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7. Simple statements
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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.

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

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8. Compound statements

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Basic Statements in Python

Table of contents, what is a statement in python, statement set, multi-line statements, simple statements, expression statements, the assert statement, the try statement.

In Python, statements are instructions or commands that you write to perform specific actions or tasks. They are the building blocks of a Python program.

A statement is a line of code that performs a specific action. It is the smallest unit of code that can be executed by the Python interpreter.

Assignment Statement

In this example, the value 10 is assigned to the variable x using the assignment statement.

Conditional Statement

In this example, the if-else statement is used to check the value of x and print a corresponding message.

By using statements, programmers can instruct the computer to perform a variety of tasks, from simple arithmetic operations to complex decision-making processes. Proper use of statements is crucial to writing efficient and effective Python code.

Here's a table summarizing various types of statements in Python:

Please note that this table provides a brief overview of each statement type, and there may be additional details and variations for each statement.

Multi-line statements are a convenient way to write long code in Python without making it cluttered. They allow you to write several lines of code as a single statement, making it easier for developers to read and understand the code. Here are two examples of multi-line statements in Python:

Using backslash:
Using parentheses:

Simple statements are the smallest unit of execution in Python programming language and they do not contain any logical or conditional expressions. They are usually composed of a single line of code and can perform basic operations such as assigning values to variables , printing out values, or calling functions .

Examples of simple statements in Python:

Simple statements are essential to programming in Python and are often used in combination with more complex statements to create robust programs and applications.

Expression statements in Python are lines of code that evaluate and produce a value. They are used to assign values to variables, call functions, and perform other operations that produce a result.

In this example, we assign the value 5 to the variable x , then add 3 to x and assign the result ( 8 ) to the variable y . Finally, we print the value of y .

In this example, we define a function square that takes one argument ( x ) and returns its square. We then call the function with the argument 5 and assign the result ( 25 ) to the variable result . Finally, we print the value of result .

Overall, expression statements are an essential part of Python programming and allow for the execution of mathematical and computational operations.

The assert statement in Python is used to test conditions and trigger an error if the condition is not met. It is often used for debugging and testing purposes.

Where condition is the expression that is tested, and message is the optional error message that is displayed when the condition is not met.

In this example, the assert statement tests whether x is equal to 5 . If the condition is met, the statement has no effect. If the condition is not met, an error will be raised with the message x should be 5 .

In this example, the assert statement tests whether y is not equal to 0 before performing the division. If the condition is met, the division proceeds as normal. If the condition is not met, an error will be raised with the message Cannot divide by zero .

Overall, assert statements are a useful tool in Python for debugging and testing, as they can help catch errors early on. They are also easily disabled in production code to avoid any unnecessary overhead.

The try statement in Python is used to catch exceptions that may occur during the execution of a block of code. It ensures that even when an error occurs, the code does not stop running.

Examples of Error Processing

Dive deep into the topic.

Match Statements
Operators in Python Statements
The IF Statement

Contribute with us!

Do not hesitate to contribute to Python tutorials on GitHub: create a fork, update content and issue a pull request.

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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 ] .

Python Numerical Methods

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists , the content is also available at Berkeley Python Numerical Methods .

The copyright of the book belongs to Elsevier. We also have this interactive book online for a better learning experience. The code is released under the MIT license . If you find this content useful, please consider supporting the work on Elsevier or Amazon !

< 2.0 Variables and Basic Data Structures | Contents | 2.2 Data Structure - Strings >

Variables and Assignment ¶

When programming, it is useful to be able to store information in variables. A variable is a string of characters and numbers associated with a piece of information. The assignment operator , denoted by the “=” symbol, is the operator that is used to assign values to variables in Python. The line x=1 takes the known value, 1, and assigns that value to the variable with name “x”. After executing this line, this number will be stored into this variable. Until the value is changed or the variable deleted, the character x behaves like the value 1.

TRY IT! Assign the value 2 to the variable y. Multiply y by 3 to show that it behaves like the value 2.

A variable is more like a container to store the data in the computer’s memory, the name of the variable tells the computer where to find this value in the memory. For now, it is sufficient to know that the notebook has its own memory space to store all the variables in the notebook. As a result of the previous example, you will see the variable “x” and “y” in the memory. You can view a list of all the variables in the notebook using the magic command %whos .

TRY IT! List all the variables in this notebook

Note that the equal sign in programming is not the same as a truth statement in mathematics. In math, the statement x = 2 declares the universal truth within the given framework, x is 2 . In programming, the statement x=2 means a known value is being associated with a variable name, store 2 in x. Although it is perfectly valid to say 1 = x in mathematics, assignments in Python always go left : meaning the value to the right of the equal sign is assigned to the variable on the left of the equal sign. Therefore, 1=x will generate an error in Python. The assignment operator is always last in the order of operations relative to mathematical, logical, and comparison operators.

