๋ฌธ3) ๋‹ค์Œ ๋ฒกํ„ฐ(emp)๋Š” '์ž…์‚ฌ๋…„๋„์ด๋ฆ„๊ธ‰์—ฌ'์ˆœ์œผ๋กœ ์‚ฌ์›์˜ ์ •๋ณด๊ฐ€ ๊ธฐ๋ก๋œ ๋ฐ์ดํ„ฐ ์žˆ๋‹ค.
      ์ด ๋ฒกํ„ฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ถœ๋ ฅ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚˜๋„๋ก ํ•จ์ˆ˜๋ฅผ ์ •์˜ํ•˜์‹œ์˜ค. 

<์ถœ๋ ฅ ๊ฒฐ๊ณผ>
 ์ „์ฒด ์‚ฌ์› ๊ธ‰์—ฌ ํ‰๊ท  : 260

 

from re import findall
from statistics import mean


<Vector ์ค€๋น„>

emp = ["2014ํ™๊ธธ๋™220", "2002์ด์ˆœ์‹ 300", "2010์œ ๊ด€์ˆœ260"]


ํ•จ์ˆ˜ ์ •์˜

def pay_pro(emp):
    # list + for
    pays = [] # ๊ธ‰์—ฌ ์ €์žฅ 
    for e in emp :
        pay = findall('[0-9]{3}$', e) # ['220']
        pays.append(int(pay[0])) # '220' -> 220 
                    
    print(pays) # [220, 300, 260]
    
    # list ๋‚ดํฌ : ๋ณ€์ˆ˜ = [์‹คํ–‰๋ฌธ for๋ฌธ]
    pays2 = [int(findall('[0-9]{3}$', e)[0]) for e in emp]
    return mean(pays), mean(pays2)

 

ํ•จ์ˆ˜ ํ˜ธ์ถœ 

pays_mean, pays_mean2 = pay_pro(emp)
print('์ „์ฒด ์‚ฌ์›์˜ ๊ธ‰์—ฌ ํ‰๊ท  :', pays_mean)
print('์ „์ฒด ์‚ฌ์›์˜ ๊ธ‰์—ฌ ํ‰๊ท  :', pays_mean2)

 

 

 

 

 

๋ฌธ4) tot ํ•จ์ˆ˜๋ฅผ ์ธ์ˆ˜๋กœ ๋ฐ›์•„์„œ dataset ๊ฐ ์›์†Œ์˜ ํ•ฉ์„ ๊ณ„์‚ฐํ•˜๋Š” ํ•จ์ˆ˜๋ฅผ ์™„์„ฑํ•˜์‹œ์˜ค.

<์ถœ๋ ฅ ๊ฒฐ๊ณผ>
tot = [12.5, 7, 22.3]


tot ํ•จ์ˆ˜ ์ •์˜ 

def tot(x):
    return sum(x)


tot ํ•จ์ˆ˜๋ฅผ ์ธ์ˆ˜๋กœ ๋ฐ›๋Š” ํ•จ์ˆ˜ ์ •์˜ 

def my_func(func, datas):
    # list ๋‚ดํฌ 
    re = [func(data) for data in datas]
    
    return re


dataset

dataset = [[2,4.5,6], [3,4], [5,8.3,9]]


ํ•จ์ˆ˜ ํ˜ธ์ถœ 

tot = my_func(tot, dataset)
print('tot = ', tot) # tot =  [12.5, 7, 22.3]

 

 

 

 

๋ฌธ5) ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๋‹จ ์ˆ˜๋ฅผ ์ธ์ˆ˜๋กœ ๋„˜๊ฒจ์„œ ํ•ด๋‹น ๊ตฌ๊ตฌ๋‹จ์„ ์žฅ์‹ํ•˜์—ฌ ํ•จ์ˆ˜ ์žฅ์‹์ž๋ฅผ ์ •์˜ํ•˜์‹œ์˜ค.
<์ถœ๋ ฅ ์˜ˆ์‹œ>
*** 2๋‹จ ***
2 * 1 = 2
2 * 2 = 4
   :
2 * 9 = 18
***********

 

def gugu_deco(gugu) :
    def inner(dan) :
        print(f'*** {dan}๋‹จ ***') # ์ถ•์•ฝํ˜• format
        gugu(dan)
        print('***********')
    return inner

@gugu_deco 
def gugu_dan(dan):
    for i in range(1, 10) :
        print('%d * %d = %d'%(dan, i, dan*i))

gugu_dan(9)

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