python assignment 2 3

Assignment 2.3 | Week-4 | Programming for Everybody (Getting Started with Python) By Coursera

Coursera Programming for Everybody (Getting Started with Python) Week 4 Assignment 2.3

Question: 2.3 Write a program to prompt the user for hours and rate per hour using input to compute gross pay. Use 35 hours and a rate of 2.75 per hour to test the program (the pay should be 96.25). You should use input to read a string and float() to convert the string to a number. Do not worry about error checking or bad user data.

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Multiple assignment in Python: Assign multiple values or the same value to multiple variables

In Python, the = operator is used to assign values to variables.

You can assign values to multiple variables in one line.

Assign multiple values to multiple variables

Assign the same value to multiple variables.

You can assign multiple values to multiple variables by separating them with commas , .

You can assign values to more than three variables, and it is also possible to assign values of different data types to those variables.

When only one variable is on the left side, values on the right side are assigned as a tuple to that variable.

If the number of variables on the left does not match the number of values on the right, a ValueError occurs. You can assign the remaining values as a list by prefixing the variable name with * .

For more information on using * and assigning elements of a tuple and list to multiple variables, see the following article.

Unpack a tuple and list in Python

You can also swap the values of multiple variables in the same way. See the following article for details:

Swap values in a list or values of variables in Python

You can assign the same value to multiple variables by using = consecutively.

For example, this is useful when initializing multiple variables with the same value.

After assigning the same value, you can assign a different value to one of these variables. As described later, be cautious when assigning mutable objects such as list and dict .

You can apply the same method when assigning the same value to three or more variables.

Be careful when assigning mutable objects such as list and dict .

If you use = consecutively, the same object is assigned to all variables. Therefore, if you change the value of an element or add a new element in one variable, the changes will be reflected in the others as well.

If you want to handle mutable objects separately, you need to assign them individually.

after c = []; d = [] , c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d .) 3. Data model — Python 3.11.3 documentation

You can also use copy() or deepcopy() from the copy module to make shallow and deep copies. See the following article.

Shallow and deep copy in Python: copy(), deepcopy()

Related Categories

Assignment Operators in Python

Operators are used to perform operations on values and variables. These are the special symbols that carry out arithmetic, logical, bitwise computations. The value the operator operates on is known as Operand .

Here, we will cover Assignment Operators in Python. So, Assignment Operators are used to assigning values to variables.

Now Let’s see each Assignment Operator one by one.

1) Assign: This operator is used to assign the value of the right side of the expression to the left side operand.

2) Add and Assign: This operator is used to add the right side operand with the left side operand and then assigning the result to the left operand.

Syntax:

3) Subtract and Assign: This operator is used to subtract the right operand from the left operand and then assigning the result to the left operand.

Example –

4) Multiply and Assign: This operator is used to multiply the right operand with the left operand and then assigning the result to the left operand.

5) Divide and Assign: This operator is used to divide the left operand with the right operand and then assigning the result to the left operand.

6) Modulus and Assign: This operator is used to take the modulus using the left and the right operands and then assigning the result to the left operand.

7) Divide (floor) and Assign: This operator is used to divide the left operand with the right operand and then assigning the result(floor) to the left operand.

8) Exponent and Assign: This operator is used to calculate the exponent(raise power) value using operands and then assigning the result to the left operand.

9) Bitwise AND and Assign: This operator is used to perform Bitwise AND on both operands and then assigning the result to the left operand.

10) Bitwise OR and Assign: This operator is used to perform Bitwise OR on the operands and then assigning result to the left operand.

11) Bitwise XOR and Assign: This operator is used to perform Bitwise XOR on the operands and then assigning result to the left operand.

12) Bitwise Right Shift and Assign: This operator is used to perform Bitwise right shift on the operands and then assigning result to the left operand.

13) Bitwise Left Shift and Assign: This operator is used to perform Bitwise left shift on the operands and then assigning result to the left operand.

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Module 2: The Essentials of Python »
Variables & Assignment
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Variables & Assignment 

There are reading-comprehension exercises included throughout the text. These are meant to help you put your reading to practice. Solutions for the exercises are included at the bottom of this page.

Variables permit us to write code that is flexible and amendable to repurpose. Suppose we want to write code that logs a student’s grade on an exam. The logic behind this process should not depend on whether we are logging Brian’s score of 92% versus Ashley’s score of 94%. As such, we can utilize variables, say name and grade , to serve as placeholders for this information. In this subsection, we will demonstrate how to define variables in Python.

In Python, the = symbol represents the “assignment” operator. The variable goes to the left of = , and the object that is being assigned to the variable goes to the right:

Attempting to reverse the assignment order (e.g. 92 = name ) will result in a syntax error. When a variable is assigned an object (like a number or a string), it is common to say that the variable is a reference to that object. For example, the variable name references the string "Brian" . This means that, once a variable is assigned an object, it can be used elsewhere in your code as a reference to (or placeholder for) that object:

Valid Names for Variables 

A variable name may consist of alphanumeric characters ( a-z , A-Z , 0-9 ) and the underscore symbol ( _ ); a valid name cannot begin with a numerical value.

var : valid

_var2 : valid

ApplePie_Yum_Yum : valid

2cool : invalid (begins with a numerical character)

I.am.the.best : invalid (contains . )

They also cannot conflict with character sequences that are reserved by the Python language. As such, the following cannot be used as variable names:

for , while , break , pass , continue

in , is , not

if , else , elif

def , class , return , yield , raises

import , from , as , with

try , except , finally

There are other unicode characters that are permitted as valid characters in a Python variable name, but it is not worthwhile to delve into those details here.

