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.
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.
This document discusses function parameters in Python. It explains that parameters hold the values passed into a function as arguments. Parameters act as variables that accept the values passed during a function call. Functions can have multiple parameters, and the number and order of arguments passed must match the function definition. Arguments are passed by value, so changes to a parameter do not affect the original argument. A function can return multiple values using return, and functions themselves can be passed as parameters to other functions.
Functions allow programmers to organize code into reusable blocks. There are built-in functions and user-defined functions. Functions make code easier to develop, test and reuse. Variables inside functions can be local, global or nonlocal. Parameters pass data into functions, while functions can return values. Libraries contain pre-defined functions for tasks like mathematics and string manipulation.
Python main function. Main function is the entry point of any program. But python interpreter executes the source file code sequentially and doesn't call any method if it's not part of the code. But if it's directly part of the code then it will be executed when the file is imported as a module.
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 document discusses user-defined functions in C++. It covers defining functions with return types and parameters, using return statements, function prototypes, and the flow of execution when a function is called. Functions help make programs more modular and understandable by breaking tasks into reusable blocks of code. Defining functions properly allows the compiler to understand how to execute function calls within a program.
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.
This document discusses functions in C++. It defines a function as a block of code that performs a specific task and can be reused. The key points made are:
- Functions allow for modular and reusable code. They group statements and give them a name to be called from other parts of a program.
- The document demonstrates simple functions in C++ through examples, including defining, declaring, calling, passing arguments to, and returning values from functions.
- Other function concepts covered include function overloading, recursion, inline functions, default arguments, scope and storage class, and global vs local variables.
This document provides an outline and overview of functions in C++. It discusses:
- The definition of a function as a block of code that performs a specific task and can be called from other parts of the program.
- The standard library that is included in C++ and provides useful tools like containers, iterators, algorithms and more.
- The parts of a function definition including the return type, name, parameters, and body.
- How to declare functions, call functions by passing arguments, and how arguments are handled.
- Scope rules for local and global variables as they relate to functions.
This presentation introduces built-in functions in C programming. It defines built-in functions as functions provided by the C library that perform common tasks like file access, math operations, and graphics without needing to be defined by the programmer. It provides examples of commonly used built-in functions from header files like stdio.h for input/output, string.h for string manipulation, and math.h for math functions. The presentation concludes by noting advantages like code reusability but also potential disadvantages like increased complexity from functions.
The document discusses functions, modules, and how to modularize Python programs. It provides examples of defining functions, using parameters, returning values, and function scope. It also discusses creating modules, importing modules, and the difference between running a Python file as a module versus running it as the main script using the __name__ == "__main__" check. The key points are that functions help break programs into reusable and readable components, modules further help organize code, and the __name__ check allows code to run differently depending on how it is imported or run directly.
This document discusses predefined and user-defined functions in JavaScript. It explains that functions allow code to be reused by passing control between the call and definition. Both built-in functions like alert() and user-defined functions can be created. User-defined functions are defined with the function keyword and name, may accept parameters, and control returns to the call site after execution. Functions encapsulate reusable blocks of code.
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.
1) A Python module allows you to organize related code into a logical group and makes the code easier to understand and use.
2) Modules are imported using import statements and can contain functions, classes, and variables that can then be accessed from other code.
3) The import process searches specific directories to locate the module file based on the module name and import path.
This document provides an introduction to the Python programming language. It discusses Python's design philosophy emphasizing readability. It also covers printing messages, reading input, variables and data types, operators, and basic syntax like comments and identifiers. Arithmetic, relational, logical and bitwise operators are explained along with examples.
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.
Functions are blocks of reusable code that perform specific tasks. There are three types of functions in Python: built-in functions, anonymous lambda functions, and user-defined functions. Functions help organize code by breaking programs into smaller, modular chunks. They reduce code duplication, decompose complex problems, improve clarity, and allow code reuse. Functions can take arguments and return values. Built-in functions are pre-defined to perform operations and return results when given the required arguments.
This document discusses function overloading in C++. It explains that function overloading allows multiple functions to have the same name but different parameters. This improves readability and allows functions to be distinguished at compile time based on their signatures. It provides an example of overloading the sum() function to take integer or double parameters. The key requirements for overloading are that functions must differ in number or type of parameters, and their return types may be different. Overloaded functions are distinguished by their signatures, which include the function name and parameter types.
