assignment functions python

Python Numerical Methods

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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 operators are used to assign values to variables:

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Python Matrix Exercises

Python program to a Sort Matrix by index-value equality count
Python Program to Reverse Every Kth row in a Matrix
Python Program to Convert String Matrix Representation to Matrix
Python - Count the frequency of matrix row length
Python - Convert Integer Matrix to String Matrix
Python Program to Convert Tuple Matrix to Tuple List
Python - Group Elements in Matrix
Python - Assigning Subsequent Rows to Matrix first row elements
Adding and Subtracting Matrices in Python
Python - Convert Matrix to dictionary
Python - Convert Matrix to Custom Tuple Matrix
Python - Matrix Row subset
Python - Group similar elements into Matrix
Python - Row-wise element Addition in Tuple Matrix
Create an n x n square matrix, where all the sub-matrix have the sum of opposite corner elements as even

Python Functions Exercises

Python splitfields() Method
How to get list of parameters name from a function in Python?
How to Print Multiple Arguments in Python?
Python program to find the power of a number using recursion
Sorting objects of user defined class in Python

Assign Function to a Variable in Python

Returning a function from a function - Python
What are the allowed characters in Python function names?
Defining a Python function at runtime
Explicitly define datatype in a Python function
Functions that accept variable length key value pair as arguments
How to find the number of arguments in a Python function?
How to check if a Python variable exists?
Python - Get Function Signature
Python program to convert any base to decimal by using int() method

Python Lambda Exercises

Python - Lambda Function to Check if value is in a List
Difference between Normal def defined function and Lambda
Python: Iterating With Python Lambda
How to use if, else & elif in Python Lambda Functions
Python - Lambda function to find the smaller value between two elements
Lambda with if but without else in Python
Python Lambda with underscore as an argument
Difference between List comprehension and Lambda in Python
Nested Lambda Function in Python
Python lambda
Python | Sorting string using order defined by another string
Python | Find fibonacci series upto n using lambda
Overuse of lambda expressions in Python
Python program to count Even and Odd numbers in a List
Intersection of two arrays in Python ( Lambda expression and filter function )

Python Pattern printing Exercises

Simple Diamond Pattern in Python
Python - Print Heart Pattern
Python program to display half diamond pattern of numbers with star border
Python program to print Pascal's Triangle
Python program to print the Inverted heart pattern
Python Program to print hollow half diamond hash pattern
Program to Print K using Alphabets
Program to print half Diamond star pattern
Program to print window pattern
Python Program to print a number diamond of any given size N in Rangoli Style
Python program to right rotate n-numbers by 1
Python Program to print digit pattern
Print with your own font using Python !!
Python | Print an Inverted Star Pattern
Program to print the diamond shape

Python DateTime Exercises

Python - Iterating through a range of dates
How to add time onto a DateTime object in Python
How to add timestamp to excel file in Python
Convert string to datetime in Python with timezone
Isoformat to datetime - Python
Python datetime to integer timestamp
How to convert a Python datetime.datetime to excel serial date number
How to create filename containing date or time in Python
Convert "unknown format" strings to datetime objects in Python
Extract time from datetime in Python
Convert Python datetime to epoch
Python program to convert unix timestamp string to readable date
Python - Group dates in K ranges
Python - Divide date range to N equal duration
Python - Last business day of every month in year

Python OOPS Exercises

Get index in the list of objects by attribute in Python
Python program to build flashcard using class in Python
How to count number of instances of a class in Python?
Shuffle a deck of card with OOPS in Python
What is a clean and Pythonic way to have multiple constructors in Python?
How to Change a Dictionary Into a Class?
How to create an empty class in Python?
Student management system in Python
How to create a list of object in Python class

Python Regex Exercises

Validate an IP address using Python without using RegEx
Python program to find the type of IP Address using Regex
Converting a 10 digit phone number to US format using Regex in Python
Python program to find Indices of Overlapping Substrings
Python program to extract Strings between HTML Tags
Python - Check if String Contain Only Defined Characters using Regex
How to extract date from Excel file using Pandas?
Python program to find files having a particular extension using RegEx
How to check if a string starts with a substring using regex in Python?
How to Remove repetitive characters from words of the given Pandas DataFrame using Regex?
Extract punctuation from the specified column of Dataframe using Regex
Extract IP address from file using Python
Python program to Count Uppercase, Lowercase, special character and numeric values using Regex
Categorize Password as Strong or Weak using Regex in Python
Python - Substituting patterns in text using regex

Python LinkedList Exercises

Python program to Search an Element in a Circular Linked List
Implementation of XOR Linked List in Python
Pretty print Linked List in Python
Python Library for Linked List
Python | Stack using Doubly Linked List
Python | Queue using Doubly Linked List
Program to reverse a linked list using Stack
Python program to find middle of a linked list using one traversal
Python Program to Reverse a linked list