Mutable and Immutable Objects 

The mutability of an object refers to its ability to have its state changed. A mutable object can have its state changed, whereas an immutable object cannot. For instance, a list is an example of a mutable object. Once formed, we are able to update the contents of a list - replacing, adding to, and removing its elements.

To spell out what is transpiring here, we:

Create (initialize) a list with the state [1, 2, 3] .

Assign this list to the variable x ; x is now a reference to that list.

Using our referencing variable, x , update element-0 of the list to store the integer -4 .

This does not create a new list object, rather it mutates our original list. This is why printing x in the console displays [-4, 2, 3] and not [1, 2, 3] .

A tuple is an example of an immutable object. Once formed, there is no mechanism by which one can change of the state of a tuple; and any code that appears to be updating a tuple is in fact creating an entirely new tuple.

Mutable & Immutable Types of Objects 

The following are some common immutable and mutable objects in Python. These will be important to have in mind as we start to work with dictionaries and sets.

Some immutable objects

numbers (integers, floating-point numbers, complex numbers)

“frozen”-sets

Some mutable objects

dictionaries

NumPy arrays

Referencing a Mutable Object with Multiple Variables 

It is possible to assign variables to other, existing variables. Doing so will cause the variables to reference the same object:

What this entails is that these common variables will reference the same instance of the list. Meaning that if the list changes, all of the variables referencing that list will reflect this change:

We can see that list2 is still assigned to reference the same, updated list as list1 :

In general, assigning a variable b to a variable a will cause the variables to reference the same object in the system’s memory, and assigning c to a or b will simply have a third variable reference this same object. Then any change (a.k.a mutation ) of the object will be reflected in all of the variables that reference it ( a , b , and c ).

Of course, assigning two variables to identical but distinct lists means that a change to one list will not affect the other:

Reading Comprehension: Does slicing a list produce a reference to that list?

Suppose x is assigned a list, and that y is assigned a “slice” of x . Do x and y reference the same list? That is, if you update part of the subsequence common to x and y , does that change show up in both of them? Write some simple code to investigate this.

Reading Comprehension: Understanding References

Based on our discussion of mutable and immutable objects, predict what the value of y will be in the following circumstance:

Reading Comprehension Exercise Solutions: 

Does slicing a list produce a reference to that list?: Solution

Based on the following behavior, we can conclude that slicing a list does not produce a reference to the original list. Rather, slicing a list produces a copy of the appropriate subsequence of the list:

Understanding References: Solutions

Integers are immutable, thus x must reference an entirely new object ( 9 ), and y still references 3 .

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2.3: Arithmetic Operations and Assignment Statements

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Page ID 206261

Robert Belford
University of Arkansas at Little Rock

hypothes.is tag: s20iostpy03ualr Download Assignment: S2020py03

Learning Objectives

Students will be able to:

Explain each Python arithmetic operator
Explain the meaning and use of an assignment statement
Explain the use of "+" and "*" with strings and numbers
Use the int() and float() functions to convert string input to numbers for computation
Incorporate numeric formatting into print statements
Recognize the four main operations of a computer within a simple Python program
Create input statements in Python
Create Python code that performs mathematical and string operations
Create Python code that uses assignment statements
Create Python code that formats numeric output

Prior Knowledge

Understanding of Python print and input statements
Understanding of mathematical operations
Understanding of flowchart input symbols

Model 1: Arithmetic Operators in Python

Python includes several arithmetic operators: addition, subtraction, multiplication, two types of division, exponentiation and mod .

Critical Thinking Questions:

1. Draw a line between each flowchart symbol and its corresponding line of Python code. Make note of any problems.

2. Execute the print statements in the previous Python program

a. Next to each print statement above, write the output. b. What is the value of the following line of code?

c. Predict the values of 17%3 and 18%3 without using your computer.

3. Explain the purpose of each arithmetic operation:

a. + ____________________________

b. - ____________________________

c. * ____________________________

d. ** ____________________________

e. / ____________________________

f. // ____________________________

g. % ____________________________

An assignment statement is a line of code that uses a "=" sign. The statement stores the result of an operation performed on the right-hand side of the sign into the variable memory location on the left-hand side.

4. Enter and execute the following lines of Python code in the editor window of your IDE (e.g. Thonny):

a. What are the variables in the above python program? b. What does the assignment statement : MethaneMolMs = 16 do? c. What happens if you replace the comma (,) in the print statements with a plus sign (+) and execute the code again? Why does this happen?

5. What is stored in memory after each assignment statement is executed?

Note: Concatenating Strings in python

The "+" concatenates the two strings stored in the variables into one string. "+" can only be used when both operators are strings.

