List¶
any: Check if Any Element of an Iterable is True¶
If you want to check if any element of an iterable is True, use any. In the code below, I use any to find if any element in the text is in uppercase.
text = "abcdE"
any(c for c in text if c.isupper())
True
filter: Get the Elements of an Iterable that a Function Returns True¶
If you want to get the elements of an iterable that a function returns true, use filter.
In the code below, I use the filter method to get items that are fruits.
def get_fruit(val: str):
fruits = ['apple', 'orange', 'grape']
if val in fruits:
return True
else:
return False
items = ['chair', 'apple', 'water', 'table', 'orange']
fruits = filter(get_fruit, items)
print(list(fruits))
['apple', 'orange']
How to Unpack Iterables in Python¶
To assign items of a Python iterables (such as list, tuple, string) to different variables, you can unpack the iterable like below.
nested_arr = [[1, 2, 3], ["a", "b"], 4]
num_arr, char_arr, num = nested_arr
num_arr
[1, 2, 3]
char_arr
['a', 'b']
Extended Iterable Unpacking: Ignore Multiple Values when Unpacking a Python Iterable¶
If you want to ignore multiple values when unpacking a Python iterable, add * to _ as shown below.
This is called “Extended Iterable Unpacking” and is available in Python 3.x.
a, *_, b = [1, 2, 3, 4]
print(a)
1
b
4
_
[2, 3]
random.choice: Get a Randomly Selected Element from a Python List¶
Besides getting a random number, you can also get a random element from a Python list using random. In the code below, “stay at home” was picked randomly from a list of options.
import random
to_do_tonight = ['stay at home', 'attend party', 'do exercise']
random.choice(to_do_tonight)
'stay at home'
heapq: Find n Max Values of a Python List¶
If you want to extract n max values from a large Python list, using heapq will speed up the code.
In the code below, using heapq is more than 2 times faster than using sorting and indexing. Both methods try to find the max values of a list of 10000 items.
import heapq
import random
from timeit import timeit
random.seed(0)
l = random.sample(range(0, 10000), 10000)
def get_n_max_sorting(l: list, n: int):
l = sorted(l, reverse=True)
return l[:n]
def get_n_max_heapq(l: list, n: int):
return heapq.nlargest(n, l)
expSize = 1000
n = 100
time_sorting = timeit("get_n_max_sorting(l, n)", number=expSize,
globals=globals())
time_heapq = timeit('get_n_max_heapq(l, n)', number=expSize,
globals=globals())
ratio = round(time_sorting/time_heapq, 3)
print(f'Run {expSize} experiments. Using heapq is {ratio} times'
' faster than using sorting')
Run 1000 experiments. Using heapq is 2.636 times faster than using sorting