To be honest, I started my adventure with Python only because I was tired of all the difficulty of writing software in C and C++. In fact, it is very common that programmers start to learn Python when they realize that other languages do not deliver what the users need. Programming in Python, when compared to C, C++, or Java, is a breeze. Everything seems to be simple and well designed. You might think that there are no places where you can trip and there are no other programming languages required anymore.
And of course nothing could be more wrong. Yes, Python is an amazing language with a lot of cool features and it is used in many fields. But it does not mean that it is perfect and does not have any downsides. It is easy to understand and write, but this easiness comes with a price. It is not as slow as many think, but will never be as fast as C. It is highly portable, but its interpreter is not available on as many architectures as compilers for other languages are. We could go with that list forever.
One of the solutions to that problem is to write extensions, so we can bring of some of the advantages of good old C back to Python. And in most cases, it works well. The question is: are we really using Python because we want to extend it with C? The answer is no. This is only an inconvenient necessity in situations where we don't have any better option.
It is not a secret that developing applications in many different languages is not an easy task. Python and C are completely different technologies and it is very hard to find anything that they have in common. It is also true that there is no application that is free of bugs. If extensions become common in your codebase, debugging can become painful. Not only because debugging of C code requires completely different workflow and tools, but also because you will need to switch context between two different languages very often.
We are all humans and all have limited cognitive capabilities. There are, of course, people who can handle multiple layers of abstraction and technology stacks at the same time efficiently but they seem to be very rare specimens. No matter how skilled you are, there is always an additional price to pay for maintaining such hybrid solutions. This will either involve extra effort and time required to switch between C and Python, or additional stress that will make you eventually less efficient.
According to the TIOBE index, C is still one of the most popular programming languages. Despite this fact, it is very common for Python programmers to know very little or almost nothing about it. Personally, I think that C should be lingua franca in the programming world, but my opinion is very unlikely to change anything in this matter. Python also is so seductive and easy to learn that a lot of programmers forget about all their previous experiences and completely switch to the new technology. And programming is not like riding a bike. This particular skill erodes faster if not used and polished sufficiently. Even programmers with strong C background are risking to gradually lose their previous knowledge if they decide to dive into Python for too long. All of the above leads to one simple conclusion—it is harder to find people who will be able to understand and extend your code. For open source packages, this means fewer voluntary contributors. In closed source, this means that not all of your teammates will be able to develop and maintain extensions without breaking things.
When it comes to failures, extensions may break, very badly. Static typing gives you a lot of advantages over Python and allows you to catch a lot of issues during the compilation step that would be hard to notice in Python without a rigorous testing routine and full test coverage. On the other hand, all memory management must be performed manually. And faulty memory management is the main reason of most programming errors in C. In the best case scenario, such mistakes will only result in some memory leaks that will gradually eat all of your environment resources. The best case does not mean easy to handle. Memory leaks are really tricky to find without using proper external tools such as Valgrind. Anyway, in most cases, the memory management issues in your extension's code will result in a segmentation fault that is unrecoverable in Python and will cause the interpreter to crash without raising any exception. This means that you will eventually need to arm up with additional tools that most Python programmers don't need to use. This adds complexity to your development environment and workflow.