TRY IT! The mathematical statement x=x+1 has no solution for any value of x . In programming, if we initialize the value of x to be 1, then the statement makes perfect sense. It means, “Add x and 1, which is 2, then assign that value to the variable x”. Note that this operation overwrites the previous value stored in x .

There are some restrictions on the names variables can take. Variables can only contain alphanumeric characters (letters and numbers) as well as underscores. However, the first character of a variable name must be a letter or underscores. Spaces within a variable name are not permitted, and the variable names are case-sensitive (e.g., x and X will be considered different variables).

TIP! Unlike in pure mathematics, variables in programming almost always represent something tangible. It may be the distance between two points in space or the number of rabbits in a population. Therefore, as your code becomes increasingly complicated, it is very important that your variables carry a name that can easily be associated with what they represent. For example, the distance between two points in space is better represented by the variable dist than x , and the number of rabbits in a population is better represented by nRabbits than y .

Note that when a variable is assigned, it has no memory of how it was assigned. That is, if the value of a variable, y , is constructed from other variables, like x , reassigning the value of x will not change the value of y .

EXAMPLE: What value will y have after the following lines of code are executed?

WARNING! You can overwrite variables or functions that have been stored in Python. For example, the command help = 2 will store the value 2 in the variable with name help . After this assignment help will behave like the value 2 instead of the function help . Therefore, you should always be careful not to give your variables the same name as built-in functions or values.

TIP! Now that you know how to assign variables, it is important that you learn to never leave unassigned commands. An unassigned command is an operation that has a result, but that result is not assigned to a variable. For example, you should never use 2+2 . You should instead assign it to some variable x=2+2 . This allows you to “hold on” to the results of previous commands and will make your interaction with Python must less confusing.

You can clear a variable from the notebook using the del function. Typing del x will clear the variable x from the workspace. If you want to remove all the variables in the notebook, you can use the magic command %reset .

In mathematics, variables are usually associated with unknown numbers; in programming, variables are associated with a value of a certain type. There are many data types that can be assigned to variables. A data type is a classification of the type of information that is being stored in a variable. The basic data types that you will utilize throughout this book are boolean, int, float, string, list, tuple, dictionary, set. A formal description of these data types is given in the following sections.

Python Variables – The Complete Beginner's Guide

Variables are an essential part of Python. They allow us to easily store, manipulate, and reference data throughout our projects.

This article will give you all the understanding of Python variables you need to use them effectively in your projects.

If you want the most convenient way to review all the topics covered here, I've put together a helpful cheatsheet for you right here:

Download the Python variables cheatsheet (it takes 5 seconds).

What is a Variable in Python?

So what are variables and why do we need them?

Variables are essential for holding onto and referencing values throughout our application. By storing a value into a variable, you can reuse it as many times and in whatever way you like throughout your project.

You can think of variables as boxes with labels, where the label represents the variable name and the content of the box is the value that the variable holds.

In Python, variables are created the moment you give or assign a value to them.

How Do I Assign a Value to a Variable?

Assigning a value to a variable in Python is an easy process.

You simply use the equal sign = as an assignment operator, followed by the value you want to assign to the variable. Here's an example:

In this example, we've created two variables: country and year_founded. We've assigned the string value "United States" to the country variable and integer value 1776 to the year_founded variable.

There are two things to note in this example:

Variables in Python are case-sensitive . In other words, watch your casing when creating variables, because Year_Founded will be a different variable than year_founded even though they include the same letters
Variable names that use multiple words in Python should be separated with an underscore _ . For example, a variable named "site name" should be written as "site_name" . This convention is called snake case (very fitting for the "Python" language).

How Should I Name My Python Variables?

There are some rules to follow when naming Python variables.

Some of these are hard rules that must be followed, otherwise your program will not work, while others are known as conventions . This means, they are more like suggestions.

Variable naming rules

Variable names must start with a letter or an underscore _ character.
Variable names can only contain letters, numbers, and underscores.
Variable names cannot contain spaces or special characters.

Variable naming conventions

Variable names should be descriptive and not too short or too long.
Use lowercase letters and underscores to separate words in variable names (known as "snake_case").

What Data Types Can Python Variables Hold?

One of the best features of Python is its flexibility when it comes to handling various data types.

Python variables can hold various data types, including integers, floats, strings, booleans, tuples and lists:

Integers are whole numbers, both positive and negative.

Floats are real numbers or numbers with a decimal point.

Strings are sequences of characters, namely words or sentences.

Booleans are True or False values.

Lists are ordered, mutable collections of values.

Tuples are ordered, immutable collections of values.

There are more data types in Python, but these are the most common ones you will encounter while working with Python variables.

Python is Dynamically Typed

Python is what is known as a dynamically-typed language. This means that the type of a variable can change during the execution of a program.