This document provides an overview of pointers and dynamic arrays in C++. It discusses pointer variables, memory management, pointer arithmetic, array variables as pointers, functions for manipulating strings like strcpy and strcmp, and advanced pointer notation for multi-dimensional arrays. Code examples are provided to demonstrate concepts like passing pointers to functions, dereferencing pointers, pointer arithmetic on arrays, and using string functions. The overall objective is to introduce pointers and how they enable dynamic memory allocation and manipulation of data structures in C++.
The document discusses functions in C programming. It defines functions as blocks of code that perform a specific task and can be called by other parts of the code. It covers the different components of functions like declarations, definitions, calls, passing arguments, and recursive functions. Recursive functions are functions that call themselves to break down a problem into smaller sub-problems until a base case is reached.
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.
This document defines and explains functions in C programming. It states that a function is a block of code that performs a specific task and contains its own definition. There are two types of functions: predefined functions that are included in header files, and user-defined functions that are created by the user. User-defined functions can have no arguments with no return type, no arguments with a return type, arguments with no return type, or arguments with a return type. Arguments pass data values from the calling function to the called function. A function is called by another function and will only execute when called.
This document discusses recursion in programming. It defines recursion as a technique for solving problems by repeatedly applying the same procedure to reduce the problem into smaller sub-problems. The key aspects of recursion covered include recursive functions, how they work by having a base case and recursively calling itself, examples of recursive functions in Python like calculating factorials and binary search, and the differences between recursion and iteration approaches.
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.
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.
The document discusses functions in Python. It defines a function as a block of code that performs a specific task and can be called when needed. Functions make code reusable, readable, and help divide programs into modular pieces. The document covers built-in functions, user-defined functions, passing arguments to functions, scope of variables, mutable and immutable objects, and functions available in Python libraries like math and string functions.
This document discusses functions in C++. It defines a function as a block of code that performs a specific task and can be reused. The key points made are:
- Functions allow for modular and reusable code. They group statements and give them a name to be called from other parts of a program.
- The document demonstrates simple functions in C++ through examples, including defining, declaring, calling, passing arguments to, and returning values from functions.
- Other function concepts covered include function overloading, recursion, inline functions, default arguments, scope and storage class, and global vs local variables.
This document provides an outline and overview of functions in C++. It discusses:
- The definition of a function as a block of code that performs a specific task and can be called from other parts of the program.
- The standard library that is included in C++ and provides useful tools like containers, iterators, algorithms and more.
- The parts of a function definition including the return type, name, parameters, and body.
- How to declare functions, call functions by passing arguments, and how arguments are handled.
- Scope rules for local and global variables as they relate to functions.
This presentation introduces built-in functions in C programming. It defines built-in functions as functions provided by the C library that perform common tasks like file access, math operations, and graphics without needing to be defined by the programmer. It provides examples of commonly used built-in functions from header files like stdio.h for input/output, string.h for string manipulation, and math.h for math functions. The presentation concludes by noting advantages like code reusability but also potential disadvantages like increased complexity from functions.
The document discusses functions, modules, and how to modularize Python programs. It provides examples of defining functions, using parameters, returning values, and function scope. It also discusses creating modules, importing modules, and the difference between running a Python file as a module versus running it as the main script using the __name__ == "__main__" check. The key points are that functions help break programs into reusable and readable components, modules further help organize code, and the __name__ check allows code to run differently depending on how it is imported or run directly.
This document discusses predefined and user-defined functions in JavaScript. It explains that functions allow code to be reused by passing control between the call and definition. Both built-in functions like alert() and user-defined functions can be created. User-defined functions are defined with the function keyword and name, may accept parameters, and control returns to the call site after execution. Functions encapsulate reusable blocks of code.
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.
1) A Python module allows you to organize related code into a logical group and makes the code easier to understand and use.
2) Modules are imported using import statements and can contain functions, classes, and variables that can then be accessed from other code.
3) The import process searches specific directories to locate the module file based on the module name and import path.
This document provides an introduction to the Python programming language. It discusses Python's design philosophy emphasizing readability. It also covers printing messages, reading input, variables and data types, operators, and basic syntax like comments and identifiers. Arithmetic, relational, logical and bitwise operators are explained along with examples.
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.
Functions are blocks of reusable code that perform specific tasks. There are three types of functions in Python: built-in functions, anonymous lambda functions, and user-defined functions. Functions help organize code by breaking programs into smaller, modular chunks. They reduce code duplication, decompose complex problems, improve clarity, and allow code reuse. Functions can take arguments and return values. Built-in functions are pre-defined to perform operations and return results when given the required arguments.