6. Run the following program in the editor window of your IDE (e.g. Thonny) to see what happens if you try to use the "+" with strings instead of numbers?

a. The third line of code contains an assignment statement. What is stored in fullName when the line is executed? b. What is the difference between the two output lines? c. How could you alter your assignment statements so that print(fullName) gives the same output as print(firstName,lastName) d. Only one of the following programs will work. Which one will work, and why doesn’t the other work? Try doing this without running the programs!

e. Run the programs above and see if you were correct. f. The program that worked above results in no space between the number and the street name. How can you alter the code so that it prints properly while using a concatenation operator?

7. Before entering the following code into the Python interpreter (Thonny IDE editor window), predict the output of this program.

Now execute it. What is the actual output? Is this what you thought it would do? Explain.

8. Let’s take a look at a python program that prompts the user for two numbers and subtracts them.

Execute the following code by entering it in the editor window of Thonny.

a. What output do you expect? b. What is the actual output c. Revise the program in the following manner:

Between lines two and three add the following lines of code: num1 = int(firstNumber) num2 = int(secondNumber)
Next, replace the statement: difference = firstNumber – secondNumber with the statement: difference = num1 – num2
Execute the program again. What output did you get?

d. Explain the purpose of the function int(). e. Explain how the changes in the program produced the desired output.

Model 3: Formatting Output in Python

There are multiple ways to format output in python. The old way is to use the string modulo %, and the new way is with a format method function.

9. Look closely at the output for python program 7.

a. How do you indicate the number of decimals to display using

the string modulo (%) ______________________________________________________

the format function ________________________________________________________

b. What happens to the number if you tell it to display less decimals than are in the number, regardless of formatting method used?

c. What type of code allows you to right justify your numbers?

10. Execute the following code by entering it in the editor window of Thonny.

a. Does the output look like standard output for something that has dollars and cents associated with it?

b. Replace the last line of code with the following:

print("Total cost of laptops: $%.2f" % price)

print("Total cost of laptops:" ,format(price, '.2f.))

Discuss the change in the output.

c. Replace the last line of code with the following:

print("Total cost of laptops: $", format(price,'.2f') print("Total cost of laptops: $" ,format(price, '.2f.))

Discuss the change in the output.

d. Experiment with the number ".2" in the ‘0.2f’ of the print above statement by substituting the following numbers and explain the results.

.4 ___________________________________________________

.0 ___________________________________________________

.1 ___________________________________________________

.8 ___________________________________________________

e. Now try the following numbers in the same print statement. These numbers contain a whole number and a decimal. Explain the output for each number.

02.5 ___________________________________________________

08.2 ___________________________________________________

03.1 ___________________________________________________

f. Explain what each part of the format function: format(variable, "%n.nf") does in a print statement where n.n represents a number.

variable ____________________________ First n _________________________

Second n_______________________ f _________________________

g. Revise the print statement by changing the "f" to "d" and laptopCost = 600 . Execute the statements and explain the output format.

print("Total cost of laptops: %2d" % price) print("Total cost of laptops: %10d" % price)

h. Explain how the function format(var,'10d') formats numeric data. var represents a whole number.

11. Use the following program and output to answer the questions below.

a. From the code and comments in the previous program, explain how the four main operations are implemented in this program. b. There is one new function in this sample program. What is it? From the corresponding output, determine what it does.

Application Questions: Use the Python Interpreter to check your work

8 to the 4 th power
The sum of 5 and 6 multiplied by the quotient of 34 and 7 using floating point arithmetic
Write an assignment statement that stores the remainder obtained from dividing 87 and 8 in the variable leftover
Assume:

courseLabel = "CHEM" courseNumber = "3350"

Write a line of Python code that concatenates the label with the number and stores the result in the variable courseName . Be sure that there is a space between the course label and the course number when they are concatenated.

Write one line of Python code that will print the word "Happy!" one hundred times.
Write one line of code that calculates the cost of 15 items and stores the result in the variable totalCost
Write one line of code that prints the total cost with a label, a dollar sign, and exactly two decimal places. Sample output: Total cost: $22.5
Assume:

height1 = 67850 height2 = 456

Use Python formatting to write two print statements that will produce the following output exactly at it appears below:

Homework Assignment: s2020py03

Download the assignment from the website, fill out the word document, and upload to your Google Drive folder the completed assignment along with the two python files.

1. (5 pts) Write a Python program that prompts the user for two numbers, and then gives the sum and product of those two numbers. Your sample output should look like this:

Enter your first number:10 Enter your second number:2 The sum of these numbers is: 12 The product of these two numbers is: 20

Your program must contain documentation lines that include your name, the date, a line that states "Py03 Homework question 1" and a description line that indicates what the program is supposed to do.
Paste the code this word document and upload to your Google drive when the assignment is completed, with file name [your last name]_py03_HWQ1
Save the program as a python file (ends with .py), with file name [your last name]_py03Q1_program and upload that to the Google Drive.

2. (10 pts) Write a program that calculates the molarity of a solution. Molarity is defined as numbers of moles per liter solvent. Your program will calculate molarity and must ask for the substance name, its molecular weight, how many grams of substance you are putting in solution, and the total volume of the solution. Report your calculated value of molarity to 3 decimal places. Your output should also be separated from the input with a line containing 80 asterixis.

