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.
Lambda functions in Python are anonymous functions that can take any number of arguments but can only have a single expression. They are defined using the lambda keyword followed by arguments and an expression. Lambda functions can be used wherever function objects are required and are useful for passing functions as arguments. For example, a lambda function can double an input argument or add two arguments and return the result.
Lambda expressions in Java 8 enable functional programming and allow code blocks to be treated as values. They provide a more readable and concise syntax than traditional anonymous classes. Lambdas improve APIs and support parallel processing. Functional programming separates code from objects and allows independent functions. Lambda expressions infer types from functional interfaces and can implement interfaces like Runnable more concisely than anonymous classes. Java 8 defines functional interfaces to have a single abstract method to be compatible with lambdas. Common functional interfaces include Predicate and Consumer. Streams and Optional are new classes that work with lambdas to support functional-style operations on collections and optional values.
What is Python Lambda Function? Python Tutorial | EdurekaEdureka!
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This Edureka PPT on 'Python Lambda' is to educate you about the Lambda functions of Python and help you understand how to use them in various scenarios. Below are the topics covered in this PPT:
What are Python Lambda functions?
Why are they used?
How to write anonymous functions?
Lambda functions within user-defined functions
Using Anonymous functions within
- filter()
- map()
- reduce()
Solving algebric expressions using Lambda
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This document discusses functions in Python. It defines functions as collections of statements that perform specific tasks. There are three types of functions: built-in functions, module functions, and user-defined functions. Built-in functions are predefined in Python, module functions are contained in .py files, and user-defined functions are created by the user. The document provides examples of various types of functions and how they can be called and used.
This document discusses lambda expressions in Java. It defines lambda as an anonymous function without a name. Lambda expressions allow functional programming by defining classes or functions with a single method using a simple syntax. Functional interfaces are interfaces with only one abstract method, and some examples are Runnable, Callable, Comparator. The document shows examples of using lambda expressions with these built-in interfaces and traversing collections. It also demonstrates defining a new functional interface and using it with lambda expressions.
This document discusses functional interfaces and method references in Java. It covers the main functional interfaces in the java.util.function package like Function, Predicate, and Consumer. It explains how to compose functions using andThen() and how method references provide a compact way to refer to existing methods rather than defining anonymous methods. Examples are provided to demonstrate method references with Thread, Runnable, and filtering a list.
This document introduces lambda expressions in Java 8. It provides background on why lambda expressions were added to Java, including to allow for more functional programming and parallel processing. It covers the syntax of lambda expressions, when they should and should not be used, functional interfaces, method and constructor references, referencing external variables, debugging lambda expressions, and new lambda methods added in Java 8. The document also advertises exercises for the reader to complete to practice using lambda expressions.
This document summarizes new C# 3.0 features including implicit typing, lambda expressions, and extension methods. Implicit typing allows declaring variables without an explicit type using the 'var' keyword. Lambda expressions provide a concise way to pass code as arguments using the '=>' operator. Extension methods allow adding methods to existing types without modifying them by defining methods in static classes that take the extended type as the first parameter.
The document discusses lambda expressions in Java 8. It provides background on the lambda calculus and functional programming. Lambda expressions allow anonymous functions and are implemented using functional interfaces in Java 8. This enables a more functional style of programming. Lambda expressions can access variables from their enclosing scope and method references provide a concise way to pass existing methods. The streams API allows functional-style operations on collections and supports sequential and parallel processing.
Default methods and static methods allow interfaces to define new methods without breaking existing implementations. The lambda expressions feature introduces functional programming to Java through functional interfaces like Consumer and Function. The streams API provides a functional-style way to process and analyze collections through parallelization, optional return values, and collectors.