Another feature of dynamic typing is that it is not necessary to manually declare the type of each variable, unlike other programming languages such as Java.

You can use the type() function to determine the type of a variable. For instance:

What Operations Can Be Performed?

Variables can be used in various operations, which allows us to transform them mathematically (if they are numbers), change their string values through operations like concatenation, and compare values using equality operators.

Mathematic Operations

It's possible to perform basic mathematic operations with variables, such as addition, subtraction, multiplication, and division:

It's also possible to find the remainder of a division operation by using the modulus % operator as well as create exponents using the ** syntax:

String operators

Strings can be added to one another or concatenated using the + operator.

Equality comparisons

Values can also be compared in Python using the < , > , == , and != operators.

These operators, respectively, compare whether values are less than, greater than, equal to, or not equal to each other.

Finally, note that when performing operations with variables, you need to ensure that the types of the variables are compatible with each other.

For example, you cannot directly add a string and an integer. You would need to convert one of the variables to a compatible type using a function like str() or int() .

Variable Scope

The scope of a variable refers to the parts of a program where the variable can be accessed and modified. In Python, there are two main types of variable scope:

Global scope : Variables defined outside of any function or class have a global scope. They can be accessed and modified throughout the program, including within functions and classes.

Local scope : Variables defined within a function or class have a local scope. They can only be accessed and modified within that function or class.

In this example, attempting to access local_var outside of the function_with_local_var function results in a NameError , as the variable is not defined in the global scope.

Don't be afraid to experiment with different types of variables, operations, and scopes to truly grasp their importance and functionality. The more you work with Python variables, the more confident you'll become in applying these concepts.

Finally, if you want to fully learn all of these concepts, I've put together for you a super helpful cheatsheet that summarizes everything we've covered here.

Just click the link below to grab it for free. Enjoy!

Download the Python variables cheatsheet

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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 assignment operators.

Assignment operators are used to assign values to variables:

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Augmented Assignment Operators in Python

An assignment operator is an operator that is used to assign some value to a variable. Like normally in Python, we write “ a = 5 “ to assign value 5 to variable ‘a’. Augmented assignment operators have a special role to play in Python programming. It basically combines the functioning of the arithmetic or bitwise operator with the assignment operator. So assume if we need to add 7 to a variable “a” and assign the result back to “a”, then instead of writing normally as “ a = a + 7 “, we can use the augmented assignment operator and write the expression as “ a += 7 “. Here += has combined the functionality of arithmetic addition and assignment.

So, augmented assignment operators provide a short way to perform a binary operation and assigning results back to one of the operands. The way to write an augmented operator is just to write that binary operator and assignment operator together. In Python, we have several different augmented assignment operators like +=, -=, *=, /=, //=, **=, |=, &=, >>=, <<=, %= and ^=. Let’s see their functioning with the help of some exemplar codes:

1. Addition and Assignment (+=): This operator combines the impact of arithmetic addition and assignment. Here,

a = a + b can be written as a += b

2. Subtraction and Assignment (-=): This operator combines the impact of subtraction and assignment.

a = a – b can be written as a -= b

Example:

3. Multiplication and Assignment (*=): This operator combines the functionality of multiplication and assignment.

a = a * b can be written as a *= b

4. Division and Assignment (/=): This operator has the combined functionality of division and assignment.

a = a / b can be written as a /= b

5. Floor Division and Assignment (//=): It performs the functioning of floor division and assignment.

a = a // b can be written as a //= b

6. Modulo and Assignment (%=): This operator combines the impact of the modulo operator and assignment.

a = a % b can be written as a %= b

7. Power and Assignment (**=): This operator is equivalent to the power and assignment operator together.

a = a**b can be written as a **= b

8. Bitwise AND & Assignment (&=): This operator combines the impact of the bitwise AND operator and assignment operator.

a = a & b can be written as a &= b

9. Bitwise OR and Assignment (|=): This operator combines the impact of Bitwise OR and assignment operator.

a = a | b can be written as a |= b

10. Bitwise XOR and Assignment (^=): This augmented assignment operator combines the functionality of the bitwise XOR operator and assignment operator.

a = a ^ b can be written as a ^= b

11. Bitwise Left Shift and Assignment (<<=): It puts together the functioning of the bitwise left shift operator and assignment operator.

a = a << b can be written as a <<= b

12. Bitwise Right Shift and Assignment (>>=): It puts together the functioning of the bitwise right shift operator and assignment operator.

a = a >> b can be written as a >>= b

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Python Operators

Harsh Pandey

Last updated on 19 Mar 2024

Python, one of the most popular programming languages today, is known for its simplicity and readability. One fundamental aspect of Python that contributes to its ease of use is its operators. Operators are the constructs which can manipulate the value of operands. In this blog, we'll explore the different types of operators available in Python and their applications.

