Is Lisp still worth learning?

The truth is; programming languages have varying power. Some programmers hail Lisp as one of the most powerful programming languages. Of course, some will dispute. But is it really worth learning Lisp?

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There are reasons, which make Lisp a worthwhile language. The apparent reason is it is the oldest langue after Fortran, and it is widespread. Look, as we speak, Clojure, Scheme, and Common Lisp are still widely used. However, it isn’t a super popular language; if you look at the amount of code developed by Lisp, the number is pretty low. 

The choice to use Lisp is highly subjective, i.e., it depends on the situation at hand. For instance, if you learn Lisp to seek a job, it might be a terrible choice. In this case, choosing a more practical programming language might work well for you. 

However, looking at it from a broader perspective, a functional programming language like Lisp or Scheme might be a nice asset. Such languages widen exposure and experience to different types of thinking when it comes to programming. This can be handy in your programming adventures.

Ideally, learning Lisp exposes you to advanced programming concepts not found in other programming languages. And learning a new concept is golden in programming. Catching up with different programming concepts is a thing of varying programming languages currently. Still, Lisp can teach you many new and advanced concepts, making it easy for you to implement Lisp paradigms.   

Here are some reasons to learn Lisp:

Lisp is influential in design more recent programming languages. Python, Scala, Ruby, Swift, Haskell, and JavaScript all take inspiration from Lisp. What’s more, Lisp influences less popular languages such as Nim, Lua, Perl, Forth, Elixir, etc. So learning Lisp can give a deeper insight into a variety of other programming languages. 

Lisp teaches you S-expressions. The S-expressions were invented explicitly for Lisp. Though not broadly used today, they are still a practical and efficient data serialization format for tree-structured data. When you learn Lisp, practically you learn to use them; thus, getting another option for data serialization, which much more efficient than JSON, XML, or YAML while dealing with simple data structures.

An interactive environment in Lisp. Lisp offers an entirely interactive platform in REPL, even while developing web applications. There is always an interactive debugger with the stacktrace when an error occurs in the editor, and when you press V, it automatically moves you to the problematic line. Of course, you can focus on catching the errors to avoid the debugger. But also, you can disable it using the global settings. 

What’s so great about Lisp?

Lisp has a rich history, a strong foundation, and offers unique ways of doing things. Typically, Lisp allows you to do something in the most powerful yet unique way. While many people claim that the Lisp code is homoiconicity, i.e., code-is-like-data, and complain of terms like Lambda Calculus, endless parentheses, etc., is much more depth to Lisp than this. Take a look:

Lisp Macros

Macros are a significant advantage in Lisp as a programming language. But also macros are an advantage of other programming languages. Macros can allow you to do things in Lisp that you cannot do in different programming languages – you can use the macros to transform Lisp into other programming languages and back.  

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Any programmer who can gain experience with Lisp will discover that other languages are just skins on top of Lisp. That means that programming with a lisp is programming at a higher level than other languages. 

The majority of other programming languages invent and enforce syntactic and semantic rules. However, Lisp allows you to make the rules as it is general and malleable. 

Even so, comprehending the importance of macros requires a deeper understanding of Lisp’s features but are absent in other programming languages. If you are a top percentile programmer, learning the depths of programming is a must. Therefore, you must know some sort of way to write macros, i.e., programs that write programs. 

Mostly, if you consider macros, you may find yourself relying on stylistic aphorisms like:

• Macros are changing the lisp code syntax.
• Macros are working on your program’s parse tree.
• Only using macros when a function won’t do.

How often would you want to use programs that write programs? Few programs and programmers are fond of that, except Lisp and maybe Cobol. Lisp is more convenient. 

Proper numbers

Lisp uses proper numbers. For instance, in Python and Cee Language, 3/5 mean 0. In contrast, in Perl and Fortran programming languages, it means an inexact floating-point of 0.6. in Lisp, that value 3/5 means a rational number 3/5., giving you an exact integer three and not 2.99999999 after multiplying it by 5. 

Another example is 100000000*100000000 – in Lisp, the value doesn’t silently overflow to give a wrong answer as in the case of C. again, it won’t fall with an exception like in Python or result in a floating-point number of doubtful exactitude as in Perl. What you get is just the number 10000000000000000.

Other good things about Lisp include:

• Good built-in types
• Dynamic typing:
• Robust Error Handling, thanks to a complete and flexible environment 

Is Lisp the most powerful language?

Beyond being a flexible language, Lisp has immense meta ability, allowing developers to invent their worlds. But it takes a little longer to figure out your little world while working in Lisp. 

