How to use Map() Filter() and Reduce() functions in Python | Edureka

Dec 9, 20191 like1,071 views

The document provides an introduction to three essential functions in Python: map(), filter(), and reduce(). The map() function applies a specified function to all elements in an iterable and returns a new list, filter() filters elements based on a condition, and reduce() accumulates values from an iterable into a single result. It also discusses how these functions can be combined to process data efficiently.

Technology

Most read

Copyright © 2017, edureka and/or its affiliates. All rights reserved.

➢ Introduction to map() filter() reduce()
▪ The map() function
▪ The filter() function
▪ The reduce() function
➢ Using map() filter() and reduce() together
www.edureka.co/python
▪ filter() within map()
▪ map() within filter()
▪ filter() and map() within reduce()

Introduction to map(), filter() and
reduce()
www.edureka.co/python

Applies a given
function to all the
iterables and returns
a new list
Creates an output list
consisting of values for
which the function
returns true
Applies a given
function to the
iterables and returns a
single value
map() filter() reduce()
Introduction to map(), filter() and reduce()
www.edureka.co/python

1 2 3 4
map():
Applies a given function to all the iterables and returns a
new list.
output = map(lambda x: x+3 , [1,2,3,4])
www.edureka.co/python

1 2 3 4
4 5 6 7
map():
Applies a given function to all the iterables and returns a
new list.
output = map(lambda x: x+3 , [1,2,3,4])
www.edureka.co/python

filter()
Used to filter the given iterables(lists, sets, etc) with the help
of another function passed as an argument to test all the
elements to be true or false.
output = map(lambda x: (x>=3) , [1,2,3,4])
1 2 3 4
www.edureka.co/python

output = map(lambda x,y: x+y, [1,2,3,4])
reduce():
Applies some other function to a list of elements that are
passed as a parameter to it and finally returns a single
value.
1 2 3 4

How to use Map() Filter() and Reduce() functions in Python | Edureka

c = map(lambda x:x+x, )filter(lambda x: (x>=3), (1,2,3,4))
www.edureka.co/python

1 2 3 4
3 443
filter(lambda x: (x>=3), (1,2,3,4))
c = map(lambda x:x+x, )
www.edureka.co/python

,( ) )
1 2 3 4
3 4
43c = map(lambda x:x+x,
filter(lambda x: (x>=3), (1,2,3,4))
www.edureka.co/python
Filters out integers >=3 resulting in [3,4], then maps this using (x+x)
condition, you will get [6,8], which is the output.

c = filter(lambda x: (x>=3), )map(lambda x:x+x, (1,2,3,4))
www.edureka.co/python

c = filter(lambda x: (x>=3), )
map(lambda x:x+x, (1,2,3,4))
2 3 4
5 6 7
1
4
www.edureka.co/python

map(lambda x:x+x, (1,2,3,4))
1 2 3 4
c = filter(lambda x: (x>=3), )4 5 6 7( )), , ,
www.edureka.co/python

d = reduce ( lambda x, y: x+y, )map ( lambda x:x+x, filter ( lambda x: (x>=3) , (1,2,3,4)))
www.edureka.co/python

d = reduce ( lambda x, y: x+y, )
map ( lambda x:x+x, filter ( lambda x: (x>=3) , (1,2,3,4)))
1 2 3 4
3 443
86
www.edureka.co/python

Copyright © 2017, edureka and/or its affiliates. All rights reserved.
www.edureka.co

The document provides an introduction and overview of the Python programming language. It discusses that Python is an interpreted, object-oriented, high-level programming language that is easy to learn and read. It also covers Python features such as portability, extensive standard libraries, and support for functional, structured, and object-oriented programming. The document then discusses Python data types including numbers, strings, and various Python syntax elements before concluding with the history and evolution of the Python language through various versions.

Python Seminar PPTShivam Gupta

The document provides an overview of Python, detailing its nature as both a programming and scripting language, its history, and various applications across industries like web development and scientific computing. It discusses Python's features, including its object-oriented design, ease of installation, and usability on different platforms. Additionally, it outlines fundamental concepts such as data types, variables, and control structures within the Python programming environment.

Python Lambda FunctionMd Soyaib

Advanced Python : DecoratorsBhanwar Singh Meena

Np cooks theoremNarayana Galla

The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.

Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn

The document provides a comprehensive overview of data science, emphasizing the importance of Python for data analysis due to its speed and extensive libraries like Pandas, NumPy, and Scikit-learn. It covers essential concepts such as exploratory data analysis, data wrangling, and building predictive models, specifically using logistic regression for a loan prediction problem. The content is structured to guide readers through the process of data handling, model building, and deriving insights from data.

Machine LearningGirish Khanzode

The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.

AES.pptBincySam2

The document discusses the Advanced Encryption Standard (AES). It describes AES as a symmetric block cipher selected by the U.S. National Institute of Standards and Technology (NIST) in 2001 to replace the Data Encryption Standard (DES). AES uses a variable block size of 128 bits and a key size of 128, 192, or 256 bits. The cipher operates on a 4x4 column-byte state and has 10, 12, or 14 rounds depending on the key size. Each round consists of byte substitution, shift rows, mix columns, and add round key transformations.

