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Python is a high-level object-oriented programming language. It allows developers to use built-in data structures, combined with dynamic binding and typing.
Python is a scripting language like Perl, PHP, and Ruby that allows you to create desktop and web applications.
Python ranks second after R as one of the widely-used programming languages for data science and statistical analysis - especially machine learning. It’s an open source language providing optimized features for code readability, programmer productivity, and software quality. Python is diverse and allows developers to create a variety of programs ranging from simple to complex programs.
There is no direct and easy answer to this question. Both languages are very popular due to their powerful features. The purpose of this article is to help you understand their strengths and weaknesses to identify which one suits you the best and fulfills your purpose.
Learnability refers to the ease of learning. Learnability is very important when you are learning a new programming language (even with no prior programming experience).
Time is precious. You need to chose a programming language that is user-friendly and easy to learn with minimum effort.
From this perspective, Python has a short learning curve and you could learn it in a few months. Python is designed as a beginner-friendly language that provides more simple functions and variables.
Overall, from a learning perspective, I would say that Python is the better choice.
Scalability is an important feature of any programming language when it comes to developing applications. It is measured by two parameters: i) the ability to support a large number of users, and ii) its capacity of process number of transactions and data with a minimal server utilization.
Data science has been a hot area in software and web development for many years. The focus of data science is allowing developers to analyze, manipulate and understand raw or structured data and to make inferences based on that data.
Data scientists usually use one of two languages. Python is one of them (with the other being R).
Here are some reasons why:
Python provides a wide range of built-in frameworks and libraries that empower developers with thousands of useful functions. These frameworks and libraries not only help developers save time and write more performant code.
Visualizing data through different appealing charts or graphs is one of the core activities in data science. Python provides a list of libraries that allows you to create very appealing visualizations. Some of the top visualization libraries of Python include Matplotlib, Plotly, Seaborn, gplot, and Altair.
Like other object-oriented languages, Python supports inheritance. Inheritance allows base classes (child classes) to inherit objects and behavior from parent class (parent class). It uses a class-based inheritance model.
5. Function Arguments
If we talk about function parameters in both languages, we can also see a difference here. If a function is incorrectly called or provides a wrong number of parameters in Python, an exception with a custom message occurs at the time of function execution. This indicates that the developer doesn’t know the actual number of parameters until functioning calling.
Python contains mutable and immutable data types. Mutability allows change in content or state of an object after it is created. Immutable objects cannot modify their state after their creation. Examples of some mutable objects are set, list, and dict are whereas objects like int, tuple, bool, Unicode are immutable.
7. Need of an Interpreter
An interpreter is a program that directly interprets and executes a code written in a programming or scripting language without converting it into machine code.
Like other high-level programming languages, Python requires an interpreter to execute code.
8. Programming Paradigms Support
This means that they provide programming support for more than one programming approach. Both languages provide functional, object-oriented, and imperative approaches for programming.
9. Modules and libraries
Python comes with several modules for programmers. It’s often called a “batteries-included programming language” because of this. As discussed earlier, it includes a wide list of built-in libraries for performing data science, data analytics, and machine learning tasks.
10. Hash Tables
11. Numeric Data Types
Python allows developers to use multiple numeric data types such as int, float, and decimal point.