# APL

> functional, symbolic programming language for operating on multidimensional arrays

**Wikidata**: [Q296187](https://www.wikidata.org/wiki/Q296187)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/APL_(programming_language))  
**Source**: https://4ort.xyz/entity/apl

## Summary
APL (A Programming Language) is a functional, symbolic programming language designed for operating on multidimensional arrays. Developed by Kenneth E. Iverson at IBM in 1966, it is known for its concise syntax and powerful array manipulation capabilities, making it particularly useful for mathematical and scientific computing.

## Key Facts
- APL was created in 1966 by Kenneth E. Iverson, a Canadian computer scientist and mathematician.
- It is classified as an array programming language, functional programming language, and interpreted language.
- APL is multi-paradigm, supporting structured, modular, and functional programming.
- The language was developed by IBM, with contributions from Lawrence M. Breed.
- APL is standardized under ISO 13751.
- It is dynamically typed and known for its symbolic notation.
- Notable implementations include Dyalog APL.
- APL competes with languages like Mathematica, Fortran, MATLAB, and Python for numerical and scientific computing.

## FAQs
### Q: What is APL used for?
A: APL is primarily used for mathematical and scientific computing, data analysis, and array manipulation due to its powerful array-oriented syntax.

### Q: Who created APL?
A: APL was created by Kenneth E. Iverson, a Canadian computer scientist, with contributions from IBM and Lawrence M. Breed.

### Q: What makes APL unique?
A: APL is known for its concise, symbolic notation and its ability to operate on multidimensional arrays efficiently, making it highly expressive for mathematical operations.

### Q: Is APL still used today?
A: Yes, APL is still used in specific domains like financial modeling, scientific research, and data analysis, though it is less common than languages like Python or MATLAB.

### Q: What are some alternatives to APL?
A: Alternatives to APL include Mathematica, Fortran, MATLAB, R, GNU Octave, and Python, all of which are used for numerical computing and data analysis.

## Why It Matters
APL revolutionized programming by introducing a highly expressive, array-oriented syntax that allowed complex mathematical operations to be written concisely. Its influence can be seen in modern array programming languages and tools used in scientific computing, data analysis, and financial modeling. APL's unique notation and efficiency in handling multidimensional arrays have made it a valuable tool for researchers, engineers, and analysts who require precise and compact code for numerical computations. Despite its niche status, APL's contributions to programming paradigms and its impact on subsequent languages underscore its significance in the history of computer science.

## Notable For
- Being one of the first array programming languages, enabling efficient manipulation of multidimensional arrays.
- Its concise, symbolic notation, which allows complex operations to be expressed in minimal code.
- Influence on later programming languages and tools, particularly in mathematical and scientific computing.
- Standardization under ISO 13751, ensuring consistency and portability across implementations.
- Development by Kenneth E. Iverson, a pioneering computer scientist, and IBM, a leading technology corporation.

## Body
### Overview
APL (A Programming Language) is a high-level programming language designed for array processing. It was created by Kenneth E. Iverson in 1966 and developed by IBM. APL is known for its powerful array manipulation capabilities and concise syntax, which uses a unique set of symbols to represent operations.

### Development and History
- APL was first introduced in 1966 by Kenneth E. Iverson, who was inspired by mathematical notation.
- The language was further developed by IBM, with contributions from Lawrence M. Breed.
- APL has been standardized under ISO 13751, ensuring consistency across different implementations.

### Features and Capabilities
- **Array Programming**: APL is designed to operate on multidimensional arrays, making it highly efficient for mathematical and scientific computations.
- **Functional Programming**: APL supports functional programming principles, allowing for the creation of concise and expressive code.
- **Interpreted Language**: APL is typically implemented as an interpreted language, meaning that instructions are executed directly without the need for compilation.
- **Dynamic Typing**: APL uses dynamic typing, which allows for flexible and adaptable programming.
- **Symbolic Notation**: APL uses a unique set of symbols to represent operations, which can make the code very concise but also requires familiarity with the notation.

### Implementations
- **Dyalog APL**: One of the most well-known implementations of APL, Dyalog APL is widely used in industry and academia.
- **Other Implementations**: There are several other implementations of APL, each with its own features and optimizations.

### Applications
- **Mathematical and Scientific Computing**: APL is particularly well-suited for mathematical and scientific computations due to its array processing capabilities.
- **Data Analysis**: APL is used in data analysis for its ability to handle large datasets efficiently.
- **Financial Modeling**: APL is used in financial modeling for its precision and efficiency in handling complex calculations.

### Competitors
- **Mathematica**: A computational software program used for mathematical computation, data analysis, and scientific modeling.
- **Fortran**: A general-purpose programming language designed for high-performance numerical and scientific computing.
- **MATLAB**: A numerical computing environment used for numerical computing and data analysis.
- **R**: A programming language for statistical analysis and data manipulation.
- **GNU Octave**: A numerical computation software used for numerical computation and data analysis.
- **Python**: A general-purpose programming language used for data analysis and numerical computing.

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## References

1. [Source](https://github.com/JohnMarkOckerbloom/ftl/blob/master/data/wikimap)
2. Freebase Data Dumps. 2013
3. [Source](http://datos.bne.es/tema/XX528234.html)
4. Quora
5. National Library of Israel