Map, Filter and Reduce In PythonSimplilearn

The document explains the functionality of three key Python functions: map(), filter(), and reduce(). The map() function applies a given function to each item in a sequence, the filter() function filters elements based on a condition, and the reduce() function condenses a sequence into a single value. Additionally, contact information for Simplilearn Solutions Pvt. Ltd. in India and the United States is provided.

Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!

The document is about Python certification training, focusing on functions, their importance, and different types. It explains the concept of functions, including built-in and user-defined functions, provides examples of common built-in functions, and discusses the usage of lambda functions. Additionally, it emphasizes the benefits of using functions to reduce code repetition and outlines how functions can be structured and invoked in Python.

Chapter 05 classes and objectsPraveen M Jigajinni

Here is a Python class with the specifications provided in the question: class PICTURE: def __init__(self, pno, category, location): self.pno = pno self.category = category self.location = location def FixLocation(self, new_location): self.location = new_location This defines a PICTURE class with three instance attributes - pno, category and location as specified in the question. It also defines a FixLocation method to assign a new location as required.

Python functionsProf. Dr. K. Adisesha

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.

Fundamentals of Python ProgrammingKamal Acharya

The document provides an introduction to Python programming including its features, uses, history, and installation process. Some key points covered include: - Python is an interpreted, object-oriented programming language that is used for web development, scientific computing, and desktop applications. - It was created by Guido van Rossum in 1991 and named after the Monty Python comedy group. - To install Python on Windows, users download the latest version from python.org and run the installer, which also installs the IDLE development environment. - The document then covers basic Python concepts like variables, data types, operators, and input/output functions.

Introduction to Python amiable_indian

Python GeneratorsAkshar Raaj

The document explains the concept of generators in Python, emphasizing their benefits such as memory optimization and simpler code writing. It details how generators work, demonstrating their use of the yield keyword for lazy evaluation, and contrasts them with traditional iterators. Additionally, it highlights various real-world applications of generators and their advantages over iterators in terms of convenience and reduced boilerplate code.

Python OOPsBinay Kumar Ray

This document provides an introduction to object oriented programming in Python. It discusses key OOP concepts like classes, methods, encapsulation, abstraction, inheritance, polymorphism, and more. Each concept is explained in 1-2 paragraphs with examples provided in Python code snippets. The document is presented as a slideshow that is meant to be shared and provide instruction on OOP in Python.

Packages In Python TutorialSimplilearn

Python : FunctionsEmertxe Information Technologies Pvt Ltd

Polymorphism presentation in javaAhsan Raja

Polymorphism in Java allows an object to take on multiple forms. There are two types of polymorphism: compile-time polymorphism (method overloading) and runtime polymorphism (method overriding). Method overloading involves methods with the same name but different parameters, while method overriding involves subclasses providing their own implementation of a superclass method. Runtime polymorphism determines which version of a method to call based on the object's actual type at runtime. Abstraction in Java allows hiding implementation details and showing only essential functionality through the use of abstract classes and methods.

Object oriented programming in pythonbaabtra.com - No. 1 supplier of quality freshers

This document provides an introduction to object-oriented programming in Python. It discusses key concepts like classes, instances, inheritance, and modules. Classes group state and behavior together, and instances are created from classes. Methods defined inside a class have a self parameter. The __init__ method is called when an instance is created. Inheritance allows classes to extend existing classes. Modules package reusable code and data, and the import statement establishes dependencies between modules. The __name__ variable is used to determine if a file is being run directly or imported.

File handling in PythonMegha V

The document discusses file handling in Python. It explains that a file is used to permanently store data in non-volatile memory. It describes opening, reading, writing, and closing files. It discusses opening files in different modes like read, write, append. It also explains attributes of file objects like name, closed, and mode. The document also covers reading and writing text and binary files, pickle module for serialization, and working with CSV files and the os.path module.

Python-Polymorphism.pptxKarudaiyar Ganapathy

Polymorphism refers to a function having the same name but being used in different ways and different scenarios, making programming easier and more intuitive. Polymorphism is a fundamental concept in object-oriented programming that allows one function to display multiple functionalities through inheritance, where a child class inherits all methods from the parent class. In Python, polymorphism can be implemented through classes having different methods with the same name, inheritance where child classes can override parent methods, and by defining functions that can accept different object types.

What is Python Lambda Function? Python Tutorial | EdurekaEdureka!

The document explains Python lambda functions, which are anonymous, one-time-use functions that can simplify code by being used as inputs or outputs in higher-order functions. It details how to write and utilize lambda functions within user-defined functions and explains their use with filter(), map(), and reduce() functions to manipulate iterables. The syntax of lambda functions is provided along with example usage, demonstrating their efficiency in coding.

Functions in PythonKamal Acharya

The document provides a comprehensive overview of Python functions, including their definition, purpose, and types like built-in and user-defined functions. It also covers concepts such as variable scope and lifetime, recursion with its advantages and disadvantages, and the use of lambda functions. Additionally, it explains Python modules and packages, detailing how to import them and the structure required for organizing related files.