Python Searching Exercises

Binary Search (bisect) in Python
Python Program for Linear Search
Python Program for Anagram Substring Search (Or Search for all permutations)
Python Program for Binary Search (Recursive and Iterative)
Python Program for Rabin-Karp Algorithm for Pattern Searching
Python Program for KMP Algorithm for Pattern Searching

Python Sorting Exercises

Python Code for time Complexity plot of Heap Sort
Python Program for Stooge Sort
Python Program for Recursive Insertion Sort
Python Program for Cycle Sort
Bisect Algorithm Functions in Python
Python Program for BogoSort or Permutation Sort
Python Program for Odd-Even Sort / Brick Sort
Python Program for Gnome Sort
Python Program for Cocktail Sort
Python Program for Bitonic Sort
Python Program for Pigeonhole Sort
Python Program for Comb Sort
Python Program for Iterative Merge Sort
Python Program for Binary Insertion Sort
Python Program for ShellSort

Python DSA Exercises

Saving a Networkx graph in GEXF format and visualize using Gephi
Dumping queue into list or array in Python
Python program to reverse a stack
Python - Stack and StackSwitcher in GTK+ 3
Multithreaded Priority Queue in Python
Python Program to Reverse the Content of a File using Stack
Priority Queue using Queue and Heapdict module in Python
Box Blur Algorithm - With Python implementation
Python program to reverse the content of a file and store it in another file
Check whether the given string is Palindrome using Stack
Take input from user and store in .txt file in Python
Change case of all characters in a .txt file using Python
Finding Duplicate Files with Python

Python File Handling Exercises

Python Program to Count Words in Text File
Python Program to Delete Specific Line from File
Python Program to Replace Specific Line in File
Python Program to Print Lines Containing Given String in File
Python - Loop through files of certain extensions
Compare two Files line by line in Python
How to keep old content when Writing to Files in Python?
How to get size of folder using Python?
How to read multiple text files from folder in Python?
Read a CSV into list of lists in Python
Python - Write dictionary of list to CSV
Convert nested JSON to CSV in Python
How to add timestamp to CSV file in Python

Python CSV Exercises

How to create multiple CSV files from existing CSV file using Pandas ?
How to read all CSV files in a folder in Pandas?
How to Sort CSV by multiple columns in Python ?
Working with large CSV files in Python
How to convert CSV File to PDF File using Python?
Visualize data from CSV file in Python
Python - Read CSV Columns Into List
Sorting a CSV object by dates in Python
Python program to extract a single value from JSON response
Convert class object to JSON in Python
Convert multiple JSON files to CSV Python
Convert JSON data Into a Custom Python Object
Convert CSV to JSON using Python

Python JSON Exercises

Flattening JSON objects in Python
Saving Text, JSON, and CSV to a File in Python
Convert Text file to JSON in Python
Convert JSON to CSV in Python
Convert JSON to dictionary in Python
Python Program to Get the File Name From the File Path
How to get file creation and modification date or time in Python?
Menu driven Python program to execute Linux commands
Menu Driven Python program for opening the required software Application
Open computer drives like C, D or E using Python

Python OS Module Exercises

Rename a folder of images using Tkinter
Kill a Process by name using Python
Finding the largest file in a directory using Python
Python - Get list of running processes
Python - Get file id of windows file
Python - Get number of characters, words, spaces and lines in a file
Change current working directory with Python
How to move Files and Directories in Python
How to get a new API response in a Tkinter textbox?
Build GUI Application for Guess Indian State using Tkinter Python
How to stop copy, paste, and backspace in text widget in tkinter?
How to temporarily remove a Tkinter widget without using just .place?
How to open a website in a Tkinter window?

Python Tkinter Exercises

Create Address Book in Python - Using Tkinter
Changing the colour of Tkinter Menu Bar
How to check which Button was clicked in Tkinter ?
How to add a border color to a button in Tkinter?
How to Change Tkinter LableFrame Border Color?
Looping through buttons in Tkinter
Visualizing Quick Sort using Tkinter in Python
How to Add padding to a tkinter widget only on one side ?
Python NumPy - Practice Exercises, Questions, and Solutions
Pandas Exercises and Programs
How to get the Daily News using Python
How to Build Web scraping bot in Python
Scrape LinkedIn Using Selenium And Beautiful Soup in Python
Scraping Reddit with Python and BeautifulSoup
Scraping Indeed Job Data Using Python