This document provides an introduction and overview of Java lambda expressions and functional programming concepts in Java 8. It begins with getting started instructions and a review of pre-lambda approaches like anonymous inner classes. It then covers the basics of lambda expressions like syntax, type inference, implied returns, and effectively final variables. It discusses the @FunctionalInterface annotation and introduces Java's built-in java.util.function package for common functional interfaces. The document provides examples throughout and concludes with a wrap-up of lambda expressions in Java 8.
The document provides an overview of the Lua programming language. It covers Lua's data types including numbers, strings, booleans, tables, functions, threads, and users. It also discusses Lua's control structures such as if/else, while, for, and repeat. Additionally, the document outlines Lua's basic syntax for functions, variables, operators, and comments. The goal of the document is to introduce programmers to the core components of the Lua language.
This document contains the slides for a presentation on Java 8 Lambdas and Streams. The presentation will cover lambdas, including their concept, syntax, functional interfaces, variable capture, method references, and default methods. It will also cover streams. The slides provide some incomplete definitions that will be completed during the presentation. Questions from attendees are welcome. A quick survey asks about past experience with lambdas and streams.
Scala is a general purpose programming language that supports both object-oriented and functional programming. It was developed by Martin Odersky in 2001. Scala runs on the Java Virtual Machine and is fully interoperable with Java. Everything in Scala is an object, including functions, which are first-class citizens that can be passed as arguments to other functions. Scala uses the REPL (Read-Evaluate-Print-Loop) for interactive use and supports features like immutable collections, pattern matching, lazy evaluation, and traits similar to interfaces.
This document provides an overview of new features in Java 8 including lambda expressions, streams, date/time API improvements, Nashorn JavaScript engine, and JavaFX updates. The presentation discusses how lambda expressions add functional programming capabilities to Java, how streams provide functional-style data processing, and improvements made in Java 8 to the date/time API. Nashorn and its ability to call Java from JavaScript as well as call JavaScript from Java is also summarized. Finally, an overview of updates to the JavaFX rich client platform is mentioned.
The document discusses lambda expressions in Java. It defines a lambda expression as a concise representation of an anonymous function that can be passed around. Lambda expressions allow for passing functions as arguments or returning them. They make code shorter and cleaner compared to anonymous classes. Functional interfaces are required for lambda expressions, where a functional interface defines a single abstract method that lambda expressions can implement.
This document discusses lambda expressions and anonymous functions in C#. It defines lambda expressions as shorthand for anonymous functions that simplify defining anonymous methods. Lambda expressions allow omitting the type of input parameters, making them more flexible than anonymous functions. Anonymous functions are methods without a name defined using the delegate keyword and inferred return type. They must be assigned to a delegate. The document provides examples of lambda expression syntax and usage in C# code to add and cube numbers.
Scala is a programming language that combines object-oriented and functional programming. It runs on the Java Virtual Machine and is designed to grow with user demands through libraries. Play 2.0 is a web application framework written in Scala that allows for rapid development through its Model-View-Controller architecture and local development server. Cloud Foundry is an open Platform as a Service that allows developers to easily deploy and scale applications, including those written in Scala and Play 2.0.
Functions are the core of Elixir. There are two types: anonymous functions defined with fn/end and named functions defined inside modules. Anonymous functions are closures that can access outer scope bindings. Named functions can be public or private and allow default parameters and pattern matching. Pattern matching selects function clauses by matching argument patterns. Guards extend pattern matching with conditional checks. Functions transform data rather than change it, making Elixir functional.
This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.
The TRB AJE35 RIIM Coordination and Collaboration Subcommittee has organized a series of webinars focused on building coordination, collaboration, and cooperation across multiple groups. All webinars have been recorded and copies of the recording, transcripts, and slides are below. These resources are open-access following creative commons licensing agreements. The files may be found, organized by webinar date, below. The committee co-chairs would welcome any suggestions for future webinars. The support of the AASHTO RAC Coordination and Collaboration Task Force, the Council of University Transportation Centers, and AUTRI’s Alabama Transportation Assistance Program is gratefully acknowledged.
