps:

find a module, loading and initializing it if necessary

define a name or names in the local namespace for the scope where the import statement occurs.

When the statement contains multiple clauses (separated by commas) the two steps are carried out separately for each clause, just as though the clauses had been separated out into individual import statements.

The details of the first step, finding and loading modules, are described in greater detail in the section on the import system , which also describes the various types of packages and modules that can be imported, as well as all the hooks that can be used to customize the import system. Note that failures in this step may indicate either that the module could not be located, or that an error occurred while initializing the module, which includes execution of the module’s code.

If the requested module is retrieved successfully, it will be made available in the local namespace in one of three ways:

If the module name is followed by as , then the name following as is bound directly to the imported module.

If no other name is specified, and the module being imported is a top level module, the module’s name is bound in the local namespace as a reference to the imported module

If the module being imported is not a top level module, then the name of the top level package that contains the module is bound in the local namespace as a reference to the top level package. The imported module must be accessed using its full qualified name rather than directly

The from form uses a slightly more complex process:

find the module specified in the from clause, loading and initializing it if necessary;

for each of the identifiers specified in the import clauses:

check if the imported module has an attribute by that name

if not, attempt to import a submodule with that name and then check the imported module again for that attribute

if the attribute is not found, ImportError is raised.

otherwise, a reference to that value is stored in the local namespace, using the name in the as clause if it is present, otherwise using the attribute name

If the list of identifiers is replaced by a star ( '*' ), all public names defined in the module are bound in the local namespace for the scope where the import statement occurs.

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 wild card form of import — from module import * — is only allowed at the module level. Attempting to use it in class or function definitions 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 in the Package Relative Imports section.

importlib.import_module() is provided to support applications that determine dynamically the modules to be loaded.

Raises an auditing event import with arguments module , filename , sys.path , sys.meta_path , sys.path_hooks .

7.11.1. Future statements ¶

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 where the feature becomes standard.

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.

A future statement must appear near the top of the module. The only lines that can appear before a future statement are:

the module docstring (if any),

blank lines, and

other future statements.

The only feature that requires using the future statement is annotations (see PEP 563 ).

All historical features enabled by the future statement are still recognized by Python 3. The list includes absolute_import , division , generators , generator_stop , unicode_literals , print_function , nested_scopes and with_statement . They are all redundant because they are always enabled, and only kept for backwards compatibility.

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:

That is not a future statement; it’s an ordinary import statement with no special semantics or syntax restrictions.

Code compiled by calls to the built-in functions exec() and compile() 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 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.

The original proposal for the __future__ mechanism.

7.12. The global statement ¶

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 as targets in with statements or except clauses, or in a for target list, class definition, function definition, import statement, or variable annotation.

CPython implementation detail: The current implementation does not enforce some of these 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: 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 a string or code object supplied to the built-in exec() function does not affect the code block containing the function call, and code contained in such a string is unaffected by global statements in the code containing the function call. The same applies to the eval() and compile() functions.

7.13. The nonlocal statement ¶

When the definition of a function or class is nested (enclosed) within the definitions of other functions, its nonlocal scopes are the local scopes of the enclosing functions. The nonlocal statement causes the listed identifiers to refer to names previously bound in nonlocal scopes. It allows encapsulated code to rebind such nonlocal identifiers. If a name is bound in more than one nonlocal scope, the nearest binding is used. If a name is not bound in any nonlocal scope, or if there is no nonlocal scope, a SyntaxError is raised.

The nonlocal statement applies to the entire scope of a function or class body. A SyntaxError is raised if a variable is used or assigned to prior to its nonlocal declaration in the scope.

The specification for the nonlocal statement.

Programmer’s note: nonlocal is a directive to the parser and applies only to code parsed along with it. See the note for the global statement.

7.14. The type statement ¶

The type statement declares a type alias, which is an instance of typing.TypeAliasType .

For example, the following statement creates a type alias:

This code is roughly equivalent to:

annotation-def indicates an annotation scope , which behaves mostly like a function, but with several small differences.

The value of the type alias is evaluated in the annotation scope. It is not evaluated when the type alias is created, but only when the value is accessed through the type alias’s __value__ attribute (see Lazy evaluation ). This allows the type alias to refer to names that are not yet defined.

Type aliases may be made generic by adding a type parameter list after the name. See Generic type aliases for more.

type is a soft keyword .

New in version 3.12.

Introduced the type statement and syntax for generic classes and functions.

Table of Contents

• 7.1. Expression statements
• 7.2.1. Augmented assignment statements
• 7.2.2. Annotated assignment statements
• 7.3. The assert statement
• 7.4. The pass statement
• 7.5. The del statement
• 7.6. The return statement
• 7.7. The yield statement
• 7.8. The raise statement
• 7.9. The break statement
• 7.10. The continue statement
• 7.11.1. Future statements
• 7.12. The global statement
• 7.13. The nonlocal statement
• 7.14. The type statement

Previous topic

6. Expressions

8. Compound statements

• Report a Bug
• Show Source

Python Enhancement Proposals

• Python »
• PEP Index »

PEP 572 – Assignment Expressions

The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics.

This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr .

As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value).

During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally).

Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts.

Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer.

During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”.

The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”).

Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below.

However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code.

Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing:

they would write:

Another example illustrates that programmers sometimes do more work to save an extra level of indentation:

This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been:

Syntax and semantics

In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier.

The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value:

There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion:

This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid.

Again, this rule is included to avoid two visually similar ways of saying the same thing.

This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.

This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet).

The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly.

This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code.

This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended.

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example:

Second, it allows a compact way of updating mutable state from a comprehension, for example:

However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword):

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError :

(The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)

See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code.

The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used.

The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable:

This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.)

In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order:

• In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.)

Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions.

Conversely, assignment expressions don’t support the advanced features found in assignment statements:

• Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0)))
• Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = []
• Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1
• Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact
• Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None
• Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax)

The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released:

• for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3]
• due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases.

Examples from the Python standard library

env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block.

• Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base
• Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base

Avoid nested if and remove one indentation level.

• Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans
• Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans

Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.)

• Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )
• Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )

tz is only used for s += tz , moving its assignment inside the if helps to show its scope.

• Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s
• Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s

Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand.

• Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0
• Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0

A list comprehension can map and filter efficiently by capturing the condition:

Similarly, a subexpression can be reused within the main expression, by giving it a name on first use:

Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ).

