Notebook

Chapter 8: Map, Filter, & Reduce (Review Questions)¶

The questions below assume that you have read the first , second , and third part in Chapter 8.

Be concise in your answers! Most questions can be answered in one sentence.

Essay Questions¶

Q1: With the map() and filter() built-ins and the reduce() function from the functools module in the standard library , we can replace many tedious for-loops and if statements. What are some advantages of doing so?

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Q2: Looking at the lambda expression inside reduce() below, what built-in function is mimicked here?

from functools import reduce

numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]

reduce(lambda x, y: x if x > y else y, numbers)

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Q3: What is the primary use case of list comprehensions? Why do we describe them as eager?

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Q4: generator expressions may replace list objects and list comprehensions in many scenarios. When evaluated, they create a lazy generator object that does not materialize its elements right away. What do we mean by that? What does it mean for a generator object to be exhausted?

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Q5: What does it mean for the boolean reducers, the built-in all() and any() functions, to follow the short-circuiting strategy?

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Q6: What is an iterator? How does it relate to an iterable?

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True / False Questions¶

Motivate your answer with one short sentence!

Q7: lambda expressions are useful in the context of the map-filter-reduce paradigm, where we often do not re-use a function object more than once.

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Q8: Using generator expressions in place of list comprehensions wherever possible is a good practice as it makes our programs use memory more efficiently.

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Q9: Just as list comprehensions create list objects, tuple comprehensions create tuple objects.

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