๋ณธ๋ฌธ ๋ฐ”๋กœ๊ฐ€๊ธฐ

phython7

ํŒŒ์ด์ฌ - list, tuple list, tuple #list ,tuple #์‚ฌ์šฉ๋ฒ•์€ ๊ฑฐ์˜ ๋™์ผ #list(์ถ”๊ฐ€, ์‚ญ์ œ๊ฐ€๋Šฅ) tuple ์€ (์ถ”๊ฐ€X, ์‚ญ์ œX) [a,b] = [10,20] #list (c,d) = (11,22) #tuple print(a) print(b) print(c) print(d) a = 100 print(a) c = 111 print(c) #tuple tuple_test = (10,20,30) print(tuple_test[0]) #tuple_test[0]= 100 ์—๋Ÿฌ ๋ฐœ์ƒ mylist = list(tuple_test) #tuple์„ list๋กœ ๋ณ€๊ฒฝํ›„์—๋Š” ๊ฐ’์„ ๋ณ€๊ฒฝ ๊ฐ€๋Šฅ mylist[0] = 100 print(mylist[0]) list ์ถ”๊ฐ€ ์‚ญ์ œ #list - >array(๋™์ ) mylist = [23,56.. 2021. 6. 27.
ํŒŒ์ด์ฌ - Datetime & if ์กฐ๊ฑด๋ฌธ number = 12 if number > 0: print("0๋ณด๋‹ค ํฌ๋‹ค") if number = 12: print('ํ˜„์žฌ ์‹œ๊ฐ„์€ {}์‹œ๋กœ ์˜คํ›„์ž…๋‹ˆ๋‹ค'.format(now... 2021. 6. 26.
ํŒŒ์ด์ฌ(phython) ๊ธฐ๋ณธ print ์ปค๋ฆฌํ˜๋Ÿผ 1.python 2.numpy ๋ฐฐ์—ด, ํ–‰๋ ฌ 3.pandas ์‹œ๋ฆฌ์ฆˆ, dataframe 4.crawling 5.machine learning 6.deep learning print('์•ˆ๋…• ํŒŒ์ด์ฌ') #๊ฐœํ–‰ ์›์น˜ ์•Š์„ ์‹œ end ์‚ฌ์šฉ print("test python", end="") print("test anaconda", "conn") #ํ•œ์ค„ ์ฃผ์„๋ฌธ ''' ๋ฒ”์œ„ ์ฃผ์„๋ฌธ 1.python form - > application web -> django, falsk 2.numpy ๋ฐฐ์—ด, ํ–‰๋ ฌ 3.pandas ์‹œ๋ฆฌ์ฆˆ, dataframe 4.crawling 5.machine learning ->data -> new data ์ง€๋„ํ•™์Šต ๋น„์ง€๋„ํ•™์Šต 6.deep learning ''' """ ๋ฒ”์œ„ ์ฃผ์„๋ฌธ """ .. 2021. 6. 26.