Assuming you are using sodium chloride, your input and output should look like:

Your program must contain documentation lines that include your name, the date, a line that states "Py03 Homework question 2" and a description line that indicates what the program is supposed to do.
Paste the code to question two below
Save the program as a python file (ends with .py), with file name [your last name]_py03Q2_program and upload that to the Google Drive.

3. (4 pts) Make two hypothes.is annotations dealing with external open access resources on formatting with the format function method of formatting. These need the tag of s20iostpy03ualr .

Copyright Statement

Python Enhancement Proposals

Python »
PEP Index »

PEP 572 – Assignment Expressions

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

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

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

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

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

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

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

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

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

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

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

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

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

they would write:

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

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

Syntax and semantics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Examples from the Python standard library

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

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

Avoid nested if and remove one indentation level.

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

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

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

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

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

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

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

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

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

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

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

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

An example from the low-level UNIX world:

Rejected alternative proposals

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

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

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

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

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

Additional reasons to prefer := over this spelling include:

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

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

NAME = EXPR
if NAME := EXPR

reinforces the visual recognition of assignment expressions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Frequently Raised Objections

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

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

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

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

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

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

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

Appendix A: Tim Peters’s findings

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

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

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

is a vast improvement over the briefer:

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

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

as the briefer:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Let’s add a simple assignment expression.

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

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

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

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

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

Finally, let’s nest two comprehensions.

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

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

This document has been placed in the public domain.

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

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

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

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

Types of Python Operators

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

Arithmetic Operators
Assignment Operators
Comparison Operators
Logical Operators
Bitwise Operators
Special Operators

1. Python Arithmetic Operators

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

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

Example 1: Arithmetic Operators in Python

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

+ to add a and b
- to subtract b from a
* to multiply a and b
/ to divide a by b
// to floor divide a by b
% to get the remainder
** to get a to the power b

2. Python Assignment Operators

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

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

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

Example 2: Assignment Operators

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

Similarly, we can use any other assignment operators as per our needs.

3. Python Comparison Operators

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

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

Example 3: Comparison Operators

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

4. Python Logical Operators

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

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

Example 4: Logical Operators

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

5. Python Bitwise operators

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

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

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

6. Python Special operators

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

Identity operators

In Python, is and is not are used to check if two values are located at the same memory location.

It's important to note that having two variables with equal values doesn't necessarily mean they are identical.

Example 4: Identity operators in Python

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

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

Membership operators

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

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

Example 5: Membership operators in Python

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

Similarly, 1 is key, and 'a' is the value in dictionary dict1 . Hence, 'a' in y returns False .

Precedence and Associativity of operators in Python

Introduction
Python Arithmetic Operators
Python Assignment Operators
Python Comparison Operators
Python Logical Operators
Python Bitwise operators
Python Special operators

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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 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.

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Assignment operator in python.

Last Updated on June 8, 2023 by Prepbytes

To fully comprehend the assignment operators in Python, it is important to have a basic understanding of what operators are. Operators are utilized to carry out a variety of operations, including mathematical, bitwise, and logical operations, among others, by connecting operands. Operands are the values that are acted upon by operators. In Python, the assignment operator is used to assign a value to a variable. The assignment operator is represented by the equals sign (=), and it is the most commonly used operator in Python. In this article, we will explore the assignment operator in Python, how it works, and its different types.

What is an Assignment Operator in Python?

The assignment operator in Python is used to assign a value to a variable. The assignment operator is represented by the equals sign (=), and it is used to assign a value to a variable. When an assignment operator is used, the value on the right-hand side is assigned to the variable on the left-hand side. This is a fundamental operation in programming, as it allows developers to store data in variables that can be used throughout their code.

For example, consider the following line of code:

Explanation: In this case, the value 10 is assigned to the variable a using the assignment operator. The variable a now holds the value 10, and this value can be used in other parts of the code. This simple example illustrates the basic usage and importance of assignment operators in Python programming.

Types of Assignment Operator in Python

There are several types of assignment operator in Python that are used to perform different operations. Let’s explore each type of assignment operator in Python in detail with the help of some code examples.

1. Simple Assignment Operator (=)

The simple assignment operator is the most commonly used operator in Python. It is used to assign a value to a variable. The syntax for the simple assignment operator is:

Here, the value on the right-hand side of the equals sign is assigned to the variable on the left-hand side. For example

Explanation: In this case, the value 25 is assigned to the variable a using the simple assignment operator. The variable a now holds the value 25.

2. Addition Assignment Operator (+=)

The addition assignment operator is used to add a value to a variable and store the result in the same variable. The syntax for the addition assignment operator is:

Here, the value on the right-hand side is added to the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is incremented by 5 using the addition assignment operator. The result, 15, is then printed to the console.

3. Subtraction Assignment Operator (-=)

The subtraction assignment operator is used to subtract a value from a variable and store the result in the same variable. The syntax for the subtraction assignment operator is

Here, the value on the right-hand side is subtracted from the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is decremented by 5 using the subtraction assignment operator. The result, 5, is then printed to the console.