This document discusses function overloading in C++. It explains that function overloading allows multiple functions to have the same name but different parameters. This improves readability and allows functions to be distinguished at compile time based on their signatures. It provides an example of overloading the sum() function to take integer or double parameters. The key requirements for overloading are that functions must differ in number or type of parameters, and their return types may be different. Overloaded functions are distinguished by their signatures, which include the function name and parameter types.
This document provides an overview of pointers and dynamic arrays in C++. It discusses pointer variables, memory management, pointer arithmetic, array variables as pointers, functions for manipulating strings like strcpy and strcmp, and advanced pointer notation for multi-dimensional arrays. Code examples are provided to demonstrate concepts like passing pointers to functions, dereferencing pointers, pointer arithmetic on arrays, and using string functions. The overall objective is to introduce pointers and how they enable dynamic memory allocation and manipulation of data structures in C++.
The document discusses functions in C programming. It defines functions as blocks of code that perform a specific task and can be called by other parts of the code. It covers the different components of functions like declarations, definitions, calls, passing arguments, and recursive functions. Recursive functions are functions that call themselves to break down a problem into smaller sub-problems until a base case is reached.
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.
This document defines and explains functions in C programming. It states that a function is a block of code that performs a specific task and contains its own definition. There are two types of functions: predefined functions that are included in header files, and user-defined functions that are created by the user. User-defined functions can have no arguments with no return type, no arguments with a return type, arguments with no return type, or arguments with a return type. Arguments pass data values from the calling function to the called function. A function is called by another function and will only execute when called.
This document discusses recursion in programming. It defines recursion as a technique for solving problems by repeatedly applying the same procedure to reduce the problem into smaller sub-problems. The key aspects of recursion covered include recursive functions, how they work by having a base case and recursively calling itself, examples of recursive functions in Python like calculating factorials and binary search, and the differences between recursion and iteration approaches.
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.
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.
The document discusses functions in Python. It defines a function as a block of code that performs a specific task and can be called when needed. Functions make code reusable, readable, and help divide programs into modular pieces. The document covers built-in functions, user-defined functions, passing arguments to functions, scope of variables, mutable and immutable objects, and functions available in Python libraries like math and string 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. 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 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
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.
Textbook Solutions refer https://pythonxiisolutions.blogspot.com/
Practical's Solutions refer https://prippython12.blogspot.com/
It is difficult to manage a single list of instructions. Thus large program are broken down into smaller units known as functions. Functions can be invoked from other parts of the program. it make program more readable and understandable making program easy to manage.
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
Functions are blocks of code that perform tasks and are called when needed. User-defined functions in Python are created using the def keyword. Functions make code reusable, increase readability and modularity. Variables inside functions have local scope unless declared as global or nonlocal. Functions can take arguments and return values. Libraries contain many built-in functions for tasks like math operations and string manipulation.
This document discusses functions in Python. It defines a function as a block of code that performs a specific task and can be called when needed. Functions make programs easier to develop, test and reuse code. The document covers creating and calling user-defined functions, variable scope, passing arguments and return values, lambda functions, mutable and immutable objects, and built-in functions for common tasks like math operations and string manipulation.
The document discusses functions in the Python math module. It provides a list of common mathematical functions in the math module along with a brief description of what each function does, such as ceil(x) which returns the smallest integer greater than or equal to x, copysign(x, y) which returns x with the sign of y, and factorial(x) which returns the factorial of x. It also includes trigonometric functions like sin(x), cos(x), and tan(x), their inverse functions, and functions for exponentials, logarithms, and other common mathematical operations.
FME as an Orchestration Tool - Peak of Data & AI 2025Safe Software
Processing huge amounts of data through FME can have performance consequences, but as an orchestration tool, FME is brilliant! We'll take a look at the principles of data gravity, best practices, pros, cons, tips and tricks. And of course all spiced up with relevant examples!
How to purchase, license and subscribe to Microsoft Azure_PDF.pdfvictordsane
Microsoft Azure is a cloud platform that empowers businesses with scalable computing, data analytics, artificial intelligence, and cybersecurity capabilities.
Arguably the biggest hurdle for most organizations is understanding how to get started.
Microsoft Azure is a consumption-based cloud service. This means you pay for what you use. Unlike traditional software, Azure resources (e.g., VMs, databases, storage) are billed based on usage time, storage size, data transfer, or resource configurations.
