Quiz: Why are generator expressions more memory efficient than list comprehensions when processing large datasets?
Generators evaluate lazily on demand using an iterator protocol, maintaining a minimal memory footprint regardless of dataset size.
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Generators evaluate lazily on demand using an iterator protocol, maintaining a minimal memory footprint regardless of dataset size.
### Python Exercise โ Keep or Drop Cities from a List Create a list that contains different values, including **another list nested inside the โฆ
Flask returns 200 OK by default. You can override it by returning a tuple: return response, 201 .
[x**2 for x in range(5)] is a list comprehension producing [0, 1, 4, 9, 16] .
Context managers implement __enter__() (called on entry) and __exit__() (called on block exit, handling exceptions).
dict.get(key, default) safely retrieves a value or returns the fallback default without raising KeyError .
The else block executes only if the try block succeeds without throwing any exceptions .
defaultdict(factory) calls the given factory function (e.g. int , list ) to generate a default value whenever a missing key is accessed.