Java interfacesRaja Sekhar

The document explains Java interfaces, which allow for the implementation of multiple inheritance by enabling classes to adhere to multiple interfaces rather than extending a single class. It discusses the creation, implementation, and benefits of using interfaces over abstract classes, highlighting their role in supporting dynamic method resolution and decoupling method definitions from the class hierarchy. Additionally, the document covers examples of interface usage, including patterns for comparisons and cloning, underscoring the significance of interfaces in Java programming.

Object oriented approach in python programmingSrinivas Narasegouda

Exception Handling In Python | Exceptions In Python | Python Programming Tuto...Edureka!

Function in PythonYashdev Hada

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.

TI1220 Lecture 6: First-class FunctionsEelco Visser

Functions in advanced programmingVisnuDharsini

The document discusses Python's built-in functions for functional programming: map(), filter(), reduce(), and lambda. It provides examples of using each function to transform sequences. Map applies a function to each item in a sequence. Filter filters items based on a function that tests each item. Reduce combines items via a function to produce a single value. These functions allow functional-style programming in Python.

More Related Content

What's hot (20)

Map, Filter and Reduce In PythonSimplilearn

Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!

Chapter 05 classes and objectsPraveen M Jigajinni

Python functionsProf. Dr. K. Adisesha

Fundamentals of Python ProgrammingKamal Acharya

Introduction to Python amiable_indian

Python GeneratorsAkshar Raaj

Python OOPsBinay Kumar Ray

Packages In Python TutorialSimplilearn

Python : FunctionsEmertxe Information Technologies Pvt Ltd

Polymorphism presentation in javaAhsan Raja

Object oriented programming in pythonbaabtra.com - No. 1 supplier of quality freshers

File handling in PythonMegha V

Python-Polymorphism.pptxKarudaiyar Ganapathy

What is Python Lambda Function? Python Tutorial | EdurekaEdureka!

Functions in PythonKamal Acharya

Java interfacesRaja Sekhar

Object oriented approach in python programmingSrinivas Narasegouda

Exception Handling In Python | Exceptions In Python | Python Programming Tuto...Edureka!

Function in PythonYashdev Hada

Map, Filter and Reduce In PythonSimplilearn

Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!

Chapter 05 classes and objectsPraveen M Jigajinni

Python functionsProf. Dr. K. Adisesha

Fundamentals of Python ProgrammingKamal Acharya

Introduction to Python amiable_indian

Python GeneratorsAkshar Raaj

Python OOPsBinay Kumar Ray

Packages In Python TutorialSimplilearn

Python : FunctionsEmertxe Information Technologies Pvt Ltd

Polymorphism presentation in javaAhsan Raja

Object oriented programming in pythonbaabtra.com - No. 1 supplier of quality freshers

File handling in PythonMegha V

Python-Polymorphism.pptxKarudaiyar Ganapathy

What is Python Lambda Function? Python Tutorial | EdurekaEdureka!

Functions in PythonKamal Acharya

Java interfacesRaja Sekhar

Object oriented approach in python programmingSrinivas Narasegouda

Exception Handling In Python | Exceptions In Python | Python Programming Tuto...Edureka!

Function in PythonYashdev Hada

Similar to How to use Map() Filter() and Reduce() functions in Python | Edureka (20)

TI1220 Lecture 6: First-class FunctionsEelco Visser

Functions in advanced programmingVisnuDharsini

purrr.pdfMateus S. Xavier

The document provides a cheat sheet on using purrr functions to apply functions to lists and vectors. It summarizes the main map functions like map(), map2(), and pmap() to apply functions element-wise over lists. It also describes reduce() and accumulate() functions to recursively apply functions over lists. Finally it discusses working with list columns in data frames, applying map functions within mutate() and transmute() to manipulate list elements column-wise.

4 R Tutorial DPLYR Apply FunctionSakthi Dasans

- Apply functions in R are used to apply a specified function to each column or row of R objects. Common apply functions include apply(), lapply(), sapply(), tapply(), vapply(), and mapply(). - The dplyr package is a powerful R package for data manipulation. It provides verbs like select(), filter(), arrange(), mutate(), and summarize() to work with tabular data. - Functions like apply(), lapply(), sapply() apply a function over lists or matrices. Arrange() reorders data, mutate() adds new variables, and summarize() collapses multiple values into single values.

Python High Level Functions_Ch 11.pptAnishaJ7

This document provides an overview of higher-order functions in Python. It discusses functions as parameters, examples of higher-order functions like map, filter and reduce, and how they work. It also covers anonymous functions, examples and problems demonstrating the use of map, filter and reduce. Additional topics covered include regular expressions, metacharacters, and solving problems using regex patterns.

Spark workshopWojciech Pituła

This document provides an agenda and overview for a Spark workshop covering Spark basics and streaming. The agenda includes sections on Scala, Spark, Spark SQL, and Spark Streaming. It discusses Scala concepts like vals, vars, defs, classes, objects, and pattern matching. It also covers Spark RDDs, transformations, actions, sources, and the spark-shell. Finally, it briefly introduces Spark concepts like broadcast variables, accumulators, and spark-submit.

