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assignment in python

I know that "variable assignment" in python is in fact a binding / re-bindign of a name (the variable) to an object.

This brings the question: is it possible to have proper assignment in python, eg make an object equal to another object?

I guess there is no need for that in python:

Inmutable objects cannot be 'assigned to' since they can't be changed

Mutable objects could potentially be assigned to, since they can change, and this could be useful, since you may want to manipulate a copy of dictionary separately from the original one. However, in these cases the python philosophy is to offer a cloning method on the mutable object, so you can bind a copy rather than the original.

So I guess the answer is that there is no assignment in python, the best way to mimic it would be binding to a cloned object

I simply wanted to share the question in case I'm missing something important here

Both Lie Ryan and Sven Marnach answers are good, I guess the overall answer is a mix of both:

For user defined types, use the idiom:

a. dict = dict(b. dict )

(I guess this has problems as well if the assigned class has redefined attribute access methods, but lets not be fussy :))

For mutable built-ins (lists and dicts) use the cloning / copying methods they provide (eg slices, update)

finally inmutable built-ins can't be changed so can't be assigned

I'll choose Lie Ryan because it's an elegant idiom that I hadn't thought of.

Are you asking about a deep copy? Is that what you mean by "binding to a cloned object"? – S.Lott Nov 17, 2010 at 11:58

3 Answers 3

I think you are right with your characterization of assignment in Python -- I just would like to add a different method of cloning and ways of assignment in special cases.

"Copy-constructing" a mutable built-in Python object will yield a (shallow) copy of that object:

[ Edit : As pointed out by Paul McGuire in the comments, the behaviour of a "copy contructor" (forgive me the C++ terminology) for a immutable built-in Python object is implementation dependent -- you might get a copy or just the same object. But because the object is immutable anyway, you shouldn't care.]

The copy constructor could be called generically by y = type(x)(x) , but this seems a bit cryptic. And of course, there is the copy module which allows for shallow and deep copies.

Some Python objects allow assignment. For example, you can assign to a list without creating a new object:

For dictionaries, you could use the clear() method followed by update(otherdict) to assign to a dictionary without creating a new object. For a set s , you can use

Yes you can:

will do the default assignment semantic in C/C++ (i.e. do a shallow assignment).

The problem with such generalized assignment is that it never works for everybody. In C++, you can override the assignment operator since you always have to pick whether you want a fully shallow assignment, fully deep assignment, or any shade between fully deep copy and fully shallow copy.

I don't think you are missing anything.

I like to picture variables in python as the name written on 'labels' that are attached to boxes but can change its placement by assignment, whereas in other languages, assignment changes the box's contents (and the assignment operator can be overloaded).

Beginners can write quite complex applications without being aware of that, but they are usually messy programs.

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Nuri Halperin

Python variables and assignment.

Introduction

Variables and type, mixing types, the none type, checking for type equality.

Python is a great language for many tasks. It is commonly used for system administration tasks, as well as building websites, processing data, and text. It is also shaping up to be the language of choice for Machine Learning (ML), leveraging powerful modules for performing math and for visualizations.

As with the basis of most programming languages, you may use variables in Python to hold and manipulate values. This guide shows you the basics of creation and use of variables in Python.

To best benefit from this guide, you may want to follow along and run the code examples throughout this guide. The code examples are entered into Python's REPL interpreter. If your system does not already have a python interpreter, you can download one from here . Just pick the version matching your operating system and follow the installation instructions. This guide targets Python version 3.6 and the sample code was tested against that version.

Variables hold values. In Python, variables do not require forward declaration - all you need to do is provide a variable name and assign it some value.

The Python interpreter shows you a prompt that looks like this: >>> . Each line you type into the interpreter is taken one at a time, parsed by the interpreter, and if the line is complete, executed as well.

If you enter one = 1 in the Python interpreter and hit "Enter", the interpreter will just show you a new line prompt.

The new line prompt >>> is empty. But Python actually did a few things:

This is not apparent from the blank line output. But the interpreter can show you the value of any variable if you just type the variable name and hit enter:

The value 1 is shown because Python evaluates the line and reports the value returned. Previously, the line contained a statement. The variable one was assigned a value. That operation evaluated a statement, so nothing was printed as a result. A more explicit way to print the value of a variable is to use the print() function.