Python Web Scraping Exercises

How to Scrape all PDF files in a Website?
How to Scrape Multiple Pages of a Website Using Python?
Quote Guessing Game using Web Scraping in Python
How to extract youtube data in Python?
How to Download All Images from a Web Page in Python?
Test the given page is found or not on the server Using Python
How to Extract Wikipedia Data in Python?
How to extract paragraph from a website and save it as a text file?
Automate Youtube with Python
Controlling the Web Browser with Python
How to Build a Simple Auto-Login Bot with Python
Download Google Image Using Python and Selenium
How To Automate Google Chrome Using Foxtrot and Python

Python Selenium Exercises

How to scroll down followers popup in Instagram ?
How to switch to new window in Selenium for Python?
Python Selenium - Find element by text
How to scrape multiple pages using Selenium in Python?
Python Selenium - Find Button by text
Web Scraping Tables with Selenium and Python
Selenium - Search for text on page
Python Projects - Beginner to Advanced

In this article, we are going to see how to assign a function to a variable in Python. In Python, we can assign a function to a variable. And using that variable we can call the function as many as times we want. Thereby, increasing code reusability.

Implementation

Simply assign a function to the desired variable but without () i.e. just with the name of the function. If the variable is assigned with function along with the brackets (), None will be returned.

Output:

The following programs will help you understand better:

Example 1:

Example 2: parameterized function

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Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity, Membership, Bitwise

Operators are special symbols that perform some operation on operands and returns the result. For example, 5 + 6 is an expression where + is an operator that performs arithmetic add operation on numeric left operand 5 and the right side operand 6 and returns a sum of two operands as a result.

Python includes the operator module that includes underlying methods for each operator. For example, the + operator calls the operator.add(a,b) method.

Above, expression 5 + 6 is equivalent to the expression operator.add(5, 6) and operator.__add__(5, 6) . Many function names are those used for special methods, without the double underscores (dunder methods). For backward compatibility, many of these have functions with the double underscores kept.

Python includes the following categories of operators:

Arithmetic Operators

Assignment operators, comparison operators, logical operators, identity operators, membership test operators, bitwise operators.

Arithmetic operators perform the common mathematical operation on the numeric operands.

The arithmetic operators return the type of result depends on the type of operands, as below.

If either operand is a complex number, the result is converted to complex;
If either operand is a floating point number, the result is converted to floating point;
If both operands are integers, then the result is an integer and no conversion is needed.

The following table lists all the arithmetic operators in Python:

The assignment operators are used to assign values to variables. The following table lists all the arithmetic operators in Python:

The comparison operators compare two operands and return a boolean either True or False. The following table lists comparison operators in Python.

The logical operators are used to combine two boolean expressions. The logical operations are generally applicable to all objects, and support truth tests, identity tests, and boolean operations.

The identity operators check whether the two objects have the same id value e.i. both the objects point to the same memory location.

The membership test operators in and not in test whether the sequence has a given item or not. For the string and bytes types, x in y is True if and only if x is a substring of y .

Bitwise operators perform operations on binary operands.

Compare strings in Python
Convert file data to list
Convert User Input to a Number
Convert String to Datetime in Python
How to call external commands in Python?
How to count the occurrences of a list item?
How to flatten list in Python?
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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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Python Fundamentals

Python Variables and Literals
Python Type Conversion
Python Basic Input and Output
Python Operators

Python Flow Control

Python if...else Statement
Python for Loop
Python while Loop
Python break and continue
Python pass Statement

Python Data types

Python Numbers, Type Conversion and Mathematics
Python List
Python Tuple
Python Sets
Python Dictionary

Python Functions

Python Function Arguments

Python Variable Scope
Python Global Keyword
Python Recursion
Python Modules
Python Package
Python Main function

Python Files

Python Directory and Files Management
Python CSV: Read and Write CSV files
Reading CSV files in Python
Writing CSV files in Python
Python Exception Handling
Python Exceptions
Python Custom Exceptions

Python Object & Class

Python Objects and Classes
Python Inheritance
Python Multiple Inheritance
Polymorphism in Python
Python Operator Overloading

Python Advanced Topics

List comprehension

Python Lambda/Anonymous Function

Python Iterators
Python Generators
Python Namespace and Scope
Python Closures

Python Decorators

Python @property decorator
Python RegEx

Python Date and Time

Python datetime
Python strftime()
Python strptime()
How to get current date and time in Python?
Python Get Current Time
Python timestamp to datetime and vice-versa
Python time Module
Python sleep()

Additional Topic

Precedence and Associativity of Operators in Python
Python Keywords and Identifiers
Python Asserts
Python Json
Python *args and **kwargs

Python Tutorials

Python User-defined Functions

A function is a block of code that performs a specific task.

Suppose we need to create a program to make a circle and color it. We can create two functions to solve this problem:

function to create a circle
function to color the shape

Dividing a complex problem into smaller chunks makes our program easy to understand and reuse.

Create a Function

Let's create our first function.

Here are the different parts of the program:

Here, we have created a simple function named greet() that prints Hello World!