Functional ProgrammingOlexandra Dmytrenko

The document discusses functional programming and lambda expressions in Java 8. It begins by defining functional programming and predicates from predicate logic. It then discusses the key properties of functional programming including no states, passing control, single large function, and no cycles. The document provides examples of determining if a number is prime in both imperative and declarative styles using Java 8 lambda expressions. It also provides examples of getting the first doubled number greater than 3 from a list using both declarative and imperative approaches. The examples demonstrate the use of streams, filters, maps and other functional operations.

Java 8 lambda expressionsLogan Chien

This document provides an overview of Java 8 lambda expressions. It begins with an introduction and background on anonymous classes in previous Java versions. The main topics covered include lambda syntax, lambda expressions vs closures, capturing variables in lambda expressions, type inference, and functional interfaces. It also discusses stream operations like filter, map, flatMap, and peek. Finally, it covers parallelism and how streams can leverage multiple cores to improve performance.

Function in Python.pptx by Faculty at gla university in mathura uttar pradeshbloodskullgoswami

The document provides an overview of function definitions and usage in Python, including concepts such as parameters, passing by reference versus value, and different types of arguments like required, keyword, default, and variable-length arguments. It also illustrates the use of lambda functions and higher-order functions like filter(), map(), and reduce() for processing collections. Additionally, it explains the distinction between global and local variables within functions.

Why Haskell Mattersromanandreg

Haskell is a pure, statically typed, lazy functional programming language that emphasizes correctness and succinct code through features like recursion, high-order functions, and a robust type system. It is particularly well-suited for systems programming and applications requiring high reliability, such as financial systems. Despite its advantages, Haskell's unconventional approach and limited popularity hinder its broader adoption.

Functional Programming for OO Programmers (part 2)Calvin Cheng

This document explores key concepts of functional programming aimed at object-oriented (OO) programmers, covering topics such as pure vs. impure functions, currying, and higher-order functions like map, filter, and reduce. It explains different functional programming concepts, including functors, applicative functors, and monads, with practical examples in JavaScript and Haskell. Additionally, it illustrates the importance of avoiding mutable state to enable parallel thread execution without race conditions.

Scala. Introduction to FP. MonadsKirill Kozlov

The document provides an introduction to functional programming in Scala, emphasizing that functions are treated as first-class objects. It covers essential concepts such as case classes, pattern matching, lists, higher-order functions like map and flatMap, and the definition and application of monads, including option and try, to manage computations and errors explicitly. The document also explores the reactive manifesto and how to handle asynchronous operations with futures.

関数潮流(Function Tendency)riue

The document shows examples of using lambda functions and functional programming techniques in various languages like JavaScript, Python, C#, and Java. It demonstrates how to define anonymous functions, map, filter and reduce collections, use closures, and more. Key examples include summing a list of integers with reduce, filtering even numbers from a list, mapping string transformations, and converting a list of strings to uppercase.

Frp2016 3Kirill Kozlov

Functions are treated as objects in Scala, with the function type A => B being an abbreviation for the class scala.Function1[A, B]. Functions are objects that have an apply method. Case classes implicitly define companion objects with apply methods, allowing constructor-like syntax. Pattern matching provides a way to generalize switch statements to class hierarchies. The for expression provides a cleaner syntax than higher-order functions like map and flatMap for working with collections, but compiles to calls to these functions. Types like Option and Try are examples of monads in Scala, making failure or missing data explicit in the type while hiding boilerplate code.

R Cheat Sheet for Data Analysts and Statisticians.pdfTimothy McBush Hiele

This document provides a cheat sheet for common commands and functions in R for data manipulation, statistical analysis, and graphics. It summarizes key topics such as accessing and manipulating data, conducting statistical tests, fitting linear and generalized linear models, performing clustering and multivariate analyses, and creating basic plots and graphics. The cheat sheet is organized into sections covering basics, vectors and data types, data frames, input/output, indexing, missing values, numerical and tabulation functions, programming, operators, graphics, and statistical models and distributions.

Composition birds-and-recursionDavid Atchley

The document discusses function composition and recursion in JavaScript. It introduces basic functions like double, square, and inc. It then shows how to work with these functions in an imperative style using a for loop, and in a declarative style using map. It discusses how composition allows building complexity by combining functions. The document also discusses combinators, introducing birds that represent functions like identity, mockingbird, bluebird (compose), lark, and meadowlark. It concludes by explaining the Y-combinator and how it allows recursion using fixed points.

Practical catsRaymond Tay

The document discusses various programming constructs within the Cats library, such as functors, monads, and applicatives, emphasizing their roles and laws in functional programming. It provides examples of how these constructs can be used for effective code separation, state management, and error handling, showcasing practical applications in Scala. Additionally, it highlights the use of type classes as a means of creating reusable code, along with detailed descriptions and examples of monoids, semigroups, and the validation of configurations.

Swift 3.0 で変わったところ - 厳選 13 項目 #love_swift #cswiftTomohiro Kumagai

Differential calculus Santhanam Krishnan

The document discusses key concepts in calculus including: - Differential calculus examines how quantities change by looking at their rates of change, represented by derivatives. - Integration is used to determine quantities like material needs or structure weights by calculating the area under a curve. - Calculus has various applications in fields like engineering, physics, and robotics where quantities change continuously over time. - The document provides examples of how differential and integral calculus are used in applications such as space travel planning, architecture, and robotics.