Let's create another variable named greeting and assign it the value 'hi!' :

Here we created a variable and assigned it a string value. Note the variable name greeting . It was chosen to contain, well, a greeting of some sort. Python, of course, has no way to tell that the string value 'hi' is indeed a greeting. A variable is, well, variable ! We can re-assign a variable later. The value stored in a variable is simply the last one assigned to it.

The initial value 'hi once' is lost once the second assignment to the value 'hi again!' was evaluated. The current value of the variable remains 'hi again! for the duration of the session unless otherwise assigned a new value later.

Both variable names x and greeting consist of characters only. Python allows you to name variables to your liking, as long as the names follow these rules:

Following the rules above, all of these variable assignments are legal:

All the above variable names are acceptable. But just because they are acceptable does not mean you should use them. The Python community has further developed naming conventions which should be followed. For example, even though a single-character identifier is perfectly legal, you are strongly discouraged from using the characters l (lower case el) or O (uppercase oh) or I (uppercase eye). This is because in some fonts these are hard to distinguish from the digits 1 (one) and 0 (zero). For more on variable naming, see this reference .

The following variable names are not acceptable. If you attempt to use them, python will produce an error and no variable would be created.

An initial character which is not an underscore or a letter from A-Z or a-z will produce an error. The backtick (`) character for example:

An identifier starting with a digit is not legal.

An identifier containing a space isn't legal:

Also, we can't use reserved words as variable names. In python, the word and is a reserved word. The following assignment will therefore fail:

In all of the failed cases above, the Python interpreter raised an error and refused to carry out the assignment or creation of the variable. You may note that the caret ^ character points to different position in the erroneous identifier. This is due to the interpreter's attempt to match the identifier to an acceptable syntax. But either way, the outcome is the same: invalid variable names result in an error.

As a reference, Python's reserved words list includes:

Python does not require you to declare a variable. You do not need to tell Python ahead of time that you intend to reserve space for a variable. All you do is assign a variable a value. But this does not mean you can use a variable without having Python allocate it first. For example, the following line will fail in my session:

This error appears because there is no identifier named imaginary_thing as far as Python can tell. Python will joyfully accept a variable by that name, but it requires that any variable being used must already be assigned .

The act of assignment to a variable allocates the name and space for the variable to contain a value.

We saw that we can assign a variable a numeric value as well as a string (text) value. We also saw that we can re-assign a variable, providing it a new value which replaces any previous value it contained.

Python tracks the value of a variable by letting you access it via the variable name. Python also tracks the type of the value assigned to a variable. To tell what the value type is, you can use the built-in type() function. In the following examples, we use the type() function to display the value type :

In each of the examples above, Python infers the value type by parsing the right-hand part of the assignment and deciding the type accordingly. The existence of the decimal point in the value 3.14 clued Python to assign the type float whereas the bare number 42 produced an int .

Python also supports boolean data types. Booleans are assigned a value of True or False (both of which are keywords by the way).

An integer data type is also created when you use hexadecimal or octal or binary literals. To type a value as octal, prefix the number with 0o . To type a value is hexadecimal, prefix it with 0x . For a binary literal, prefix with 0b .

If you want to ensure the value of a variable is of int type, you may use the built-in int() class constructor:

The above statement assigned the variable type class int to x . In order to store the number 3.14 to an integer value, the int() function discarded the fraction part.

We saw that values do indeed have a type and that Python tracks variable value as well as type. Lastly though - what does this type mean? Python will allow you to perform operations that fit the type.

For example, you may wish to divide the value of a variable by 3:

But the division operator does not work on a string. So you can't divide the string 'one two three' into 3:

The error Python raises is descriptive of the fact that the operator / (used for numeric division) is not defined for the types string and integer. Python is aware of the type assigned to the variable x which is how it made that determination. While you may be able to define your own operators on any types you wish, the point remains that Python does need the type system in order to map values, operators, and variables to the correct internal function. Python is a dynamically typed, but typed nonetheless.

