Python is the most powerful, open-source, interpreted, object-oriented, and a very high-level programming language for data scientists. They need dynamic semantics, in-built data structures, a large community, and excellent libraries to deal with various data science projects/applications.
It is a general-purpose programming language that can be used for developing both web and desktop applications, including complex numeric and scientific applications. Due to its versatility, it has become the fastest-growing worldwide use coding language and the right choice to kick-start a career in Data Science.
So, How Python emerged as the foundation of tech giants to win a Kaggle competition in Data Science? Let’s dig deeper into the topic: Python for Data Science.
History of Python Programming Language
- Python was created by Guido Van Rossum and was first released in 1991 to help programmers write a logical and clear code for small to large-scale projects.
- Python 1.0 was issued in 1994 with new features such as map, lambda, filter, and reduce.
- The final version of Python 2.0 came into the market on October 16, 2000, with additional features like garbage collection systems and list comprehensions.
- Python 3.0 was published on December 3, 2008, to improve the language’s fundamental flaw.
- Python 3.9 was declared on October 5th, 2020, and its next version is expected to come between 2020 and 2025.
Features that Attract Techies to Use Python for Data Science
Python is faster to learn and easy to use in contrast to other programming languages. Its syntax and grammar are very close to the natural language that helps programmers increase their productivity while working on Data Science.
Faster to Learn & Easy to Use
Python for Data Science is the recommended programming language for beginners because it has no use of curly-brackets or semicolons. If a single skip in C-like languages, it would be impossible for you to compile the program.
From Google, Instagram, Facebook to Spotify like world-class companies use Python because it allows them to write a program on Macintosh computers and run it on different platforms such as Linux, Unix, Windows, and vice versa.
It is a very expressive and portable language. Overall, we can say that one language is sufficient for everything. Whether you want to build web applications and machine learning tools, it has everything you need in one language and simplifies your project using less time and money.
Numerous Data Science/Machine Learning Libraries
Python for Data Science is the key to the future. The reason for the Python popularity is its numerous free data science, data analysis, and machine learning libraries like Scikit-Learn, Pandas, Numpy, etc. All these libraries of Python help in data mining, data analysis, and data manipulation. With Python in-built libraries, you can quickly solve real-world problems and implement popular algorithms on datasets.
Apart from that, there are some deep learning frameworks in Python for data science, likewise TensorFlow, Caffee, PyTorch that allows you to build deep learning architectures by just using a few lines of Python code.
Hadoop, an open-source software framework, supports Python for big data. The Python package labelled as Pydoop enables you to access APIs for Hadoop, and you can write several Hadoop programs using Python.
With this package, you can quickly write quality codes and solve complex problems by putting minimal effort.
Largest Community Support
Python has the largest community support for beginners to experience coders. The popularity of data science using Python is increasing day-by-day, but we can’t forget the language is also widespread due to its broad Python developer community.
They help everyone from Python programmers to data scientists and whenever they find it difficult while writing a code for web scraping or any other project related to Python.
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