Modular Module Systemsleague

Modular Module Systems: a survey discusses different approaches to modularity in programming languages. It covers modules as a way to separate compilation, manage namespaces, and hide/abstract types and values. Different languages take different approaches, from separate files in C to more robust modules in ML and Haskell that allow nesting, signatures, functors, and type classes. The document uses examples from C, C++, ML, Haskell to illustrate how modules have evolved to support separate compilation, abstraction, and reuse through mechanisms like functors and signatures.

TI1220 Lecture 6: First-class FunctionsEelco Visser

Functions in advanced programmingVisnuDharsini

purrr.pdfMateus S. Xavier

4 R Tutorial DPLYR Apply FunctionSakthi Dasans

Python High Level Functions_Ch 11.pptAnishaJ7

Spark workshopWojciech Pituła

Functional ProgrammingOlexandra Dmytrenko

Java 8 lambda expressionsLogan Chien

Function in Python.pptx by Faculty at gla university in mathura uttar pradeshbloodskullgoswami

Why Haskell Mattersromanandreg

Functional Programming for OO Programmers (part 2)Calvin Cheng

Scala. Introduction to FP. MonadsKirill Kozlov

関数潮流(Function Tendency)riue

Frp2016 3Kirill Kozlov

R Cheat Sheet for Data Analysts and Statisticians.pdfTimothy McBush Hiele

Composition birds-and-recursionDavid Atchley

Practical catsRaymond Tay

Swift 3.0 で変わったところ - 厳選 13 項目 #love_swift #cswiftTomohiro Kumagai

Differential calculus Santhanam Krishnan

Modular Module Systemsleague

More from Edureka! (20)

What to learn during the 21 days Lockdown | EdurekaEdureka!

The document outlines a 21-day learning plan recommended during the nationwide lockdown imposed by India's Prime Minister to combat the coronavirus. It suggests focusing on various technology topics such as software development, web app development, and cybersecurity, with structured learning milestones for freshers and experienced professionals. Additionally, it addresses frequently asked questions related to transitioning into tech careers and the potential career benefits from the proposed learning activities.

Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!

The document discusses the decline and current challenges faced by ten outdated programming languages, including Perl, Objective C, and COBOL 60. Each language is critiqued for specific pitfalls such as increasing disuse, lack of community support, and poor performance. The overall theme highlights the shifting landscape of the programming job market and the need for developers to adapt to more modern languages.

Top 5 Trending Business Intelligence Tools | EdurekaEdureka!

The document analyzes top business intelligence (BI) tools based on a survey of 600 buyers, highlighting key features and advantages of each tool. It ranks the tools from Tableau as the most popular to SAS, showcasing their respective benefits such as interactive visualizations, ease of use, and fast processing. The document emphasizes the importance of BI and data governance in meeting buyer expectations and maximizing data utilization.

Tableau Tutorial for Data Science | EdurekaEdureka!

The document provides an introduction to Tableau, an analytics platform designed for data visualization and business intelligence. It presents two problem statements: the first focuses on analyzing profit issues at a superstore from 2012-2015, while the second aims to analyze TED talk transcripts to understand persuasive communication through NLP techniques. The document outlines objectives, expectations, and methods for both use cases to extract insights and visualize data effectively.

Python Programming Tutorial | EdurekaEdureka!

The document provides an overview of Python programming, highlighting its ease of learning, versatility, and support from a large community of developers. It discusses various applications in web development, data science, machine learning, and artificial intelligence, along with common beginner mistakes and suggested remedies for effective learning. Additionally, it offers information on Python versions, important programming concepts, and project ideas.

Top 5 PMP Certifications | EdurekaEdureka!

The document outlines various project management certifications, including details about their prerequisites, exam fees, validity, and potential salary increases. Key certifications mentioned include CAPM, CSM, PMI-ACP, PRINCE2, and PMP, each highlighting their relevance in today's competitive job market. It emphasizes the importance of choosing the right certification based on market demand and personal career goals.

Top Maven Interview Questions in 2020 | EdurekaEdureka!

The document provides a comprehensive overview of Apache Maven, covering its features, functionalities, and distinctions from other build tools. It discusses core concepts like the Project Object Model (POM), build lifecycles, dependency management, and types of repositories, along with commands for project management. Additionally, it explains Maven plugins, archetypes, and various dependency scopes to facilitate efficient project builds and management.

Linux Mint Tutorial | EdurekaEdureka!

How to Deploy Java Web App in AWS| EdurekaEdureka!

Amazon Web Services (AWS) is a secure cloud services platform that offers a range of functionalities including compute power, database storage, and content delivery to aid business growth. It supports the deployment of Java web applications through tools like AWS Elastic Beanstalk, enhancing automation and management. Prerequisites for deployment include JDK 8 or higher, Tomcat 8 or higher, and Eclipse IDE for Java EE.

Importance of Digital Marketing | EdurekaEdureka!

The document discusses the significant growth of digital marketing, predicting it will reach $118 billion by 2021, with a focus on the rise of AI and voice search impacting marketing strategies. It highlights the diverse career opportunities in the field, emphasizing an increase in job demand and the importance of staying updated with trends to maintain competitiveness. Additionally, it advocates for learning digital marketing as a means to upskill and adapt in a continuously evolving industry.