Many programming languages support the notion of null . Null is treated as a special value denoting "not-a-value", something which would let us denote an "empty" or undefined value. Python's version of that is the keyword None , which is backed by the class NoneType . Note that assigning a variable to None does not get rid of the variable. Space is still allocated for the variable - only the value is set to None . If you want to remove the variable altogether you may use the del statement:

In the example above, after deleting the variable, any attempt to use that variable produces an error stating it is not (or no longer) defined.

While the type() function lets us glean which type a variable contains. When comparing numbers, we may need to check that they are identical - that both their value and type match. The is operator provides for such identity checking. Numeric values may compare as equal to each other using the equality test == yet not match on their type. Consider this example:

In the above example, x is assigned the integer value 1 and y is assigned the float value 1.0 . When tested using the equality match == , the result is True . Yet when tested using the object identity operator is , the result is False since float and int are different types.

Python does let you define your own operators on your objects, so you could add support for both the equality and the identity operators on your classes. The default behavior of most non-numeric classes though is that two instances of an object would not evaluate as equal or identical to each other.

Strings are a bit different. Strings in Python are immutable reference types. To complicate thing more, two strings containing the same exact sequence of characters and compared for object identity may produce either True or False .This is due to internal implementation details and may vary across Python interpreters.

To summarize: Python lets you create variables simply by assigning a value to the variable, without the need to declare the variable upfront. The value assigned to a variable determines the variable type. Different types may support some operations which others don't. If you want to control the type of variable assigned, you may use the specific class constructor to assign the value, such as int() or float() . Bare numbers expressed without a decimal point - or as hex or octal literals - will produce an integer. You can get the class type of a variable by using the type() function, or test whether a type matches some specific type using the is operator.

Python variables provide a simple and dynamic way to create variables, yet maintains a powerful type system to ensure safe operations on your data.

Explore these Python courses from Pluralsight to continue learning:

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/datum pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed).

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, which is writable. 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:

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 .

New in version 3.3: 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 ¶

The nonlocal statement causes the listed identifiers to refer to previously bound variables in the nearest enclosing scope excluding globals. This is important because the default behavior for binding is to search the local namespace first. The statement allows encapsulated code to rebind variables outside of the local scope besides the global (module) scope.

Names listed in a nonlocal statement, unlike those listed in a global statement, must refer to pre-existing bindings in an enclosing scope (the scope in which a new binding should be created cannot be determined unambiguously).

Names listed in a nonlocal statement must not collide with pre-existing bindings in the local scope.

The specification for the nonlocal statement.

Previous topic

6. Expressions

8. Compound statements

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Assignment in Python

Pass by Reference in Python: Best Practices Howard Francis 05:01

00:00 Since Python’s argument passing mechanism relies so much on how Python deals with assignment, the next couple of lessons will go into a bit more depth about how assignment works in Python.

00:12 Recall some things I’ve already mentioned: Assignment is the process of binding a name to an object. Parameter names are also bound to objects on function entry in Python. And again, this is how Python’s argument passing mechanism gets its name.

00:29 But how exactly does that work?

00:33 Let’s take a closer look.

00:36 In other languages like C++, assignment is quite simple, especially for basic types. In the first statement, a space of memory which has been designated for the variable x has the value 5 stored in it.

00:51 Then, in that second statement, that same piece of memory is overwritten with the value of 10 , and the value 5 is lost completely.

01:01 Python works much differently. In the first statement, an object representing the number 5 is created, if one doesn’t already exist, and then x is made to refer— or we sometimes say “point”—to that object, basically by storing the memory address to where the object 5 is stored.

01:21 In the second statement, x is reassigned to refer to an object representing the value 10 . Again, if one didn’t already exist, one is created.

01:31 In other words, x is rebound to a new object. The object representing 5 may still exist because there could be other variables or names referring to that object.

01:42 Let’s look at this process in more detail. There’s a particular structure used to implement objects in Python. A reference counter keeps track of how many references a specific object has. So again, in the statement x = 5 , an object representing the value 5 is found or created, the reference counter for that object is incremented, and an entry is made binding that variable name to the object.

02:14 This is basically done in a dictionary, and depending on the namespace of the variable, it can be found using either the locals() or globals() function.