Assignment expressions can be used to good effect in the header of an if or while statement:

Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value.

An example from the low-level UNIX world:

Rejected alternative proposals

Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above.

A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions.

Broadly the same semantics as the current proposal, but spelled differently.

Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements).

(Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .)

Additional reasons to prefer := over this spelling include:

• In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y .
• import foo as bar
• except Exc as var
• with ctxmgr() as var

To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well.

• NAME = EXPR
• if NAME := EXPR

reinforces the visual recognition of assignment expressions.

This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition.

This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder.

Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ).

This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative.

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions.

Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements.

Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions.

This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library).

As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own.

As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax.

Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein.

There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it:

Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful.

While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma).

Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent:

(With the current version of the proposal, the latter would be equivalent to ((point := x), y) .)

However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence:

The less confusing option is to make := bind more tightly than comma.

It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant:

Frequently Raised Objections

C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction.

The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts.

Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore.

(The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] )

As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested.

• If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent.
• If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead.

The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation.

Appendix A: Tim Peters’s findings

Here’s a brief essay Tim Peters wrote on the topic.

I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of:

instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled:

is a vast improvement over the briefer:

The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane.

In other cases, combining related logic made it harder to understand, such as rewriting:

as the briefer:

The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way.

But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form:

I find that clearer, and certainly a bit less typing and pattern-matching reading, as:

It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me!

There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py :

The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”:

Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it.

A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level:

That if is about as long as I want my lines to get, but remains easy to follow.

So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment.

I have another example that quite impressed me at the time.

Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration):

It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding.

If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression.

To my eyes, the original form is harder to understand:

This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding.

Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code.

Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope).

Let’s start with a reminder of what code is generated for a generator expression without assignment expression.

• Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ]
• Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a simple assignment expression.

• Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a global TARGET declaration in f() .

• Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Or instead let’s add a nonlocal TARGET declaration in f() .

• Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Finally, let’s nest two comprehensions.

• Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2
• Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET )

Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example:

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst

Last modified: 2023-10-11 12:05:51 GMT

Learning Python by doing

• suggest edit

Variables, Expressions, and Assignments

Variables, expressions, and assignments 1 #, introduction #.

In this chapter, we introduce some of the main building blocks needed to create programs–that is, variables, expressions, and assignments. Programming related variables can be intepret in the same way that we interpret mathematical variables, as elements that store values that can later be changed. Usually, variables and values are used within the so-called expressions. Once again, just as in mathematics, an expression is a construct of values and variables connected with operators that result in a new value. Lastly, an assignment is a language construct know as an statement that assign a value (either as a constant or expression) to a variable. The rest of this notebook will dive into the main concepts that we need to fully understand these three language constructs.

Values and Types #

A value is the basic unit used in a program. It may be, for instance, a number respresenting temperature. It may be a string representing a word. Some values are 42, 42.0, and ‘Hello, Data Scientists!’.

Each value has its own type : 42 is an integer ( int in Python), 42.0 is a floating-point number ( float in Python), and ‘Hello, Data Scientists!’ is a string ( str in Python).

The Python interpreter can tell you the type of a value: the function type takes a value as argument and returns its corresponding type.

Observe the difference between type(42) and type('42') !

Expressions and Statements #

On the one hand, an expression is a combination of values, variables, and operators.

A value all by itself is considered an expression, and so is a variable.

When you type an expression at the prompt, the interpreter evaluates it, which means that it calculates the value of the expression and displays it.

In boxes above, m has the value 27 and m + 25 has the value 52 . m + 25 is said to be an expression.

On the other hand, a statement is an instruction that has an effect, like creating a variable or displaying a value.

The first statement initializes the variable n with the value 17 , this is a so-called assignment statement .

The second statement is a print statement that prints the value of the variable n .

The effect is not always visible. Assigning a value to a variable is not visible, but printing the value of a variable is.

Assignment Statements #

We have already seen that Python allows you to evaluate expressions, for instance 40 + 2 . It is very convenient if we are able to store the calculated value in some variable for future use. The latter can be done via an assignment statement. An assignment statement creates a new variable with a given name and assigns it a value.

The example in the previous code contains three assignments. The first one assigns the value of the expression 40 + 2 to a new variable called magicnumber ; the second one assigns the value of π to the variable pi , and; the last assignment assigns the string value 'Data is eatig the world' to the variable message .

Programmers generally choose names for their variables that are meaningful. In this way, they document what the variable is used for.

Do It Yourself!

Let’s compute the volume of a cube with side \(s = 5\) . Remember that the volume of a cube is defined as \(v = s^3\) . Assign the value to a variable called volume .

Well done! Now, why don’t you print the result in a message? It can say something like “The volume of the cube with side 5 is \(volume\) ”.

Beware that there is no checking of types ( type checking ) in Python, so a variable to which you have assigned an integer may be re-used as a float, even if we provide type-hints .

Names and Keywords #

Names of variable and other language constructs such as functions (we will cover this topic later), should be meaningful and reflect the purpose of the construct.

In general, Python names should adhere to the following rules:

It should start with a letter or underscore.

It cannot start with a number.

It must only contain alpha-numeric (i.e., letters a-z A-Z and digits 0-9) characters and underscores.

They cannot share the name of a Python keyword.

If you use illegal variable names you will get a syntax error.

By choosing the right variables names you make the code self-documenting, what is better the variable v or velocity ?

The following are examples of invalid variable names.

These basic development principles are sometimes called architectural rules . By defining and agreeing upon architectural rules you make it easier for you and your fellow developers to understand and modify your code.

If you want to read more on this, please have a look at Code complete a book by Steven McConnell [ McC04 ] .

Every programming language has a collection of reserved keywords . They are used in predefined language constructs, such as loops and conditionals . These language concepts and their usage will be explained later.

The interpreter uses keywords to recognize these language constructs in a program. Python 3 has the following keywords:

False class finally is return

None continue for lambda try

True def from nonlocal while

and del global not with

as elif if or yield

assert else import pass break

except in raise

Reassignments #

It is allowed to assign a new value to an existing variable. This process is called reassignment . As soon as you assign a value to a variable, the old value is lost.

The assignment of a variable to another variable, for instance b = a does not imply that if a is reassigned then b changes as well.

You have a variable salary that shows the weekly salary of an employee. However, you want to compute the monthly salary. Can you reassign the value to the salary variable according to the instruction?