RPA in 2020 | EdurekaEdureka!

The document discusses the history and statistics of Robotic Process Automation (RPA), highlighting its efficiency improvements, error reduction, and growth in adoption among European companies. It features key players in the RPA field, such as UiPath and Blue Prism, and outlines the skills and salary ranges for RPA developers in India and the United States. The document also emphasizes the integration of RPA with AI technologies and the increasing demand for automation tools.

Email Notifications in Jenkins | EdurekaEdureka!

EA Algorithm in Machine Learning | EdurekaEdureka!

The document discusses the Expectation-Maximization (EM) algorithm in machine learning, particularly in relation to latent variables and the Gaussian Mixture Model (GMM). It outlines how the algorithm works, highlighting its advantages such as guaranteed likelihood increase and ease of M-step solutions, as well as disadvantages like slow convergence and susceptibility to local optima. Additionally, it covers the role of probability density estimation in analyzing observed data.

Cognitive AI Tutorial | EdurekaEdureka!

Cognitive computing aims to mimic human reasoning and behavior to solve complex problems. It works by simulating human thought processes through adaptive, interactive, iterative and contextual means. Cognitive computing supplements human decision making in sectors like customer service and healthcare, while artificial intelligence focuses more on autonomous decision making with applications in finance, security and more. A use case of cognitive AI is using it to assess skills, find relevant jobs, negotiate pay, suggest career paths and provide salary comparisons and job openings to help humans.

AWS Cloud Practitioner Tutorial | EdurekaEdureka!

The document discusses the benefits and features of Amazon Web Services (AWS), highlighting its extensive global infrastructure, with 64 availability zones across 21 regions and a rapid increase in services. It offers information on the AWS Cloud Practitioner certification, including exam details, course objectives, and its value in the job market. The text further emphasizes AWS's industry dominance and projected market growth.

Blue Prism Top Interview Questions | EdurekaEdureka!

The document presents a comprehensive set of interview questions and answers related to Blue Prism, an RPA tool, covering various aspects such as differences between RPA and automation, stages of the RPA lifecycle, and features of RPA. It also discusses Blue Prism's architecture, components, and security measures, emphasizing the tool's user-friendly features that do not require coding skills. Additionally, it outlines practical steps for executing processes and managing work queues within the Blue Prism environment.

Big Data on AWS Tutorial | Edureka Edureka!

A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!

Kubernetes Installation on Ubuntu | EdurekaEdureka!

The document provides detailed steps for installing Kubernetes, including turning off swap space, updating repositories, setting hostnames, and installing necessary components like Docker and kubeadm on both master and slave nodes. It also outlines the process for initiating the Kubernetes cluster, installing the pod network, and setting up the Kubernetes dashboard at the master node, while detailing the steps for slave nodes to join the cluster. Additionally, it highlights the availability of Kubernetes certification training through Edureka.

Introduction to DevOps | EdurekaEdureka!

The document is a comprehensive tutorial on DevOps, covering its definition, phases, and tools, as well as comparing DevOps with the traditional waterfall and agile methodologies. It discusses the limitations of these models and emphasizes the importance of specific DevOps practices such as continuous integration, continuous delivery, and continuous monitoring. Additionally, practical use cases illustrate the application of DevOps in software development through version control and containerization techniques.

What to learn during the 21 days Lockdown | EdurekaEdureka!

Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!

Top 5 Trending Business Intelligence Tools | EdurekaEdureka!

Tableau Tutorial for Data Science | EdurekaEdureka!

Python Programming Tutorial | EdurekaEdureka!

Top 5 PMP Certifications | EdurekaEdureka!

Top Maven Interview Questions in 2020 | EdurekaEdureka!

Linux Mint Tutorial | EdurekaEdureka!

How to Deploy Java Web App in AWS| EdurekaEdureka!

Importance of Digital Marketing | EdurekaEdureka!

RPA in 2020 | EdurekaEdureka!

Email Notifications in Jenkins | EdurekaEdureka!

EA Algorithm in Machine Learning | EdurekaEdureka!

Cognitive AI Tutorial | EdurekaEdureka!

AWS Cloud Practitioner Tutorial | EdurekaEdureka!

Blue Prism Top Interview Questions | EdurekaEdureka!

Big Data on AWS Tutorial | Edureka Edureka!

A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!

Kubernetes Installation on Ubuntu | EdurekaEdureka!

Introduction to DevOps | EdurekaEdureka!

Recently uploaded (20)

FIDO Seminar: New Data: Passkey Adoption in the Workforce.pptxFIDO Alliance

The Future of Technology: 2025-2125 by Saikat Basu.pdfSaikat Basu

Cyber Defense Matrix Workshop - RSA ConferencePriyanka Aash

Lessons Learned from Developing Secure AI Workflows.pdfPriyanka Aash

OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC

The OpenACC organization focuses on enhancing parallel computing skills and advancing interoperability in scientific applications through hackathons and training. The upcoming 2025 Open Accelerated Computing Summit (OACS) aims to explore the convergence of AI and HPC in scientific computing and foster knowledge sharing. This year's OACS welcomes talk submissions from a variety of topics, from Using Standard Language Parallelism to Computer Vision Applications. The document also highlights several open hackathons, a call to apply for NVIDIA Academic Grant Program and resources for optimizing scientific applications using OpenACC directives.

GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash

A Constitutional Quagmire - Ethical Minefields of AI, Cyber, and Privacy.pdfPriyanka Aash

Coordinated Disclosure for ML - What's Different and What's the Same.pdfPriyanka Aash

Securing AI - There Is No Try, Only Do!.pdfPriyanka Aash

FIDO Seminar: Evolving Landscape of Post-Quantum Cryptography.pptxFIDO Alliance

Tech-ASan: Two-stage check for Address Sanitizer - Yixuan Cao.pdfcaoyixuan2019

CapCut Pro Crack For PC Latest Version {Fully Unlocked} 2025pcprocore

👉𝗡𝗼𝘁𝗲:𝗖𝗼𝗽𝘆 𝗹𝗶𝗻𝗸 & 𝗽𝗮𝘀𝘁𝗲 𝗶𝗻𝘁𝗼 𝗚𝗼𝗼𝗴𝗹𝗲 𝗻𝗲𝘄 𝘁𝗮𝗯> https://pcprocore.com/ 👈◀ CapCut Pro Crack is a powerful tool that has taken the digital world by storm, offering users a fully unlocked experience that unleashes their creativity. With its user-friendly interface and advanced features, it’s no wonder why aspiring videographers are turning to this software for their projects.

Information Security Response Team Nepal_npCERT_Vice_President_Sudan_Jha.pdfICT Frame Magazine Pvt. Ltd.

Enhance GitHub Copilot using MCP - Enterprise version.pdfNilesh Gule

The Future of AI Agent Development Trends to Watch.pptxLisa ward

OWASP Barcelona 2025 Threat Model LibraryPetraVukmirovic

Improving Data Integrity: Synchronization between EAM and ArcGIS Utility Netw...Safe Software

Utilities and water companies play a key role in the creation of clean drinking water. The creation and maintenance of clean drinking water is becoming a critical problem due to pollution and pressure on the environment. A lot of data is necessary to create clean drinking water. For fieldworkers, two types of data are key: Asset data in an asset management system (EAM for example) and Geographic data in a GIS (ArcGIS Utility Network ). Keeping this type of data up to date and in sync is a challenge for many organizations, leading to duplicating data and creating a bulk of extra attributes and data to keep everything in sync. Using FME, it is possible to synchronize Enterprise Asset Management (EAM) data with the ArcGIS Utility Network in real time. Changes (creation, modification, deletion) in ArcGIS Pro are relayed to EAM via FME, and vice versa. This ensures continuous synchronization of both systems without daily bulk updates, minimizes risks, and seamlessly integrates with ArcGIS Utility Network services. This presentation focuses on the use of FME at a Dutch water company, to create a sync between the asset management and GIS.

Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software

Managing airport and airspace data is no small task, especially when you’re expected to deliver it in AIXM format without spending a fortune on specialized tools. But what if there was a smarter, more affordable way? Join us for a behind-the-scenes look at how Sweco partnered with Swedavia, the Swedish airport operator, to solve this challenge using FME and Esri. Learn how they built automated workflows to manage periodic updates, merge airspace data, and support data extracts – all while meeting strict government reporting requirements to the Civil Aviation Administration of Sweden. Even better? Swedavia built custom services and applications that use the FME Flow REST API to trigger jobs and retrieve results – streamlining tasks like securing the quality of new surveyor data, creating permdelta and baseline representations in the AIS schema, and generating AIXM extracts from their AIS data. To conclude, FME expert Dean Hintz will walk through a GeoBorders reading workflow and highlight recent enhancements to FME’s AIXM (Aeronautical Information Exchange Model) processing and interpretation capabilities. Discover how airports like Swedavia are harnessing the power of FME to simplify aviation data management, and how you can too.

Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand

AI VIDEO MAGAZINE - June 2025 - r/aivideo1pcity Studios, Inc

FIDO Seminar: New Data: Passkey Adoption in the Workforce.pptxFIDO Alliance

The Future of Technology: 2025-2125 by Saikat Basu.pdfSaikat Basu

Cyber Defense Matrix Workshop - RSA ConferencePriyanka Aash

Lessons Learned from Developing Secure AI Workflows.pdfPriyanka Aash

OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC

GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash

A Constitutional Quagmire - Ethical Minefields of AI, Cyber, and Privacy.pdfPriyanka Aash

Coordinated Disclosure for ML - What's Different and What's the Same.pdfPriyanka Aash

Securing AI - There Is No Try, Only Do!.pdfPriyanka Aash

FIDO Seminar: Evolving Landscape of Post-Quantum Cryptography.pptxFIDO Alliance

Tech-ASan: Two-stage check for Address Sanitizer - Yixuan Cao.pdfcaoyixuan2019

CapCut Pro Crack For PC Latest Version {Fully Unlocked} 2025pcprocore

Information Security Response Team Nepal_npCERT_Vice_President_Sudan_Jha.pdfICT Frame Magazine Pvt. Ltd.