What Are Python Operators?

Operators in Python are special symbols or keywords that are used to perform operations on one or more operands. An operand can be a value, a variable, or an expression. Operators are the building blocks of Python programs and are used to perform tasks like arithmetic calculations, logical comparisons, and memory operations.

Types Of Python Operators

The various types of operators in python are:

Arithmetic Operators
Comparison Operators
Logical Operators
Bitwise Operators
Assignment Operators
Identity Operators and Membership Operators

Arithmetic Operators In Python

Arithmetic operators in Python are used to perform basic mathematical operations and are fundamental to any kind of numerical manipulation in Python programming. These operators include addition (+), subtraction (-), multiplication (*), division (/), modulus (%), exponentiation (**), and floor division (//).

Addition (+) Adds two operands. For example. result = 3 + 2 print(result) # Output: 5
Subtraction (-) Subtracts the right operand from the left operand. result = 5 - 3 print(result) # Output: 2
Multiplication (*) Multiplies two operands. result = 4 * 3 print(result) # Output: 12
Division (/) Divides the left operand by the right operand. The result is a floating-point number. result = 8 / 2 print(result) # Output: 4.0
Modulus (%) Returns the remainder of the division. result = 5 % 2 print(result) # Output: 1
Exponentiation (**) Raises the first operand to the power of the second operand. result = 3 ** 2 print(result) # Output: 9
Floor Division (//) Performs division but discards the fractional part, returning an integer result. result = 7 // 2 print(result) # Output: 3

Understanding these arithmetic operators is essential for anyone beginning with Python, as they are used widely in various types of numerical calculations and data manipulation tasks.

Comparison Operators In Python

Comparison operators in Python are used to compare values. They evaluate to either True or False based on the condition. These operators are essential in decision-making processes in Python scripts.

Equal to (==) Checks if the values of two operands are equal. print(5 == 5) # Output: True print(5 == 3) # Output: False
Not Equal to (!=) Checks if the values of two operands are not equal. print(5 != 3) # Output: True print(5 != 5) # Output: False
Greater than (>) Checks if the value of the left operand is greater than the right operand. print(5 > 3) # Output: True print(3 > 5) # Output: False
Less than (<) Checks if the value of the left operand is less than the right operand. print(3 < 5) # Output: True print(5 < 3) # Output: False
Greater than or Equal to (>=) Checks if the left operand is greater than or equal to the right operand. print(5 >= 5) # Output: True print(4 >= 5) # Output: False
Less than or Equal to (<=) Checks if the left operand is less than or equal to the right operand. print(3 <= 5) # Output: True print(5 <= 3) # Output: False

Understanding these comparison operators is crucial for controlling the flow of a Python program, as they are commonly used in conditional statements and loops to evaluate conditions and make decisions.

Logical Operators In Python

Logical operators in Python are used to combine conditional statements, and they are fundamental in controlling the flow of logic in Python code. These operators include and, or, and not, each serving a specific purpose in evaluating conditions.

and Operator The and operator returns True if both the operands (conditions) are true. print((5 > 3) and (5 > 4)) # Output: True print((5 > 3) and (5 < 4)) # Output: False
or Operator The or operator returns True if at least one of the operands is true. print((5 > 3) or (5 < 4)) # Output: True print((5 < 3) or (5 < 4)) # Output: False
not Operator The not operator reverses the result of the condition. It returns True if the condition is false and vice versa. print(not(5 > 3)) # Output: False print(not(5 < 3)) # Output: True

Logical operators are integral in Python for building complex logical expressions, making them essential in decision-making structures like if-else statements and loops.

Bitwise Operators In Python

Bitwise operators in Python are used to perform operations on binary numbers at the bit level. These operators are essential for manipulating individual bits and performing bit-by-bit operations.

AND Operator (&) The & operator performs a bitwise AND, where each bit of the output is 1 if both corresponding bits of the operand are 1. print(5 & 3) # Output: 1
OR Operator (|) The | operator performs a bitwise OR, where each bit of the output is 1 if at least one of the corresponding bits of the operands is 1. print(5 | 3) # Output: 7
XOR Operator (^) The ^ operator performs a bitwise XOR, where each bit of the output is 1 if the corresponding bits of the operands are different. print(5 ^ 3) # Output: 6
NOT Operator (~) The ~ operator performs a bitwise NOT, inverting each bit of the number. print(~5) # Output: -6
Shift Operators Left Shift (<<): Shifts the bits of the number to the left and fills 0 on voids left as a result. The left operand specifies the value to be shifted, and the right operand specifies the number of positions to shift. print(5 << 1) # Output: 10 Right Shift (>>): Shifts the bits of the number to the right. The left operand specifies the value to be shifted, and the right operand specifies the number of positions to shift. print(5 >> 1) # Output: 2

Bitwise operators are particularly useful in low-level programming, such as device driver writing or protocol development, where manipulation of data at the bit level is crucial.

