According to a survey by JetBrains (which additionally launched Kotlin, the up-and-coming language for Android development), some 49 % say they use Python for information analytics, ahead of web development (46 %), machine learning (42 %), and system administration (37 %).
Considerable numbers of developers additionally use the language for software testing (25 %), software program prototyping (22 %), and “educational functions” (20 %). Fewer chose it for graphics development or video games/mobile development.
This data reinforces the overall idea that Python is swallowing the data-analytics space whole. Though extremely functional languages corresponding to R have their place amongst academics and extra analysis-centric data analysts, it’s clear that Python’s relative ease of use (not to point out its ubiquity) has made it many friends amongst those who have to crunch data for some aspect of their jobs.
This trend has additionally been underway for fairly some time: In February 2018, a KDnuggets poll confirmed a slow decline in R utilization in favor of Python among tech professionals who utilized both languages. Throughout that very same period, a separate survey from Burtch Works revealed that the language’s use amongst analytics professionals grew from 53 % to 69 % over that same time two-yr interval, while the R user-base shrank by nearly a 3rd.
However, you also can’t ignore Python’s use in machine learning, which is extensively viewed as an essential part of virtually every firm’s future tech technique. If developers are utilizing Python to construct out machine learning instruments, which means the language may have a big lock on the ML/A.I. Ecosystem considered so centrally to how future software develops.
For those who don’t know Python, it’s an important language to learn. Thankfully, there’s quite a lot of web sites, books, and different sources that can get you up to speed quickly.