Updating Variables #

A frequently used reassignment is for updating puposes: the value of a variable depends on the previous value of the variable.

This statement expresses “get the current value of x , add one, and then update x with the new value.”

Beware, that the variable should be initialized first, usually with a simple assignment.

Do you remember the salary excercise of the previous section (cf. 13. Reassignments)? Well, if you have not done it yet, update the salary variable by using its previous value.

Updating a variable by adding 1 is called an increment ; subtracting 1 is called a decrement . A shorthand way of doing is using += and -= , which stands for x = x + ... and x = x - ... respectively.

Order of Operations #

Expressions may contain multiple operators. The order of evaluation depends on the priorities of the operators also known as rules of precedence .

For mathematical operators, Python follows mathematical convention. The acronym PEMDAS is a useful way to remember the rules:

Parentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3 - 1) is 4 , and (1 + 1)**(5 - 2) is 8 . You can also use parentheses to make an expression easier to read, even if it does not change the result.

Exponentiation has the next highest precedence, so 1 + 2**3 is 9 , not 27 , and 2 * 3**2 is 18 , not 36 .

Multiplication and division have higher precedence than addition and subtraction . So 2 * 3 - 1 is 5 , not 4 , and 6 + 4 / 2 is 8 , not 5 .

Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi , the division happens first and the result is multiplied by pi . To divide by 2π, you can use parentheses or write: degrees / 2 / pi .

In case of doubt, use parentheses!

Let’s see what happens when we evaluate the following expressions. Just run the cell to check the resulting value.

Floor Division and Modulus Operators #

The floor division operator // divides two numbers and rounds down to an integer.

For example, suppose that driving to the south of France takes 555 minutes. You might want to know how long that is in hours.

Conventional division returns a floating-point number.

Hours are normally not represented with decimal points. Floor division returns the integer number of hours, dropping the fraction part.

You spend around 225 minutes every week on programming activities. You want to know around how many hours you invest to this activity during a month. Use the \(//\) operator to give the answer.

The modulus operator % works on integer values. It computes the remainder when dividing the first integer by the second one.

The modulus operator is more useful than it seems.

For example, you can check whether one number is divisible by another—if x % y is zero, then x is divisible by y .

String Operations #

In general, you cannot perform mathematical operations on strings, even if the strings look like numbers, so the following operations are illegal: '2'-'1' 'eggs'/'easy' 'third'*'a charm'

But there are two exceptions, + and * .

The + operator performs string concatenation, which means it joins the strings by linking them end-to-end.

The * operator also works on strings; it performs repetition.

Speedy Gonzales is a cartoon known to be the fastest mouse in all Mexico . He is also famous for saying “Arriba Arriba Andale Arriba Arriba Yepa”. Can you use the following variables, namely arriba , andale and yepa to print the mentioned expression? Don’t forget to use the string operators.

Asking the User for Input #

The programs we have written so far accept no input from the user.

To get data from the user through the Python prompt, we can use the built-in function input .

When input is called your whole program stops and waits for the user to enter the required data. Once the user types the value and presses Return or Enter , the function returns the input value as a string and the program continues with its execution.

Try it out!

You can also print a message to clarify the purpose of the required input as follows.

The resulting string can later be translated to a different type, like an integer or a float. To do so, you use the functions int and float , respectively. But be careful, the user might introduce a value that cannot be converted to the type you required.

We want to know the name of a user so we can display a welcome message in our program. The message should say something like “Hello \(name\) , welcome to our hello world program!”.

Script Mode #

So far we have run Python in interactive mode in these Jupyter notebooks, which means that you interact directly with the interpreter in the code cells . The interactive mode is a good way to get started, but if you are working with more than a few lines of code, it can be clumsy. The alternative is to save code in a file called a script and then run the interpreter in script mode to execute the script. By convention, Python scripts have names that end with .py .

Use the PyCharm icon in Anaconda Navigator to create and execute stand-alone Python scripts. Later in the course, you will have to work with Python projects for the assignments, in order to get acquainted with another way of interacing with Python code.

This Jupyter Notebook is based on Chapter 2 of the books Python for Everybody [ Sev16 ] and Think Python (Sections 5.1, 7.1, 7.2, and 5.12) [ Dow15 ] .

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4 Variables, expressions and statements

2.2  variable names.

Programmers generally choose names for their variables that are meaningful—they document what the variable is used for.

Variable names can be as long as you like. They can contain both letters and numbers, but they can’t begin with a number. It is legal to use uppercase letters, but it is conventional to use only lower case for variables names.

The underscore character, _ , can appear in a name. It is often used in names with multiple words, such as your_name or airspeed_of_unladen_swallow .

If you give a variable an illegal name, you get a syntax error:

76trombones is illegal because it begins with a number. more@ is illegal because it contains an illegal character, @ . But what’s wrong with class ?

It turns out that class is one of Python’s keywords . The interpreter uses keywords to recognize the structure of the program, and they cannot be used as variable names.

Python 3 has these keywords:

You don’t have to memorize this list. In most development environments, keywords are displayed in a different color; if you try to use one as a variable name, you’ll know.

2.3  Expressions and statements

An expression is a combination of values, variables, and operators. A value all by itself is considered an expression, and so is a variable, so the following are all legal expressions:

When you type an expression at the prompt, the interpreter evaluates it, which means that it finds the value of the expression. In this example, n has the value 17 and n + 25 has the value 42.

A statement is a unit of code that has an effect, like creating a variable or displaying a value.

The first line is an assignment statement that gives a value to n . The second line is a print statement that displays the value of n .

When you type a statement, the interpreter executes it, which means that it does whatever the statement says. In general, statements don’t have values.

2.4  Script mode

So far we have run Python in interactive mode , which means that you interact directly with the interpreter. Interactive mode is a good way to get started, but if you are working with more than a few lines of code, it can be clumsy.

The alternative is to save code in a file called a script and then run the interpreter in script mode to execute the script. By convention, Python scripts have names that end with .py .

If you know how to create and run a script on your computer, you are ready to go. Otherwise I recommend using PythonAnywhere again. I have posted instructions for running in script mode at http://tinyurl.com/thinkpython2e .

Because Python provides both modes, you can test bits of code in interactive mode before you put them in a script. But there are differences between interactive mode and script mode that can be confusing.

For example, if you are using Python as a calculator, you might type

The first line assigns a value to miles , but it has no visible effect. The second line is an expression, so the interpreter evaluates it and displays the result. It turns out that a marathon is about 42 kilometers.

