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Python is a versatile and accessible programming language that has secured its place in the IT industry since its introduction in 1991. Since then, it has been adopted as the common programming language in a variety of areas from data science to video game development. Python is easy to learn compared to many other languages currently in use. This is in part due to its natural and intuitive syntax and use of indentation and line breaks to define blocks of code. Although using curly braces and semicolons, common in languages such as Java, is not significantly more difficult, it makes these languages less accessible to those learning programming.
Another significant feature that adds to the language’s accessibility is its dynamic typing. It allows programmers to write their code without defining what data type each variable will hold. Because of this, one can write simple Python programs with little planning. However, the same benefit can turn into a significant disadvantage in larger projects, when new code incorrectly assumes the content of old variables. This is especially true for working with others, who may not know the expectations and internal conventions of existing code. Therefore, commenting code and adhering to conventions become critically important in such projects.
Finally, Python requires no compilation ahead of time, allowing one to write simple, practical applications faster. Furthermore, small adjustments can be made with minimal required effort, thus allowing such applications to be created and adjusted to one’s needs quickly and easily. This makes Python ideal for such small, personal applications for automating one’s common tasks, implementing regularly used mathematical functions and calculations, or similar purposes.
These advantages combine to give Python its versatility; the language is used in a variety of fields. Some of these fields are not directly related to developing stand-alone applications, but rather use Python as a tool for data and mathematical features. The language is often used in non-IT-specific fields such as sociology or statistics to process or analyze data.
Python is often used for writing scripts that are executed in concert with other, lower-level programming languages. This sees common use in the implementation of particular scripts as plugins through solutions such as Jython for Java or embedded directly in C++ code. This allows less performance-critical parts of larger applications to be developed faster, or provide end-users of such an application with significant customization and scripting opportunities.
The versatility and breadth of Python’s use scenarios contribute to its popularity and availability of support. Documentation, code examples, and advice are common on the internet; most common issues have already been described and solutions are easy to find. The language’s popularity also means that questions on resources such as StackExchange are answered quickly and accurately. This further contributes to the language ease of learning and collaboration.
From a technical standpoint, Python is a high-level programming language, which has advantages and drawbacks. There is dynamic typing and built-in memory management, which means that these relatively low-level concepts do not need to be considered in Python development. At the same time, that limits Python applications’ memory performance and potential stability, especially with user input. Furthermore, Python is generally not as performant, limiting its use in performance-critical applications. Therefore, while Python components and scripts are often used in applications written in other languages, large-scale Python applications are uncommon.
Python’s similarities and differences with other languages stem from these same principles. Compared to Java, C#, or C++, other popular languages, Python’s syntax is simple to write and easy to read, even with limited programming experience. Python code also produces shorter lines for similar operations, and occasionally offers built-in functions that other languages do not. Elements such as loops or iterators are generally faster and more intuitive to implement in Python. Moreover, Python has features that allow the same actions to be performed in less code; for instance, list comprehensions allow one to populate lists with values, including those that require recursion to obtain. In Python, this can be achieved in one line of code; whereas in Java, such a list would have to be populated by an iterative loop.
Compared to Java, Python has fewer built-in data types for its variables. For instance, an integer in Python is type int, whereas in Java it can be any byte, short, int, or long, depending on its size and, therefore, memory requirements. Because of this, Java applications can be written to use the memory more efficiently by only allocating as much memory to a variable as required. However, the same property can cause overflow issues if one attempts to write a number that is too large into a variable. Furthermore, Java’s strict typing can add complexity when trying to perform operations that involve integers of different data types (e. g. int and long); in these cases, the additional casting of variables may be necessary.
All other languages mentioned above require compiling programs before they can be launched. This is a time-consuming process, especially when working with larger applications, where a minor code change can require hours to recompile the code. In contrast, Python is compiled at run-time, practically eliminating this step. This ultimately accelerates development and reduces the barrier to entry, reducing the amount of software that needs to be installed and set up to work with Python.
The ability to use Python as a real-time scripting utility is another significant advantage. Executing lines of code in real-time allows one to perform minor tasks without developing a full utility application. This is useful in cases where the task is large enough to be automated, but not common enough to require such a utility. Additionally, Python’s ability to be used as a mathematical engine is useful in this scenario, as relatively complex calculations can be accomplished by simply writing the expression. These features can be combined to execute more involved calculations, for instance, those that require recursion.
Overall, these differences make Python a more convenient and accessible programming language. As such, it has found its niche as a scripting language that functions alongside or within applications written in other, lower-level, and more performant languages. In this capacity, it allows applications or scripts to be developed, tested, and deployed relatively quickly. It can be used as a standalone utility for mathematical calculations, or as a tool for scripting or operations in fields that do not directly involve application development. Its relatively simple syntax, dynamic typing, accessibility and convenience features such as list comprehensions allow one to use it without significant experience in programming or computer science. However, the same strengths limit its potential performance and create opportunities for errors, particularly in larger collaborative projects.
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