Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!
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This Edureka PPT on Python Functions tutorial covers all the important aspects of functions in Python right from the introduction to what functions are, all the way till checking out the major functions and using the code-first approach to understand them better.
Agenda
Why use Functions?
What are the Functions?
Types of Python Functions
Built-in Functions in Python
User-defined Functions in Python
Python Lambda Function
Conclusion
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This document discusses functions and methods in Python. It defines functions and methods, and explains the differences between them. It provides examples of defining and calling functions, returning values from functions, and passing arguments to functions. It also covers topics like local and global variables, function decorators, generators, modules, and lambda functions.
Functions allow programmers to organize code into reusable blocks. A function is defined using the def keyword and can accept parameters. The body of a function contains a set of statements that run when the function is called. Functions can return values and allow code to be reused, reducing errors and improving readability. Parameters allow information to be passed into functions, while return values allow functions to provide results.
Basic Python Programming: Part 01 and Part 02Fariz Darari
This document discusses basic Python programming concepts including strings, functions, conditionals, loops, imports and recursion. It begins with examples of printing strings, taking user input, and calculating areas of shapes. It then covers variables and data types, operators, conditional statements, loops, functions, imports, strings, and recursion. Examples are provided throughout to demonstrate each concept.
What is Dictionary In Python? Python Dictionary Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/rZjhId0VkuY
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This Edureka PPT on 'Dictionary In Python' will help you understand the concept of dictionary, why and how we can use dictionary in python and various operations that we can perform on a dictionary. Below are the topics covered in this PPT:
What Is A Dictionary In Python?
Why Use A Python Dictionary?
Lists vs Dictionary
How To Implement A Dictionary In Python?
Operations In Python Dictionary
Use Case - Nested Dictionary
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The document provides an introduction to Python programming. It discusses installing and running Python, basic Python syntax like variables, data types, conditionals, and functions. It emphasizes that Python uses references rather than copying values, so assigning one variable to another causes both to refer to the same object.
A class is a code template for creating objects. Objects have member variables and have behaviour associated with them. In python a class is created by the keyword class.
An object is created using the constructor of the class. This object will then be called the instance of the class.
This document provides an introduction to Python dictionaries. It explains that dictionaries are collections of key-value pairs that are mutable and unordered. It describes how to create a dictionary by placing key-value pairs within curly braces, with unique immutable keys and values that can be of any data type. The document also outlines how to access elements from a dictionary using keys, change and add dictionary elements using assignment, and remove elements. It notes that dictionaries can be nested inside one another to model more complex information.
Python functions allow for reusable code through defining functions, passing arguments, returning values, and setting scopes. Functions can take positional or keyword arguments, as well as variable length arguments. Default arguments allow functions to specify default values for optional parameters. Functions are objects that can be assigned to variables and referenced later.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
Modules allow grouping of related functions and code into reusable files. Packages are groups of modules that provide related functionality. There are several ways to import modules and their contents using import and from statements. The document provides examples of creating modules and packages in Python and importing from them.
What is Tuple in python? | Python Tuple Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/GstQPTWpt88
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Tuple In Python' will help you understand how we can use Tuple in Python with various examples for better understanding. Following are the topics discussed:
What Is Tuple In Python?
Accessing Elements In A Tuple
Changing A Tuple
Concatenating Two Tuples
Deleting A Tuple
Tuple Methods
List vs Tuple
Tuple Constructor
Other Examples
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This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
The document discusses Python data types. It describes the numeric data types integer, float, and complex which are used to represent numbers. Integer is a whole number without decimals, float has decimals, and complex numbers have real and imaginary parts. None is described as a null value. Strings are arrays of characters and can be indexed. Tuples and lists are ordered collections that can hold heterogeneous data types. Sets are unordered collections of unique items. Dictionaries are unordered collections of key-value pairs that allow accessing values via keys.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Python If Else | If Else Statement In Python | EdurekaEdureka!
YouTube Link: https://youtu.be/nMEFZ6TvkDA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'If Else In Python' will help you understand how you can use a conditional if and else statements in python for decision making with concepts like shorthand if and else, nested if-else, etc. Following are the topics discussed:
What Are Python Conditions?
What Is If And If Else In Python?
Syntax For If Else In Python
Shorthand If Else
Use Case - Nested If Else
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The document discusses various concepts related to functions in Python including defining functions, passing arguments, default arguments, arbitrary argument lists, lambda expressions, function annotations, and documentation strings. Functions provide modularity and code reusability. Arguments can be passed by value or reference and default values are evaluated once. Keyword, arbitrary and unpacked arguments allow flexible calling. Lambda expressions define small anonymous functions. Annotations provide type metadata and docstrings document functions.
