Python is a rather full language with succinct syntax. If you were developing an algorithm on a whiteboard in pseudo code, you could probably convert your text into compiling Python with relatively little effort. Its string manipulation syntax is concise and natural (which I've seen a lot), lists and dictionaries function with no overhead, and it features an interpreter for quick iteration. In the first hour of a Python project, you will be far more productive than in more powerful languages.

In comparison, the two most popular full-featured languages, Java and C++, require you to learn a lot more syntax and produce a lot more text to get the same result. That is appropriate for a full-time software engineer. Java and C++ provide a lot of speed and power in exchange for all of the extra information you must learn. They compel you to think through exactly what you want to do, which is bad in the first hour but excellent in the days, weeks, and years that follow. Both, for example, have strong typing, which necessitates a lot more typing and thought but also significantly reduces the number of ways your program may bite your head off.

For a data scientist who is mostly developing terminal code (programming that will not be referenced by other code), the time spent knowing all of their complexities isn't worth it.