4. Multiplication Assignment Operator (*=)

The multiplication assignment operator is used to multiply a variable by a value and store the result in the same variable. The syntax for the multiplication assignment operator is:

Here, the value on the right-hand side is multiplied by the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is multiplied by 5 using the multiplication assignment operator. The result, 50, is then printed to the console.

5. Division Assignment Operator (/=)

The division assignment operator is used to divide a variable by a value and store the result in the same variable. The syntax for the division assignment operator is:

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 5 using the division assignment operator. The result, 2.0, is then printed to the console.

6. Modulus Assignment Operator (%=)

The modulus assignment operator is used to find the remainder of the division of a variable by a value and store the result in the same variable. The syntax for the modulus assignment operator is

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the remainder is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 3 using the modulus assignment operator. The remainder, 1, is then printed to the console.

7. Floor Division Assignment Operator (//=)

The floor division assignment operator is used to divide a variable by a value and round the result down to the nearest integer, and store the result in the same variable. The syntax for the floor division assignment operator is:

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the result is rounded down to the nearest integer. The rounded result is then stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 3 using the floor division assignment operator. The result, 3, is then printed to the console.

8. Exponentiation Assignment Operator (**=)

The exponentiation assignment operator is used to raise a variable to the power of a value and store the result in the same variable. The syntax for the exponentiation assignment operator is:

Here, the variable on the left-hand side is raised to the power of the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is raised to the power of 3 using the exponentiation assignment operator. The result, 8, is then printed to the console.

9. Bitwise AND Assignment Operator (&=)

The bitwise AND assignment operator is used to perform a bitwise AND operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise AND assignment operator is:

Here, the variable on the left-hand side is ANDed with the value on the right-hand side using the bitwise AND operator, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is ANDed with 3 using the bitwise AND assignment operator. The result, 2, is then printed to the console.

10. Bitwise OR Assignment Operator (|=)

The bitwise OR assignment operator is used to perform a bitwise OR operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise OR assignment operator is:

Here, the variable on the left-hand side is ORed with the value on the right-hand side using the bitwise OR operator, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is ORed with 3 using the bitwise OR assignment operator. The result, 7, is then printed to the console.

11. Bitwise XOR Assignment Operator (^=)

The bitwise XOR assignment operator is used to perform a bitwise XOR operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise XOR assignment operator is:

Here, the variable on the left-hand side is XORed with the value on the right-hand side using the bitwise XOR operator, and the result are stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is XORed with 3 using the bitwise XOR assignment operator. The result, 5, is then printed to the console.

12. Bitwise Right Shift Assignment Operator (>>=)

The bitwise right shift assignment operator is used to shift the bits of a variable to the right by a specified number of positions, and store the result in the same variable. The syntax for the bitwise right shift assignment operator is:

Here, the variable on the left-hand side has its bits shifted to the right by the number of positions specified by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is shifted 2 positions to the right using the bitwise right shift assignment operator. The result, 2, is then printed to the console.

13. Bitwise Left Shift Assignment Operator (<<=)

The bitwise left shift assignment operator is used to shift the bits of a variable to the left by a specified number of positions, and store the result in the same variable. The syntax for the bitwise left shift assignment operator is:

Here, the variable on the left-hand side has its bits shifted to the left by the number of positions specified by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example,

Conclusion Assignment operator in Python is used to assign values to variables, and it comes in different types. The simple assignment operator (=) assigns a value to a variable. The augmented assignment operators (+=, -=, *=, /=, %=, &=, |=, ^=, >>=, <<=) perform a specified operation and assign the result to the same variable in one step. The modulus assignment operator (%) calculates the remainder of a division operation and assigns the result to the same variable. The bitwise assignment operators (&=, |=, ^=, >>=, <<=) perform bitwise operations and assign the result to the same variable. The bitwise right shift assignment operator (>>=) shifts the bits of a variable to the right by a specified number of positions and stores the result in the same variable. The bitwise left shift assignment operator (<<=) shifts the bits of a variable to the left by a specified number of positions and stores the result in the same variable. These operators are useful in simplifying and shortening code that involves assigning and manipulating values in a single step.

Here are some Frequently Asked Questions on Assignment Operator in Python:

Q1 – Can I use the assignment operator to assign multiple values to multiple variables at once? Ans – Yes, you can use the assignment operator to assign multiple values to multiple variables at once, separated by commas. For example, "x, y, z = 1, 2, 3" would assign the value 1 to x, 2 to y, and 3 to z.

Q2 – Is it possible to chain assignment operators in Python? Ans – Yes, you can chain assignment operators in Python to perform multiple operations in one line of code. For example, "x = y = z = 1" would assign the value 1 to all three variables.

Q3 – How do I perform a conditional assignment in Python? Ans – To perform a conditional assignment in Python, you can use the ternary operator. For example, "x = a (if a > b) else b" would assign the value of a to x if a is greater than b, otherwise it would assign the value of b to x.

Q4 – What happens if I use an undefined variable in an assignment operation in Python? Ans – If you use an undefined variable in an assignment operation in Python, you will get a NameError. Make sure you have defined the variable before trying to assign a value to it.

