Module 3: Functional programming III

Question 1

What is an iterable?

Answer 1

• Iterables act as containers of other objects i.e. collection of items
• Iterations can be used to access the contained objects individually

Question 2

Give me examples of iterables.

Answer 2

• Lists
• Tuples
• Dictionaries
• Strings

Question 3

What are ways to create iterables?

Answer 3

• for…in… expressions
• List comprehension
• Set comprehension
• Dictionary comprehension

Question 4

What does this code showcase?
myList = [‘blue’, ‘red’, ‘yellow’]
for color in myList:
print(color)

Answer 4

for …in… expression

Question 5

What is a list comprehension?

Answer 5

It is a useful and compact way to create a list based on an operation or condition applied to another iterable

Question 6

What does this code show?
colors=[‘red’, ‘blue’, ‘yellow’]

li=[” “+s+” “ for s in colors]

for s in li:
print(s)

Answer 6

List comprehension

colors=[‘red’, ‘blue’, ‘yellow’]
#List comprehension:
li=[” “+s+” “ for s in colors]
#Access elements:
for s in li:
print(s)

(set and dict comprehensions look basically the same)

Question 7

What is an iterator?

Answer 7

• Objects that store a state (normally an index)
  NOT collections
• An iterator is an object that contains a __next__ method

Question 8

What is the relation between an iterable and an iterator?

Answer 8

When looping over an iterable, the iterator of that iterable will tell us which item of the collection is the next to be read.

Question 9

What happens when we iterate over iterable objects?

Answer 9

We are implicitly calling the iter() built-in function with the iterable object as an argument
- iter(x) checks if object x has an iter method

• iter returns an iterator for the iterable

Question 10

What does iter() return

Answer 10

iter() returns an iterable object

Question 11

What does the __next__ method return?

Answer 11

Returns the next item stored in a collection. When there are more items left, this method will raise a StopIteration exception, which will be caught by iterating expression.

Question 12

What does the order of access depend on when using iterators?

Answer 12

The order of access (i.e. which object is the next) depends on the type of collection

Question 13

What are types of collections?

Answer 13

• Ordered types
• Unordered types

Question 14

Explain ordered types and give examples.

Answer 14

The order of access is the same as the insertion order.
e.g. lists, strings, tuples, and dicts

Question 15

Explained unordered types and give example.

Answer 15

The order of access is unknown to the programmer
e.g. sets

Question 16

mySet=set((5,4,3,2,1))
for s in mySet:
print(s, end=”,”)
Do we know in which order these items will be printed? Why or why not.

Answer 16

We don’t know the print order as a set is a unordered type, meaning the order of access is unknown to the programmer.

Question 17

myList=[5,4,3,2,1]
for item in myList:
print(item, end=”,”)
Do we know the order in which these items will be printed? Why or why not?

Answer 17

We know the order as a list is a ordered types, mening the order of access is the same as the insertion order

Question 18

Give examples of built-in iterators.

Answer 18

• range
• enumerate
• map
• filter
• zip

Question 19

What is the difference between an iterable and an iterator?

Answer 19

• Iterable objects implement an __iter__ method which results in an iterator
• Iterator objects implement a __next__ method, which attempt to return the next item in the sequence (until the sequence is exhausted) and also an __iter__ method that returns self

Question 20

Are iterators iterable?

Answer 20

Yes. Iterator objects also implement (next to the __next method) __iter__ method that returns self.

Question 21

Can iterables be iterators?

Answer 21

No.

Question 22

What is a generator?

Answer 22

Generators are one type of iterators.

Question 23

How are generators created?

Answer 23

• Defining a yield statement
  OR
• Using a generator expression ‘

Question 24

What does this code shocase?
def firstn(n):
num = 0
while num < n:
yield num
num += 1

g=firstn(4)

for n in g:
print(n)

Answer 24

firstn has a yield statement,

therefore it ‘returns’ a generator:
def firstn(n):
num = 0
while num < n:
yield num
num += 1

g=firstn(4)

for n in g:
print(n)

Question 25

What is the difference in usage between List comprehensions and Generator expressions?

Answer 25

Generators are lazy!! & different brackets Otherwise similar syntax & usage

Question 26

What is an iterable and give examples?(summary)

Answer 26

- Stores a collection of data that can be looped over - Has an __iter__ method to obtain an iterator from the iterable ⇒ this is what allows us to loop over the data! - E.g.: lists, strings, tuples, sets, dictionaries, list comprehensions, generators, generator expressions

Question 27

What is an iterator

Answer 27

- Object with a state that indicates next item to return in an iteration - Has a __next__ method - Can be created with __iter__() or with generator functions - E.g.: range, enumerate, generator, generator expressions

Question 28

What happens when we run this code? mylist=[1, 2, 3] if mylist[7] > 0 and mylist[0] < 1: print("We are here!")

Answer 28

IndexError: list index out of range

Question 29

What happens when we run this code? mylist=[1, 2, 3] if mylist[0] < 1 and mylist[7] > 0: print("We are here!")

Answer 29

The result of the if expression is False ● Python performs lazy evaluation here: when the left condition is False, the result of the and operator will be False regardless of the right-hand condition. Therefore, Python decides to not evaluate the right part! ● This form of evaluation is also known as non-strict

Question 30

Does this code work? if myList is not None and len(myList)>1: do_something()

Answer 30

Yes, the code works even when mylist is None thanks to lazy evaluation.

Question 31

if myList is not None and len(myList)>1: do_something() What would happen if mylist was None and we had eager (strict) evaluation (i.e. the Python interpreter evaluates both sides of the and operator)?

Answer 31

TypeError: object of type 'NoneType' has no len()

Question 32

Why is lazy evaluation useful?

Answer 32

It does not require indented conditions

Question 33

Are most evaluations in Python eager(strict) or lazy?

Answer 33

Eager(strict).

Question 34

What are eager/strict evaluations and give an example?

Answer 34

They go from left to right and evaluate all the elements. >>> mylist=[1, 2, 3] >>> 0 * mylist[1] 0 >>> 0 * mylist[7] IndexError: list index out of range

Question 35

What are operations that use lazy evaluation?

Answer 35

- or , - and , - if-else logical operators - generator functions

Question 36

What is a lazy evaluation?

Answer 36

Stop evaluation when the outcome is already known.

Question 37

Is the evaluation of generators lazy or strict?

Answer 37

Lazy

Question 38

Is the evaluation of list comprehensions lazy or strict?

Answer 38

Strict.

Question 39

Can you think of any case in which list comprehension is a better solution?

Answer 39

- While generators save memory and computation time due to lazy evaluation, they are not always preferable. - List comprehensions are better when you need immediate access to all elements, random access, or repeated iteration over the data, as generators can only be iterated over once and do not support indexing.

Question 40

What are the pros of generators usage of lazy evaluation?

Answer 40

- Efficiency - We only perform operations as we need them - Saves memory and computation time

Question 41

When is it ideal to use list/generator comprehensions?

Answer 41

List comprehension is useful for smaller datasets where the entire list can be held in memory, # and you need fast access to elements. # Generators are ideal for large datasets where memory efficiency is critical.