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Published:
February 6, 2019

Functional Programming For Dummies

Overview

Your guide to the functional programming paradigm

Functional programming mainly sees use in math computations, including those used in Artificial Intelligence and gaming. This programming paradigm makes algorithms used for math calculations easier to understand and provides a concise method of coding algorithms by people who aren't developers. Current books on the market have a significant learning curve because they're written for developers, by developers—until now.

Functional Programming for Dummies explores the differences between the pure (as represented by the Haskell language) and impure (as represented by the Python language) approaches to functional programming for readers just like you. The pure approach is best suited to researchers who have no desire to create production code but do need to test algorithms fully and demonstrate

their usefulness to peers. The impure approach is best suited to production environments because it's possible to mix coding paradigms in a single application to produce a result more quickly. Functional Programming For Dummies uses this two-pronged approach to give you an all-in-one approach to a coding methodology that can otherwise be hard to grasp.

  • Learn pure and impure when it comes to coding
  • Dive into the processes that most functional programmers use to derive, analyze and prove the worth of algorithms
  • Benefit from examples that are provided in both Python and Haskell
  • Glean the expertise of an expert author who has written some of the market-leading programming books to date

If you’re ready to massage data to understand how things work in new ways, you’ve come to the right place!

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About The Author

John Paul Mueller has written more than 100 books and 600+ articles on everything from networking and home security to database management and heads-down programming. His technical editing talents have helped more than 70 authors refine and polish their manuscripts. John's books address every level of skill from beginning to advanced.

Sample Chapters

functional programming for dummies

CHEAT SHEET

Using functional programming techniques can make you incredibly efficient when solving certain problems or when you need to make full use of multiprocessing techniques to ensure that you get maximum benefit from each processor (or core). The functional programming paradigm supports the concept of pure languages (such as Haskell, which uses only functional techniques) and impure languages (such as Python, which actually supports multiple programming paradigms).

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Haskell supports a broad range of libraries, which is why it’s such a good product to use. If you are headed down the path as a functional programmer, you should check out the rather lengthy list of available Haskell libraries. Chances are that you’ll find a Haskell library to meet almost any need in functional programming.
For many people, the reason to learn a new language or a new programming paradigm focuses on the ability to obtain gainful employment. Yes, functional programmers also have the joy of learning something new. However, to be practical, the something new must also provide a tangible result. The purpose of the information here is to help you see the way to a new occupation that builds on the skills you discover through the functional programming paradigm.
You can find a number of extremely confusing references to the term map in functional programming. For example, a map is associated with database management, in which data elements are mapped between two distinct data models. However, for what you see here in regards to functional programming, mapping refers to a process of applying a high-order function to each member of a list.
Using functional programming techniques can make you incredibly efficient when solving certain problems or when you need to make full use of multiprocessing techniques to ensure that you get maximum benefit from each processor (or core). The functional programming paradigm supports the concept of pure languages (such as Haskell, which uses only functional techniques) and impure languages (such as Python, which actually supports multiple programming paradigms).
Functional programming is a paradigm, which means that it doesn’t have an implementation. The basis of functional programming is lambda calculus, which is actually a math abstraction. Consequently, when you want to perform tasks by using the functional programming paradigm, you're really looking for a programming language that implements functional programming in a manner that meets your needs.
Haskell has a huge library support base in which you can find all sorts of useful functions. Using library code for functional programming is a time saver because libraries usually contain well-constructed and debugged code. The import function allows you to use external code. The following steps take you through a simple Haskell library usage example: Open GHCi, if necessary.
You're unlikely to find a common dataset used with Python that doesn't provide relatively good documentation. You need to find the documentation online if you want the full story about how the dataset is put together, what purpose it serves, and who originated it, as well as any needed statistics you need to suit your functional programming goals.
When you mention the term data manipulation, you convey different information to different people, depending on their particular specialty. An overview of data manipulation may include the term CRUD, which stands for Create, Read, Update, and Delete. A database manager may view data solely from this low-level perspective that involves just the mechanics of working with data.
You use three different operations to perform tasks using lambda calculus: creating functions to pass as variables; binding a variable to the expression (abstraction); and applying a function to an argument. The following describes all three operations that functional programmers can view as rules that govern all aspects of working with lambda calculus.
Functional programming has somewhat different goals and approaches than other paradigms use. Goals define what the functional programming paradigm is trying to do in forging the approaches used by languages that support it. However, the goals don’t specify a particular implementation; doing that is within the purview of the individual languages.
Functional programming is much easier when you have a standard dataset. A standard dataset is one that provides a specific number of records using a specific format. It normally appears in the public domain and is used by professionals around the world for various sorts of tests. Professionals categorize these datasets in various ways for functional programming and other programming paradigms: Kinds of fields (features or attributes) Number of fields Number of records (cases) Complexity of data Task categories (such as classification) Missing values Data orientation (such as biology) Popularity Depending on where you search, you can find all sorts of other information, such as who donated the data and when.
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