Assignment Operator In Python

Assignment operators in Python are used to assign values to variables. These operators make the code more concise and improve its readability by combining arithmetic operations with assignment.

The Basic Assignment Operator (=) The = operator assigns the value on the right to the variable on the left. x = 10 print(x) # Output: 10
Compound Assignment Operators These operators combine assignment with other operations. Add and Assign (+=): Adds the right operand to the left operand and assigns the result to the left operand. x = 5 x += 3 # Same as x = x + 3 print(x) # Output: 8 Subtract and Assign (-=): Subtracts the right operand from the left operand and assigns the result to the left operand. x = 5 x -= 3 # Same as x = x - 3 print(x) # Output: 2 Multiply and Assign (*=): Multiplies the right operand with the left operand and assigns the result to the left operand. x = 5 x *= 3 # Same as x = x * 3 print(x) # Output: 15 Divide and Assign (/=): Divides the left operand by the right operand and assigns the result to the left operand. x = 10 x /= 2 # Same as x = x / 2 print(x) # Output: 5.0

Assignment operators are essential in Python for variable manipulation, simplifying expressions, and making code more efficient and easier to understand.

Identity Operators And Membership Operators In Python

Identity and Membership operators in Python are used to compare the identity of objects and check for membership within data structures, respectively.

Identity Operators

Identity operators compare the memory locations of two objects. There are two identity operators in Python: is and is not.

is: Evaluates to true if the variables on either side of the operator point to the same object. x = ["apple", "banana"] y = x print(x is y) # Output: True
is not: Evaluates to true if the variables on either side of the operator do not point to the same object. x = ["apple", "banana"] y = ["apple", "banana"] print(x is not y) # Output: True

Membership Operators

Membership operators test if a sequence is presented in an object. The two membership operators in Python are in and not in.

in: Returns True if a sequence with the specified value is present in the object. x = ["apple", "banana"] print("banana" in x) # Output: True
not in: Returns True if a sequence with the specified value is not present in the object. print("cherry" not in x) # Output: True

Identity and Membership operators are crucial in Python for validating the identity of objects and checking for the presence of elements within various data structures like lists, tuples, and strings. They are widely used in conditional statements and loops to enhance the logic and functionality of Python scripts.

Operators in Python are essential for performing various operations on data. Understanding these operators and their applications is crucial for anyone looking to master Python programming. Whether it's for data manipulation, arithmetic calculations, or logical comparisons, Python operators provide a simple yet powerful tool for developers.

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My first coding assignment in Python

I am having major issues with a simple assignment for school. I need to know what is wrong with this code. Error: File “/home/runner/work/9780357880876_pld-10e-python-145ba7d2-9f0b-4116-afd3-3be7dea0c8ae/9780357880876_pld-10e-python-145ba7d2-9f0b-4116-afd3-3be7dea0c8ae/Programming.py”, line 1 $ python /workspaces/9780357880876-pld-10e-python-145ba7d2-9f0b-4116-afd3-3be7dea0c8ae/chapter1/ex01/student/Programming.py ^ SyntaxError: invalid decimal literal

Timestamp: 2024-03-23 21:23:35.469897

Task 2: Modify the Python program so it prints two lines of output. Add a second output statement that displays That’s awesome!. Execute the program.

An example of the program is shown below:

I’m learning how to program in Python. That’s awesome!

I tried to refresh in order to obtain the original task in the terminal area, but it didn’t work for me

Open the file in your editor or IDE and look at line 1 of the file. Is it valid python code?

My guess is that /home/runner/work/9780357880876_pld-10e-python-145ba7d2-9f0b-4116-afd3-3be7dea0c8ae/9780357880876_pld-10e-python-145ba7d2-9f0b-4116-afd3-3be7dea0c8ae/Programming.py contains the text

If so then that looks like the start of a tutorial and not the python code itself.

When pasting code and output here use the </> button that will include the text as pre-formmmated text like this:

Please start by reading the pinned thread in order to understand how to make code show up with proper formatting. It’s important to view the code with a monospace font and the proper indentation. The forum software doesn’t know it’s code unless you mark it up, and the result can’t be properly read or understood.

Next, make sure to use complete English sentences in order to explain clearly what steps you take in order to try creating and running the program. We need to be able to understand what parts are code that you typed; what parts are your understanding of the actions you’re taking; what parts are what the assignment says; and what parts are things that you see on the screen (aside from what you actually typed).

It’s especially important to understand the environment that you used to try to write the code. For example, did you open a text editor and start writing code in a new file? Did you use the interactive Python prompt (by running python in a command window)? Are you using an IDE (a program for editing code - such as IDLE, which comes with Python, or PyCharm, or VSCode, or Spyder, etc.? Which one?)? Something else?

thank you so much!