There are three primary Azure purchasing models:
• Pay-As-You-Go (PAYG): Ideal for flexibility. Billed monthly based on actual usage.
• Azure Reserved Instances (RI): Commit to 1- or 3-year terms for predictable workloads. This model offers up to 72% cost savings.
• Enterprise Agreements (EA): Best suited for large organizations needing comprehensive Azure solutions and custom pricing.
Licensing Azure: What You Need to Know
Azure doesn’t follow the traditional “per seat” licensing model. Instead, you pay for:
• Compute Hours (e.g., Virtual Machines)
• Storage Used (e.g., Blob, File, Disk)
• Database Transactions
• Data Transfer (Outbound)
Purchasing and subscribing to Microsoft Azure is more than a transactional step, it’s a strategic move.
Get in touch with our team of licensing experts via [email protected] to further understand the purchasing paths, licensing options, and cost management tools, to optimize your investment.
How AI Can Improve Media Quality Testing Across Platforms (1).pptxkalichargn70th171
Media platforms, from video streaming to OTT and Smart TV apps, face unprecedented pressure to deliver seamless, high-quality experiences across diverse devices and networks. Ensuring top-notch Quality of Experience (QoE) is critical for user satisfaction and retention.
Design by Contract - Building Robust Software with Contract-First DevelopmentPar-Tec S.p.A.
In the fast-paced world of software development, code quality and reliability are paramount. This SlideShare deck, presented at PyCon Italia 2025 by Antonio Spadaro, DevOps Engineer at Par-Tec, introduces the “Design by Contract” (DbC) philosophy and demonstrates how a Contract-First Development approach can elevate your projects.
Beginning with core DbC principles—preconditions, postconditions, and invariants—these slides define how formal “contracts” between classes and components lead to clearer, more maintainable code. You’ll explore:
The fundamental concepts of Design by Contract and why they matter.
How to write and enforce interface contracts to catch errors early.
Real-world examples showcasing how Contract-First Development improves error handling, documentation, and testability.
Practical Python demonstrations using libraries and tools that streamline DbC adoption in your workflow.
Best Inbound Call Tracking Software for Small BusinessesTheTelephony
The best inbound call tracking software for small businesses offers features like call recording, real-time analytics, lead attribution, and CRM integration. It helps track marketing campaign performance, improve customer service, and manage leads efficiently. Look for solutions with user-friendly dashboards, customizable reporting, and scalable pricing plans tailored for small teams. Choosing the right tool can significantly enhance communication and boost overall business growth.
Bonk coin airdrop_ Everything You Need to Know.pdfHerond Labs
The Bonk airdrop, one of the largest in Solana’s history, distributed 50% of its total supply to community members, significantly boosting its popularity and Solana’s network activity. Below is everything you need to know about the Bonk coin airdrop, including its history, eligibility, how to claim tokens, risks, and current status.
https://blog.herond.org/bonk-coin-airdrop/
Revolutionize Your Insurance Workflow with Claims Management SoftwareInsurance Tech Services
Claims management software enhances efficiency, accuracy, and satisfaction by automating processes, reducing errors, and speeding up transparent claims handling—building trust and cutting costs. Explore More - https://www.damcogroup.com/insurance/claims-management-software
Top 5 Task Management Software to Boost Productivity in 2025Orangescrum
In this blog, you’ll find a curated list of five powerful task management tools to watch in 2025. Each one is designed to help teams stay organized, improve collaboration, and consistently hit deadlines. We’ve included real-world use cases, key features, and data-driven insights to help you choose what fits your team best.
Micro-Metrics Every Performance Engineer Should Validate Before Sign-OffTier1 app
When it comes to performance testing, most engineers instinctively gravitate toward the big-picture indicators—response time, memory usage, throughput. But what about the smaller, more subtle indicators that quietly shape your application’s performance and stability? we explored the hidden layer of performance diagnostics that too often gets overlooked: micro-metrics. These small but mighty data points can reveal early signs of trouble long before they manifest as outages or degradation in production.
From garbage collection behavior and object creation rates to thread state transitions and blocked thread patterns, we unpacked the critical micro-metrics every performance engineer should assess before giving the green light to any release.
This session went beyond the basics, offering hands-on demonstrations and JVM-level diagnostics that help identify performance blind spots traditional tests tend to miss. We showed how early detection of these subtle anomalies can drastically reduce post-deployment issues and production firefighting.