Note: When writing a function, pay attention to indentation, which are the spaces at the start of a code line.

In the above code, the print() statement is indented to show it's part of the function body, distinguishing the function's definition from its body.

Calling a Function

In the above example, we have declared a function named greet() .

If we run the above code, we won't get an output.

It's because creating a function doesn't mean we are executing the code inside it. It means the code is there for us to use if we want to.

To use this function, we need to call the function.

Function Call

Example: Python Function Call

In the above example, we have created a function named greet() . Here's how the control of the program flows:

When the function greet() is called, the program's control transfers to the function definition.
All the code inside the function is executed.
The control of the program jumps to the next statement after the function call.

Arguments are inputs given to the function.

Sample Output 1

Here, we passed ' John' as an argument to the greet() function.

We can pass different arguments in each call, making the function re-usable and dynamic.

Let's call the function with a different argument.

Sample Output 2

Example: Function to Add Two Numbers

In the above example, we have created a function named add_numbers() with arguments: num1 and num2 .

Parameters are the variables listed inside the parentheses in the function definition. They act like placeholders for the data the function can accept when we call them.

Think of parameters as the blueprint that outlines what kind of information the function expects to receive.

In this example, the print_age() function takes age as its input. However, at this stage, the actual value is not specified.

The age parameter is just a placeholder waiting for a specific value to be provided when the function is called.

Arguments are the actual values that we pass to the function when we call it.

Arguments replace the parameters when the function executes.

Here, during the function call, the argument 25 is passed to the function.

The return Statement

We return a value from the function using the return statement.

In the above example, we have created a function named find_square() . The function accepts a number and returns the square of the number.

Note: The return statement also denotes that the function has ended. Any code after return is not executed.

The pass Statement

The pass statement serves as a placeholder for future code, preventing errors from empty code blocks.

It's typically used where code is planned but has yet to be written.

Note : To learn more, visit Python Pass Statement .

Python Library Functions

Python provides some built-in functions that can be directly used in our program.

We don't need to create the function, we just need to call them.

Some Python library functions are:

print() - prints the string inside the quotation marks
sqrt() - returns the square root of a number
pow() - returns the power of a number

These library functions are defined inside the module. And to use them, we must include the module inside our program.

For example, sqrt() is defined inside the math module.

Note : To learn more about library functions, please visit Python Library Functions .

Example: Python Library Function

Here, we imported a math module to use the library functions sqrt() and pow() .

Video: Introduction to Python Functions

Sorry about that.

Optimizing Job Assignments with Python: A Greedy Approach

In this article, we will learn the skill of job assignment which is a very important topic in the field of Operations Research. For this, we will utilize Python programming language and the Numpy library for the same. We will also solve a small case on a job assignment.

Job assignment involves allocating tasks to workers while minimizing overall completion time or cost. Python’s greedy algorithm, combined with NumPy, can solve such problems by iteratively assigning jobs based on worker skills and constraints, enabling efficient resource management in various industries.

Recommended: Maximizing Cost Savings Through Offshore Development: A Comprehensive Guide

Recommended: Delivery Route Optimization using Python: A Step-by-Step Guide

What is a Job Assignment?

Let us understand what a job assignment is with an example. In our example, three tasks have to be completed. Three workers have different sets of skills and take different amounts of time to complete the above-mentioned tasks. Now our goal is to assign the jobs to the workers to minimize the period of completing the three tasks.

Now, we solve the above problem using the concepts of Linear programming. Now there are certain constraints as well, each worker can be assigned only a single job at a time. Our objective function is the sum of all the time taken by the workers and minimize it. Let us now solve this problem using the power of the Numpy library of Python programming language.

Let us now look at the output of the problem.

From the output, we can see that The assignment is complete and optimized. Let us now look at a small case and understand the job assignment further.

A Real-World Job Assignment Scenario

Continuing with the example of assigning workers some jobs, in this case, a company is looking to get some work done with the help of some freelancers. There are 15 jobs and we have 10 freelancers. We have to assign jobs to workers in such a way, that we minimize the time as well as the cost of the whole operation. Let us now model this in the Python programming language.

This problem is solved using the greedy algorithm. In short, the greedy algorithm selects the most optimal choice available and does not consider what will happen in the future while making this choice. In the above code, we have randomly generated data on freelancer details. Let us now look at the output of the code.

Thus, we complete our agenda of job assignment while minimizing costs as evidenced by the output.

Assigning jobs optimally is crucial for maximizing productivity and minimizing costs in today’s competitive landscape. Python’s powerful libraries like NumPy make it easy to implement greedy algorithms and solve complex job assignment problems, even with larger sets of jobs and workers. How could you adapt this approach to accommodate dynamic changes in job requirements or worker availability?

