Is Lisp better than Python?

This is highly debatable. Python is a popular choice for beginners to learn to program, but Lisp has been around since the 1950s and was one of the first real-time computer languages created by John McCarthy in 1958 at MIT. It’s still used today with its dialect called Clojure (commonly pronounced clojur).

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Debating about which language is powerful is always biased. A true expert, however, can use any tool to solve a problem at hand. Choosing between Python and Lisp should narrow down to your level of experience and features you need, i.e., the types of problem at hand. 

Python is typically the same old system within a new syntax. This differs a lot from Lisp because Lispers can notice the past Syntax immediately as most of the programming language is stripped away. 

A group of Lisp programmers has used the SICP – Structure, and Interpretation of Computer Programs, which stresses and exploits lambdas’ importance almost from the beginning. At the begging of Python, there is no lambda function- they only appear at higher levels. Now, a close look at lambda functions in Python, you’ll realize that they are very stunted. That means arbitrary control flow is almost impossible. The worst part is they aren’t proper closures. 

The good thing is; with a few tweaks and hacks, you can make them emulate closures like mutable list cells within the closure. However, they still aren’t part of the core of the language. Beyond that, the BDFL of Python does not agree with fundamentally crucial language design changes, like eliminating tail calls. This feature is critical for codes written in the continuation-passing style or codes whose control flow is determined by the function calling instead of 60s-style loops. The standard way to solve problems in Lisp is embracing the best and the most current means of tackling issues as opposed to Python’s old-age means. 

Is Lisp still useful?

Is Lisp still an essential language for programmers to learn? The question of whether or not the Lisp programming language is still functional has been debated extensively. Some argue that it’s no longer relevant in this day and age. But others believe any other modern language can’t match its expressive power. In fact, one author even argues that learning a second dialect of Lisp makes you better at higher-level concepts like recursion.

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By now, you know that Lisp was initially created as a practical mathematical notation for computer programs, and it quickly became a choice programming language for artificial intelligence (AI) research. Ideally, Lisp inspires a peculiar universal reverence – it’s a language on its own level because it transcends the utilitarian criteria used to judge other languages.

The median programmer has never utilized Lisp to create anything practical, yet the reverence for Lisp runs so deep that Lisp is often ascribed mystical properties. Lisp’s sad part seems very poorly understood; thus, the many reasons are given why it isn’t popular. But the main thing is; it is pretty hard to learn Lisp than any other language, which doesn’t make it irrelevant, anyway. 

Keep in mind that Lisp is an abstract syntax tree (AST), and they let you think of your program in pure, abstract building blocks; hence a considerable amount of “hygiene of thought” absent in other programming languages.

If you learn to program in Lisp, you’ll definitely have an easy time programming in other languages. Ideally, learning Lisp offers a better way of tackling programming problems that are transferable to other programming languages. 

While Lisp is the best language for enhancing your coding style, it is one of the hardest languages to do real things in practice. Its interaction with the external environment is extremely poor and awkward. It features many slightly incompatible implementations with a relatively light library.

Why is Lisp not popular?

LISP was released to the public domain back in 1958 with its roots in artificial intelligence with a paper called “Programming Languages – Plans And Prospects.” The language uses lists as one of its primary data structures used recursively to represent trees or hierarchical arrays. It took years before it became mainstream due to high demand from academics interested in AI research.

Many have moved onto Python instead since they find it more accessible than other languages that require knowledge outside of what’s needed. There are many reasons why Lisp isn’t famous. It could be that the syntax is too strange and complex for most programmers. Besides, Lisp is hugely different from other programming languages like Python or Java, considering it was created during the last century. 

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But the real thing remains that Lisp is very powerful that it lets you do so much. But a few things will discourage you from using it:

Too strong integration with Emacs and no alternatives: everyone is writing in Emacs, and therefore choosing any other IDE means no help. For Lispers, macros are the order of the day. But some definitions and functions are not apparent during the first times, and you’ll need Slime and Emacs to expand the view. Otherwise, you’ll sit longer on a code before understanding it. 

Not working software: Lispers write codes in Emacs, i.e., few essential functions that use each other. Simply compile them and run using Slime. In case of problems, fix the bug then recompile; otherwise, the software won’t work. 

There is no big company standing behind: while the community is huge, there is a big corporation behind Lisp because it seems risky for senior management. Big corporations prefer to hire developers with experience in popular and easy-to-learn programming languages backed by corporations like Oracle. 

