Iterators & List Comprehensions

Question 1

iterable

Answer 1

object that can return an iterator

Question 2

iterator

Answer 2

object that keeps state and produces the next value after calling next()

Question 3

2 ways to iterate over iterable

Answer 3

1. for loop

2. iter(iterable) and then next(iter(iterable))

Question 4

a couple iterables that are not lists

Answer 4

strings and range() function

Question 5

enumerate()

Answer 5

returns enumerate object (iterator) - series of index-value pair tuples

Question 6

zip()

Answer 6

zips multiple lists into a zip object (similar to list of tuples)

Question 7

how to unzip

Answer 7

1. for loop

2. *zip(x, y) - unpacks iterable into positional arguments in a function call

Question 8

one good use of iterators

Answer 8

processing data in chunks

Question 9

pd.read_csv arguments

Answer 9

(‘.csv file’, chunksize=x)

Question 10

list comprehension format

Answer 10

[output expression for iterator variable in iterable if predicate expression]

Question 11

list comprehensions and iterables

Answer 11

list comps can be built over iterables

Question 12

nested list comprehension

Answer 12

[[output expression] for iterator variable in iterable]

Question 13

if/else list comprehension

Answer 13

[x if … else … for x in list]

Question 14

dict comprehension format

Answer 14

{key: output expression for key in iterable}

Question 15

generator

Answer 15

looks like a list comp but with () instead of [] and creates a generator object

Question 16

iterating over a dictionary

Answer 16

use dictionary.items()

Question 17

how to build a generator function

Answer 17

use “yield” instead of “return”

Question 18

generating a DataFrame from a list of dicts

Answer 18

this is easy, just use pandas.DataFrame(list of dicts)