Recommended: Splitting Lists into Sub-Lists in Python: Techniques and Advantages

Recommended: Object Detection with OpenCV: A Step-by-Step Tutorial

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5. Data Structures
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5. Data Structures ¶

This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.

5.1. More on Lists ¶

The list data type has some more methods. Here are all of the methods of list objects:

Add an item to the end of the list. Equivalent to a[len(a):] = [x] .

Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable .

Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x) .

Remove the first item from the list whose value is equal to x . It raises a ValueError if there is no such item.

Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. It raises an IndexError if the list is empty or the index is outside the list range.

Remove all items from the list. Equivalent to del a[:] .

Return zero-based index in the list of the first item whose value is equal to x . Raises a ValueError if there is no such item.

The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.

Return the number of times x appears in the list.

Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).

Reverse the elements of the list in place.

Return a shallow copy of the list. Equivalent to a[:] .

An example that uses most of the list methods:

You might have noticed that methods like insert , remove or sort that only modify the list have no return value printed – they return the default None . [ 1 ] This is a design principle for all mutable data structures in Python.

Another thing you might notice is that not all data can be sorted or compared. For instance, [None, 'hello', 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison.

5.1.1. Using Lists as Stacks ¶

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append() . To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

5.1.2. Using Lists as Queues ¶

It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:

5.1.3. List Comprehensions ¶

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using:

or, equivalently:

which is more concise and readable.

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

and it’s equivalent to:

Note how the order of the for and if statements is the same in both these snippets.

If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.

List comprehensions can contain complex expressions and nested functions:

5.1.4. Nested List Comprehensions ¶

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:

The following list comprehension will transpose rows and columns:

As we saw in the previous section, the inner list comprehension is evaluated in the context of the for that follows it, so this example is equivalent to:

which, in turn, is the same as:

In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:

See Unpacking Argument Lists for details on the asterisk in this line.

5.2. The del statement ¶

There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

del can also be used to delete entire variables:

Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.

5.3. Tuples and Sequences ¶

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple .

A tuple consists of a number of values separated by commas, for instance:

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.

Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable , and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples ). Lists are mutable , and their elements are usually homogeneous and are accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

The statement t = 12345, 54321, 'hello!' is an example of tuple packing : the values 12345 , 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

5.4. Sets ¶

Python also includes a data type for sets . A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set() , not {} ; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

Similarly to list comprehensions , set comprehensions are also supported:

5.5. Dictionaries ¶

Another useful data type built into Python is the dictionary (see Mapping Types — dict ). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys , which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend() .

It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {} . Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del . If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.

Here is a small example using a dictionary:

The dict() constructor builds dictionaries directly from sequences of key-value pairs:

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

5.6. Looping Techniques ¶

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.

It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.

5.7. More on Conditions ¶

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c .

Comparisons may be combined using the Boolean operators and and or , and the outcome of a comparison (or of any other Boolean expression) may be negated with not . These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C . As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C . When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator := . This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

5.8. Comparing Sequences and Other Types ¶

Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:

Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.

5.1.1. Using Lists as Stacks
5.1.2. Using Lists as Queues
5.1.3. List Comprehensions
5.1.4. Nested List Comprehensions
5.2. The del statement
5.3. Tuples and Sequences
5.5. Dictionaries
5.6. Looping Techniques
5.7. More on Conditions
5.8. Comparing Sequences and Other Types

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Issue on XMLRPC working with LoadBalancer

I am working on a Python assignment for Distributed Computing Systems and running into some issues . I have 3 VM’s running: AirlineServer1 , AirlineServer2 and a LoadBalancer which calls the two airline services in a RoundRobin mode. The LoadBalancer has functions to get list of flights, reserve a flight or cancel a flight. Then, finally, from my Mac node, I run a VacationClient which instead of calling the airline functions, it will call the LoadBalancer.

I run into the issue below after running the client. I checked DNS names, ports and firewall is disabled in the VM:

This is the VacationClient code:

This is the LoadBalancer:

This is one of the AirlineServers (AirlineServer2 is same code but different DNS and same port)

Can someone help me figure it out since I am new to Python and possible point out more error that may occur please? Thanks

This error or something similar will occur if the server is not accepting connections (or if the client attempts to connect to the wrong server). Before jumping to separate VMs, rule out a Network, Firewall, or DNS issue by a) setting ThiagoAirlineServer and all IP addresses to 127.0.0.1 (or localhost ), b) picking new distinct ports for each connection if needs be, and c) by running each server’s code in a separate Python process, e.g. in its own terminal session (by the way, it’s best practise to set config data like URLs and IP addresses in config files or in env variables - it makes testing and debugging much easier, amongst other benefits. The Twelve-Factor App and launching and tearing down a stack like this is very easy with Docker compose if you containerise the services - the containers can still be run on VMs afterwards).