Lisp vs. Python

Here is something you should know about Lisp: for over 50 years, Lisp has been somewhat impractical and too slow for common programmers. Besides, the procedure calling process is expensive, making it the best option for academic fields and not other developers. 

Even so, the ideas within Lisp have been extended to other programming languages. Look, when Lisp was invented, recursions were a significant milestone as its predecessor – Fortran didn’t have it until later. Currently, Lisp is still ruling in certain aspects – the Lisp heterogeneous list. This is something that was in high demand in developers needing a strongly typed language. 

Still, the first-class procedure was an idea borrowed from Lisp, and it is still invading the mainstream of programming languages. So, it is not easy to wish away Lisp. 

Python inventor – Guido van Rossum dislikes Lisp, but he had to borrow the idea of Lambda from Lisp courtesy of high demand from Python users – it was half-baked. 

So, Lisp vs. Python; which way to go? 

You might consider several factors while selecting a language – taste and preference should be the last thing on the List. Any developers should look at the entire ecosystem and its relationship to the problem at hand. For instance, if you need to use NumPy or Tensorflow, then Python is the right choice.

So frequently, you’ll realize it is not about the language itself because you may find that a portion of the entire task might be in one language, and the remaining part might be in another one. That calls for a significant amount of flexibility. However, any general-purpose language with automatic memory management can work well for any application.

Remember, learning a new language and its ecosystem is costly. But Python offers a real-world that most modern developers won’t cede to Lisp, but you can write a working code much faster in Lisp than Python. 

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Is Lisp a functional programming language?

Lisp is a functional language with imperative features. Typically, the overall style of Lisp is that the language is organized around functions and expressions instead of statements and subroutines. Each expression in Lisp normally returns a value, and all procedure is syntactically a function, which when called, it returns a particular data object as its value. 

Calling Lisp imperative means that some procedures and expressions within Lisp have side effects like array positions or storing into variables. As a result, all Lisp procedures are always not functions in a pure sense. However, in practice, developers commonly refer to them as functions, even those with side effects, to emphasize the fact that a computed result is always returned. 

Note that, within Lisp, the use of imperative features is restricted, but you can also translate a Fortran code into Lisp directly.

Procedures in Lisp are typically small and specialized, and they perform one conceptual task. A complete Lisp program can have hundreds of minor procedures, each with between thirty to sixty lines of code. To traverse complex data structures in Lisp, developers often use a procedure called recursion. The presence of parentheses indicates program structure and precedence – the indentation of the code is usually in a conventional style to assist the eye.

There are multiple dialects of Lisp, and they differ in various tiny details. The most notable difference is that some dialects replace car and cdr with the friendlier names first and rest. Still, others offer an “IF” construct as an alternative for cond. However, with time, there was a need for standardization, and two dialects – Common Lisp and Scheme, have been the subject of standardization efforts by ANSI and IEEE. The two dialects borrow features from each other to simplify the presentation but retain Lisp’s historical tinge. 

Lisp programming example

The LISP expressions are known as s-expressions or symbolic expressions. These s-expressions are made up of three valid objects, lists, atoms, and strings. All s-expressions are valid programs, and they can run either as compiled or interpreted codes.

The Lisp interpreter checks the source code in a read-evaluate-print loop (REPL), aka repeated loop. What it means is that it will read the program code, evaluate it and finally print the value it returns. 

Starting up a common Lisp environment leads to a prompt, meaning that the Lisp is ready for your Lisp expression. 

For example: 

USER(1):

When interacting with users, Lisp follows the algorithm below:

loop

    read in an expression from the console;

    evaluate the expression;

    print the result to the console;

end loop.

Take a look at this simple program: 

You can write an s-expression to determine the sum of four numbers 7, 9, 11, and 13. First, types the interpreter prompt: 

(+ 7 9 11 13)

LISP returns

40

You can run this program as a compiled code, but you’ll need to create a Lisp source code file – myprog.lisp and type the code below in it.

(write (+ 7 9 11 13))

Upon clicking the execute button or typing Ctrl+E, LISP will execute it immediately, and it returns the results 

  40

Another example:

You can computer value the value from (2 * cos(0) * (4 + 6)). So begin by typing in:

USER(1): 

(* 2 (cos 0) (+ 4 6))

Lisp returns 

20.0

Prefix Notation

Lisp utilizes prefix notation. From the above examples, the positive (+) symbol is the function name for the process of addition of the numbers. Note that the operator comes before the operands in prefix notations. 

a * ( b + c ) / d

Shall be: 

(/ (* a (+ b c) ) d)

Another example: converting Fahrenheit temp of 60o F to the centigrade: 

The expression is as: 

(60 * 9 / 5) + 32

Create a source code file – main.lisp and type code in it.