Whether you're a performance testing veteran or new to JVM tuning, this session helped shift your validation strategies left—empowering you to detect and resolve risks earlier in the lifecycle.
The rise of e-commerce has redefined how retailers operate—and reconciliation...Prachi Desai
As payment flows grow more fragmented, the complexity of reconciliation and revenue recognition increases. The result? Mounting operational costs, silent revenue leakages, and avoidable financial risk.
Spot the inefficiencies. Automate what’s slowing you down.
https://www.taxilla.com/ecommerce-reconciliation
Insurance policy management software transforms complex, manual insurance operations into streamlined, efficient digital workflows, enhancing productivity, accuracy, customer service, and profitability for insurers. Visit https://www.damcogroup.com/insurance/policy-management-software for more details!
In a tight labor market and tighter economy, PMOs and resource managers must ensure that every team member is focused on the highest-value work. This session explores how AI reshapes resource planning and empowers organizations to forecast capacity, prevent burnout, and balance workloads more effectively, even with shrinking teams.
Generative Artificial Intelligence and its ApplicationsSandeepKS52
The exploration of generative AI begins with an overview of its fundamental concepts, highlighting how these technologies create new content and ideas by learning from existing data. Following this, the focus shifts to the processes involved in training and fine-tuning models, which are essential for enhancing their performance and ensuring they meet specific needs. Finally, the importance of responsible AI practices is emphasized, addressing ethical considerations and the impact of AI on society, which are crucial for developing systems that are not only effective but also beneficial and fair.
Build enterprise-ready applications using skills you already have!PhilMeredith3
Process Tempo is a rapid application development (RAD) environment that empowers data teams to create enterprise-ready applications using skills they already have.
With Process Tempo, data teams can craft beautiful, pixel-perfect applications the business will love.
Process Tempo combines features found in business intelligence tools, graphic design tools and workflow solutions - all in a single platform.
Process Tempo works with all major databases such as Databricks, Snowflake, Postgres and MySQL. It also works with leading graph database technologies such as Neo4j, Puppy Graph and Memgraph.
It is the perfect platform to accelerate the delivery of data-driven solutions.
For more information, you can find us at www.processtempo.com
Automating Map Production With FME and PythonSafe Software
People still love a good paper map, but every time a request lands on a GIS team’s desk, it takes time to create that perfect, individual map—even when you're ready and have projects prepped. Then come the inevitable changes and iterations that add even more time to the process. This presentation explores a solution for automating map production using FME and Python. FME handles the setup of variables, leveraging GIS reference layers and parameters to manage details like map orientation, label sizes, and layout elements. Python takes over to export PDF maps for each location and template size, uploading them monthly to ArcGIS Online. The result? Fresh, regularly updated maps, ready for anyone to grab anytime—saving you time, effort, and endless revisions while keeping users happy with up-to-date, accessible maps.
Marketo & Dynamics can be Most Excellent to Each Other – The SequelBradBedford3
So you’ve built trust in your Marketo Engage-Dynamics integration—excellent. But now what?
This sequel picks up where our last adventure left off, offering a step-by-step guide to move from stable sync to strategic power moves. We’ll share real-world project examples that empower sales and marketing to work smarter and stay aligned.
If you’re ready to go beyond the basics and do truly most excellent stuff, this session is your guide.
Alt-lenders are scaling fast, but manual loan reconciliation is cracking under pressure. See how automation solves revenue leakage and compliance chaos.
https://www.taxilla.com/loan-repayment-reconciliation
6. Function
▸ Function is a named block of code that is design to do a specific job.
▸ The basic idea is write the code once and use it again and again. The first
step to code reuse.
▸ You can call the function whenever you like in your code.
1. def function_name(paramter_1,parameter_2,…):
2. someCode
3. someCode
4. someCode
5. return someValue
“function name” is an identifier so it must follow identifier naming rules!
