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There are subtle differences and distinctions in the use of the terms "generator" and "iterator", which vary between authors and languages. [5] In Python, a generator is an iterator constructor: a function that returns an iterator. An example of a Python generator returning an iterator for the Fibonacci numbers using Python's yield statement ...
Numeric literals in Python are of the normal sort, e.g. 0, -1, 3.4, 3.5e-8. Python has arbitrary-length integers and automatically increases their storage size as necessary. Prior to Python 3, there were two kinds of integral numbers: traditional fixed size integers and "long" integers of arbitrary size.
The actual statement is in columns 7 through 72 of a line. Any non-space character in column 6 indicates that this line is a continuation of the prior line. A 'C' in column 1 indicates that this entire line is a comment. Columns 1 though 5 may contain a number which serves as a label.
foreach loops are almost always used to iterate over items in a ... a foreach-construct was introduced in Java Development Kit (JDK) 1.5.0. ... Python's tuple ...
For-loops have two parts: a header and a body. The header defines the iteration and the body is the code executed once per iteration. The header often declares an explicit loop counter or loop variable. This allows the body to know which iteration is being executed.
for X := 0.1 step 0.1 to 1.0 do might be repeated 9 or 10 times, depending on rounding errors and/or the hardware and/or the compiler version. Furthermore, if the increment of X occurs by repeated addition, accumulated rounding errors may mean that the value of X in each iteration can differ quite significantly from the expected sequence 0.1, 0 ...
In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
Adverse impact on estimation: because of the fact presented under point #1, estimates based on lines of code can adversely go wrong, in all possibility. Developer's experience: implementation of a specific logic differs based on the level of experience of the developer. Hence, number of lines of code differs from person to person.