This chapter discusses dictionaries and sets in Python. It covers how to create, manipulate, and iterate over dictionaries and sets. Some key dictionary topics include adding and retrieving key-value pairs, checking for keys, and using dictionary methods. For sets, the chapter discusses set operations like union, intersection, difference and symmetric difference. It also covers serializing objects using the pickle module.
Arrays in Python can hold multiple values and each element has a numeric index. Arrays can be one-dimensional (1D), two-dimensional (2D), or multi-dimensional. Common operations on arrays include accessing elements, adding/removing elements, concatenating arrays, slicing arrays, looping through elements, and sorting arrays. The NumPy library provides powerful capabilities to work with n-dimensional arrays and matrices.
The Key Difference between a List and a Tuple. The main difference between lists and a tuples is the fact that lists are mutable whereas tuples are immutable. A mutable data type means that a python object of this type can be modified. Let's create a list and assign it to a variable.
1. Python Presented By: Rajesh Kumar Guided By: Mr. Jaishankar Bhatt
2. Content Python Introduction Python Code Execution Python Comments & Indentation Variables Data Types Strings Collections (Arrays)
3. Python Introduction What is Python? Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum Released in 1991
4. Python Code Execution Source code extension is .py Byte code extension is .pyc (compiled python code) Python’s runtime execution model:
5. Comments •Creating a Comment: Ex: Comments starts with a # Output:
6. Comments •Multi Line Comments: Ex: or:
7. Python Indentation •Indentation refers to the spaces at the beginning of a code line. Ex1: Ex2:
8. Variables •Variables are containers for storing data values. Ex:
9. Data Types •Built-in Data Types
10. Getting the Data Type •You can get the data type of any object by using the type() method. Ex: Print the data type of the variable x: Output:
11. Setting the Data Type •In Python, the data type is set when you assign a value to a variable:
12. Strings •String literals in python are surrounded by either single quotation marks, or double quotation marks. •'hello' is the same as "hello". Ex:
13. Multiline Strings •You can assign a multiline string to a variable by using three quotes Ex: Output:
14. Slicing •You can return a range of characters by using the slice syntax. Ex:Get the characters from position 2 to position 5. Output:
15. String Methods Method Description len() Returns the length of a string. lower() Returns the string in lower case. upper() Returns the string in upper case. count() Returns the number of times a specified value appears in the string.
16. Collections (Arrays) •There are four collection data types in the Python programming language. Types: 1. List 2. Tuple 3. Set 4. Dictionary
17. Python Lists •A list is a collection which is ordered and changeable. In Python lists are written with square brackets. Ex: Create a List: Output:
18. Python Tuples •A tuple is a collection which is ordered and unchangeable. In Python tuples are written with round brackets. Ex: Create a Tuple: Output:
19. Python Sets •A set is a collection which is unordered and unindexed. In Python sets are written with curly brackets. Ex: Create a Set:
20. Python Dictionaries •A dictionary is a collection which is unordered, changeable and indexed. In Python dictionaries are written with curly brackets. Ex: Create a Dictionary:
21. Conclusion Python is a great option, whether you are a beginning programmer looking to learn the basics, an experienced programmer designing a large application, or anywhere in between. The basics of Python are easily grasped, and yet its capabilities are vast.
22. Reference https://www.udemy.com/course/learn- programming-with-python https://www.w3schools.com/python/default.asp
2D array in C++ language ,define the concept of c++ Two-Dimensional array .with example .and also Accessing Array Components concept.and Processing Two-Dimensional Arrays.
The document discusses various Python datatypes. It explains that Python supports built-in and user-defined datatypes. The main built-in datatypes are None, numeric, sequence, set and mapping types. Numeric types include int, float and complex. Common sequence types are str, bytes, list, tuple and range. Sets can be created using set and frozenset datatypes. Mapping types represent a group of key-value pairs like dictionaries.
This document discusses Python functions. It defines a function as a block of code that performs a specific task. Functions help break programs into smaller, modular chunks. The document explains how to define functions using the def keyword, how to call functions, and how functions can take arguments. It also covers default arguments, keyword arguments, arbitrary arguments, recursion, anonymous functions, and provides examples of each.
The document contains tips and best practices for productivity, Git, code reviews, editors, code style, Asana usage, and coding in general. Key points include committing code often, using pull requests, following code style guidelines, adding all tasks to Asana, writing readable and commented code, and prioritizing usability over optimizations.
This document provides an introduction to Python dictionaries. It explains that dictionaries are collections of key-value pairs that are mutable and unordered. It describes how to create a dictionary by placing key-value pairs within curly braces, with unique immutable keys and values that can be of any data type. The document also outlines how to access elements from a dictionary using keys, change and add dictionary elements using assignment, and remove elements. It notes that dictionaries can be nested inside one another to model more complex information.