Q5 – Can I use assignment operators with non-numeric data types in Python? Ans – Yes, you can use assignment operators with non-numeric data types in Python, such as strings or lists. For example, "my_list += [4, 5, 6]" would append the values 4, 5, and 6 to the end of the list named my_list.

Functions Defined

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3

Compound Data Types

Lists (known as arrays in other languages) are one of the compound data types that Python understands. Lists can be indexed, sliced and manipulated with other built-in functions. More about lists in Python 3

Intuitive Interpretation

Calculations are simple with Python, and expression syntax is straightforward: the operators + , - , * and / work as expected; parentheses () can be used for grouping. More about simple math functions in Python 3 .

All the Flow You’d Expect

Python knows the usual control flow statements that other languages speak — if , for , while and range — with some of its own twists, of course. More control flow tools in Python 3

Quick & Easy to Learn

Experienced programmers in any other language can pick up Python very quickly, and beginners find the clean syntax and indentation structure easy to learn. Whet your appetite with our Python 3 overview.

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The Python Language Reference
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The Python Language Reference ¶

This reference manual describes the syntax and “core semantics” of the language. It is terse, but attempts to be exact and complete. The semantics of non-essential built-in object types and of the built-in functions and modules are described in The Python Standard Library . For an informal introduction to the language, see The Python Tutorial . For C or C++ programmers, two additional manuals exist: Extending and Embedding the Python Interpreter describes the high-level picture of how to write a Python extension module, and the Python/C API Reference Manual describes the interfaces available to C/C++ programmers in detail.

1.1. Alternate Implementations
1.2. Notation
2.1. Line structure
2.2. Other tokens
2.3. Identifiers and keywords
2.4. Literals
2.5. Operators
2.6. Delimiters
3.1. Objects, values and types
3.2. The standard type hierarchy
3.3. Special method names
3.4. Coroutines
4.1. Structure of a program
4.2. Naming and binding
4.3. Exceptions
5.1. importlib
5.2. Packages
5.3. Searching
5.4. Loading
5.5. The Path Based Finder
5.6. Replacing the standard import system
5.7. Package Relative Imports
5.8. Special considerations for __main__
5.9. References
6.1. Arithmetic conversions
6.3. Primaries
6.4. Await expression
6.5. The power operator
6.6. Unary arithmetic and bitwise operations
6.7. Binary arithmetic operations
6.8. Shifting operations
6.9. Binary bitwise operations
6.10. Comparisons
6.11. Boolean operations
6.12. Assignment expressions
6.13. Conditional expressions
6.14. Lambdas
6.15. Expression lists
6.16. Evaluation order
6.17. Operator precedence
7.1. Expression statements
7.2. 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. The import statement
7.12. The global statement
7.13. The nonlocal statement
7.14. The type statement
8.1. The if statement
8.2. The while statement
8.3. The for statement
8.4. The try statement
8.5. The with statement
8.6. The match statement
8.7. Function definitions
8.8. Class definitions
8.9. Coroutines
8.10. Type parameter lists
9.1. Complete Python programs
9.2. File input
9.3. Interactive input
9.4. Expression input
10. Full Grammar specification

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6. Editors and IDEs

1. Introduction

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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!

Well, first and foremost thing: never, ever post screenshots of your program. If you are asking about your program, please post the program, not an image of it.

Also, when we are at posting your program: you have also do it in a specific way. Unfortunately the way of how this forum software works requires from you to post it this way:

