[DE NanoDegree Extra course 1] Python 추가 개념 정리

데이터 엔지니어링에 대해 공부할 때, 사전에 필요한 지식 중 하나인 파이썬에 대해서도 Udacity에서 정리를 해두어서 복습차 쭉 정리해서 듣고 있다. 물론 기본적인 개념이고 현재 내가 메인으로 쓰고 있는 언어도 파이썬이라 듣지않아도 되겠다 생각했지만,

가장 기본적인 개념이 제일 중요한 만큼 한번 쭉 정리하다가 내가 새로 이해하게 된 부분은 추가적으로 정리해보려 한다.

 

While "True" is a valid boolean expression, it's not useful as a condition since it always evaluates to True, so the indented code will always get run. Similarly, if False is not a condition you should use either - the statement following this if statement would never be executed.

 

Recall that the dictionary get method is another way to retrieve the value of a key in a dictionary. Except unlike indexing, this will return a default value if the key is not found. If unspecified, this default value is set to None. We can use get with a default value of 0 to simplify the code from the first method above.

book_title =  ['great', 'expectations','the', 'adventures', 'of', 'sherlock','holmes','the','great','gasby','hamlet','adventures','of','huckleberry','fin']
word_counter = {}
for word in book_title:
    word_counter[word] = word_counter.get(word, 0) + 1

word_counter = {'great': 2, 'expectations': 1, 'the': 2, 'adventures': 2, 'of': 2, 'sherlock': 1, 'holmes': 1, 'gasby': 1, 'hamlet': 1, 'huckleberry': 1, 'fin': 1}

for loops are ideal when the number of iterations is known or finite

while loops are ideal when the iterations need to continue until a condition is met.

 

ZIP

letters = ['a', 'b', 'c']
nums = [1, 2, 3]

for letter, num in zip(letters, nums):
    print("{}: {}".format(letter, num))

some_list = [('a', 1), ('b', 2), ('c', 3)]
letters, nums = zip(*some_list)

 

Enumerate

letters = ['a', 'b', 'c', 'd', 'e']
for i, letter in enumerate(letters):
    print(i, letter)

0 a
1 b
2 c
3 d
4 e

squares = [x**2 for x in range(9) if x % 2 == 0]
squares = [x**2 if x % 2 == 0 else x + 3 for x in range(9)]

Lambda Expressions

You can use lambda expressions to create anonymous functions. That is, functions that don’t have a name.

multiply = lambda x, y: x * y

가장 유용한 라이브러리

1. NumPy

Overview — NumPy v1.22.dev0 Manual

2. Pandas

pandas documentation — pandas 1.3.1 documentation