Python functions allow for reusable code through defining functions, passing arguments, returning values, and setting scopes. Functions can take positional or keyword arguments, as well as variable length arguments. Default arguments allow functions to specify default values for optional parameters. Functions are objects that can be assigned to variables and referenced later.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
Modules allow grouping of related functions and code into reusable files. Packages are groups of modules that provide related functionality. There are several ways to import modules and their contents using import and from statements. The document provides examples of creating modules and packages in Python and importing from them.
What is Tuple in python? | Python Tuple Tutorial | EdurekaEdureka!
YouTube Link: https://youtu.be/GstQPTWpt88
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Tuple In Python' will help you understand how we can use Tuple in Python with various examples for better understanding. Following are the topics discussed:
What Is Tuple In Python?
Accessing Elements In A Tuple
Changing A Tuple
Concatenating Two Tuples
Deleting A Tuple
Tuple Methods
List vs Tuple
Tuple Constructor
Other Examples
Follow us to never miss an update in the future.
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This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
The document discusses Python data types. It describes the numeric data types integer, float, and complex which are used to represent numbers. Integer is a whole number without decimals, float has decimals, and complex numbers have real and imaginary parts. None is described as a null value. Strings are arrays of characters and can be indexed. Tuples and lists are ordered collections that can hold heterogeneous data types. Sets are unordered collections of unique items. Dictionaries are unordered collections of key-value pairs that allow accessing values via keys.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Python If Else | If Else Statement In Python | EdurekaEdureka!
YouTube Link: https://youtu.be/nMEFZ6TvkDA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'If Else In Python' will help you understand how you can use a conditional if and else statements in python for decision making with concepts like shorthand if and else, nested if-else, etc. Following are the topics discussed:
What Are Python Conditions?
What Is If And If Else In Python?
Syntax For If Else In Python
Shorthand If Else
Use Case - Nested If Else
Follow us to never miss an update in the future.
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The document discusses various concepts related to functions in Python including defining functions, passing arguments, default arguments, arbitrary argument lists, lambda expressions, function annotations, and documentation strings. Functions provide modularity and code reusability. Arguments can be passed by value or reference and default values are evaluated once. Keyword, arbitrary and unpacked arguments allow flexible calling. Lambda expressions define small anonymous functions. Annotations provide type metadata and docstrings document functions.
This chapter discusses dictionaries and sets in Python. It covers how to create, manipulate, and iterate over dictionaries and sets. Some key dictionary topics include adding and retrieving key-value pairs, checking for keys, and using dictionary methods. For sets, the chapter discusses set operations like union, intersection, difference and symmetric difference. It also covers serializing objects using the pickle module.
Arrays in Python can hold multiple values and each element has a numeric index. Arrays can be one-dimensional (1D), two-dimensional (2D), or multi-dimensional. Common operations on arrays include accessing elements, adding/removing elements, concatenating arrays, slicing arrays, looping through elements, and sorting arrays. The NumPy library provides powerful capabilities to work with n-dimensional arrays and matrices.
The Key Difference between a List and a Tuple. The main difference between lists and a tuples is the fact that lists are mutable whereas tuples are immutable. A mutable data type means that a python object of this type can be modified. Let's create a list and assign it to a variable.
1. Python Presented By: Rajesh Kumar Guided By: Mr. Jaishankar Bhatt
2. Content Python Introduction Python Code Execution Python Comments & Indentation Variables Data Types Strings Collections (Arrays)
3. Python Introduction What is Python? Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum Released in 1991
4. Python Code Execution Source code extension is .py Byte code extension is .pyc (compiled python code) Python’s runtime execution model:
5. Comments •Creating a Comment: Ex: Comments starts with a # Output:
6. Comments •Multi Line Comments: Ex: or:
7. Python Indentation •Indentation refers to the spaces at the beginning of a code line. Ex1: Ex2:
8. Variables •Variables are containers for storing data values. Ex:
9. Data Types •Built-in Data Types
10. Getting the Data Type •You can get the data type of any object by using the type() method. Ex: Print the data type of the variable x: Output:
11. Setting the Data Type •In Python, the data type is set when you assign a value to a variable:
12. Strings •String literals in python are surrounded by either single quotation marks, or double quotation marks. •'hello' is the same as "hello". Ex:
13. Multiline Strings •You can assign a multiline string to a variable by using three quotes Ex: Output:
14. Slicing •You can return a range of characters by using the slice syntax. Ex:Get the characters from position 2 to position 5. Output:
15. String Methods Method Description len() Returns the length of a string. lower() Returns the string in lower case. upper() Returns the string in upper case. count() Returns the number of times a specified value appears in the string.