02:26 Then, when x is reassigned to the value 10 , the reference count for the object representing 5 is reduced by one, the reference counter for the object representing 10 is increased by one, and finally, the appropriate dictionary is updated to indicate that x is now bound to that new object 10 . To see this happen, you can write a small program which uses the sys.getrefcount() function.

02:54 This function takes an object as an argument and returns the number of references to it. Here is an example. It will track the number of references to two objects, cleverly named "value_1" and "value_2" . First, it displays the number of references to each object before either are assigned to a variable.

03:20 Then it assigns x to be bound to the "value_1" . It repeats calls to getrefcount() to see how the counts have been changed as a result of that assignment statement.

03:33 Then it reassigns x from "value_1" to "value_2" and displays the ref counts again. Let’s try this out.

03:48 Well, that’s interesting! Before either object is bound to x , they each already have three references. Why is that? Well, one is an internal reference to the object when it was created.

04:01 Python has to know where it is so it can be bound to something else as the program runs. Second, it’s being used as an argument to this function call, so there’s a reference. And it’s used in the parameter variable inside getrefcount() .

04:17 So you likely always get a count of at least 3 for any object you try. The point to notice is that once x was assigned to be "value_1" , the ref count for "value_1" was incremented.

04:32 Then when x was reassigned to "value_2" , the ref count for "value_1" went back down to 3 and the ref count for "value_2" increased to 4 .

04:44 So, you can see the ref counts are going up and down as objects are bound and unbound to the variable x .

04:54 Next, you’ll see how this notion of assignments and bindings works with function arguments.

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= Assignment ¶

Description ¶.

Assigns a value to a variable(s).

Return Value ¶

According to coercion rules.

Time Complexity ¶

Assignment operation always works from right to left. The object that was referenced by the variable prior to the assignment is now dereferenced.

Example 1 ¶

Multiple assignment also follows the right-to-left rule. Consider the following example:

Example 2 ¶

First the integer 10 is assigned to the variable c, then the value of c (10) is assigned to b and b is assigned to a. After evaluating this expression all the variables are referencing the same object - integer 10.

Another case of assignment is a multi-variable assignment. Consider the following example:

Example 3 ¶

Following right-to-left rule a tuple containing (1, 2, 3) is created, then it is iterated over and its consequent values are assigned to the comma separated list of variables on the left.

This syntax can be used to swap the values of two variables:

Example 4 ¶

Another common use is when we want to assign new values based on existing values:

Example 5a ¶

this is different than:

Example 5b ¶

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Python Introduction

Python Operators

Python Namespace

Python Flow Control

Python Functions

Python Datatypes

Python Files

Python Object & Class

Python Advanced Topics

Python Date and time

Video: Operators in Python

Operators are special symbols that perform operations on variables and values. For example,

Here, + is an operator that adds two numbers: 5 and 6 .

Types of Python Operators

Here's a list of different types of Python operators that we will learn in this tutorial.

1. Python Arithmetic Operators

Arithmetic operators are used to perform mathematical operations like addition, subtraction, multiplication, etc. For example,

Here, - is an arithmetic operator that subtracts two values or variables.

Example 1: Arithmetic Operators in Python

In the above example, we have used multiple arithmetic operators,

2. Python Assignment Operators

Assignment operators are used to assign values to variables. For example,

Here, = is an assignment operator that assigns 5 to x .

Here's a list of different assignment operators available in Python.

Example 2: Assignment Operators

Here, we have used the += operator to assign the sum of a and b to a .

3. Python Comparison Operators

Comparison operators compare two values/variables and return a boolean result: True or False . For example,

Here, the > comparison operator is used to compare whether a is greater than b or not.

Example 3: Comparison Operators

Note: Comparison operators are used in decision-making and loops. We'll discuss more of the comparison operator and decision-making in later tutorials.

4. Python Logical Operators

Logical operators are used to check whether an expression is True or False . They are used in decision-making. For example,

Here, and is the logical operator AND . Since both a > 2 and b >= 6 are True , the result is True .

Example 4: Logical Operators

Note : Here is the truth table for these logical operators.