If localhost alone doesn’t narrow it down, try it without the load balancer. And then if not, with only one AirlineServer . If there’s a problem running your code as a simple client / server connection over local host, then it’s worth looking at the Python code in detail.

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Python bindings for llama.cpp

abetlen/llama-cpp-python

Folders and files, repository files navigation, 🦙 python bindings for llama.cpp.

Simple Python bindings for @ggerganov's llama.cpp library. This package provides:

Low-level access to C API via ctypes interface.
OpenAI-like API
LangChain compatibility
LlamaIndex compatibility
Local Copilot replacement
Function Calling support
Vision API support
Multiple Models

Documentation is available at https://llama-cpp-python.readthedocs.io/en/latest .

Installation

Requirements:

Python 3.8+
Linux: gcc or clang
Windows: Visual Studio or MinGW
MacOS: Xcode

To install the package, run:

This will also build llama.cpp from source and install it alongside this python package.

If this fails, add --verbose to the pip install see the full cmake build log.

Pre-built Wheel (New)

It is also possible to install a pre-built wheel with basic CPU support.

Installation Configuration

llama.cpp supports a number of hardware acceleration backends to speed up inference as well as backend specific options. See the llama.cpp README for a full list.

All llama.cpp cmake build options can be set via the CMAKE_ARGS environment variable or via the --config-settings / -C cli flag during installation.

They can also be set via pip install -C / --config-settings command and saved to a requirements.txt file:

Supported Backends

Below are some common backends, their build commands and any additional environment variables required.

To install with OpenBLAS, set the LLAMA_BLAS and LLAMA_BLAS_VENDOR environment variables before installing:

To install with CUDA support, set the LLAMA_CUDA=on environment variable before installing:

It is also possible to install a pre-built wheel with CUDA support. As long as your system meets some requirements:

CUDA Version is 12.1, 12.2 or 12.3
Python Version is 3.10, 3.11 or 3.12

Where <cuda-version> is one of the following:

cu121 : CUDA 12.1
cu122 : CUDA 12.2
cu123 : CUDA 12.3

For example, to install the CUDA 12.1 wheel:

To install with Metal (MPS), set the LLAMA_METAL=on environment variable before installing:

It is also possible to install a pre-built wheel with Metal support. As long as your system meets some requirements:

MacOS Version is 11.0 or later

To install with CLBlast, set the LLAMA_CLBLAST=on environment variable before installing:

To install with hipBLAS / ROCm support for AMD cards, set the LLAMA_HIPBLAS=on environment variable before installing:

To install with Vulkan support, set the LLAMA_VULKAN=on environment variable before installing:

To install with Kompute support, set the LLAMA_KOMPUTE=on environment variable before installing:

To install with SYCL support, set the LLAMA_SYCL=on environment variable before installing:

Windows Notes

If you run into issues where it complains it can't find 'nmake' '?' or CMAKE_C_COMPILER, you can extract w64devkit as mentioned in llama.cpp repo and add those manually to CMAKE_ARGS before running pip install:

See the above instructions and set CMAKE_ARGS to the BLAS backend you want to use.

MacOS Notes

Detailed MacOS Metal GPU install documentation is available at docs/install/macos.md

Note: If you are using Apple Silicon (M1) Mac, make sure you have installed a version of Python that supports arm64 architecture. For example:

Otherwise, while installing it will build the llama.cpp x86 version which will be 10x slower on Apple Silicon (M1) Mac.

Try installing with

Upgrading and Reinstalling

To upgrade and rebuild llama-cpp-python add --upgrade --force-reinstall --no-cache-dir flags to the pip install command to ensure the package is rebuilt from source.

High-level API

API Reference

The high-level API provides a simple managed interface through the Llama class.

Below is a short example demonstrating how to use the high-level API to for basic text completion:

Text completion is available through the __call__ and create_completion methods of the Llama class.

Pulling models from Hugging Face Hub

You can download Llama models in gguf format directly from Hugging Face using the from_pretrained method. You'll need to install the huggingface-hub package to use this feature ( pip install huggingface-hub ).

By default from_pretrained will download the model to the huggingface cache directory, you can then manage installed model files with the huggingface-cli tool.

Chat Completion

The high-level API also provides a simple interface for chat completion.

Chat completion requires that the model knows how to format the messages into a single prompt. The Llama class does this using pre-registered chat formats (ie. chatml , llama-2 , gemma , etc) or by providing a custom chat handler object.

The model will will format the messages into a single prompt using the following order of precedence:

Use the chat_handler if provided
Use the chat_format if provided
Use the tokenizer.chat_template from the gguf model's metadata (should work for most new models, older models may not have this)
else, fallback to the llama-2 chat format

Set verbose=True to see the selected chat format.