But if you type the same code into a script and run it, you get no output at all. In script mode an expression, all by itself, has no visible effect. Python evaluates the expression, but it doesn’t display the result. To display the result, you need a print statement like this:

This behavior can be confusing at first. To check your understanding, type the following statements in the Python interpreter and see what they do:

Now put the same statements in a script and run it. What is the output? Modify the script by transforming each expression into a print statement and then run it again.

2.5  Order of operations

When an expression contains more than one operator, the order of evaluation depends on the order of operations . For mathematical operators, Python follows mathematical convention. The acronym PEMDAS is a useful way to remember the rules:

• P arentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3-1) is 4, and (1+1)**(5-2) is 8. You can also use parentheses to make an expression easier to read, as in (minute * 100) / 60 , even if it doesn’t change the result.
• E xponentiation has the next highest precedence, so 1 + 2**3 is 9, not 27, and 2 * 3**2 is 18, not 36.
• M ultiplication and D ivision have higher precedence than A ddition and S ubtraction. So 2*3-1 is 5, not 4, and 6+4/2 is 8, not 5.
• Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi , the division happens first and the result is multiplied by pi . To divide by 2 π, you can use parentheses or write degrees / 2 / pi .

I don’t work very hard to remember the precedence of operators. If I can’t tell by looking at the expression, I use parentheses to make it obvious.

2.6  String operations

In general, you can’t perform mathematical operations on strings, even if the strings look like numbers, so the following are illegal:

But there are two exceptions, + and * .

The + operator performs string concatenation , which means it joins the strings by linking them end-to-end. For example:

The * operator also works on strings; it performs repetition. For example, 'Spam'*3 is 'SpamSpamSpam' . If one of the values is a string, the other has to be an integer.

This use of + and * makes sense by analogy with addition and multiplication. Just as 4*3 is equivalent to 4+4+4 , we expect 'Spam'*3 to be the same as 'Spam'+'Spam'+'Spam' , and it is. On the other hand, there is a significant way in which string concatenation and repetition are different from integer addition and multiplication. Can you think of a property that addition has that string concatenation does not?

2.7  Comments

As programs get bigger and more complicated, they get more difficult to read. Formal languages are dense, and it is often difficult to look at a piece of code and figure out what it is doing, or why.

For this reason, it is a good idea to add notes to your programs to explain in natural language what the program is doing. These notes are called comments , and they start with the # symbol:

In this case, the comment appears on a line by itself. You can also put comments at the end of a line:

Everything from the # to the end of the line is ignored—it has no effect on the execution of the program.

Comments are most useful when they document non-obvious features of the code. It is reasonable to assume that the reader can figure out what the code does; it is more useful to explain why .

This comment is redundant with the code and useless:

This comment contains useful information that is not in the code:

Good variable names can reduce the need for comments, but long names can make complex expressions hard to read, so there is a tradeoff.

2.8  Debugging

Three kinds of errors can occur in a program: syntax errors, runtime errors, and semantic errors. It is useful to distinguish between them in order to track them down more quickly.

2.9  Glossary

2.10  exercises.

Repeating my advice from the previous chapter, whenever you learn a new feature, you should try it out in interactive mode and make errors on purpose to see what goes wrong.

• We’ve seen that n = 42 is legal. What about 42 = n ?
• How about x = y = 1 ?
• In some languages every statement ends with a semi-colon, ; . What happens if you put a semi-colon at the end of a Python statement?
• What if you put a period at the end of a statement?
• In math notation you can multiply x and y like this: x y . What happens if you try that in Python?

Practice using the Python interpreter as a calculator:

• The volume of a sphere with radius r is 4/3 π r 3 . What is the volume of a sphere with radius 5?
• Suppose the cover price of a book is $24.95, but bookstores get a 40% discount. Shipping costs $3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?
• If I leave my house at 6:52 am and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?

If you would like to make a contribution to support my books, you can use the button below and pay with PayPal. Thank you!

Are you using one of our books in a class?

We’d like to know about it. Please consider filling out this short survey .

Building Skills for Data Science Copyright © by Dr. Nouhad Rizk. All Rights Reserved.

Share This Book

Assignment Statements

The last thing we discussed in the previous unit were variables. We use variables to store values of an evaluated expression. To store this value, we use an assignment statement . A simple assignment statement consists of a variable name, an equal sign ( assignment operator ) and the value to be stored.

a in the above expression is assigned the value 7.

Here we see that the variable a has 2 added to it's previous value. The resulting number is 9, the addition of 7 and 2.

To break it down into easy steps:

• a = 7 -> Variable is initialized when we store a value in it
• a = a + 2 -> Variable is assigned a new value and forgets the old value

This is called overwriting the variable. a 's value was overwritten in the process. The expression on the right of the = operator is evaluated down to a single value before it is assigned to the variable on the left. During the evaluation stage, a still carries the number 7 , this is added by 2 which results in 9 . 9 is then assigned to the variable a and overwrites the previous value.

Another example:

Hence, when a new value is assigned to a variable, the old one is forgotten.

Variable Names

There are three rules for variable names:

• It can be only one word, no spaces are allowed.
• It can use only letters, numbers, and the underscore (_) character.
• It can’t begin with a number.

Note: Variable names are case-sensitive. This means that hello, Hello, hellO are three different variables. The python convention is to start a variable name with lowercase characters. Tip: A good variable name describes the data it contains. Imagine you have a cat namely Kitty. You could say cat = 'Kitty' .

What would this output: 'name' + 'namename' ? a. 'name' b. 'namenamename' c. 'namename' d. namenamename

What would this output: 'name' * 3 a. 'name' b. 'namenamename' c. 'namename' d. namenamename

results matching " "

No results matching " ".

Python Basics: Keywords, Identifiers and Indention 😊

Welcome to the first article in my first series ever: "Python: Explain Like I'm Five" !

In this first article, I'll be introducing you to a few beginner Python concepts! 😊

• reserved keywords
• identifiers & the rules for creating them

Strap in, folks. Keep all hands, feet, arms, legs inside the ride. We're about to learn some Python 😎

Identifiers

Keywords are the reserved words in Python. We can't use these keywords as identifiers (variable names), function names, etc.! They're used to define the syntax & structure of Python. ( Watch out-- they're case-sensitive! Notice how only True, None and False are capitalized! )

There are 33 keywords in Python! To access these keywords in your Python shell, simply type help> keywords and they'll pop up! Check 'em out below 😊

A Python identifier is a name used to identify a variable, function, class, module or other object. 😎

Identifiers can be a combination of letters in lowercase (a to z), uppercase (A to Z), digits (0 to 9) or underscores (_). Names like: myClass , var_1 and print_this_to_screen , all are valid examples.