This webinar overviews proven methods for collaborating with USDOT University Transportation Centers (UTCs), emphasizing state departments of transportation and other stakeholders. It will cover partnerships at all UTC stages, from the Notice of Funding Opportunity (NOFO) release through proposal development, research and implementation. Successful USDOT UTC research, education, workforce development, and technology transfer best practices will be highlighted. Dr. Larry Rilett, Director of the Auburn University Transportation Research Institute will moderate.
For more information, visit: https://aub.ie/trbwebinars
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This document discusses functions in Python. It defines functions as collections of statements that perform specific tasks. There are three types of functions: built-in functions, module functions, and user-defined functions. Built-in functions are predefined in Python, module functions are contained in .py files, and user-defined functions are created by the user. The document provides examples of various types of functions and how they can be called and used.
This document discusses lambda expressions in Java. It defines lambda as an anonymous function without a name. Lambda expressions allow functional programming by defining classes or functions with a single method using a simple syntax. Functional interfaces are interfaces with only one abstract method, and some examples are Runnable, Callable, Comparator. The document shows examples of using lambda expressions with these built-in interfaces and traversing collections. It also demonstrates defining a new functional interface and using it with lambda expressions.
This document discusses functional interfaces and method references in Java. It covers the main functional interfaces in the java.util.function package like Function, Predicate, and Consumer. It explains how to compose functions using andThen() and how method references provide a compact way to refer to existing methods rather than defining anonymous methods. Examples are provided to demonstrate method references with Thread, Runnable, and filtering a list.
This document introduces lambda expressions in Java 8. It provides background on why lambda expressions were added to Java, including to allow for more functional programming and parallel processing. It covers the syntax of lambda expressions, when they should and should not be used, functional interfaces, method and constructor references, referencing external variables, debugging lambda expressions, and new lambda methods added in Java 8. The document also advertises exercises for the reader to complete to practice using lambda expressions.
This document summarizes new C# 3.0 features including implicit typing, lambda expressions, and extension methods. Implicit typing allows declaring variables without an explicit type using the 'var' keyword. Lambda expressions provide a concise way to pass code as arguments using the '=>' operator. Extension methods allow adding methods to existing types without modifying them by defining methods in static classes that take the extended type as the first parameter.
The document discusses lambda expressions in Java 8. It provides background on the lambda calculus and functional programming. Lambda expressions allow anonymous functions and are implemented using functional interfaces in Java 8. This enables a more functional style of programming. Lambda expressions can access variables from their enclosing scope and method references provide a concise way to pass existing methods. The streams API allows functional-style operations on collections and supports sequential and parallel processing.
Default methods and static methods allow interfaces to define new methods without breaking existing implementations. The lambda expressions feature introduces functional programming to Java through functional interfaces like Consumer and Function. The streams API provides a functional-style way to process and analyze collections through parallelization, optional return values, and collectors.
This document provides an introduction and overview of Java lambda expressions and functional programming concepts in Java 8. It begins with getting started instructions and a review of pre-lambda approaches like anonymous inner classes. It then covers the basics of lambda expressions like syntax, type inference, implied returns, and effectively final variables. It discusses the @FunctionalInterface annotation and introduces Java's built-in java.util.function package for common functional interfaces. The document provides examples throughout and concludes with a wrap-up of lambda expressions in Java 8.
The document provides an overview of the Lua programming language. It covers Lua's data types including numbers, strings, booleans, tables, functions, threads, and users. It also discusses Lua's control structures such as if/else, while, for, and repeat. Additionally, the document outlines Lua's basic syntax for functions, variables, operators, and comments. The goal of the document is to introduce programmers to the core components of the Lua language.
This document contains the slides for a presentation on Java 8 Lambdas and Streams. The presentation will cover lambdas, including their concept, syntax, functional interfaces, variable capture, method references, and default methods. It will also cover streams. The slides provide some incomplete definitions that will be completed during the presentation. Questions from attendees are welcome. A quick survey asks about past experience with lambdas and streams.