5. Python Bitwise operators

Bitwise operators act on operands as if they were strings of binary digits. They operate bit by bit, hence the name.

For example, 2 is 10 in binary and 7 is 111 .

In the table below: Let x = 10 ( 0000 1010 in binary) and y = 4 ( 0000 0100 in binary)

6. Python Special operators

Python language offers some special types of operators like the identity operator and the membership operator. They are described below with examples.

Identity operators

In Python, is and is not are used to check if two values are located on the same part of the memory. Two variables that are equal does not imply that they are identical.

Example 4: Identity operators in Python

Here, we see that x1 and y1 are integers of the same values, so they are equal as well as identical. Same is the case with x2 and y2 (strings).

But x3 and y3 are lists. They are equal but not identical. It is because the interpreter locates them separately in memory although they are equal.

Membership operators

In Python, in and not in are the membership operators. They are used to test whether a value or variable is found in a sequence ( string , list , tuple , set and dictionary ).

In a dictionary we can only test for presence of key, not the value.

Example 5: Membership operators in Python

Here, 'H' is in x but 'hello' is not present in x (remember, Python is case sensitive).

Sorry about that.

Different Forms of Assignment Statements in Python

We use Python assignment statements to assign objects to names. The target of an assignment statement is written on the left side of the equal sign (=), and the object on the right can be an arbitrary expression that computes an object.

There are some important properties of assignment in Python :-

Assignment statement forms :-

1. Basic form:

This form is the most common form.

2. Tuple assignment:

When we code a tuple on the left side of the =, Python pairs objects on the right side with targets on the left by position and assigns them from left to right. Therefore, the values of x and y are 50 and 100 respectively.

3. List assignment:

This works in the same way as the tuple assignment.

4. Sequence assignment:

In recent version of Python, tuple and list assignment have been generalized into instances of what we now call sequence assignment – any sequence of names can be assigned to any sequence of values, and Python assigns the items one at a time by position.

5. Extended Sequence unpacking:

It allows us to be more flexible in how we select portions of a sequence to assign.

Here, p is matched with the first character in the string on the right and q with the rest. The starred name (*q) is assigned a list, which collects all items in the sequence not assigned to other names.

This is especially handy for a common coding pattern such as splitting a sequence and accessing its front and rest part.

6. Multiple- target assignment:

In this form, Python assigns a reference to the same object (the object which is rightmost) to all the target on the left.

7. Augmented assignment :

The augmented assignment is a shorthand assignment that combines an expression and an assignment.

There are several other augmented assignment forms:

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// Tutorial //

Python operators - a quick reference.

While we believe that this content benefits our community, we have not yet thoroughly reviewed it. If you have any suggestions for improvements, please let us know by clicking the “report an issue“ button at the bottom of the tutorial.

Python operators allow us to do common processing on variables. We will look into different types of operators with examples and also operator precedence. They are the special symbols that can manipulate the values of one or more operands.

List of Python Operators

Python operators can be classified into several categories.

Python Assignment Operators

Assignment operators include the basic assignment operator equal to sign (=).

But to simplify code, and reduce redundancy, Python also includes arithmetic assignment operators.

This includes the += operator in Python used for addition assignment, //= floor division assignment operator , and others.

Here’s a list of all the arithmetic assignment operators in Python.

Using assignment operators

Python Arithmetic Operators

Python Comparison Operators

Python Bitwise Operators

Python Logical Operators

Python Operator Precedence

Precedence of these operators means the priority level of operators. This becomes vital when an expression has multiple operators in it. For example consider the following expression:

Now, what do you think the series of operation would be? We can add 2 and 3, then multiply the result by 4. Also, we can multiply 3 and 4 first, then add 2 with it. Here we can see that the operators’ precedence is important.

Below is a list of operators indicating the precedence level. It’s in descending order. That means the upper group has more precedence than that of the lower group.

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For Python Logical Operators Section, upon running the example I receive this error: Traceback (most recent call last): File “C:\Users\leeri\OneDrive\Documents\Python\Python Tutorial Codes.py ”, line 249, in a = int(input()) ValueError: invalid literal for int() with base 10: ‘’ Do you know what I am doing wrong?

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Python Tutorial

File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python operators.