An identifier cannot start with a digit. 1variable is invalid, but variable1 is a valid name

Keywords (in the chart above) cannot be used as identifiers

We also cannot use special symbols like !, @, #, $, % etc. in our identifiers!

Look what happens if you use a keyword as an identifier for a variable ⬇️

Look what happens if use special symbols like !, @, #, $, % etc. in our identifiers! ⬇️

Tips to remember:

• Python is a case-sensitive language! So variable and Variable aren't the same😎
• Always give identifiers a name that makes sense for what you're creating. While b = 10 is a valid name, writing books = 10 would make more sense, and it would be easier to figure out what it represents when you're looking back at your code (or having others read it!).
• Multiple words can be separated using an underscore, like this_is_an_acceptable_identifier

Instructions that a Python interpreter can execute are called statements. For example, a = 1 is an assignment statement.

Multi-line statements

In Python, the end of a statement is usually marked by a newline character (\n) like, print("This is using the newline char\n") . But, we can make a statement extend over multiple lines with the line continuation character (). Here's an example ⬇️

In Python, a line continuation is implied inside parentheses (), brackets [], and braces {}. For instance, we can implement the above multi-line statement like so ⬇️

Here's an example with brackets [] ⬇️

You can also put multiple statements in one line using semicolons ⬇️

Comments are super easy! And very important. Use them to describe exactly what you're doing for anyone who has to look at your source code, as well as for your own personal use.

Here's an example of comments in Python:

You can also create multi-line statements! You can use hashtags # or a set of 3 single or double quotes: ' ' ' or """ Look below ⬇️

An example with quotes:

Indention is SO IMPORTANT in Python!

Most programming languages like C, C++, and Java use braces { } to define a block of code. Python uses INDENTATION 😎

Here's the breakdown: a code block (the body of a function, loop, etc.) starts with indentation and ends with the first unindented line. The amount of indentation is up to you, but it has to be consistent throughout that entire block.

Usually, four whitespaces are used for indentation and are preferred over tabs. Let's look at some examples ⬇️

See how nice and clean the above code looks? You can thank white space for that 😉

Indention can be ignored in single line continuations, like below ⬇️

But, doesn't it look a bit unorganized compared to this indented version? ⬇️

You made it to the end! Thanks for reading! 😃

In Part 2, I'll be going over:

• Variables (Python variables, constants, literals and their use cases)
• Collections (to hold variables, literals, constants, etc. --> Dictionaries, lists, tuples, sets)

I'll be uploading parts of this series weekly! Part 2 coming 8/21/2021! See you then 😉

• # web-development
• # python-beginner

CS105: Introduction to Python

Variables and assignment statements.

Computers must be able to remember and store data. This can be accomplished by creating a variable to house a given value. The assignment operator = is used to associate a variable name with a given value. For example, type the command:

in the command line window. This command assigns the value 3.45 to the variable named a . Next, type the command:

in the command window and hit the enter key. You should see the value contained in the variable a echoed to the screen. This variable will remember the value 3.45 until it is assigned a different value. To see this, type these two commands:

You should see the new value contained in the variable a echoed to the screen. The new value has "overwritten" the old value. We must be careful since once an old value has been overwritten, it is no longer remembered. The new value is now what is being remembered.

Although we will not discuss arithmetic operations in detail until the next unit, you can at least be equipped with the syntax for basic operations: + (addition), - (subtraction), * (multiplication), / (division)

For example, entering these command sequentially into the command line window:

would result in 12.32 being echoed to the screen (just as you would expect from a calculator). The syntax for multiplication works similarly. For example:

would result in 35 being echoed to the screen because the variable b has been assigned the value a * 5 where, at the time of execution, the variable a contains a value of 7.

After you read, you should be able to execute simple assignment commands using integer and float values in the command window of the Repl.it IDE. Try typing some more of the examples from this web page to convince yourself that a variable has been assigned a specific value.

In programming, we associate names with values so that we can remember and use them later. Recall Example 1. The repeated computation in that algorithm relied on remembering the intermediate sum and the integer to be added to that sum to get the new sum. In expressing the algorithm, we used th e names current and sum .

In programming, a name that refers to a value in this fashion is called a variable . When we think of values as data stored somewhere i n the computer, we can have a mental image such as the one below for the value 10 stored in the computer and the variable x , which is the name we give to 10. What is most important is to see that there is a binding between x and 10.

The term variable comes from the fact that values that are bound to variables can change throughout computation. Bindings as shown above are created, and changed by assignment statements . An assignment statement associates the name to the left of the symbol = with the value denoted by the expression on the right of =. The binding in the picture is created using an assignment statemen t of the form x = 10 . We usually read such an assignment statement as "10 is assigned to x" or "x is set to 10".

If we want to change the value that x refers to, we can use another assignment statement to do that. Suppose we execute x = 25 in the state where x is bound to 10.Then our image becomes as follows:

Choosing variable names

Suppose that we u sed the variables x and y in place of the variables side and area in the examples above. Now, if we were to compute some other value for the square that depends on the length of the side , such as the perimeter or length of the diagonal, we would have to remember which of x and y , referred to the length of the side because x and y are not as descriptive as side and area . In choosing variable names, we have to keep in mind that programs are read and maintained by human beings, not only executed by machines.

Note about syntax

In Python, variable identifiers can contain uppercase and lowercase letters, digits (provided they don't start with a digit) and the special character _ (underscore). Although it is legal to use uppercase letters in variable identifiers, we typically do not use them by convention. Variable identifiers are also case-sensitive. For example, side and Side are two different variable identifiers.

There is a collection of words, called reserved words (also known as keywords), in Python that have built-in meanings and therefore cannot be used as variable names. For the list of Python's keywords See 2.3.1 of the Python Language Reference.