Chat completion is available through the create_chat_completion method of the Llama class.

For OpenAI API v1 compatibility, you use the create_chat_completion_openai_v1 method which will return pydantic models instead of dicts.

JSON and JSON Schema Mode

To constrain chat responses to only valid JSON or a specific JSON Schema use the response_format argument in create_chat_completion .

The following example will constrain the response to valid JSON strings only.

JSON Schema Mode

To constrain the response further to a specific JSON Schema add the schema to the schema property of the response_format argument.

Function Calling

The high-level API supports OpenAI compatible function and tool calling. This is possible through the functionary pre-trained models chat format or through the generic chatml-function-calling chat format.

The various gguf-converted files for this set of models can be found here . Functionary is able to intelligently call functions and also analyze any provided function outputs to generate coherent responses. All v2 models of functionary supports parallel function calling . You can provide either functionary-v1 or functionary-v2 for the chat_format when initializing the Llama class.

Due to discrepancies between llama.cpp and HuggingFace's tokenizers, it is required to provide HF Tokenizer for functionary. The LlamaHFTokenizer class can be initialized and passed into the Llama class. This will override the default llama.cpp tokenizer used in Llama class. The tokenizer files are already included in the respective HF repositories hosting the gguf files.

NOTE : There is no need to provide the default system messages used in Functionary as they are added automatically in the Functionary chat handler. Thus, the messages should contain just the chat messages and/or system messages that provide additional context for the model (e.g.: datetime, etc.).

Multi-modal Models

llama-cpp-python supports the llava1.5 family of multi-modal models which allow the language model to read information from both text and images.

You'll first need to download one of the available multi-modal models in GGUF format:

llava-v1.5-7b
llava-v1.5-13b
bakllava-1-7b

Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images.

Images can be passed as base64 encoded data URIs. The following example demonstrates how to do this.

Speculative Decoding

llama-cpp-python supports speculative decoding which allows the model to generate completions based on a draft model.

The fastest way to use speculative decoding is through the LlamaPromptLookupDecoding class.

Just pass this as a draft model to the Llama class during initialization.

To generate text embeddings use create_embedding or embed . Note that you must pass embedding=True to the constructor upon model creation for these to work properly.

There are two primary notions of embeddings in a Transformer-style model: token level and sequence level . Sequence level embeddings are produced by "pooling" token level embeddings together, usually by averaging them or using the first token.

Models that are explicitly geared towards embeddings will usually return sequence level embeddings by default, one for each input string. Non-embedding models such as those designed for text generation will typically return only token level embeddings, one for each token in each sequence. Thus the dimensionality of the return type will be one higher for token level embeddings.

It is possible to control pooling behavior in some cases using the pooling_type flag on model creation. You can ensure token level embeddings from any model using LLAMA_POOLING_TYPE_NONE . The reverse, getting a generation oriented model to yield sequence level embeddings is currently not possible, but you can always do the pooling manually.

Adjusting the Context Window

The context window of the Llama models determines the maximum number of tokens that can be processed at once. By default, this is set to 512 tokens, but can be adjusted based on your requirements.

For instance, if you want to work with larger contexts, you can expand the context window by setting the n_ctx parameter when initializing the Llama object:

OpenAI Compatible Web Server

llama-cpp-python offers a web server which aims to act as a drop-in replacement for the OpenAI API. This allows you to use llama.cpp compatible models with any OpenAI compatible client (language libraries, services, etc).

To install the server package and get started:

Similar to Hardware Acceleration section above, you can also install with GPU (cuBLAS) support like this:

Navigate to http://localhost:8000/docs to see the OpenAPI documentation.

To bind to 0.0.0.0 to enable remote connections, use python3 -m llama_cpp.server --host 0.0.0.0 . Similarly, to change the port (default is 8000), use --port .

You probably also want to set the prompt format. For chatml, use

That will format the prompt according to how model expects it. You can find the prompt format in the model card. For possible options, see llama_cpp/llama_chat_format.py and look for lines starting with "@register_chat_format".

If you have huggingface-hub installed, you can also use the --hf_model_repo_id flag to load a model from the Hugging Face Hub.

Web Server Features

Docker image.

A Docker image is available on GHCR . To run the server:

Docker on termux (requires root) is currently the only known way to run this on phones, see termux support issue

Low-level API

The low-level API is a direct ctypes binding to the C API provided by llama.cpp . The entire low-level API can be found in llama_cpp/llama_cpp.py and directly mirrors the C API in llama.h .

Below is a short example demonstrating how to use the low-level API to tokenize a prompt:

Check out the examples folder for more examples of using the low-level API.

Documentation

Documentation is available via https://llama-cpp-python.readthedocs.io/ . If you find any issues with the documentation, please open an issue or submit a PR.

Development

This package is under active development and I welcome any contributions.