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NPTEL The Joy of Computing using Python Jan-2024 Week 9 to 10 | Swayam

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1. Programming Assignment | Week 9

word is a string that contains one or more parentheses of the following types: "{ }", "[ ]", "( )". The string is said to be balanced if all the following conditions are satisfied.

When read from left to right: (1) The number of opening parentheses of a given type is equal to the number of closing parentheses of the same type. (2) An opening parenthesis cannot be immediately followed by a closing parenthesis of a different type. (3) Every opening parenthesis should be eventually closed by a closing parenthesis of the same type.

Write a function named balanced that accepts the string word as an argument. Return True if the string is balanced, and False otherwise. You can assume that the string doesn't contain any characters other than parentheses.

You do not have to accept the input from the user or print output to the console. You just have to write the function definition.

2. Programming Assignment | Week 9

Consider the problem about balanced expressions discussed in 1. Programming Assignment | Week 9 . We have a balanced expression (string) that has only the flower brackets: '( )'. We can recursively define a concept called nesting depth for each pair of opening and closing brackets.

The nesting depth of a pair that lies within another pair is one more than the nesting depth of the pair that immediately englobes it. For a pair that is not surrounded by any other pair, the nesting depth is 1.

Write a function named depth that accepts a balanced expression (string) as an argument. It should return the maximum nesting depth in this expression.

3. Programming Assignment | Week 9

Write a recursive function named power that accepts a square matrix A and a positive integer m as arguments and returns A m .

You do not have to accept input from the user or print the output to the console. You just have to write the function definition.

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this repo contain sll the assignment design for academic python classes

milamailo/python-assignments

Folders and files, repository files navigation, employee management system in python.

The repository comprises assignment designs crafted for academic Python classes!

Project Introduction:

In today's professional landscape, efficient employee data management is crucial across various industries. Whether it's maintaining accurate records, updating information, or generating insightful reports, having a robust system is essential.

Python, known for its simplicity and versatility, is well-suited for such tasks. Its clear syntax and extensive library support make it a powerful tool for building applications that handle data effectively.

To set the context for the project, build a console application tailored for managing employee information. This application will empower users to perform a variety of operations, including:

Adding new employees to the system
Deleting existing employees from the records
Updating employee details as needed
Generating comprehensive reports based on various criteria

To ensure data persistence, use a text file as the storage mechanism. This approach provides a practical solution and also allows us to explore file handling concepts in Python.

Let's embark on this academic journey to explore Python's capabilities in managing employee data and hone your programming skills along the way!

Functional Requirements

This section outlines the key functionalities expected from the application and provides guidance on implementation details.

Expected Functionalities:

Adding Employee Information: Users should be able to add new employee details to the system, including attributes such as ID, first name, last name, date of birth, department, and salary.

Deleting Employee Information: Users should have the capability to delete existing employee records from the system based on unique identifiers such as employee ID.

Updating Employee Information: Users should be able to modify existing employee details as needed, allowing for changes in attributes such as first name, last name, date of birth, department, and salary.

Data Persistence with Text File: The application should interact with a text file to store employee data persistently. This ensures that the information remains accessible across different sessions of the application.

Specified Fields for Employee Information:

For Adding and Updating: The following fields are required: ID, first name, last name, date of birth, department, and salary.
List of departments.
List of all employees with ID, full name, and department.
List of all departments with "the Average age and salary of all employees in each department.(BONUS)
List of employees in each department with ID, full name, date of birth, salary, and total salary for department's employees.(BONUS)

Guidance on Implementation

This section provides a structured approach to implementing the functionalities outlined in the project requirements.

Basic Structure:

Main Menu Interface: Begin by creating a main menu interface that allows users to interact with the application. The menu should provide options for adding, deleting, updating employee information, generating reports, and exiting the application.

Functions for Employee Operations: Develop separate functions for adding, deleting, and updating employee information. These functions should handle the respective operations based on user input.

File Handling Functions: Implement functions or methods to read from and write to the text file that stores employee data. These functions should facilitate seamless interaction between the application and the external data source.

Python Data Structures:

Use of Lists and Dictionaries: Utilize Python's built-in data structures such as lists and dictionaries to manage employee data efficiently. Use lists to store collections of employee records and dictionaries to represent individual employee information.

Error Handling and Data Validation:

Ensure Data Integrity: Emphasize the importance of error handling and data validation to maintain the integrity of the stored data. Implement robust error-checking mechanisms to handle invalid input and ensure consistency in employee records.

Modular Programming Practices:

Organize Code into Functions: Encourage modular programming practices by breaking down the application's functionalities into smaller, reusable functions or methods. This promotes code reusability, readability, and maintainability.

