For an advanced programmer new to Python, understanding Python's unique idioms, "Pythonic" ways, and potential gotchas is essential to write efficient, readable, and Pythonic code. Here's a compiled list of Pythonisms, idioms, and gotchas to look out for:

### Pythonisms and Idioms

1. **List Comprehensions**: Compact way to process all or part of the elements in a sequence and return a list with the results.
   ```python
   squares = [x**2 for x in range(10)]
   ```

2. **Dictionary Comprehensions**: Similar to list comprehensions but for dictionaries.
   ```python
   square_dict = {x: x**2 for x in range(10)}
   ```

3. **Using Underscore for Unused Variables**: It's a Pythonic way to indicate that a variable is intentionally unused.
   ```python
   for _ in range(10):
       do_something()
   ```

4. **Unpacking**: A way to unpack values from a sequence or iterable into variables.
   ```python
   a, b, *rest = range(10)
   ```

5. **The Zen of Python**: Accessible by typing `import this`, it's a collection of Python's design principles.

6. **Using `enumerate()` for Loops**: To get both the index and the value of an item in a list.
   ```python
   for index, value in enumerate(my_list):
       print(index, value)
   ```

7. **Function Arguments**: Python supports default, keyword, positional, and arbitrary arguments.
   ```python
   def func(a, b, c=5, *args, **kwargs):
       pass
   ```

8. **The Walrus Operator (`:=`)**: Introduced in Python 3.8, allows you to assign values to variables as part of an expression.
   ```python
   if (n := len(a)) > 10:
       print(f"List is too long ({n} elements)")
   ```

### Gotchas

1. **Mutable Default Arguments**: Default argument values are evaluated only once at function definition time, which means that modifying a default argument will affect all subsequent calls to the function.
   ```python
   def append_to(element, to=[]):
       to.append(element)
       return to
   ```

2. **Dynamic Typing**: While flexible, it can lead to unexpected behaviors if not carefully managed, especially when coming from statically-typed languages.

3. **Indentation**: Python uses indentation to define blocks, making code readability a part of the syntax. However, incorrect indentation can lead to `IndentationError` or unexpected behavior.

4. **`==` vs `is`**: `==` checks for equality, while `is` checks for identity. This distinction is crucial for understanding mutable and immutable objects.
   ```python
   a = [1, 2, 3]
   b = a
   print(a == b)  # True
   print(a is b)  # True
   c = a.copy()
   print(a == c)  # True
   print(a is c)  # False
   ```

5. **Looping Pitfalls**: Modifying a list while iterating over it can lead to unexpected behavior. Use slicing or create a new list instead.

6. **Global vs Local Variables**: Variables declared inside a function are local unless explicitly declared global.

7. **Floating Point Arithmetic**: Like many languages, Python's floating-point numbers can have rounding errors.
   ```python
   print(0.1 + 0.2 == 0.3)  # False
   ```

8. **GIL (Global Interpreter Lock)**: Python's GIL means that only one thread can execute Python bytecodes at a time. This can be a gotcha for CPU-bound multithreading applications.

Understanding these Python-specific idioms and gotchas is crucial for leveraging Python's strengths while avoiding common pitfalls, ensuring your transition to Python is smooth and productive.