16. Collections (Arrays) •There are four collection data types in the Python programming language. Types: 1. List 2. Tuple 3. Set 4. Dictionary
17. Python Lists •A list is a collection which is ordered and changeable. In Python lists are written with square brackets. Ex: Create a List: Output:
18. Python Tuples •A tuple is a collection which is ordered and unchangeable. In Python tuples are written with round brackets. Ex: Create a Tuple: Output:
19. Python Sets •A set is a collection which is unordered and unindexed. In Python sets are written with curly brackets. Ex: Create a Set:
20. Python Dictionaries •A dictionary is a collection which is unordered, changeable and indexed. In Python dictionaries are written with curly brackets. Ex: Create a Dictionary:
21. Conclusion Python is a great option, whether you are a beginning programmer looking to learn the basics, an experienced programmer designing a large application, or anywhere in between. The basics of Python are easily grasped, and yet its capabilities are vast.
22. Reference https://www.udemy.com/course/learn- programming-with-python https://www.w3schools.com/python/default.asp
2D array in C++ language ,define the concept of c++ Two-Dimensional array .with example .and also Accessing Array Components concept.and Processing Two-Dimensional Arrays.
The document discusses various Python datatypes. It explains that Python supports built-in and user-defined datatypes. The main built-in datatypes are None, numeric, sequence, set and mapping types. Numeric types include int, float and complex. Common sequence types are str, bytes, list, tuple and range. Sets can be created using set and frozenset datatypes. Mapping types represent a group of key-value pairs like dictionaries.
This document discusses Python functions. It defines a function as a block of code that performs a specific task. Functions help break programs into smaller, modular chunks. The document explains how to define functions using the def keyword, how to call functions, and how functions can take arguments. It also covers default arguments, keyword arguments, arbitrary arguments, recursion, anonymous functions, and provides examples of each.
The document contains tips and best practices for productivity, Git, code reviews, editors, code style, Asana usage, and coding in general. Key points include committing code often, using pull requests, following code style guidelines, adding all tasks to Asana, writing readable and commented code, and prioritizing usability over optimizations.
This document discusses functions in Python. It defines a function as a block of reusable code that has a name. The function header includes the name, parameters in parentheses, and return type. The function body contains the code within curly braces. Functions are called by their name and parameters are passed within parentheses. Examples are provided of defining, calling, and returning values from functions in Python.
The document discusses Python functions. It explains that functions allow programmers to define reusable blocks of code to perform tasks or operations. It provides examples of defining functions using def, passing parameters to functions, and how each function call creates a new stack frame. It also discusses how functions can return values and the concept that every function in Python returns something, even if it is None.
The document discusses Python functions. Some key points covered include:
- Functions are reusable blocks of code defined using the def keyword that can accept parameters and return values.
- To execute a function, it must be called by name with appropriate arguments.
- Functions can call themselves, which is known as recursion.
- Functions can have default, variable, and keyword parameters to provide flexibility in how they are called.
The document discusses various methods for reading data from files in Python, including reading the entire file content into a string, reading the file in chunks, reading one line at a time, and reading all lines at once into a list. It demonstrates counting the total number of characters or getting the average line length for a sample haiku file using different reading methods.
Lazy evaluation in Python delays the evaluation of expressions until their values are needed. It enables infinite data structures by lazily evaluating sequences, abstracts control structures using generators and generator expressions that follow the iterator protocol. Lazy evaluation provides performance benefits in both computation time and memory usage.
Functions in Python are defined using the 'def' keyword followed by the function name and arguments. Functions return values using the 'return' statement and do not declare argument or return types. Python allows functions to be passed as arguments to other functions or used anonymously without naming. Python is popular for scientific programming due to its easy syntax, built-in data types, ability to run on many systems, large library of scientific packages, large user community, and use in universities.
Python modules allow code reuse and organization. A module is a Python file with a .py extension that contains functions and other objects. Modules can be imported and their contents accessed using dot notation. Modules have a __name__ variable that is set to the module name when imported but is set to "__main__" when the file is executed as a script. Packages are collections of modules organized into directories, with each directory being a package. The Python path defines locations where modules can be found during imports.
This document discusses various Python functions concepts including defining simple functions, functions with arguments, default arguments, lambda functions, generators, and decorators. It provides examples of defining functions that take positional and keyword arguments, using lambda functions, creating generators using yield, and applying decorators to functions. Various introspection methods like type(), dir(), and callable() are also covered.
The document provides tips for caring for and maintaining a laptop computer. It recommends keeping liquids, food, and other debris away from the laptop. Important maintenance steps include using antivirus software, cleaning the laptop regularly by removing dust and debris, protecting the screen, and avoiding rapid temperature changes or heavy objects on top of the laptop. Proper handling and use of a laptop case can help prevent damage.