By adhering to these implementation guidelines, you can develop a well-structured and functional employee management system that effectively handles employee data while adhering to best practices in Python programming.

Tips and Best Practices

This section offers practical advice on writing clean and maintainable code, as well as emphasizing the importance of thorough testing.

Use Descriptive Variable Names and Comments:

Utilize descriptive variable names that convey the purpose or meaning of the data they represent. This enhances code readability and makes it easier for others (and your future self) to understand the code.
Add comments to explain complex logic, algorithms, or any non-obvious aspects of your code. Comments provide context and clarity, helping readers to follow the code's flow and understand its functionality.

Break Down Complex Functionalities:

Break down complex functionalities into smaller, manageable functions or methods. Each function should ideally perform a single task or handle a specific aspect of the application's functionality.
By decomposing complex functionalities into smaller units, you can improve code modularity, reusability, and maintainability. It also makes debugging and testing easier, as you can isolate and troubleshoot individual components.

Thorough Testing:

Prioritize thorough testing to identify and resolve any potential issues or bugs in your code. Test your application under various scenarios, including both typical and edge cases, to ensure its robustness and reliability.
Implement unit tests to verify the functionality of individual functions or methods. Additionally, conduct integration tests to validate the interaction between different components of your application.
Regularly review and update your test suite as your codebase evolves. Automated testing frameworks can streamline the testing process and help catch regressions early in the development cycle.

By following these tips and best practices, you can write cleaner, more readable code and build more robust applications. Remember that writing good code is an ongoing process of learning and refinement, so don't hesitate to seek feedback and iterate on your codebase.

Resources and Support

Relevant documentation links:.

Python Official Documentation : The official Python documentation provides comprehensive information on Python's syntax, standard libraries, and built-in functions.
Python Tutorial : The Python tutorial offers a guided introduction to Python programming, covering basic to advanced topics with examples.
Python Data Structures : This section of the Python tutorial explains various data structures available in Python, including lists, dictionaries, sets, and tuples.
File Handling in Python : Learn how to perform file handling operations such as reading from and writing to files in Python.

Example Code Snippets:

Python Code Examples : Programiz provides a collection of Python code examples covering a wide range of topics, from basic syntax to advanced concepts.

Assessment Criteria

This section outlines the criteria for assessing the project, including functionality, code quality, adherence to best practices, and the use of version control with GitHub.

Functionality:

Completeness of Functionalities: Evaluate whether the project implements all required functionalities, including adding, deleting, updating employee information, generating reports, and interacting with the text file for data persistence.
Accuracy of Functionality Implementation: Assess the accuracy and reliability of the implemented functionalities, ensuring they perform as expected and handle various scenarios effectively.
Error Handling: Evaluate the robustness of error handling mechanisms, checking for appropriate error messages, input validation, and graceful error recovery.

Code Quality:

Readability and Maintainability: Assess the readability and maintainability of the codebase, looking for clear variable names, consistent formatting, and meaningful comments that explain complex logic or algorithms.
Modularization: Evaluate the modularization of the codebase, checking whether functionalities are logically organized into reusable functions or methods. Encourage the use of modular programming practices to enhance code organization and reusability.
Documentation: Review the documentation provided within the codebase, including docstrings and inline comments, to ensure clarity and comprehensiveness.

Adherence to Best Practices:

Pythonic Code: Assess whether the code follows Python's best practices and idiomatic conventions, such as adhering to PEP 8 guidelines for code style and structure.
File Handling Practices: Evaluate the implementation of file handling operations, checking for proper opening, reading, writing, and closing of files, as well as error handling for file-related operations.
Testing: While testing may not be explicitly required for this project, encourage students to incorporate testing practices, such as unit tests or integration tests, to validate the functionality of their code and identify potential issues or bugs.

Version Control with GitHub:

Git Workflow: Git Workflow: Proficiency in using Git and GitHub for version control will be evaluated. Assessment will focus on the ability to create branches, commit changes, push commits to remote repositories, and merge branches using appropriate Git workflows.
Commit History: The commit history will be reviewed to assess engagement with version control practices. Attention will be given to meaningful commit messages, frequent commits, and well-structured branches demonstrating iterative development and collaboration.
Collaboration: Collaboration skills will be assessed by reviewing pull requests, code reviews, and collaboration within the GitHub repository. Evaluation will focus on the ability to provide constructive feedback, address issues, and incorporate changes effectively.

Recommended exploration of additional features or enhancements beyond the basic project requirements is highly beneficial. Highlighting the opportunity for creativity and innovation in extending the functionality of the application can be a motivating factor. By experimenting with new features or implementing advanced functionalities, one can deepen understanding of Python programming concepts and develop problem-solving skills.

