What are the drawbacks?

Aside from personal preferences, the primary drawback of Python lies in its execution speed. Typically, Python is slower than its compiled siblings. The standard implementation of Python produces, when you run an application, a compiled version of the source code called byte code (with the extension .pyc), which is then run by the Python interpreter. The advantage of this approach is portability, which we pay for with increased runtimes because Python is not compiled down to the machine level, as other languages are.

Despite this, Python speed is rarely a problem today, hence its wide use regardless of this downside. What happens is that, in real life, hardware cost is no longer a problem, and usually you can gain speed by parallelizing tasks. Moreover, many programs spend a great proportion of the time waiting for I/O operations to complete; therefore, the raw execution speed is often a secondary factor to the overall performance.

It is worth noting that Python’s core developers have put great effort into speeding up operations on the most common data structures in the last few years. This effort, in some cases very successful, has somewhat alleviated this issue.

In situations where speed really is crucial, one can switch to faster Python implementations, such as PyPy, which provides, on average, just over a four-fold speedup by implementing advanced compilation techniques (check https://pypy.org/ for reference). It is also possible to write performance-critical parts of your code in faster languages, such as C or C++, and integrate that with your Python code. Libraries such as pandas and NumPy (which are commonly used for doing data science in Python) use such techniques.

If that is not convincing enough, you can always consider that Python has been used to drive the backend of services such as Spotify and Instagram, where performance is a concern. From this, it can be seen that Python has done its job perfectly well.