Scala is a general purpose programming language that supports both object-oriented and functional programming. It was developed by Martin Odersky in 2001. Scala runs on the Java Virtual Machine and is fully interoperable with Java. Everything in Scala is an object, including functions, which are first-class citizens that can be passed as arguments to other functions. Scala uses the REPL (Read-Evaluate-Print-Loop) for interactive use and supports features like immutable collections, pattern matching, lazy evaluation, and traits similar to interfaces.
This document provides an overview of new features in Java 8 including lambda expressions, streams, date/time API improvements, Nashorn JavaScript engine, and JavaFX updates. The presentation discusses how lambda expressions add functional programming capabilities to Java, how streams provide functional-style data processing, and improvements made in Java 8 to the date/time API. Nashorn and its ability to call Java from JavaScript as well as call JavaScript from Java is also summarized. Finally, an overview of updates to the JavaFX rich client platform is mentioned.
The document discusses lambda expressions in Java. It defines a lambda expression as a concise representation of an anonymous function that can be passed around. Lambda expressions allow for passing functions as arguments or returning them. They make code shorter and cleaner compared to anonymous classes. Functional interfaces are required for lambda expressions, where a functional interface defines a single abstract method that lambda expressions can implement.
This document discusses lambda expressions and anonymous functions in C#. It defines lambda expressions as shorthand for anonymous functions that simplify defining anonymous methods. Lambda expressions allow omitting the type of input parameters, making them more flexible than anonymous functions. Anonymous functions are methods without a name defined using the delegate keyword and inferred return type. They must be assigned to a delegate. The document provides examples of lambda expression syntax and usage in C# code to add and cube numbers.
Scala is a programming language that combines object-oriented and functional programming. It runs on the Java Virtual Machine and is designed to grow with user demands through libraries. Play 2.0 is a web application framework written in Scala that allows for rapid development through its Model-View-Controller architecture and local development server. Cloud Foundry is an open Platform as a Service that allows developers to easily deploy and scale applications, including those written in Scala and Play 2.0.
Functions are the core of Elixir. There are two types: anonymous functions defined with fn/end and named functions defined inside modules. Anonymous functions are closures that can access outer scope bindings. Named functions can be public or private and allow default parameters and pattern matching. Pattern matching selects function clauses by matching argument patterns. Guards extend pattern matching with conditional checks. Functions transform data rather than change it, making Elixir functional.
This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.
The TRB AJE35 RIIM Coordination and Collaboration Subcommittee has organized a series of webinars focused on building coordination, collaboration, and cooperation across multiple groups. All webinars have been recorded and copies of the recording, transcripts, and slides are below. These resources are open-access following creative commons licensing agreements. The files may be found, organized by webinar date, below. The committee co-chairs would welcome any suggestions for future webinars. The support of the AASHTO RAC Coordination and Collaboration Task Force, the Council of University Transportation Centers, and AUTRI’s Alabama Transportation Assistance Program is gratefully acknowledged.
This webinar overviews proven methods for collaborating with USDOT University Transportation Centers (UTCs), emphasizing state departments of transportation and other stakeholders. It will cover partnerships at all UTC stages, from the Notice of Funding Opportunity (NOFO) release through proposal development, research and implementation. Successful USDOT UTC research, education, workforce development, and technology transfer best practices will be highlighted. Dr. Larry Rilett, Director of the Auburn University Transportation Research Institute will moderate.
For more information, visit: https://aub.ie/trbwebinars
In modern aerospace engineering, uncertainty is not an inconvenience — it is a defining feature. Lightweight structures, composite materials, and tight performance margins demand a deeper understanding of how variability in material properties, geometry, and boundary conditions affects dynamic response. This keynote presentation tackles the grand challenge: how can we model, quantify, and interpret uncertainty in structural dynamics while preserving physical insight?
