![]() ![]() ![]() If you’d like to learn more about these libraries, I highly recommend reading this article about the top 15 Python libraries for data science. These libraries simplify and expedite most of the tasks in data science, from data cleaning to creating machine learning models. ![]() The second reason is the numerous, extremely helpful Python libraries. Since people from various technical and non-technical backgrounds work in the data science ecosystem, a programming language that is not difficult to learn is likely to be their first choice. Its syntax is clear, intuitive, and highly readable. The first is that Python is easy to learn. There are two main reasons why Python is the most preferred language among aspiring data scientists and people who work in the field of data science. The PYPL Index is created by analyzing how often language tutorials are searched on Google. The use of Python in data science has been the most influential factor in its proliferation.Īccording to the Popularity of Programming Language Index ( PYPL Index), Python is currently the most popular language, and it grew the most in the last 5 years. However, it has gained much of its popularity in recent years. ![]() Python was first released in 1991, so it has been around for a long while. ![]()
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