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7. Simple statements
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).
Python's Assignment Operator: Write Robust Assignments
Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value. To execute an assignment statement like the above, Python runs the following steps: Evaluate the right-hand expression to produce a concrete value or object.
Different Forms of Assignment Statements in Python
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. a, b, c = 'HEY'. print('a = ', a)
Assignment Operators in Python
Syntax. =. Assign value of right side of expression to left side operand. x = y + z. +=. Add and Assign: Add right side operand with left side operand and then assign to left operand. a += b. -=. Subtract AND: Subtract right operand from left operand and then assign to left operand: True if both operands are equal.
Variables in Python
In Python, variables need not be declared or defined in advance, as is the case in many other programming languages. To create a variable, you just assign it a value and then start using it. Assignment is done with a single equals sign ( = ): Python. >>> n = 300. This is read or interpreted as " n is assigned the value 300 .".
Introduction into Python Statements: Assignment, Conditional Examples
Expression statements in Python are lines of code that evaluate and produce a value. They are used to assign values to variables, call functions, and perform other operations that produce a result. x = 5. y = x + 3. print(y) In this example, we assign the value 5 to the variable x, then add 3 to x and assign the result ( 8) to the variable y.
Assignment Expressions: The Walrus Operator
In this lesson, you'll learn about the biggest change in Python 3.8: the introduction of assignment expressions.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.
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.
Variables, Expressions, and Assignments
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 ...
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Variables and Assignment¶. When programming, it is useful to be able to store information in variables. A variable is a string of characters and numbers associated with a piece of information. The assignment operator, denoted by the "=" symbol, is the operator that is used to assign values to variables in Python.The line x=1 takes the known value, 1, and assigns that value to the variable ...
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The fundamental concept to understand is that everything is an object in Python. Python supports numbers, strings, sets, lists, tuples, and dictionaries. These are the standard data types. I will explain each of them in detail. Declare And Assign Value To Variable. Assignment sets a value to a variable. To assign variable a value, use the ...
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Assigning a value to a variable in Python is an easy process. You simply use the equal sign = as an assignment operator, followed by the value you want to assign to the variable. Here's an example: country = "United States". year_founded = 1776. In this example, we've created two variables: country and year_founded.
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Python Assignment Operators. Assignment operators are used to assign values to variables: Operator. Example. Same As. Try it. =. x = 5. x = 5.
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Different Assignment operators in Python
Simple assignment operator in Python. The Simple assignment operator in Python is denoted by = and is used to assign values from the right side of the operator to the value on the left side. Input: a = b + c Add and equal operator.
Variable Assignment
Variables in PythonMartin Breuss 06:29. Think of a variable as a name attached to a particular object. In Python, variables need not be declared or defined in advance, as is the case in many other programming languages. To create a variable, you just assign it a value and then start using it. Assignment is done with a single equals sign ( = ).
Augmented Assignment Operators in Python
An assignment operator is an operator that is used to assign some value to a variable. Like normally in Python, we write " a = 5 " to assign value 5 to variable 'a'. Augmented assignment operators have a special role to play in Python programming. It basically combines the functioning of the arithmetic or bitwise operator with the ...
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What does colon equal (:=) in Python mean?
102. This symbol := is an assignment operator in Python (mostly called as the Walrus Operator ). In a nutshell, the walrus operator compresses our code to make it a little shorter. Here's a very simple example: # without walrus. n = 30. if n > 10: print(f"{n} is greater than 10") # with walrus.
Python Operators
Assignment operators in Python are used to assign values to variables. These operators make the code more concise and improve its readability by combining arithmetic operations with assignment. The Basic Assignment Operator (=) The = operator assigns the value on the right to the variable on the left. x = 10 print(x) # Output: 10
My first coding assignment in Python
I am having major issues with a simple assignment for school. I need to know what is wrong with this code. Task 2: Modify the Python program so it prints two lines of output. Add a second output statement that displays That's awesome!. Execute the program. I'm learning how to program in Python.
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.
NPTEL The Joy of Computing using Python Jan-2024 Week 9 to 10
Consider the problem about balanced expressions discussed in 1. Programming Assignment | Week 9. We have a balanced expression (string) that has only the flower brackets: ' ( )'. We can recursively define a concept called nesting depth for each pair of opening and closing brackets. The nesting depth of a pair that lies within another pair is ...
Best way to do conditional assignment in python
Python conditional assignment operator. Related. 5. Python: avoiding if condition? 5. pythonic way to rewrite an assignment in an if statement. 3. Python shorthand for conditionally assigning a variable. 0. Is there a simpler way to allocate value to a variable given a if condition - Python? 3.
GitHub
Python Tutorial: The Python tutorial offers a guided introduction to Python programming, covering basic to advanced topics with examples. Python Data Structures : This section of the Python tutorial explains various data structures available in Python, including lists, dictionaries, sets, and tuples.
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