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(You can also use Stack Overflow Trends to compare rates of questions asked, rather than ones visited). We could compare the web frameworks Django and Flask to the data science packages NumPy, matplotlib, and pandas. How could we disentangle Python’s recent growth across these fields?įor starters, we could examine the growth in traffic to tags representing notable Python packages in each field. Python is a versatile language used for a variety of tasks, such as web development and data science. Just like in the previous post, all of these analyses are constrained to World Bank high-income countries. In combination this tells a story of data science and machine learning becoming more common in many types of companies, and Python becoming a common choice for that purpose. However, Python’s growth is spread pretty evenly across industries. As for which industries are using Python, we found that it is more visited in a few industries, such as electronics, manufacturing, software, government, and especially universities.

This is particularly visible in the growth of the pandas package, which is the fastest-growing Python-related tag on the site. First, the fastest-growing use of Python is for data science, machine learning and academic research. In this post, I’ll take another look at Stack Overflow data to understand what kinds of Python development have been growing, and in what kinds of companies and organizations it’s most used.

I’m a data scientist who uses R, so I’m certainly interested in how much of Python’s growth has been within my own field. Why is Python growing so fast? Python is used in a variety of purposes, ranging from web development to data science to DevOps, and it’s worth understanding what particular applications of Python have recently become more common.
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