Some of Python's features that make it so flexible as a development language are also seen as major drawbacks by those used to the "whole program" static analysis conducted by the compilation and linking process in languages such as C++ and Java.
- Implicit declaration of local variables
Local variables are declared using the ordinary assignment statement. This means that variable bindings in any other scope require explicit annotation to be picked up by the compiler (global and nonlocal declarations for outer scopes, attribute access notation for instance scopes). This massively reduces the amount of boilerplate needed when programming, but means that third party static analysis tools (such as pyflakes) are needed to perform checks that are handled by the compiler in languages that require explicit variable declarations.
- "Monkey patching" is supported
The contents of modules, class objects and even the builtin namespace can be modified at runtime. This is hugely powerful, allowing many extremely useful techniques. However, this flexibility means that Python does not offer some features common to statically typed OO languages. Most notably, the "self" parameter to instance methods is explicit rather than implicit (since "methods" don't have to be defined inside a class, they can be added later by modifying the class, meaning that it isn't particularly practical to pass the instance reference implicitly) and attribute access controls can't readily be enforced based on whether or not code is "inside" or "outside" the class (as that distinction only exists while the class definition is being executed).
- Far from the metal
This is also true of many other high level languages, but Python tends to abstract away most hardware details. Systems programming languages like C and C++ are still far better suited to handling direct hardware access (however, Python will quite happily talk to those either via CPython extension modules or, more portably, via the ctypes library).