This talk reflects over two decades of research at the intersection of structural mechanics, stochastic modelling, and computational dynamics. Rather than adopting black-box probabilistic methods that obscure interpretation, the approaches outlined here are rooted in engineering-first thinking — anchored in modal analysis, physical realism, and practical implementation within standard finite element frameworks.
The talk is structured around three major pillars:
1. Parametric Uncertainty via Random Eigenvalue Problems
* Analytical and asymptotic methods are introduced to compute statistics of natural frequencies and mode shapes.
* Key insight: eigenvalue sensitivity depends on spectral gaps — a critical factor for systems with clustered modes (e.g., turbine blades, panels).
2. Parametric Uncertainty in Dynamic Response using Modal Projection
* Spectral function-based representations are presented as a frequency-adaptive alternative to classical stochastic expansions.
* Efficient Galerkin projection techniques handle high-dimensional random fields while retaining mode-wise physical meaning.
3. Nonparametric Uncertainty using Random Matrix Theory
* When system parameters are unknown or unmeasurable, Wishart-distributed random matrices offer a principled way to encode uncertainty.
* A reduced-order implementation connects this theory to real-world systems — including experimental validations with vibrating plates and large-scale aerospace structures.
Across all topics, the focus is on reduced computational cost, physical interpretability, and direct applicability to aerospace problems.
The final section outlines current integration with FE tools (e.g., ANSYS, NASTRAN) and ongoing research into nonlinear extensions, digital twin frameworks, and uncertainty-informed design.
Whether you're a researcher, simulation engineer, or design analyst, this presentation offers a cohesive, physics-based roadmap to quantify what we don't know — and to do so responsibly.
Key words
Stochastic Dynamics, Structural Uncertainty, Aerospace Structures, Uncertainty Quantification, Random Matrix Theory, Modal Analysis, Spectral Methods, Engineering Mechanics, Finite Element Uncertainty, Wishart Distribution, Parametric Uncertainty, Nonparametric Modelling, Eigenvalue Problems, Reduced Order Modelling, ASME SSDM2025
How to Buy Snapchat Account A Step-by-Step Guide.pdfjamedlimmk
Scaling Growth with Multiple Snapchat Accounts: Strategies That Work
Operating multiple Snapchat accounts isn’t just a matter of logging in and out—it’s about crafting a scalable content strategy. Businesses and influencers who master this can turn Snapchat into a lead generation engine.
Key strategies include:
Content Calendars for Each Account – Plan distinct content buckets and themes per account to avoid duplication and maintain variety.
Geo-Based Content Segmentation – Use location-specific filters and cultural trends to speak directly to a region's audience.
Audience Mapping – Tailor messaging for niche segments: Gen Z, urban youth, gamers, shoppers, etc.
Metrics-Driven Storytelling – Use Snapchat Insights to monitor what type of content performs best per account.
Each account should have a unique identity but tie back to a central brand voice. This balance is crucial for brand consistency while leveraging the platform’s creative freedoms.
How Agencies and Creators Handle Bulk Snapchat Accounts
Digital agencies and creator networks often manage dozens—sometimes hundreds—of Snapchat accounts. The infrastructure to support this requires:
Dedicated teams for each cluster of accounts
Cloud-based mobile device management (MDM) systems
Permission-based account access for role clarity
Workflow automation tools (Slack, Trello, Notion) for content coordination
This is especially useful in verticals such as music promotion, event marketing, lifestyle brands, and political outreach, where each campaign needs targeted messaging from different handles.