Syntax and Sema ntic Errors

Now that we know how to write arithmetic expressions and assignment statements in Python, we can pause and think about what Python does if we write something that the Python interpreter cannot interpret. Python informs us about such problems by giving an error message. Broadly speaking there are two categories for Python errors:

• Syntax errors: These occur when we write Python expressions or statements that are not well-formed according to Python's syntax. For example, if we attempt to write an assignment statement such as 13 = age , Python gives a syntax error. This is because Python syntax says that for an assignment statement to be well-formed it must contain a variable on the left hand side (LHS) of the assignment operator "=" and a well-formed expression on the right hand side (RHS), and 13 is not a variable.
• Semantic errors: These occur when the Python interpreter cannot evaluate expressions or execute statements because they cannot be associated with a "meaning" that the interpreter can use. For example, the expression age + 1 is well-formed but it has a meaning only when age is already bound to a value. If we attempt to evaluate this expression before age is bound to some value by a prior assignment then Python gives a semantic error.

Even though we have used numerical expressions in all of our examples so far, assignments are not confined to numerical types. They could involve expressions built from any defined type. Recall the table that summarizes the basic types in Python.

The following video shows execution of assignment statements involving strings. It also introduces some commonly used operators on strings. For more information see the online documentation. In the video below, you see the Python shell displaying "=> None" after the assignment statements. This is unique to the Python shell presented in the video. In most Python programming environments, nothing is displayed after an assignment statement. The difference in behavior stems from version differences between the programming environment used in the video and in the activities, and can be safely ignored.

Distinguishing Expressions and Assignments

So far in the module, we have been careful to keep the distinction between the terms expression and statement because there is a conceptual difference between them, which is sometimes overlooked. Expressions denote values; they are evaluated to yield a value. On the other hand, statements are commands (instructions) that change the state of the computer. You can think of state here as some representation of computer memory and the binding of variables and values in the memory. In a state where the variable side is bound to the integer 3, and the variable area is yet unbound, the value of the expression side + 2 is 5. The assignment statement side = side + 2 , changes the state so that value 5 is bound to side in the new state. Note that when you type an expression in the Python shell, Python evaluates the expression and you get a value in return. On the other hand, if you type an assignment statement nothing is returned. Assignment statements do not return a value. Try, for example, typing x = 100 + 50 . Python adds 100 to 50, gets the value 150, and binds x to 150. However, we only see the prompt >>> after Python does the assignment. We don't see the change in the state until we inspect the value of x , by invoking x .

What we have learned so far can be summarized as using the Python interpreter to manipulate values of some primitive data types such as integers, real numbers, and character strings by evaluating expressions that involve built-in operators on these types. Assignments statements let us name the values that appear in expressions. While what we have learned so far allows us to do some computations conveniently, they are limited in their generality and reusability. Next, we introduce functions as a means to make computations more general and reusable.

• Free Python 3 Tutorial
• Control Flow
• Exception Handling
• Python Programs
• Python Projects
• Python Interview Questions
• Python Database
• Data Science With Python
• Machine Learning with Python
• How to Learn Python from Scratch in 2024
• Introduction To Python
• Python 3 basics
• Important differences between Python 2.x and Python 3.x with examples
• Download and Install Python 3 Latest Version

Statement, Indentation and Comment in Python

• Python | Set 2 (Variables, Expressions, Conditions and Functions)
• Global and Local Variables in Python
• Type Conversion in Python
• Private Variables in Python
• __name__ (A Special variable) in Python
• Taking input in Python
• Taking multiple inputs from user in Python
• Python | Output using print() function
• Python end parameter in print()
• Python | Output Formatting
• Python Operators
• Ternary Operator in Python
• Operator Overloading in Python
• Python | a += b is not always a = a + b
• Difference between == and is operator in Python
• Python | Set 3 (Strings, Lists, Tuples, Iterations)
• Python String
• Python Lists
• Python Tuples

Here, we will discuss Statements in Python, Indentation in Python, and Comments in Python. We will also discuss different rules and examples for Python Statement, Python Indentation, Python Comment, and the Difference Between ‘Docstrings’ and ‘Multi-line Comments.

What is Statement in Python

A Python statement is an instruction that the Python interpreter can execute. There are different types of statements in Python language as Assignment statements, Conditional statements, Looping statements, etc. The token character NEWLINE is used to end a statement in Python. It signifies that each line of a Python script contains a statement. These all help the user to get the required output.

Types of statements in Python?

The different types of Python statements are listed below:

• Multi-Line Statements
• Python If-else
• Python for loop
• Python while loop 
• Python try-except
• Python with statement  
• Python pass statement
• Python del statement
• Python return statement
• Python import statement
• Python continue and 
• Python break statement

Example: 

Statement in Python can be extended to one or more lines using parentheses (), braces {}, square brackets [], semi-colon (;), and continuation character slash (\). When the programmer needs to do long calculations and cannot fit his statements into one line, one can make use of these characters. 

What is Indentation in Python

Whitespace is used for indentation in Python . Unlike many other programming languages which only serve to make the code easier to read, Python indentation is mandatory. One can understand it better by looking at an example of indentation in Python.

Role of Indentation in Python

A block is a combination of all these statements. Block can be regarded as the grouping of statements for a specific purpose. Most programming languages like C, C++, and Java use braces { } to define a block of code for indentation. One of the distinctive roles of Python is its use of indentation to highlight the blocks of code. All statements with the same distance to the right belong to the same block of code. If a block has to be more deeply nested, it is simply indented further to the right.  

The lines print (‘Logging on to geeksforgeeks…’) and print(‘retype the URL.’) are two separate code blocks. The two blocks of code in our example if-statement are both indented four spaces. The final print(‘All set!’) is not indented, so it does not belong to the else-block. 

To indicate a block of code in Python, you must indent each line of the block by the same whitespace. The two lines of code in the while loop are both indented four spaces. It is required for indicating what block of code a statement belongs to. For example, j=1 and while(j<=5): is not indented, and so it is not within the while block. So, Python code structures by indentation.

What are Comments in Python

Python comments start with the hash symbol # and continue to the end of the line. Comments in Python are useful information that the developers provide to make the reader understand the source code. It explains the logic or a part of it used in the code. Comments in Python are usually helpful to someone maintaining or enhancing your code when you are no longer around to answer questions about it. These are often cited as useful programming convention that does not take part in the output of the program but improves the readability of the whole program. 

Comments in Python are identified with a hash symbol, #, and extend to the end of the line.