Positional
Parameters - The
order matters
8. Example of Defining and Calling a Function
▸ Function without parameters
▸ Function with parameters
▸ Calling a function with or without arguments
1. def welcome_passenger():
2. print(“Dear Passenger welcome onboard ”)
1. def welcome_passenger(passengerName):
2. print(“Dear Mr/Ms” + passengerName + “ Welcome Onboard”)
1. welcome_passenger()
2. welcome_passenger(“Tehrani”)
parameter
argument
10. P A R A M E T E R V S .
ARGUMENT
Two names for the same things. Parameters are inside
the function and Arguments are outside the function
11. Parameters Default Values
▸ We can define a default value for each parameter. If an argument for
for a parameters is provided in the function call python use the
argument value and if not its uses the parameter’s default value
1. def welcome_message(name, country =“Japan”):
2. print(“Mr./Ms. ” + name + “ from ”+country + “ Welcome To Iran” )
1. welcome_message(name=“John”, country=”Germany”)
2. welcome_message(name=“John”,)
Be careful about Arguments Error
“Japan” is the
default value
12. Return a Value
▸ Function can process some data and then return a value or set of
values
▸ Calling the function with return value
1. def format_name(first_name, last_name):
2. full_name = first_name.title() + “ “ + last_name.title()
3. return full_name
1. print(format_name(“ehsan”,”kaviani”))
2. formated_name = formated_name(“ehsan”,”Kaviani”)
13. Return Multiple Value
▸ A Function can return any object including data structures like
dictionary or list.
1. def add_key_value(dic,key,value):
2. dic[key] = value
3. return dic
5. student_dic_0 = {“name” : ”Kaveh” , “family”:”Sarkhosh”}
6. student_dic_0 = add_key_value(student_dic_0, ‘age’, 20)
7. print(student_dic_0)
It is a Dictionary
data structure
14. WHEN YOU PASS A LIST OR
DICTIONARY ANY CHANGE TO
THE OBJECT IS PERMANENT!
So be careful when you are working with list or dictionary
15. Keyword Arguments
▸ Is a name-value pair that you pass to a function
▸ Calling the function
1. def welcome_message(name, country ):
2. print(“Mr./Ms. ” + name + “ from ”+country + “ Welcome To Iran” )
1. welcome_message(name=“John”, country=”Germany”)
2. welcome_message(country=”Iran”, name=“Babak”)
You have to use the exact names of the parameters!!!! Exact
16. Pass Variable Number of Arguments
▸ *args and **kwargs are used as parameters in function definitions and
allows to pass a variable number of arguments to the calling function.
▸ *args allows you to pass a non-keyworded, variable length tuple
argument list to the
▸ **kwargs allows you to pass keyworded, variable length dictionary
argument list to the calling function
17. Example of Function With Variable Number of Parameters
▸ It should be noted that the single asterisk (*) and double asterisk (**) are
the important elements here and the words args and kwargs are used
only by convention.
1. def sample_function(name,*args,**kwargs):
2. print(f”Hello {name} here is the list of item you sent:”)
3. for item in args :
4. print(item)
5. print(f”Also here is the list of key-value you have sent:”)
6. for kw in kwargs :
7. print(kw,” : ”,kwargs[kw])
18. BY USING FUNCTION
YOU CAN SEPARATE
BLOCK OF CODE FROM
YOUR MAIN CODE
The code is much more readable and manageable
19. Using Modules
▸ You can store your functions in separate file called module and then
import the module in your main program.
▸ Module is a file with .py extension.
▸ You can put all functions in a module.
1. def add_key_value(dic,key,value):
2. dic[key] = value
3. return dic
my_module.py
20. Importing an Entire Module - All its Functions
▸ You can use a module by importing it wherever you need the module
function!
▸ Put the import at the top of your main code.
▸ The general rule is you have to define a function before you use it.
1. import my_module
2. #or
3. from my_module import *
main.py
There is a small difference between these two!!! What is it?
26. Importing Specific Function
▸ There is some big module with lots of function. But we just want to use
some specific functions so we can partially import the module.
1. from my_module import function_1, function_2
2. …
main.py
27. Aliasing Module Name or Function Name
▸ We can use alias to make module name shorter
1. import tensorflow as tf
2. from tensorflow.core import debug as tfd
main.py
28. THERE ARE MANY
MODULES THAT YOU CAN
USE IN YOUR PROGRAM
Using pip directly or using pip in pyCharm
29. Python Standard Libraries and Modules
▸ Built-in modules are part of python standard library
▸ python library reference manual
1. import math
2. import pathlib
3. import os
4. import json
5. import csv
6. …
https://docs.python.org/3/library/
34. Python Third-Party Libraries and Modules - Package
▸ Third-party modules or libraries can be installed and managed using
Python’s package manager pip.
▸ You can use Operating System Command line
▸ You can use PyCharm Command Line
▸ You can use PyCharm Package Management Tool
pip install module_name