Ansible is a tool for configuration management and application deployment. It works by connecting to target systems using SSH and executing commands. Ansible has simple definitions for describing system configurations and states using YAML files and modules written in Python. Modules allow Ansible to assess system states, make changes idempotently to ensure systems match the defined states. Ansible is highly modular and has many contributors due to its architecture, examples, documentation and an active community.
This document discusses object-oriented programming concepts in Python including multiple inheritance, method resolution order, method overriding, and static and class methods. It provides examples of multiple inheritance where a class inherits from more than one parent class. It also explains method resolution order which determines the search order for methods and attributes in cases of multiple inheritance. The document demonstrates method overriding where a subclass redefines a method from its parent class. It describes static and class methods in Python, noting how static methods work on class data instead of instance data and can be called through both the class and instances, while class methods always receive the class as the first argument.
Basics of Object Oriented Programming in PythonSujith Kumar
The document discusses key concepts of object-oriented programming (OOP) including classes, objects, methods, encapsulation, inheritance, and polymorphism. It provides examples of classes in Python and explains OOP principles like defining classes with the class keyword, using self to reference object attributes and methods, and inheriting from base classes. The document also describes operator overloading in Python to allow operators to have different meanings based on the object types.
Functions in Python with all type of argumentsriazahamed37
Functions are the structured or procedural programming way of
organizing the logic in the programs.
Functions are often compared to procedures. Both are entities
which can be invoked, but the traditional function or "black box,"
perhaps taking some or no input parameters, performs some
amount of processing and concludes by sending back a return
value to the caller.
Some functions are Boolean in nature, returning a "yes" or "no"
answer, or, more appropriately, a non-zero or zero value.
Procedures, often compared to functions, are simply special cases,
functions which do not return a value.
Python procedures are implied functions because the interpreter
implicitly returns a default value of None.
Calling Functions
Functions are called using the same pair of parentheses that are
used to. In fact, some consider ( ( ) ) to be a two-character
operator, the function operator.
Any input parameters or arguments must be placed between these
calling parentheses.
Parentheses are also used as part of function declarations to define
those arguments.
The concept of keyword arguments applies only to function
invocation. The idea here is for the caller to identify the arguments
by parameter name in a function call. This specification
allows for arguments to be missing or out-of-order because the
interpreter is able to use the provided keywords to match values to
parameters.
Default arguments are those which are declared with default
values. Parameters which are not passed on a function call are
thus allowed and are assigned the default value.
Functions are created using the def statement. The header line consists of the def keyword, the function name,
and a set of arguments (if any).
The remainder of the def clause consists of an optional but
highly-recommended documentation string and the required
function body suite
Functions may return a value back to its caller and those which are
more procedural in nature do not explicitly return anything at all.
Languages which treat procedures as functions usually have a
special type or value name for functions that "return nothing.“
Like most languages, python also return only one value/object from
a function. One difference is that in returning a container type, it will
actually return more than a single object.
In Python, functions are just like any other object. They can be
referenced (accessed or aliased to other variables), passed as
arguments to functions, be elements of container objects like lists
and dictionaries, etc.
The one unique characteristic of functions which may set them
apart from other objects is that they are callable, i.e., can be
invoked via the function operator
A Python function's set of formal arguments consists of all
parameters passed to the function on invocation for which there is
an exact correspondence to those of the argument list in the
function declaration.
These arguments include all required arguments (passed to the
function in correct ordered
Functions allow for code reusability and modularity by defining reusable blocks of code. A function is defined using the def keyword followed by the function name and parameters. Functions can take in arguments and return values. There are different types of arguments like default arguments, keyword arguments, positional arguments, and arbitrary arguments. Functions improve efficiency and reduce errors through code reuse.
This document discusses Python functions. It defines a function as a reusable block of code that performs a specific task. Functions help break programs into smaller, modular chunks. The key components of a function definition are the def keyword, the function name, parameters in parentheses, and a colon. Functions can take different types of arguments, including positional, default, keyword, and variable length arguments. Objects like lists, dictionaries, and sets are mutable and can change, while numbers, strings, tuples are immutable and cannot change. The document provides examples of passing list, tuples, and dictionaries to functions using techniques like tuples, asterisk operators, and double asterisk operators.
The document discusses functions in Python. It defines what functions are, how to define functions, pass arguments to functions, return values from functions, and scope of objects in functions. It covers built-in functions, when to write your own functions, and different types of arguments like required arguments, keyword arguments, default arguments, and variable-length arguments. It also discusses how functions communicate with their environment through parameters and arguments, and pass-by-reference vs pass-by-value in Python functions.
