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Python's is operator may be used to compare object identities (comparison by reference), and comparisons may be chained—for example, a <= b <= c. Python uses and, or, and not as Boolean operators. Python has a type of expression named a list comprehension, and a more general expression named a generator expression. [78]
In Python, functions are first-class objects that can be created and passed around dynamically. Python's limited support for anonymous functions is the lambda construct. An example is the anonymous function which squares its input, called with the argument of 5:
For example, a programmer may write a function in source code that is compiled to machine code that implements similar semantics. There is a callable unit in the source code and an associated one in the machine code, but they are different kinds of callable units – with different implications and features.
For example, in the for statement in the following pseudocode fragment, when calculating the new value for A(i), except for the first (with i = 2) the reference to A(i - 1) will obtain the new value that had been placed there in the previous step. In the for all version, however, each calculation refers only to the original, unaltered A.
A fold function runs over all elements in a structure (for lists usually left-to-right, a "left fold", called reduce in Python), accumulating a value as it goes. This can be used to combine all elements of a structure into one value, for example:
In Python, functions are first-class objects, just like strings, numbers, lists etc. This feature eliminates the need to write a function object in many cases. Any object with a __call__() method can be called using function-call syntax. An example is this accumulator class (based on Paul Graham's study on programming language syntax and ...
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
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.