Place your code between → ``` ← these three backticks (If you can’t find them on your keyboard, just copy them from my post). The backticks must appear alone in a line above and below your code. Like this way:

``` #your code goes there ``` which will result in rendering your code like this:

This is especially important as (you may not know it yet) in Python indentation serves as a part of Python’s grammar so it is crucial for your code to include them. If you post your code without these backticks the software of this forum will mangle your code making it to us as much unusable as if you were post the screenshot again.

@FelixLeg Do you find the pinned topic about formatting lacking? Maybe suggest an improvement then.

Your code looks ok to me in the screenshot. (Though we would normally put each print() on its own line. Not required, just conventional.)

Maybe your error in the right half of the screen is out of date? Do you need to press some “Run code” button?

But the real test for us is running it, and for that the easiest thing is if the code is in your post as text, not as a screenshot.

Please show your code inline as text between code fences. There’s a </> button in the compose bar for making these. Copy/paste the text of the code into that section. The same for error messages.

Cheers, Cameron Simpson [email protected]

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Guide Guide

Collection Expressions – Using C# 12 in Rider and ReSharper

Welcome to our series, where we take a closer look at the C# 12 language features and how ReSharper and Rider make it easy for you to adopt them in your codebase. If you haven’t yet, download the latest .NET 8 SDK and update your project files !

In this series, we are looking at:

Primary Constructors
Interceptors
Alias Any Type
Collection Expressions

This third part is all about collection expressions and how you can take full advantage of them with ReSharper and Rider!

Background and Syntax

In C#, we use many different types to represent collections, with various ways to create and initialize them. Many have verbose syntax, while some come with performance drawbacks, and together they make for a jumble of inconsistent code styles ( ToList vs. ToArray , IEnumerable<T> vs. params T[] , etc.). That’s plenty of motivation to sort it out and catch up with other languages like TypeScript or Python, which already successfully provide a succinct syntax. Some of the foundation for matching and deconstruction was already laid with the introduction of list patterns in C# 11 . With C# 12, we can also enjoy the power of construction!

Let’s compare how collections were constructed before C# 12 and how much cleaner they can be written now:

Since collection literals are target-typed , we cannot use var but must declare the type name for our variable. You can also see how the .. spread operator is used on the first array to integrate it into a new array with additional elements. Note that spreading could also be used between two elements.

IL Code and Conversion of Collection Expressions

You can use collection expressions to create collections for the most well-known collection types. The compiler automatically converts them to the appropriate target type:

Diving deeper into the low-level C# code generated by the compiler is always interesting. Feel free to take a look at our SharpLab snippet or to spin up the IL Viewer in ReSharper/Rider on your own solution to inspect some of these collection expressions:

Note that when the target type is IReadOnlyList<T> or IEnumerable<T> , using collection expressions comes with an additional allocation cost. In the following example, the compiler would allocate an array object for storage but also wrap this array into a compiler-generated collection class which forbids modifications at runtime:

Collection expressions also solve the issue of conversion where elements of a collection are more derived than the target’s scalar type. While the compiler initializes the collection, it implicitly takes advantage of assignment compatibility and conversion operators :

Supporting Collection Expressions on Custom Types

Similar to the duck-typing approaches for awaiting , iterating , or deconstructing objects, you can support collection expressions for your own types ! Assuming your type is a collection type, you just need to add the CollectionBuilderAttribute and provide a create method :

Do you have an interesting use case? Please let us know in the comments!

Conversion and Simplification

As with many new language features, you may find yourself eager to quickly integrate them into your daily coding routines and establish new habits. You might also feel compelled to convert the entire codebase to use these shiny additions. Unsurprisingly, ReSharper and Rider have you covered!

You can use the Use collection expression quick-fix to convert arrays, lists, and other collection-like objects to the new, more succinct syntax:

The quick-fix also takes Add calls below the initializer into account:

Of course, you can apply this quick-fix in bulk to save space and cognitive load throughout your solution, project, or file:

The bulk-fix may also bring you some peace of mind by unifying the various ways of passing empty collections :

Conversion Limitations

There are situations that ReSharper and Rider will handle more pessimistically to avoid possible runtime errors. At first, it might look like a good idea to convert the following method to use collection expressions:

However, we must anticipate that the caller may take advantage of the implementation and cast the results to a List<int> . In this case, converting to a collection expression would break the code with an InvalidCastException .

Code Style and Formatting

Every team has its unique taste regarding how code should be styled and formatted. ReSharper and Rider help you enforce your particular style through the Reformat Code action! As part of our collection expressions support, we’ve added new code style settings under some categories, like under the Line Breaks and Wrapping section:

Or under the Tabs, Indents, Alignment section:

You can try these new settings by selecting a code block and invoking the Configure code style action. Alternatively, you can grab the following snippet and adapt the formatter comments:

Future Work

We’re not done! We are still working on additional quick-fixes and context actions to make conversion easier for you. For example, we will support conversion for ToList , ToArray , and AsReadOnly invocations as well as simplifications to nested collection expressions.

In this post, we discovered several features related to the new C# 12 collection expressions. Try ReSharper 2024.1 EAP or Rider 2024.1 EAP to get the most out of the latest C# 12 in your daily work! If you see any opportunities for additional support, please let us know in the comments below!

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Python Assignment 2 Basic Programming in Python
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COMP2152-Python Assignment-2
Python Assignment Operators And Comparison Operators
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Coursera Assignment 2.3 Solved Python for Everybody
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Assignment 2.3
CourseraProgramming for Everybody (Getting Started with Python)Week 4 Assignment 2.3 Question: 2.3 Write a program to prompt the user for hours and rate per hour using input to compute gross pay. Use 35 hours and a rate of 2.75 per hour to test the program (the pay should be 96.25). You should…