To get started, clone the repository and install the package in editable / development mode:

You can also test out specific commits of lama.cpp by checking out the desired commit in the vendor/llama.cpp submodule and then running make clean and pip install -e . again. Any changes in the llama.h API will require changes to the llama_cpp/llama_cpp.py file to match the new API (additional changes may be required elsewhere).

Are there pre-built binaries / binary wheels available?

The recommended installation method is to install from source as described above. The reason for this is that llama.cpp is built with compiler optimizations that are specific to your system. Using pre-built binaries would require disabling these optimizations or supporting a large number of pre-built binaries for each platform.

That being said there are some pre-built binaries available through the Releases as well as some community provided wheels.

In the future, I would like to provide pre-built binaries and wheels for common platforms and I'm happy to accept any useful contributions in this area. This is currently being tracked in #741

How does this compare to other Python bindings of llama.cpp ?

I originally wrote this package for my own use with two goals in mind:

Provide a simple process to install llama.cpp and access the full C API in llama.h from Python
Provide a high-level Python API that can be used as a drop-in replacement for the OpenAI API so existing apps can be easily ported to use llama.cpp

Any contributions and changes to this package will be made with these goals in mind.

This project is licensed under the terms of the MIT license.

Releases 140

Contributors 131.

Python 97.6%

IMAGES

4. Multiple Assignment Function in Python
Variable Assignment in Python
Assignment Operators in Python
Functions in Python
Functions in Python
Functions in Python Programming

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Assignment
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COMMENTS

Python's Assignment Operator: Write Robust Assignments
To create a new variable or to update the value of an existing one in Python, you'll use an assignment statement. This statement has the following three components: A left operand, which must be a variable. The assignment operator ( =) A right operand, which can be a concrete value, an object, or an expression.
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x() is not the function, it's the call of the function. In python, functions are simply a type of variable, and can generally be used like any other variable. For example: def power_function(power): return lambda x : x**power power_function(3)(2) This returns 8. power_function is a function that
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x += 1. print(x) OUTPUT. 3. There are several other augmented assignment forms: -=, **=, &=, etc. Don't miss your chance to ride the wave of the data revolution! Every industry is scaling new heights by tapping into the power of data. Sharpen your skills and become a part of the hottest trend in the 21st century.
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In-place Operators¶. Many operations have an "in-place" version. Listed below are functions providing a more primitive access to in-place operators than the usual syntax does; for example, the statement x += y is equivalent to x = operator.iadd(x, y).Another way to put it is to say that z = operator.iadd(x, y) is equivalent to the compound statement z = x; z += y.
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The author selected the COVID-19 Relief Fund to receive a donation as part of the Write for DOnations program.. Introduction. Python 3.8, released in October 2019, adds assignment expressions to Python via the := syntax. The assignment expression syntax is also sometimes called "the walrus operator" because := vaguely resembles a walrus with tusks. ...
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Python Overview Python Built-in Functions Python String Methods Python List Methods Python Dictionary Methods Python Tuple Methods Python Set Methods Python File Methods Python Keywords Python Exceptions Python Glossary ... Python Assignment Operators. Assignment operators are used to assign values to variables: Operator Example Same As
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PEP 572
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The isinstance() built-in function is recommended for testing the type of an object, because it takes subclasses into account. With three arguments, return a new type object. This is essentially a dynamic form of the class statement. The name string is the class name and becomes the __name__ attribute.
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Do you want to assign different return values from two different calls to your random function or a single value to two variables generated by a single call to the function. For the former, use tuple unpacking. ... Double assignment in python. 0. Python - assigning functions to variables. 2.
Python Function Exercises
1. Write a function find_max that accepts three numbers as arguments and returns the largest number among three. Write another function main, in main () function accept three numbers from user and call find_max. Solution. 2. Write a function, is_vowel that returns the value true if a given character is a vowel, and otherwise returns false.
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Hello all, I am working on a Python assignment for Distributed Computing Systems and running into some issues . I have 3 VM's running: AirlineServer1, AirlineServer2 and a LoadBalancer which calls the two airline services in a RoundRobin mode. The LoadBalancer has functions to get list of flights, reserve a flight or cancel a flight. Then, finally, from my Mac node, I run a VacationClient ...
Python Daily Question -36 Explanation:- 1. Variable Assignment Outside
51 likes, 12 comments - coding_with_mani on April 28, 2024: "Python Daily Question -36 Explanation:- 1. Variable Assignment Outside the Function: - a is initially assigned the value 6. 2.
GitHub
Provide a simple process to install llama.cpp and access the full C API in llama.h from Python; Provide a high-level Python API that can be used as a drop-in replacement for the OpenAI API so existing apps can be easily ported to use llama.cpp; Any contributions and changes to this package will be made with these goals in mind.