Operators are used to perform operations on variables and values.

In the example below, we use the + operator to add together two values:

Python divides the operators in the following groups:

Python Arithmetic Operators

Arithmetic operators are used with numeric values to perform common mathematical operations:

Python Assignment Operators

Assignment operators are used to assign values to variables:

Python Comparison Operators

Comparison operators are used to compare two values:

Python Logical Operators

Logical operators are used to combine conditional statements:

Python Identity Operators

Identity operators are used to compare the objects, not if they are equal, but if they are actually the same object, with the same memory location:

Python Membership Operators

Membership operators are used to test if a sequence is presented in an object:

Python Bitwise Operators

Bitwise operators are used to compare (binary) numbers:

Operator Precedence

Operator precedence describes the order in which operations are performed.

Parentheses has the highest precedence, meaning that expressions inside parentheses must be evaluated first:

Multiplication * has higher precedence than addition + , and therefor multiplications are evaluated before additions:

The precedence order is described in the table below, starting with the highest precedence at the top:

If two operators have the same precedence, the expression is evaluated from left to right.

Addition + and subtraction - has the same precedence, and therefor we evaluate the expression from left to right:

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Python 3.10.10

Release Date: Feb. 8, 2023

This is the tenth maintenance release of Python 3.10

Python 3.10.10 is the newest major release of the Python programming language, and it contains many new features and optimizations.

Major new features of the 3.10 series, compared to 3.9

Among the new major new features and changes so far:

bpo-38605 : from __future__ import annotations ( PEP 563 ) used to be on this list in previous pre-releases but it has been postponed to Python 3.11 due to some compatibility concerns. You can read the Steering Council communication about it here to learn more.

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And now for something completely different

Granular convection is a phenomenon where granular material subjected to shaking or vibration will exhibit circulation patterns similar to types of fluid convection. It is sometimes described as the Brazil nut effect when the largest particles end up on the surface of a granular material containing a mixture of variously sized objects; this derives from the example of a typical container of mixed nuts, where the largest will be Brazil nuts. The phenomenon is also known as the muesli effect since it is seen in packets of breakfast cereal containing particles of different sizes but similar densities, such as muesli mix. Under experimental conditions, granular convection of variously sized particles has been observed forming convection cells similar to fluid motion.