Types of comments in Python

A comment can be written on a single line, next to the corresponding line of code, or in a block of multiple lines. Here, we will try to understand examples of comment in Python one by one:

Single-line comment in Python

Python single-line comment starts with a hash symbol (#) with no white spaces and lasts till the end of the line. If the comment exceeds one line then put a hashtag on the next line and continue the comment. Python’s single-line comments are proved useful for supplying short explanations for variables, function declarations, and expressions. See the following code snippet demonstrating single line comment:

Example 1:  

Python allows comments at the start of lines, and Python will ignore the whole line.

Example 2:  

Python also allows comments at the end of lines, ignoring the previous text.

Multiline comment in Python  

Use a hash ( #) for each extra line to create a multiline comment . In fact, Python multiline comments are not supported by Python’s syntax. Additionally, we can use Python multi-line comments by using multiline strings. It is a piece of text enclosed in a delimiter (“””) on each end of the comment. Again there should be no white space between delimiter (“””). They are useful when the comment text does not fit into one line; therefore need to span across lines. Python Multi-line comments or paragraphs serve as documentation for others reading your code. See the following code snippet demonstrating a multi-line comment:

In this example, we are using an extra # for each extra line to create a Python multiline comment.

In this example, we are using three double quotes (“) at the start and end of the string without any space to create a Python multiline comment.

In this example, we are using three single quotes (‘) at the start and end of the string without any space to create a Python multiline comment.

Docstring in Python

Python Docstrings are a type of comment that is used to show how the program works. Docstrings in Python are surrounded by Triple Quotes in Python (“”” “””). Docstrings are also neglected by the interpreter.

Difference Between ‘Docstrings’ and ‘Multi-line Comments 

Docstrings and Multi-line comments may look the same but they aren’t.

• Docstrings are written in the functions and classes to show how to use the program.
• Multi-line comments are used to show how a block of code works.

Please Login to comment...

Similar reads.

• python-basics
• CBSE Exam Format Changed for Class 11-12: Focus On Concept Application Questions
• 10 Best Waze Alternatives in 2024 (Free)
• 10 Best Squarespace Alternatives in 2024 (Free)
• Top 10 Owler Alternatives & Competitors in 2024
• 30 OOPs Interview Questions and Answers (2024)

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Simple statements

The types of simple statements are keyword, assignment, and null.

This browser is no longer supported.

Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support.

Writing assignment statements

• 7 contributors

Assignment statements assign a value or expression to a variable or constant . Assignment statements always include an equal sign ( = ).

The following example assigns the return value of the InputBox function to the variable.

The Let statement is optional and is usually omitted. For example, the preceding assignment statement can be written.

The Set statement is used to assign an object to a variable that has been declared as an object. The Set keyword is required. In the following example, the Set statement assigns a range on Sheet1 to the object variable myCell .

Statements that set property values are also assignment statements. The following example sets the Bold property of the Font object for the active cell.

• Visual Basic conceptual topics

Support and feedback

Have questions or feedback about Office VBA or this documentation? Please see Office VBA support and feedback for guidance about the ways you can receive support and provide feedback.

Was this page helpful?

Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: https://aka.ms/ContentUserFeedback .

Submit and view feedback for

Additional resources

• Assignment Statement

An Assignment statement is a statement that is used to set a value to the variable name in a program .

Assignment statement allows a variable to hold different types of values during its program lifespan. Another way of understanding an assignment statement is, it stores a value in the memory location which is denoted by a variable name.

The symbol used in an assignment statement is called as an operator . The symbol is ‘=’ .

Note: The Assignment Operator should never be used for Equality purpose which is double equal sign ‘==’.

The Basic Syntax of Assignment Statement in a programming language is :

variable = expression ;

variable = variable name

expression = it could be either a direct value or a math expression/formula or a function call

Few programming languages such as Java, C, C++ require data type to be specified for the variable, so that it is easy to allocate memory space and store those values during program execution.

data_type variable_name = value ;

In the above-given examples, Variable ‘a’ is assigned a value in the same statement as per its defined data type. A data type is only declared for Variable ‘b’. In the 3 rd line of code, Variable ‘a’ is reassigned the value 25. The 4 th line of code assigns the value for Variable ‘b’.

Assignment Statement Forms

This is one of the most common forms of Assignment Statements. Here the Variable name is defined, initialized, and assigned a value in the same statement. This form is generally used when we want to use the Variable quite a few times and we do not want to change its value very frequently.

Tuple Assignment

Generally, we use this form when we want to define and assign values for more than 1 variable at the same time. This saves time and is an easy method. Note that here every individual variable has a different value assigned to it.

(Code In Python)

Sequence Assignment

(Code in Python)

Multiple-target Assignment or Chain Assignment

In this format, a single value is assigned to two or more variables.

Augmented Assignment

In this format, we use the combination of mathematical expressions and values for the Variable. Other augmented Assignment forms are: &=, -=, **=, etc.

Browse more Topics Under Data Types, Variables and Constants

• Concept of Data types
• Built-in Data Types
• Constants in Programing Language 
• Access Modifier
• Variables of Built-in-Datatypes
• Declaration/Initialization of Variables
• Type Modifier

Few Rules for Assignment Statement

Few Rules to be followed while writing the Assignment Statements are:

• Variable names must begin with a letter, underscore, non-number character. Each language has its own conventions.
• The Data type defined and the variable value must match.
• A variable name once defined can only be used once in the program. You cannot define it again to store other types of value.
• If you assign a new value to an existing variable, it will overwrite the previous value and assign the new value.

FAQs on Assignment Statement

Q1. Which of the following shows the syntax of an  assignment statement ?

• variablename = expression ;
• expression = variable ;
• datatype = variablename ;
• expression = datatype variable ;

Answer – Option A.

Q2. What is an expression ?

• Same as statement
• List of statements that make up a program
• Combination of literals, operators, variables, math formulas used to calculate a value
• Numbers expressed in digits

Answer – Option C.

Q3. What are the two steps that take place when an  assignment statement  is executed?

• Evaluate the expression, store the value in the variable
• Reserve memory, fill it with value
• Evaluate variable, store the result
• Store the value in the variable, evaluate the expression.

Customize your course in 30 seconds

Which class are you in.

