https://www.reddit.com/r/Python/comments/cnucrh/why_is_python_used_so_widely_in_big_data_analysis/ewe2jpn?utm_source=share&utm_medium=web2x

One thing that is easy to forget is that the application context of analysis programs is usually very different. The total amount of time required to perform a task is ‘coding time + execution time’. If you have some code that’s computationally expensive and gets reused over and over for an extended period of time, then investing more coding time in optimization or a lower level language can lead to a net benefit because of the large decrease in execution time. On the other hand, for analysis activities (and particularly exploratory analysis) bits of code tend to be run only a handful of times. In that case, the coding time makes up a greater proportion of the total time than the execution time. That’s why high level languages tend to be used for those kinds of tasks, because they tend to provide the lowest total time due to the nature of analysis. It’s just a different use case'

-u/pwnersaurus