The Legality and Risk Profile of Bulk Account Operations
If your aim is to operate or acquire multiple Snapchat accounts, understand the risk thresholds:
Personal Use (Low Risk) – One or two accounts for personal and creative projects
Business Use (Medium Risk) – Accounts with aligned goals, managed ethically
Automated Bulk Use (High Risk) – Accounts created en masse or used via bots are flagged quickly
Snapchat uses advanced machine learning detection for unusual behavior, including:
Fast switching between accounts from the same IP
Identical Snap stories across accounts
Rapid follower accumulation
Use of unverified devices or outdated OS versions
To stay compliant, use manual operations, vary behavior, and avoid gray-market account providers.
Smart Monetization Through Multi-Account Snapchat Strategies
With a multi-account setup, you can open doors to diversified monetization:
Affiliate Marketing – Niche accounts promoting targeted offers
Sponsored Content – Brands paying for story placement across multiple profiles
Product Launch Funnels – Segment users by interest and lead them to specific landing pages
Influencer Takeovers – Hosting creators across multiple themed accounts for event buzz
This turns your Snapchat network into a ROI-driven asset instead of a time sink.
Conclusion: Build an Ecosystem, Not Just Accounts
When approached correctly, multiple Snapchat accounts bec
Dear SICPA Team,
Please find attached a document outlining my professional background and experience.
I remain at your disposal should you have any questions or require further information.
Best regards,
Fabien Keller
an insightful lecture on "Loads on Structure," where we delve into the fundamental concepts and principles of load analysis in structural engineering. This presentation covers various types of loads, including dead loads, live loads, as well as their impact on building design and safety. Whether you are a student, educator, or professional in the field, this lecture will enhance your understanding of ensuring stability. Explore real-world examples and best practices that are essential for effective engineering solutions.
A lecture by Eng. Wael Almakinachi, M.Sc.
この資料は、Roy FieldingのREST論文(第5章)を振り返り、現代Webで誤解されがちなRESTの本質を解説しています。特に、ハイパーメディア制御やアプリケーション状態の管理に関する重要なポイントをわかりやすく紹介しています。
This presentation revisits Chapter 5 of Roy Fielding's PhD dissertation on REST, clarifying concepts that are often misunderstood in modern web design—such as hypermedia controls within representations and the role of hypermedia in managing application state.
The use of huge quantity of natural fine aggregate (NFA) and cement in civil construction work which have given rise to various ecological problems. The industrial waste like Blast furnace slag (GGBFS), fly ash, metakaolin, silica fume can be used as partly replacement for cement and manufactured sand obtained from crusher, was partly used as fine aggregate. In this work, MATLAB software model is developed using neural network toolbox to predict the flexural strength of concrete made by using pozzolanic materials and partly replacing natural fine aggregate (NFA) by Manufactured sand (MS). Flexural strength was experimentally calculated by casting beams specimens and results obtained from experiment were used to develop the artificial neural network (ANN) model. Total 131 results values were used to modeling formation and from that 30% data record was used for testing purpose and 70% data record was used for training purpose. 25 input materials properties were used to find the 28 days flexural strength of concrete obtained from partly replacing cement with pozzolans and partly replacing natural fine aggregate (NFA) by manufactured sand (MS). The results obtained from ANN model provides very strong accuracy to predict flexural strength of concrete obtained from partly replacing cement with pozzolans and natural fine aggregate (NFA) by manufactured sand.
2. Lambda functions
• Similar to user defined functions , but without a name
• Called as Anonymous functions
• Efficient for simple expressions , ie for expressions with single line of
statement.
• Useful when functions are used once.
3. Define lambda functions
lamdba – keyword for defining anonymous functions
argument(s) – variable to hold the value to be passed into the function, can have
multiple values depending on the need.
Expression – code to be executed in the lambda function.
Anonymous function – does not have a return keyword , as it automatically returns the
result of the expression, once executed.
Donot use lambda functions for complex structures like if else , loops, define user
defined functions for the complex structure.
6. Iterables
• Anything that consists of series of values like, characters, numbers
etc. In python it is String, lists , ranges, tuples, and so on.
• Lambda is used with two other functions
• 1. filter
• 2. map