Data Types, Variables and Constants

• Variables in Programming Language
• Concept of Data Types
• Declaration of Variables
• Type Modifiers
• Access Modifiers
• Constants in Programming Language

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Press Releases

Cyber Safety Review Board Releases Report on Microsoft Online Exchange Incident from Summer 2023

WASHINGTON – Today, the U.S. Department of Homeland Security (DHS) released the Cyber Safety Review Board’s (CSRB) findings and recommendations following its independent review of the Summer 2023 Microsoft Exchange Online intrusion . The review detailed operational and strategic decisions that led to the intrusion and recommended specific practices for industry and government to implement to ensure an intrusion of this magnitude does not happen again. Secretary of Homeland Security Alejandro N. Mayorkas received the CSRB report from the Board and delivered it to President Biden. This is the third review completed by the CSRB since the Board was announced in February 2022.

“Individuals and organizations across the country rely on cloud services every day, and the security of this technology has never been more important,” said Secretary Mayorkas . “Nation-state actors continue to grow more sophisticated in their ability to compromise cloud service systems. Public-private partnerships like the CSRB are critical in our efforts to mitigate the serious cyber threat these nation-state actors pose. The Department of Homeland Security appreciates the Board’s comprehensive review and report of the Storm-0558 incident. Implementation of the Board’s recommendations will enhance our cybersecurity for years to come.”

The CSRB provides a unique forum for leading government and industry experts to review significant cybersecurity events and provide independent, strategic, and actionable recommendations to the President, the Secretary, and the Director of the Cybersecurity and Infrastructure Security Agency (CISA) to better protect our nation. The Board is made up of cybersecurity leaders from the private sector and senior officials from DHS, CISA, the Defense Department, the National Security Agency, the Department of Justice, the Federal Bureau of Investigation, the Office of the National Cyber Director, and the Federal Chief Information Officer.

In August 2023, DHS announced that the CSRB would assess the recent Microsoft Exchange Online intrusion, initially reported in July 2023, and conduct a broader review of issues relating to cloud-based identity and authentication infrastructure affecting applicable cloud service providers (CSP) and their customers. The CSRB obtained data from and conducted interviews with 20 organizations and experts including cybersecurity companies, technology companies, law enforcement organizations, security researchers, academics, as well as several impacted organizations.

The inclusive review process developed actionable findings and recommendations. As a result of the CSRB’s recommendations, CISA plans to convene major CSPs to develop cloud security practices aligned with the CSRB recommendations and a process for CSPs to regularly attest and demonstrate alignment.

“DHS is committed to efforts that meaningfully improve cybersecurity resilience and preparedness for our nation, and the work of the CSRB is reflective of our determination and dedication to this cause,” said CISA Director Jen Easterly . “I am confident that the findings and recommendations from the Board’s report will catalyze action to reduce risk to the critical infrastructure Americans rely on every day.”

The CSRB’s review found that the intrusion by Storm-0558, a hacking group assessed to be affiliated with the People’s Republic of China, was preventable. It identified a series of Microsoft operational and strategic decisions that collectively pointed to a corporate culture that deprioritized enterprise security investments and rigorous risk management, at odds with the company’s centrality in the technology ecosystem and the level of trust customers place in the company to protect their data and operations. The Board recommends that Microsoft develop and publicly share a plan with specific timelines to make fundamental, security-focused reforms across the company and its suite of products. Microsoft fully cooperated with the Board’s review.

“Cloud computing is some of the most critical infrastructure we have, as it hosts sensitive data and powers business operations across our economy,” said DHS Under Secretary of Policy and CSRB Chair Robert Silvers . “It is imperative that cloud service providers prioritize security and build it in by design. The Board has become the authoritative organization for conducting fact-finding and issuing recommendations in the wake of major cyber incidents, receiving extensive industry and expert input in each of its three reviews to date. We appreciate Microsoft’s full cooperation in the course of the Board’s seven-month, independent review. We also appreciate the input received from 19 additional companies, government agencies, and individual experts.”

“The threat actor responsible for this brazen intrusion has been tracked by industry for over two decades and has been linked to 2009 Operation Aurora and 2011 RSA SecureID compromises,” said CSRB Acting Deputy Chair Dmitri Alperovitch . “This People’s Republic of China affiliated group of hackers has the capability and intent to compromise identity systems to access sensitive data, including emails of individuals of interest to the Chinese government. Cloud service providers must urgently implement these recommendations to protect their customers against this and other persistent and pernicious threats from nation-state actors.”

The CSRB recommends specific actions to all cloud service providers and government partners to improve security and build resilience against the types of attacks conducted by Storm-0558 and associated groups. Select recommendations include:

• Cloud Service Provider Cybersecurity Practices: Cloud service providers should implement modern control mechanisms and baseline practices, informed by a rigorous threat model, across their digital identity and credential systems to substantially reduce the risk of system-level compromise.
• Audit Logging Norms: Cloud service providers should adopt a minimum standard for default audit logging in cloud services to enable the detection, prevention, and investigation of intrusions as a baseline and routine service offering without additional charge.
• Digital Identity Standards and Guidance: Cloud service providers should implement emerging digital identity standards to secure cloud services against prevailing threat vectors. Relevant standards bodies should refine, update, and incorporate these standards to address digital identity risks commonly exploited in the modern threat landscape.
• Cloud Service Provider Transparency: Cloud service providers should adopt incident and vulnerability disclosure practices to maximize transparency across and between their customers, stakeholders, and the United States government.
• Victim Notification Processes: Cloud service providers should develop more effective victim notification and support mechanisms to drive information-sharing efforts and amplify pertinent information for investigating, remediating, and recovering from cybersecurity incidents.
• Security Standards and Compliance Frameworks: The United States government should update the Federal Risk Authorization Management Program and supporting frameworks and establish a process for conducting discretionary special reviews of the program’s authorized Cloud Service Offerings following especially high-impact situations. The National Institute of Standards and Technology should also incorporate feedback about observed threats and incidents related to cloud provider security.

As directed by President Biden through Executive Order 14028 Improving the Nation’s Cybersecurity, Secretary Mayorkas established the CSRB  in February 2022.  The Board’s investigations are conducted independently, and its conclusions are independently reached. DHS and the CSRB are committed to transparency and will, whenever possible, release public versions of CSRB reports, consistent with applicable law and the need to protect sensitive information from disclosure.

To read the full report, visit the Report on the Microsoft Online Exchange Incident from Summer 2023 .

• Cybersecurity
• Secretary of Homeland Security
• Cyber Incident
• Cybersecurity and Infrastructure Security Agency (CISA)
• Secretary Alejandro Mayorkas

• Study Guides
• Homework Questions

[SPMANACS37 GROUP 2] Assignment 2 Financial Statement Analysis