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COMMENTS

python
Python tutorial introduces the _ in the very same context. I see that the behavior is carried over from certain shell behaviors. In your example: _, x = L.pop () would fail because L.pop () would return a single value and you cannot assign it to two variables and also it not a good idea to assign values to _ which has a special meaning. Share
How To Use Assignment Expressions in Python
Python 3.8, released in October 2019, adds assignment expressions to Python via the := syntax. The assignment expression syntax is also sometimes called "the walrus operator" because := vaguely resembles a walrus with tusks. Assignment expressions allow variable assignments to occur inside of larger expressions.
Assignment Expressions: The Walrus Operator
Assignment expression are written with a new notation (:=) .This operator is often called the walrus operator as it resembles the eyes and tusks of a walrus on its side. Assignment expressions allow you to assign and return a value in the same expression.
Assign within if statement Python
For Python 3.8+, PEP 572 introduces Assignment Expressions This allows assigning to variables within an expression using the notation NAME := expr. It can be used within if statements, for example: if (match := pattern.search (data)) is not None: # Do something with match Share Improve this answer Follow answered Jul 23, 2021 at 18:43 mhudnell
The Walrus Operator: Python 3.8 Assignment Expressions
Each new version of Python adds new features to the language. For Python 3.8, the biggest change is the addition of assignment expressions.Specifically, the := operator gives you a new syntax for assigning variables in the middle of expressions. This operator is colloquially known as the walrus operator.. This tutorial is an in-depth introduction to the walrus operator.
assignment in python
And of course, there is the copy module which allows for shallow and deep copies. Some Python objects allow assignment. For example, you can assign to a list without creating a new object: l = [2, 3] m = l l [:] = [3, 4, 5] m --> [3, 4, 5] For dictionaries, you could use the clear () method followed by update (otherdict) to assign to a ...
Assignment Operators in Python
1) Assign: This operator is used to assign the value of the right side of the expression to the left side operand. Syntax: x = y + z Example: Python3 a = 3 b = 5 c = a + b print(c) Output: 8 2) Add and Assign: This operator is used to add the right side operand with the left side operand and then assigning the result to the left operand. Syntax:
Solved In this assignment, you are going to implement a
Computer Science. Computer Science questions and answers. In this assignment, you are going to implement a recursive Python function to flatten a list or tuple. Unlike other languages, Python allows arbitrary type of data in a list and/or tuple, including other lists/tuples. Because of this, the users can create deeply nested lists and/or tuples.
Solved In this programming assignment, we write Python code
See Answer. Question: In this programming assignment, we write Python code to solve the 8 puzzle problem. The purpose of this programming assignment is to practice graph traversal algorithms. The 8-puzzle is played on a 3-by-3 grid with 8 square blocks labeled 1 through 8 and a blank square. The goal is to rearrange the blocks so that they are ...
Python Assign Values to Multiple Variables
Assign Value to Multiple Variables Python allows you to assign values to multiple variables in one line: Example x, y, z = "Orange", "Banana", "Cherry" print(x) print(y) print(z) Try it Yourself » And you can assign the same value to multiple variables in one line: Example x = y = z = "Orange" print(x) print(y) print(z) Try it Yourself »
Python Variables and Assignment
In each of the examples above, Python infers the value type by parsing the right-hand part of the assignment and deciding the type accordingly. The existence of the decimal point in the value 3.14 clued Python to assign the type float whereas the bare number 42 produced an int.
Variables and Assignment
Variables and Assignment — Python Numerical Methods Python Numerical Methods Python Programming And Numerical Methods: A Guide For Engineers And Scientists Preface Acknowledgment Chapter 1. Getting Started with Python Python as a Calculator Managing Packages Introduction to Jupyter Notebook
Welcome to Python.org
The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3. Python is a programming language that lets you work quickly and integrate systems more effectively. Learn More.
7. Simple statements
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.
Assignment in Python
00:00 Since Python's argument passing mechanism relies so much on how Python deals with assignment, the next couple of lessons will go into a bit more depth about how assignment works in Python.. 00:12 Recall some things I've already mentioned: Assignment is the process of binding a name to an object. Parameter names are also bound to objects on function entry in Python.
= Assignment
Example 2 ¶. >>> a = b = c = 10 >>> a 10 >>> b 10 >>> c 10. First the integer 10 is assigned to the variable c, then the value of c (10) is assigned to b and b is assigned to a. After evaluating this expression all the variables are referencing the same object - integer 10. Another case of assignment is a multi-variable assignment.
Python Operators (With Examples)
Python Assignment Operators. Assignment operators are used to assign values to variables. For example, # assign 5 to x var x = 5. Here, = is an assignment operator that assigns 5 to x. Here's a list of different assignment operators available in Python. Operator Name Example = Assignment Operator:
Different Forms of Assignment Statements in Python
We use Python assignment statements to assign objects to names. The target of an assignment statement is written on the left side of the equal sign (=), and the object on the right can be an arbitrary expression that computes an object. There are some important properties of assignment in Python :-
Python Operators
But to simplify code, and reduce redundancy, Python also includes arithmetic assignment operators. This includes the += operator in Python used for addition assignment, //= floor division assignment operator, and others. Here's a list of all the arithmetic assignment operators in Python. Operator. Description. +=. a+=b is equivalent to a=a+b ...
Python Operators
Python Assignment Operators Assignment operators are used to assign values to variables: Python Comparison Operators Comparison operators are used to compare two values: Python Logical Operators Logical operators are used to combine conditional statements: Python Identity Operators
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Python Release Python 3.10.10
Python 3.10.10. Release Date: Feb. 8, 2023. This is the tenth maintenance release of Python 3.10. Python 3.10.10 is the newest major release of the Python programming language, and it contains many new features and optimizations. Major new features of the 3.10 series, compared to 3.9. Among the new major new features and changes so far:
Learn Python: Best free coding courses
These are the best free online Python courses this week: Introduction To Python Programming. Learn Python and Become Professional Python Developer. Python OOP: Object Oriented Programming in ...