Note that programming languages are a form of culture, and they tend to enforce specific ways of doing things directly or indirectly. This comes in the form of conventions or standards. So, if you understand a language and its culture, you are bound to meet few surprises and a longer adaptation curve. 

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Lisp is close to an interpreter builder than just an interpreter or compiler. This means that developers can use it in ways they deem fit, i.e., developers can create anything they want. While Lisp is a powerful choice, it does not significantly oversimplify things than other programming languages. 

For some people, Lisp is more of a Hacker Language than a commercial language. The reason behind this notion is that hackers are loners who do not care if others can figure out their code in the role of hacking. As such, they create their own little world, fitting their need perfectly to hack faster. 

Note that Lisp doesn’t fit the style of other programming languages – a significant advantage of its heavy meta-ability. That means that it will take a considerably longer time for most people to get faster in Lisp. 

In the world of Turing-complete languages, Lisp is still powerful. That means you can accomplish more with Lisp in a finite amount of time. While using Lisp, the standard practice defines abstractions relevant to your problem domain and then re-uses them. This is much similar to domain-specific language, with the support of a whole language behind it. Creating these abstractions means combining macros and features, which have evolved over half a century. This is somewhat complex and only experienced Lispers – it’s their second nature. Therefore, Lispers can do much more in a specified period of time than non-Lispers. 

Lisper manifests itself in such a way that a single line of code can end up being equal to hundreds of lines of hand-written code. Any programming language that replaces hundreds of lines with a single code line reduces defects in software automatically by a proportional amount.

Typically, the average defect for code is about 15-50 per KLOC. This is human error and is generally independent of a programming language. Representing multiple codes as a single code can drastically reduce the time to find errors since programmers spend most time tracing and fixing defects. As a result, the programmer becomes more productive. 

What language is better than Python?

Python is a powerful programming language that excellently implements clear concepts. Additionally, it is perfect in what it offers. As a programmer, you get benefits like easy to learn, fast scripting environment, easy to extend, exploratory programming, and prototyping and algorithms. 

Python’s standard is comparable to most other interpreted languages like Java, JavaScript, TCL, Perl, and Smalltalk. But also, 

comparing Python to languages like C++, Common Lisp, and Scheme gives a host of enlightening information. 

The choice of a programming language is usually dictated by real-world issues like training, cost, availability, and prior investment. Sometimes even emotional attachment is a huge factor. These aspects are highly variable; thus, not an essential element here. 

Python is 30+ years old, and over the years, it has been an outstanding jack of all trades. However, in particular domains, some modern languages will perform better than Python. Modern languages have been designed to meet the arising issues that languages like Python aren’t solving.

• Desktop and enterprise applications: if you plan to build desktop or enterprise applications, Java and C# have a better design to handle such tasks. These languages have better runtimes than the PythonVM, making them a better choice. 
• Mobile app development: developing mobile apps requires a flexible but rich feature language. Here, Kotlin, also running on the JVM, is a much better language than Java. On the other hand, Swift is a modern language that is very well designed with some similarities to Kotlin and has a native compiler. That also makes it an admirable option. 
• Web backend programming, microservices: Golang will offer a much better environment than Python in this aspect. Beyond being strongly typed, Golang features a highly functional syntax with superior inbuilt parallel and concurrent capabilities. Moreover, it is super fast.
• Cross-platform web app (frontend): Python was never designed to develop frontend applications. So, so if you intend to build such applications, a better alternative would be TypeScript and Dart, which offer a better way for frontend development than Python.
• Data science: the design of Python wasn’t for data science. However, the data science capabilities can be extended through Pandas and Numpy. That makes it okay for this purpose, and there are many packages and other firsthand support from Tensorflow, Keras, and further ML/DL libraries and frameworks.

The reason why people might consider choosing Python is:

• Fast development speed: Python is accessible, making writing a code much easier and quicker. 
• Numerous libraries and frameworks improve your time to market when you leverage these features since you won’t be coding manually. 
• Easy maintenance: Python resembles accurate English, making it intuitive to read, so deciphering and maintain the language is effortless.

Lisp vs. Python for AI

Lisp programming languages have essential features that make it AI-centred; so, you can use them to deliver artificial intelligence development. LISP is a pioneer of many important concepts in functional programming. The key attraction is, however, how the language is close to maths. These features have found their way in most modern programming languages. 

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On the other hand, Python is used by 57% of data scientists, and 33% of developers prefer Python over other programming languages.