This document discusses functions in Python. It begins by defining what a function is and provides examples of built-in functions and functions defined in modules. It then lists some advantages of using functions such as code reusability and readability. The document discusses the different types of functions - built-in functions, functions defined in modules, and user-defined functions. It provides examples of each type. The document also covers topics such as function parameters, return values, variable scope, lambda functions, and using functions from libraries.
This document discusses functions in Python. It begins by defining what a function is - a block of code that performs a specific task and only runs when called. User-defined functions can be created in Python. The document outlines the advantages of using functions such as code reusability and readability. It provides an example of defining and calling a simple function. It discusses variable scope within functions and different types of arguments that can be passed to functions. The document also covers passing different data types like lists, dictionaries and strings to functions. Finally, it discusses using functions from library modules like math and string functions.
This document discusses functions in Python. It begins by defining what a function is - a block of code that performs a specific task and only runs when called. User-defined functions can be created in Python. The document outlines the advantages of using functions such as code reusability and readability. It provides an example of defining and calling a simple function. It also discusses variable scope in functions, including local, global, and nonlocal variables. Finally, it covers passing different data types like numbers, lists, dictionaries and strings to functions.
The document discusses functions in Python. It defines a function as a block of code that performs a specific task and only runs when called. Functions can take parameters as input and return values. Some key points covered include:
- User-defined functions can be created in Python in addition to built-in functions.
- Functions make code reusable, readable, and modular. They allow for easier testing and maintenance of code.
- Variables can have local, global, or non-local scope depending on where they are used.
- Functions can take positional/required arguments, keyword arguments, default arguments, and variable length arguments.
- Objects passed to functions can be mutable like lists, causing pass by
1. The document discusses functions in Python including types of functions, arguments, parameters, scope of variables, and returning values from functions.
2. Functions allow you to organize and reuse code, and in Python are defined using the def keyword. Arguments pass information into a function as variables called parameters.
3. Variables can have local or global scope depending on whether they are defined inside or outside of a function. The global keyword is used to read or write global variables inside a function.
The document discusses functions in Python. It defines a function as a block of code that performs a specific task and only runs when called. There are three types of functions: built-in functions, functions defined in modules, and user-defined functions. Functions can take parameters and return values. Variables used in functions can have local, global, or nonlocal scope. Functions allow for code reusability and modularity.
functions in python By Eng. Osama Ghandour الدوال فى البايثون مع مهندس اسامه ...Osama Ghandour Geris
Functions are blocks of reusable code that perform single actions. They provide modularity and code reusability. Functions can take arguments, including default arguments, and return values. Arguments can be passed by value for immutable objects or by reference for mutable objects. Functions can also take arbitrary and keyword arguments. Lambda functions are small anonymous functions. Documentation strings and annotations provide metadata about functions.
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Sensitivity labels, powered by Microsoft Purview Information Protection, serve as the foundation for classifying and protecting your sensitive data within Microsoft 365. Their importance extends beyond classification and play a crucial role in enforcing governance policies across your Microsoft 365 environment. Join me, a Data Security Consultant and Microsoft MVP, as I share practical tips and tricks to get the full potential of sensitivity labels. I discuss sensitive information types, automatic labeling, and seamless integration with Data Loss Prevention, Teams Premium, and Microsoft 365 Copilot.
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What is Oracle EPM A Guide to Oracle EPM Cloud Everything You Need to KnowSMACT Works
In today's fast-paced business landscape, financial planning and performance management demand powerful tools that deliver accurate insights. Oracle EPM (Enterprise Performance Management) stands as a leading solution for organizations seeking to transform their financial processes. This comprehensive guide explores what Oracle EPM is, its key benefits, and how partnering with the right Oracle EPM consulting team can maximize your investment.
Data Virtualization: Bringing the Power of FME to Any ApplicationSafe Software
Imagine building web applications or dashboards on top of all your systems. With FME’s new Data Virtualization feature, you can deliver the full CRUD (create, read, update, and delete) capabilities on top of all your data that exploit the full power of FME’s all data, any AI capabilities. Data Virtualization enables you to build OpenAPI compliant API endpoints using FME Form’s no-code development platform.
In this webinar, you’ll see how easy it is to turn complex data into real-time, usable REST API based services. We’ll walk through a real example of building a map-based app using FME’s Data Virtualization, and show you how to get started in your own environment – no dev team required.
What you’ll take away:
-How to build live applications and dashboards with federated data
-Ways to control what’s exposed: filter, transform, and secure responses
-How to scale access with caching, asynchronous web call support, with API endpoint level security.
-Where this fits in your stack: from web apps, to AI, to automation
Whether you’re building internal tools, public portals, or powering automation – this webinar is your starting point to real-time data delivery.