Enhance GitHub Copilot using MCP - Enterprise version.pdfNilesh Gule

The Future of AI Agent Development Trends to Watch.pptxLisa ward

OWASP Barcelona 2025 Threat Model LibraryPetraVukmirovic

Improving Data Integrity: Synchronization between EAM and ArcGIS Utility Netw...Safe Software

Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software

Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand

AI VIDEO MAGAZINE - June 2025 - r/aivideo1pcity Studios, Inc

How to use Map() Filter() and Reduce() functions in Python | Edureka

2. ➢ Introduction to map() filter() reduce() ▪ The map() function ▪ The filter() function ▪ The reduce() function ➢ Using map() filter() and reduce() together www.edureka.co/python ▪ filter() within map() ▪ map() within filter() ▪ filter() and map() within reduce()

3. Introduction to map(), filter() and reduce() www.edureka.co/python

4. Applies a given function to all the iterables and returns a new list Creates an output list consisting of values for which the function returns true Applies a given function to the iterables and returns a single value map() filter() reduce() Introduction to map(), filter() and reduce() www.edureka.co/python

5. www.edureka.co/python

6. 1 2 3 4 map(): Applies a given function to all the iterables and returns a new list. output = map(lambda x: x+3 , [1,2,3,4]) www.edureka.co/python

7. 1 2 3 4 map(): Applies a given function to all the iterables and returns a new list. output = map(lambda x: x+3 , [1,2,3,4]) www.edureka.co/python

8. 1 2 3 4 4 5 6 7 map(): Applies a given function to all the iterables and returns a new list. output = map(lambda x: x+3 , [1,2,3,4]) www.edureka.co/python

9. www.edureka.co/python

10. filter() Used to filter the given iterables(lists, sets, etc) with the help of another function passed as an argument to test all the elements to be true or false. output = map(lambda x: (x>=3) , [1,2,3,4]) 1 2 3 4 www.edureka.co/python

11. filter() Used to filter the given iterables(lists, sets, etc) with the help of another function passed as an argument to test all the elements to be true or false. output = map(lambda x: (x>=3) , [1,2,3,4]) 1 2 3 4 www.edureka.co/python

12. filter() Used to filter the given iterables(lists, sets, etc) with the help of another function passed as an argument to test all the elements to be true or false. output = map(lambda x: (x>=3) , [1,2,3,4]) 1 2 3 4 3 4 www.edureka.co/python

13. www.edureka.co/python

14. output = map(lambda x,y: x+y, [1,2,3,4]) reduce(): Applies some other function to a list of elements that are passed as a parameter to it and finally returns a single value. 1 2 3 4

15. output = map(lambda x,y: x+y, [1,2,3,4]) reduce(): Applies some other function to a list of elements that are passed as a parameter to it and finally returns a single value. 1 2 3 4 3 www.edureka.co/python

16. output = map(lambda x,y: x+y, [1,2,3,4]) reduce(): Applies some other function to a list of elements that are passed as a parameter to it and finally returns a single value. 1 2 3 4 3 6 www.edureka.co/python

17. output = map(lambda x,y: x+y, [1,2,3,4]) reduce(): Applies some other function to a list of elements that are passed as a parameter to it and finally returns a single value. 1 2 3 4 3 6 10 www.edureka.co/python

18. www.edureka.co/python

20. c = map(lambda x:x+x, )filter(lambda x: (x>=3), (1,2,3,4)) www.edureka.co/python

21. 1 2 3 4 3 443 filter(lambda x: (x>=3), (1,2,3,4)) c = map(lambda x:x+x, ) www.edureka.co/python

22. ,( ) ) 1 2 3 4 3 4 43c = map(lambda x:x+x, filter(lambda x: (x>=3), (1,2,3,4)) www.edureka.co/python Filters out integers >=3 resulting in [3,4], then maps this using (x+x) condition, you will get [6,8], which is the output.

23. www.edureka.co/python

24. c = filter(lambda x: (x>=3), )map(lambda x:x+x, (1,2,3,4)) www.edureka.co/python

25. c = filter(lambda x: (x>=3), ) map(lambda x:x+x, (1,2,3,4)) 2 3 4 5 6 7 1 4 www.edureka.co/python

26. map(lambda x:x+x, (1,2,3,4)) 1 2 3 4 c = filter(lambda x: (x>=3), )4 5 6 7( )), , , www.edureka.co/python

27. www.edureka.co/python

28. d = reduce ( lambda x, y: x+y, )map ( lambda x:x+x, filter ( lambda x: (x>=3) , (1,2,3,4))) www.edureka.co/python

29. d = reduce ( lambda x, y: x+y, ) map ( lambda x:x+x, filter ( lambda x: (x>=3) , (1,2,3,4))) 1 2 3 4 3 443 86 www.edureka.co/python

30. d = reduce ( lambda x, y: x+y, ) map ( lambda x:x+x, filter ( lambda x: (x>=3) , (1,2,3,4))) 1 2 3 4 3 443 86( , )) www.edureka.co/python

32. www.edureka.co/python

How to use Map() Filter() and Reduce() functions in Python | Edureka

Recommended

More Related Content

What's hot (20)

Similar to How to use Map() Filter() and Reduce() functions in Python | Edureka (20)

More from Edureka! (20)

Recently uploaded (20)

How to use Map() Filter() and Reduce() functions in Python | Edureka