These two languages go head to head when it comes to creating AI-based programs. Ideally, they have powerful features; thus, offering a friendly environment. 

Easiness and speed of coding

Python is undoubtedly one of the easiest programming languages, which means it is easier to code as the developer is not chained to strict procedures and sophisticated architectures embedded within the system.

On the other hand, you can describe Lisp as an easy-to-use or programming language that is fast to code because this language evolves over time, and there are constant revisions and updates. But still, it is far from Python. However, Lisp some amazing architecture and practices. Multiple options make this language an elegant choice for modern programmers. 

AI and Python

The Prebuilt Libraries within Python-like Scipy for advanced computing Numpy for scientific computation, and Pybrain for machine learning make a handy choice for AI. For general AI, libraries like AIMA, pyDatalog, SimpleAI, and EasyAI are convenient. For machine learning, PyML, MDP-Toolkit, and Scikit-learn are handy. 

Python is highly independent; thus, offering much flexibility for use across multiple technologies and platforms. Additionally, it has minor tweaks in basic coding. 

There is comprehensive support from the Python community and support from the various platforms. 

Lisp and AI

Lisp has efficient implementations (SBCL, Bigloo, or Chicken/Scheme), making it easy to drive big data or machine learning applications. However, most recent machine learning libraries are coded in more mainstream languages. Lisp is great for AI because: 

Great implementations like SBCL that compiles to machine code every expression to the REPL

Read-Eval-Print Loop – for interactive programming 

A powerful macro machinery

Garbage collecting every code 

Lisp vs. Python performance

Much is said between Lisp and Python. But understanding how these two powerful languages can be beneficial when it comes to choosing a programming language. Performance is a crucial factor when looking at a programming language. Apart from how easy it is to code in a language, the speed also matters because it determines the workflow. 

So comparing the inherent workflow and ecosystems between these two languages can give you a clear picture of what they are. 

Interactivity

Coding in Python means resetting everything when you change each code. This involves using breakpoints, and it is time-consuming, repetitive, and boring. Developers need to re-manipulate data. Even if you choose a more interactive way, you still need to restart the webserver. 

Contrarily, everything is highly interactive in Lisp in the REPL. An interactive debugger within the editor automatically takes you to the error code when you press V. It is easy to resume codding within any stackframe. No process needs to restart. 

Editing code

Editing codes in Python is done line by line or paragraph by paragraph. However, you can try editing using editor plug-ins to edit codes by semantic units. This process requires much attention, especially when adding or removing whitespaces. 

Lisp allows you to edit codes by semantic units. Most developers use the emacs’ lispy mode – it is weird at first but very convenient. It lets you navigate expressions back and forth and delete an entire “if” expression with a single keypress. But other emacs plugs make the process easier. 

Running, testing programs.

Default workflows in Python run commands in the terminal. So, you can manually read output, scroll, and copy-paste. Alternatively, you can use a terminal inside emacs.

By contrast, in Common Lisp, the default workflows perform all things interactively into the REPL. However, some developers still use a write-compile-run approach. That means there is an inbuilt completion for everything. So, you don’t need to use the shell. Besides, there is an interactive debugger that you can use to fix and re-run code and tests interactively.

Conclusion 

Lisp is a multi-paradigm, compiled, high-performance, ANSI-standardized programming language. It was initially created as a practical mathematical notation for computer programs. Common LISP is one of the most favored languages for artificial intelligence (AI) research. Since Lisp was a pioneering language in computers, it came with multiple ideas, including tree data structures, dynamic typing, automatic storage management, conditionals, recursion, higher-order functions, and the self-hosting compiler. 

On the other hand, Python is a clear and powerful object-oriented programming language, comparable to Ruby, Perl, Java, or Scheme. Being a general-purpose programming language, Python is most praised for its elegant syntax and readable code. Developers just beginning their programming career might find Python one of the best languages. 

Python and Lisp are both efficient scripting languages, but Python is generally easier to learn. For example, the syntax of a function with parameters in Python would be:

inputs = (1,) outputs = { ‘a’: 1 } def my_function(x): if x > 0: return input * 2 else: print(“error!”) ————–my_function————————–def

While Python is a good programming language for beginners, it’s not the best choice to do something complicated. Python isn’t going to offer some advantages that more experienced programmers might need to complete their projects efficiently. Such as certain advanced features found in Lisp mainly due to how extensively professional software engineers have used this type of code over time. Experienced coders typically use Lisp because of its powerful data processing abilities.