We're nearing the end of our python crash course and will get to the fun stuff for the rest of Data Science from Scratch (by Joel Grus). In this post, we'll briefly acquaint ourselves with pseudo-randomness and regular expressions.
The random module is used extensively in data science. Particularly when random numbers need to be generated and we want reproducible results the next time we run our model (in Python its random.seed(x), in R its set.seed(x)), where x is any integer we decide (we just need to be consistent when we revisit our model).
Technically, the module produces deterministic results, hence it's pseudorandom, here's an example to highlight how the randomness is deterministic:
import random
random.seed(10) # say we use 10
# this variable is from the book
four_randoms = [random.random() for _ in range(4)]
# call four_randoms - same result from Data Science from Scratch
# because the book also uses random.seed(10)
[0.5714025946899135,
0.4288890546751146,
0.5780913011344704,
0.20609823213950174]
# if we use x instead of underscore
# a different set of four "random" numbers is generated
another_four_randoms = [random.random() for x in range(4)]
[0.81332125135732,
0.8235888725334455,
0.6534725339011758,
0.16022955651881965]
Brief detour into _
Reading around from other sources suggests that the underscore "_" is used in a for loop when we don't care about the variable (its a throwaway) and have no plans to use it, for example:
# prints 'hello' five times
for _ in range(5):
print("hello")
# we could use x as well
for x in range(5):
print("hello")
In the above example, either _ or x could have been used and there doesn't seem to be much difference. We could technically call _, but its considered bad practice:
# bad practice, but prints 0, 1, 2, 3, 4
for _ in range(5):
print(_)
Nevertheless, _ matters in the context of pseudorandomness because it yields a different result:
import random
random.seed(10)
# these two yield different results, even with the same random.seed(10)
four_randoms = [random.random() for _ in range(4)]
another_four_randoms = [random.random() for x in range(4)]
But back to determinism, or pseudorandomness, we need to change the random.seed(11), then back to random.seed(10) to see this play out:
# new random.seed()
random.seed(11)
# reset four_randoms
four_randoms = [random.random() for _ in range(4)]
[0.4523795535098186,
0.559772386080496,
0.9242105840237294,
0.4656500700997733]
# change to previous random.seed()
random.seed(10)
# reset four_randoms (again)
four_randoms = [random.random() for _ in range(4)]
# get previous result (see above)
[0.5714025946899135,
0.4288890546751146,
0.5780913011344704,
0.20609823213950174]
Other features of the random module include: random.randrange, random.shuffle, random.choice and random.sample:
random.randrange(3,6) # choose randomly between [3,4,5]
# random shuffle
one_to_ten = [1,2,3,4,5,6,7,8,9,10]
random.shuffle(one_to_ten)
print(one_to_ten) # example: [8, 7, 9, 3, 5, 2, 10, 1, 6, 4]
random.shuffle(one_to_ten) # again
print(one_to_ten) # example: [3, 10, 8, 6, 9, 2, 7, 1, 4, 5]
# random choice
list_of_people = (["Bush", "Clinton", "Obama", "Biden", "Trump"])
random.choice(list_of_people) # first time, 'Clinton'
random.choice(list_of_people) # second time, 'Biden'
# random sample
lottery_numbers = range(60) # get a range of 60 numbers
winning_numbers = random.sample(lottery_numbers, 6) # get a random sample of 6 numbers
winning_numbers # example: [39, 24, 2, 37, 0, 15]
# because its pseudorandom, if you want a different set of 6 numbers
# reset the winning_numbers
winning_numbers = random.sample(lottery_numbers, 6)
winning_numbers # a different set of numbers [8, 12, 19, 34, 23, 49]
Regex
Regular Expressions
Whole books can be written about regular expressions so the author briefly highlights a couple features that may come in handy, re.match, re.search, re.split and re.sub:
import re
re_examples = [
not re.match("a", "cat"), # re.match check the word cat 'starts' letter 'a'
re.search("a", "cat"), # re.search check if word cat 'contains' letter 'a'
not re.search("c", "dog"), # 'dog' does not contain 'c'
3 == len(re.split("[ab]", "carbs")), # 3 equals length of "carbs" once you split out [ab]
"R-D-" == re.sub("[0-9]", "-", "R2D2") # sub out numbers in 'R2D2' with hyphen "-"
]
# test that all examples are true
assert all(re_examples), "all the regex examples should be True"
The final line reviews our understanding of testing (assert) and truthiness (all) applied to our regular expression examples, pretty neat.
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