(write(+ (* (/ 9 5) 60) 32))

Click the Execute button, or Ctrl+E, LISP returns: 

140

What is Lisp used for?

The foundation of Lisp’s mathematical theory of recursive functions, i.e., a function, appears in its own definition. A Lisp program is simply a function applied to data instead of being a sequence of procedural steps like in FORTRAN and ALGOL. The best thing is Lisp uses a simple notation with parenthesized lists for operations and their respective operands. 

You already know that Lisp is a family of languages, and Common Lisp was a unification of Lisp dialects such as Clojure, MacLisp & LispMachine Lisp, Scheme, Racket, etc. While being among the most powerful and dynamic languages, its syntax is more straightforward than most other languages, and that makes it the fastest dynamic language. While using Lisp, you do have to keep reasoning out about the operator precedence as it is explicit. 

That has made many developers put List into a variety of uses. Each dialect can be used in its own unique way and this is reason enough why you are likely to find many applications using Lisp. Lisp has excellent applications in Animation and Graphics, AI, B2B and Ecommerce, Bioinformatics, EDA/Semiconductor applications to give you a more comprehensive picture. 

Additionally, Lisp is powerful enough for Data Mining, Expert Systems, Finance, Intelligent Agents, Mechanical CAD, Scheduling, Modeling, and Simulation. Some developers have also created beautiful applications in Knowledge Management, Natural Language, Risk Analysis, Optimization, Research, Telecom, and Web Authoring.

Lisp is a general language that can do a lot more than the aforementioned incidental application areas. Large-scale financial systems, as well as video game servers, can utilize Lisp well. 

With AutoLISP, you can write macros besides automating different steps. Keep in mind that AutoLISP is a subset of the original high-level language developed for developers dealing with AutoCAD specifically. But currently, it doesn’t work better than VBA.

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Learn Lisp

Lisp is utterly different from other programming languages such as Python, Java, PHP, and C. it’s not just a powerful option, but learning to use it isn’t easy. So, it’s very important to find out the easiest way to learn Lisp. 

Checking through the web, you’ll realize that there are several ways to learn about Lisp. But you have to choose which of the dialects impresses you so much. Scheme and Common Lisp are a bit unique and have much depth. For instance, these two have scooping, a very detailed feature. So, if you intend to study them, begin with one at a time. 

You can get practical tips from various sites:

Scheme 

R. Kent Dybvig has a beautiful tutorial for Scheme. Jean-Pierre Hébert illustrates this third edition tutorial. It isn’t the final and exclusively the best, but it offers you what you really need to get you into the depths of Scheme. 

Like other tutorials, it begins with more superficial parts, then transitioning to intermediate and finally the most sophisticated operations and applications. Once you complete it, it’s a good idea to proceed to SICP.

Common Lisp and Practical Common Lisp 

Multiple types of tutorials are covering the common lisp area. However, the tutorial by David Lamkins might be an excellent option. You can get the book as a paperback at Amazon. However, there is also a free online version. Beyond that, you can check out a host of other online tutorials like this and this. 

Another important dialect that you can consider is the JVM dialect – Clojure. This is an excellent option because of some reasons, including:

• A simplified syntax, i.e., it requires less parathesis, and it features a unique design of collections.
• Being JVM-based gives it a stable performant runtime. Besides, the entire Java ecosystem – libraries, like database drivers, IDEs, build tools, etc., is within reach.

Resources that you might help with here include online tutorials, books such as The Joy of Clojure and Programming Clojure, and the 4Clojure website. 

Conclusion 

The design of Lisp allows it to be compilable to code that runs fast. While lisp is arguably the most powerful language existent, with an excellent compiler, this extra power won’t slow your coding process much at all.

Even with the emergence of newer languages, which tend to solve emerging issues, Lisp has maintained its power. However, it may not be a popular option because the newer languages compromise Lisp by borrowing powerful features from the different Lisp dialects. Still, they stay easy for new users – Lisp is relatively tricky for new users.

This explains the few numbers of Lisp users. Even so, there is a significant number of Lispers across the globe – they just don’t shout too loud. That makes it a worthy and relevant language in the field like gaming, finance, AI, and computing.