Soulmaite review - Find Real AI soulmate reviewSoulmaite
Looking for an honest take on Soulmaite? This Soulmaite review covers everything you need to know—from features and pricing to how well it performs as a real AI soulmate. We share how users interact with adult chat features, AI girlfriend 18+ options, and nude AI chat experiences. Whether you're curious about AI roleplay porn or free AI NSFW chat with no sign-up, this review breaks it down clearly and informatively.
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Feeling stuck in the Matrix of your training technologies? You’re not alone. Managing your training catalog, wrangling LMSs and delivering content across different tools and audiences can feel like dodging digital bullets. At some point, you hit a fork in the road: Keep patching things up as issues pop up… or follow the rabbit hole to the root of the problems.
Good news, we’ve already been down that rabbit hole. Peter Overton and Cameron Gray of Rustici Software are here to share what we found. In this webinar, we’ll break down 5 training roadblocks in delivery and management and show you how they’re easier to fix than you might think.
Boosting MySQL with Vector Search -THE VECTOR SEARCH CONFERENCE 2025 .pdfAlkin Tezuysal
As the demand for vector databases and Generative AI continues to rise, integrating vector storage and search capabilities into traditional databases has become increasingly important. This session introduces the *MyVector Plugin*, a project that brings native vector storage and similarity search to MySQL. Unlike PostgreSQL, which offers interfaces for adding new data types and index methods, MySQL lacks such extensibility. However, by utilizing MySQL's server component plugin and UDF, the *MyVector Plugin* successfully adds a fully functional vector search feature within the existing MySQL + InnoDB infrastructure, eliminating the need for a separate vector database. The session explains the technical aspects of integrating vector support into MySQL, the challenges posed by its architecture, and real-world use cases that showcase the advantages of combining vector search with MySQL's robust features. Attendees will leave with practical insights on how to add vector search capabilities to their MySQL systems.
Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...Anish Kumar
Presented by: Anish Kumar
LinkedIn: https://www.linkedin.com/in/anishkumar/
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This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights.
Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.
In this talk, Elliott explores how developers can embrace AI not as a threat, but as a collaborative partner.
We’ll examine the shift from routine coding to creative leadership, highlighting the new developer superpowers of vision, integration, and innovation.
We'll touch on security, legacy code, and the future of democratized development.
Whether you're AI-curious or already a prompt engineering, this session will help you find your rhythm in the new dance of modern development.
6th Power Grid Model Meetup
Join the Power Grid Model community for an exciting day of sharing experiences, learning from each other, planning, and collaborating.
This hybrid in-person/online event will include a full day agenda, with the opportunity to socialize afterwards for in-person attendees.
If you have a hackathon proposal, tell us when you register!
About Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
Interested in leveling up your JavaScript skills? Join us for our Introduction to TypeScript workshop.
Learn how TypeScript can improve your code with dynamic typing, better tooling, and cleaner architecture. Whether you're a beginner or have some experience with JavaScript, this session will give you a solid foundation in TypeScript and how to integrate it into your projects.
Workshop content:
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2. amitu.com Descriptors In Python
This is from a section in my “Python for
Developers” book. The book covers python
material for people who are already
developers, even python developers.
Check it out: amitu.com/python/
3. amitu.com Descriptors In Python
In this we are going to learn about function
arguments in Python
Positional and Keyword arguments, *
and ** syntax etc.
4. amitu.com Function arguments: positional and keyword
Functions can take arguments when called.
Here we have defined a function foo, that takes
a single “positional argument”, x.
When we called foo(12), 12 got assigned to x
when the control was inside foo().
6. amitu.com Function arguments: positional and keyword
One problem with function taking many arguments
is remembering the order of arguments.
7. amitu.com Function arguments: positional and keyword
Knowing that a function add_user() exists is not
enough, nor is knowing that it takes name, gender
and location. You must also remember in what order
those three must be supplied.
How do we make such obviously wrong code
look wrong?
8. amitu.com Function arguments: positional and keyword
Sometimes your code editor can help you. But we
can not always rely on that. What if you are
browsing code on GitHub?
9. amitu.com Function arguments: positional and keyword
To solve this precise problem, Python
supports function calling using what is called
“keyword argument” syntax:
… as against “positional arguments”, eg
foo(10, 20), where arguments are identified by
their position.
10. amitu.com Function arguments: positional and keyword
When using keyword arguments, you can even
change the order of keyword arguments, and
python will do the right thing.
11. amitu.com Function arguments: positional and keyword
We can also mix both positional and
keyword arguments:
isn't Python cool?
12. amitu.com Function arguments: positional and keyword
There is one constraint—we can not pass a
positional argument after a keyword argument. If we
use both, all positional arguments must come before
any keyword argument.
14. amitu.com Function arguments: positional and keyword
In Python, we can define functions with
arguments that take default values.
15. amitu.com Function arguments: positional and keyword
We defined foo() to have default value of
10 for y. If we don't pass anything for y,
python uses the default value.
