Python is a general-purpose and high-level dynamic programming language that focuses on code readability. After being founded in the year 1991 by Guido Van Rossum, Python has only soared in popularity.
Its syntax allows programmers to write codes in fewer steps as compared to Java or C++. Some of the other reasons behind Python’s popularity include its versatility, effectiveness, ease of understanding, and robust libraries.
Python’s high-level data structures and dynamic binding make it a popular choice for rapid application development. Data scientists usually prefer Python over other programming languages.
But what exactly makes Python suitable for data science? Why do data scientists prefer working with Python? Let’s find out –
A big reason why Python is widely preferred is because of the benefits it offers. Some of the major benefits of Python are –
Data science is about extrapolating useful information from large datasets. These large datasets are unsorted and difficult to correlate unless one uses machine learning to make connections between different data points. The process requires serious computation and power to make sense of this data.
Python can very well fulfill this need. Being a general programming language, it allows one to create CSV output for easy data interpretation in a spreadsheet. Python is not only multi-functional but also lightweight and efficient at executing code. It can support object-oriented, structural, and functional programming styles and thus can be used anywhere.
Python also offers many libraries specific to data science, for example, the pandas library.
So, irrespective of the application, data scientists can use Python for a variety of powerful functions including casual analytics and predictive analytics.
As discussed above, a key reason for using Python for data science is because Python offers access to numerous data science libraries.
Some popular data science libraries are –
Python is an important tool for data analysts. The reason for its huge popularity among data scientists is the slew of features it offers along with a wide range of data-science-specific libraries that it has.
Moreover, Python is tailor-made for carrying out repetitive tasks and data manipulation. Anyone who has worked with a large amount of data would be aware of how often repetition happens. Python can be thus be used to quickly and easily automate the grunt work while freeing up the data scientists to work on more interesting parts of the job.
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