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.
Operators and Expressions in Python
Note: Since version 3.8, Python also has what it calls assignment expressions. These are special types of assignments that do return a value. ... You can confirm from the output that the f(2) and f(3) calls don't occur. A similar behavior appears in an expression with multiple and operators like the following one: Python. x1 and x2 and x3 and ...
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.
Multiple assignment in Python: Assign multiple values or the same value
A tuple with one element requires a comma in Python; Swap values in a list or values of variables in Python; Take input from user with input() in Python; Try, except, else, finally in Python (Exception handling) Invert image with Python, Pillow (Negative-positive inversion) Check and add the module search path with sys.path in Python
How To Use Assignment Expressions in Python
Multiplying 0 * 0 Multiplying 1 * 1 Multiplying 2 * 2 [1, 4] You define a function named slow_calculation that multiplies the given number x with itself. A list comprehension then iterates through 0, 1, and 2 returned by range(3).An assignment expression binds the value result to the return of slow_calculation with i.You add the result to the newly built list as long as it is greater than 0.
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.
python-for-everybody/wk2
wk2 - assignment 2.3.py. Cannot retrieve latest commit at this time. # 2.3 Write a program to prompt the user for hours and rate per hour using raw_input to compute gross pay. # Use 35 hours and a rate of 2.75 per hour to test the program (the pay should be 96.25). # You should use raw_input to read a string and float () to convert the string ...
Variables & Assignment
Module 2: The Essentials of Python » Variables & Assignment; ... [-4, 2, 3] and not [1, 2, 3]. A tuple is an example of an immutable object. Once formed, there is no mechanism by which one can change of the state of a tuple; and any code that appears to be updating a tuple is in fact creating an entirely new tuple.
2.3: Arithmetic Operations and Assignment Statements
An assignment statement is a line of code that uses a "=" sign. The statement stores the result of an operation performed on the right-hand side of the sign into the variable memory location on the left-hand side. 4. Enter and execute the following lines of Python code in the editor window of your IDE (e.g. Thonny):
PEP 572
Unparenthesized assignment expressions are prohibited for the value of a keyword argument in a call. Example: foo(x = y := f(x)) # INVALID foo(x=(y := f(x))) # Valid, though probably confusing. This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.
7. Simple statements
7.2. Assignment statements ... 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 ...
Python Operators (With Examples)
2. Python Assignment Operators. Assignment operators are used to assign values to variables. For example, # assign 5 to x var x = 5. Here, = is an assignment operator that assigns 5 to x. ... [1,2,3] y3 = [1,2,3] print(x1 is not y1) # prints False print(x2 is y2) # prints True print(x3 is y3) # prints False ...
Python Assignment Operators
Python Assignment Operators. Assignment operators are used to assign values to variables: Operator. Example. Same As. Try it. =. x = 5. x = 5.
Coursera: Assignment 2.3 Python For Everybody
Coursera: Programming For Everybody Assignment 2.3 program solution Answer | Python for Everybody Assignment 2.3 program solution.Hello friends, In this vide...
The Walrus Operator: Python 3.8 Assignment Expressions
Each new version of Python adds new features to the language. For Python 3.8, the biggest change is the addition of assignment expressions.Specifically, the := operator gives you a new syntax for assigning variables in the middle of expressions. This operator is colloquially known as the walrus operator.. This tutorial is an in-depth introduction to the walrus operator.
Variables and Assignment
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 ...
python
Since Python 3.8, code can use the so-called "walrus" operator (:=), documented in PEP 572, for assignment expressions.This seems like a really substantial new feature, since it allows this form of assignment within comprehensions and lambdas.. What exactly are the syntax, semantics, and grammar specifications of assignment expressions?
Python Exercises, Practice, Challenges
These free exercises are nothing but Python assignments for the practice where you need to solve different programs and challenges. All exercises are tested on Python 3. Each exercise has 10-20 Questions. The solution is provided for every question. Practice each Exercise in Online Code Editor. These Python programming exercises are suitable ...
slice
Here is the logical equivalent code in Python. This function takes a Python object and optional parameters for slicing and returns the start, stop, step, and slice length for the requested slice. def py_slice_get_indices_ex(obj, start=None, stop=None, step=None): length = len(obj) if step is None: step = 1.
2-3 Assignment Pycharm Introduction
IT140 Project One. IT 140 Project One. "Escape the Estate" - Module 5 assignment. 4 3 Assignment Pseudocode Revisited. 2-3 Py Charm Introduction. Higher\ lower game - High Low game assignment.
Assignment Operator in Python
The simple assignment operator is the most commonly used operator in Python. It is used to assign a value to a variable. The syntax for the simple assignment operator is: variable = value. Here, the value on the right-hand side of the equals sign is assigned to the variable on the left-hand side. For example.
Welcome to Python.org
The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3. Python is a programming language that lets you work quickly and integrate systems more effectively. Learn More.
The Python Language Reference
This reference manual describes the syntax and "core semantics" of the language. It is terse, but attempts to be exact and complete. The semantics of non-essential built-in object types and of the built-in functions and modules are described in The Python Standard Library. For an informal introduction to the language, see The Python Tutorial.
My first coding assignment in Python
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!
PDF A comparison of Human, GPT-3.5, and GPT-4 Performance in a University
Can ChatGPT Pass a University Coding Course? • GPT-3.5 with prompt engineering: Here, the assignment text is modified following the guidelines in Table 2 to optimize interaction with the GPT-3.5 model, aiming for improved responses. • GPT-4 raw: Adopts the same approach as the 'GPT-3.5 Raw' but employs the more advanced gpt-4-1106-preview model.
Conditional Statements in Python
In the form shown above: <expr> is an expression evaluated in a Boolean context, as discussed in the section on Logical Operators in the Operators and Expressions in Python tutorial. <statement> is a valid Python statement, which must be indented. (You will see why very soon.) If <expr> is true (evaluates to a value that is "truthy"), then <statement> is executed.
Collection Expressions
IEnumerable<int> GetNumbers() => new List<int> { 1, 2, 3 }; However, we must anticipate that the caller may take advantage of the implementation and cast the results to a List<int>. In this case, converting to a collection expression would break the code with an InvalidCastException. Code Style and Formatting