18. amitu.com Function arguments: positional and keyword
NOTE: like we can not pass keyword arguments after
positional arguments when calling function, we
similarly cannot define functions with default values
before arguments that do not have default values.
Arguments with default values must come at the end.
20. amitu.com Function arguments: positional and keyword
One common gotcha when using default arguments is
using a mutable type as the default value.
Default value for users here is a list, [ ], which is a
mutable data type in Python.
21. amitu.com Function arguments: positional and keyword
Let us say our intention is to create a users list. If it is
not passed we want to use an empty list by default. We
append to passed list, or the default empty list, and
returned the modified list.
22. amitu.com Function arguments: positional and keyword
So far, so good, and it seems to be working fine.
Let us see when and where the problem begins.
What happened here? Why is “sam” in the
free_users list? add_user() should have taken
default empty list, and added “richard” and
returned that. Why two members?
23. amitu.com Function arguments: positional and keyword
What is happening is that, the empty list we have used
as default value of users in the function definition…
… the same instance is used as value of users,
every time we do not pass the second parameter.
And by the second time function is called,
the list is not empty any more!
24. amitu.com Function arguments: positional and keyword
The proper way to write such a function would
be using a value that would never make sense
to be passed, say None or -1.
ASIDE: such use of a value that is used for a
special purpose is called a sentinel value.
25. amitu.com Function arguments: positional and keyword
There is another subtle mistake we can make.
Let us look at another version of add_user() to
give emphasis to it.
Here, we first check if the passed in value is None.
We have used strong identity check (is None).
26. amitu.com Function arguments: positional and keyword
The difference is subtle users is None vs not users.
First creates a new list if nothing was passed (and we took
the default value of None), other if empty list was passed.
28. amitu.com Function arguments: positional and keyword
The reason such NotPassed is preferable over None
is because our code can have None assigned to
something by mistake, but getting NotPassed
assigned to some variable has to be intentional.
29. amitu.com Function arguments: positional and keyword
Variable Arguments
Sometimes we want to write functions that
take variable number of arguments.
30. amitu.com Function arguments: positional and keyword
This is how we write a function in Python that
takes variable number of arguments:
31. amitu.com Function arguments: positional and keyword
Note the *args. The * indicates to Python
that this function takes the variable number
of positional arguments, and when the
function is called, all those arguments are
stored in tuple() and passed to the function
by the name args. Instead of args, we could
have called it anything.
32. amitu.com Function arguments: positional and keyword
We can not treat args as a keyword argument.
But we can achieve the same by * syntax:
33. amitu.com Function arguments: positional and keyword
We have used * while calling the function. When
using this syntax, we must pass an iterable. It
can be a tuple, a list, or any iterable.
Note that the list was converted to a tuple.
Whatever you pass will get converted to tuple.
34. amitu.com Function arguments: positional and keyword
We can use this syntax even for functions that do
not take a variable number of arguments…
39. amitu.com Function arguments: positional and keyword
Python 3 accepts it, but treats y as a compulsory
keyword argument.
40. amitu.com Function arguments: positional and keyword
A variable number of arguments works with default
value arguments too.
… both in Python 2 and Python 3.
41. amitu.com Function arguments: positional and keyword
But there is a possibility of ambiguity here.
Python would not accept it.
42. amitu.com Function arguments: positional and keyword
Like we have *arg syntax which tells Python to
capture all remaining positional arguments,
there is a similar **kw syntax that tells Python
to capture all remaining keyword arguments
even ones we never defined.
43. amitu.com Function arguments: positional and keyword
Python will capture all the keyword
arguments in a dict(), so everything that we
know about dict() can be used with them.
44. amitu.com Function arguments: positional and keyword
A new dictionary is constructed for you every
time a function that takes variable number of
keyword arguments is called.
45. amitu.com Function arguments: positional and keyword
In this case, our function accepts no
positional argument, so we can not call it
with a positional argument.
46. amitu.com Function arguments: positional and keyword
You can mix and match all forms of
arguments in a single function.
Just be careful that keyword arguments must come after
positional ones, and there must not be any ambiguities.
47. amitu.com Function arguments: positional and keyword
Finally, you can use the **{} syntax when
calling a function too.
Even when the function being called doesn't
take keyword arguments…
48. amitu.com Function arguments: positional and keyword
With these features, we can create a "universal
function" that can be called with any combination of
positional and keyword arguments:
These play special role when we are writing generic
decorators, or when we want to override a method
without knowing the parent methods signature.
49. amitu.com Descriptors In Python
Thats it for now!
You have seen a section in my “Python for
Developers” book. The book covers
python material for people who're already
developers, even python developers.
Check it out: amitu.com/python/