[판다스] map에서 lambda 쓰기, apply 쓰기

There are only so many words you can use when describing a bottle of wine. Is a wine more likely to be "tropical" or "fruity"? Create a Series descriptor_counts counting how many times each of these two words appears in the description column in the dataset. (For simplicity, let's ignore the capitalized versions of these words.)

=> 문장 내에 "tropical" 또는 "fruity" 라고 평가한 와인은 몇개가 있냐? (대문자 신경쓰지 마=빼라는 뜻)

 

Solution:

n_trop = reviews.description.map(lambda desc: "tropical" in desc).sum()
n_fruity = reviews.description.map(lambda desc: "fruity" in desc).sum()
descriptor_counts = pd.Series([n_trop, n_fruity], index=['tropical', 'fruity'])

 

We'd like to host these wine reviews on our website, but a rating system ranging from 80 to 100 points is too hard to understand - we'd like to translate them into simple star ratings. A score of 95 or higher counts as 3 stars, a score of at least 85 but less than 95 is 2 stars. Any other score is 1 star. Also, the Canadian Vintners Association bought a lot of ads on the site, so any wines from Canada should automatically get 3 stars, regardless of points.

Create a series star_ratings with the number of stars corresponding to each review in the dataset.

def star_rate(df):
    if df.country == 'Canada' or df.points >= 95:
        return 3
    elif df.points < 95 and df.points >= 85: 
        return 2
    else : return 1
 
 # 데이터프레임에 함수 적용할 때, axis로 적용방향 설정해주기
 star_ratings = reviews.apply(star_rate, axis = 'columns')

 

 

 

 

참고

https://tykimos.github.io/2020/01/01/Python_Lambda_Map/

파이썬 람다(lambda)랑 맵(map) 케미 - 람다시리즈 3부

https://kevinitcoding.tistory.com/entry/%EB%8D%B0%EC%9D%B4%ED%84%B0-%ED%94%84%EB%A0%88%EC%9E%84%EC%97%90-%ED%95%A8%EC%88%98-%EC%A0%81%EC%9A%A9-map-apply-applymap-%EC%99%84%EB%B2%BD-%EC%A0%95%EB%A6%AC#google_vignette

 

데이터 프레임에 함수 적용: map, apply, applymap 완벽 정리

https://www.kaggle.com/code/residentmario/summary-functions-and-maps/tutorial

Summary Functions and Maps