๋ณธ๋ฌธ ๋ฐ”๋กœ๊ฐ€๊ธฐ

๋งˆ์ผ€ํŒ…/๋ฐ์ดํ„ฐ ๋ถ„์„22

[ํŒŒ์ด์ฌ] ํ†ต๊ณ„ ๊ธฐ๋ณธ ์ƒ์‹๊ณผ ๊ทธ๋ž˜ํ”„ ํ†ต๊ณ„ ๊ธฐ๋ณธ ์ƒ์‹ํ‰๊ท  mean : ๋ฐ์ดํ„ฐ ํ•ฉ๊ณ„ / ๋ฐ์ดํ„ฐ ๊ฐœ์ˆ˜์ค‘๊ฐ„๊ฐ’ median : ๋ฐ์ดํ„ฐ ์ •๋ ฌ -> ์ค‘๊ฐ„์— ์žˆ๋Š” ๊ฐ’ / ๋ฐ์ดํ„ฐ๊ฐ€ ์ง์ˆ˜๋ผ๋ฉด ๊ฐ€์šด๋ฐ ์žˆ๋Š” ์ค‘๊ฐ„๊ฐ’ 2๊ฐœ๋ฅผ ๋”ํ•˜๊ณ  2๋กœ ๋‚˜๋ˆˆ ํ‰๊ท ๊ฐ’์ตœ์†Ÿ๊ฐ’ minimum value์ตœ๋Œ“๊ฐ’ maximum value ๋ชจ๋“  ์ˆ˜๋ฅผ 4๋“ฑ๋ถ„ํ–ˆ์„ ๋•Œ 25% ์œ„์น˜ = 1์‚ฌ๋ถ„์œ„์ˆ˜ = Q1 50% ์œ„์น˜ = 2์‚ฌ๋ถ„์œ„์ˆ˜ = Q2 = ์ค‘๊ฐ„๊ฐ’75% ์œ„์น˜ = 3์‚ฌ๋ถ„์œ„์ˆ˜ = Q4 describe()4๊ฐœ ์ปฌ๋Ÿผ์ด์ง€๋งŒ ์ˆ˜์น˜ํ˜• ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ๊ณ„์‚ฐํ•˜๋Š” ํ•จ์ˆ˜๋ผ์„œ ์ˆ˜์น˜ํ˜• ๋ฐ์ดํ„ฐ 3๊ฐ€์ง€๋งŒ ๋‚˜์˜ด 4๊ฐœ ์ปฌ๋Ÿผ์„ ๋‹ค ๋ณด๊ณ  ์‹ถ๋‹ค๋ฉด ์•„๋ž˜์™€ ๊ฐ™์ด ์ž‘์„ฑํ•œ๋‹ค.๊ทธ๋Ÿฌ๋ฉด ๋ฒ”์ฃผํ˜• ๋ฐ์ดํ„ฐ๋„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. df.describe(include='all')top : ์ตœ๋นˆ๊ฐ’ : ๊ฐ€์žฅ ๋งŽ์€ ๊ฐ’freq : ํ”„๋ฆฌํ€€์‹œ : ๋ช‡ ๋ฒˆ ๋“ฑ์žฅํ•˜๋Š”์ง€ํ‰๊ท  vs.. 2025. 3. 9.
[ํŒŒ์ด์ฌ] pandas pandas์™€ DataFrameNumPy๋ณต์žกํ•œ ์ˆ˜ํ•™ ์—ฐ์‚ฐ์„ ํ•  ๋•Œ ์‚ฌ์šฉNumPy array ๋‹จ์ - ๊ฐ€๋…์„ฑ์ด ๋–จ์–ด์ง- ์ •๋ณด์— ๋Œ€ํ•œ ๋ ˆ์ด๋ธ” ์‚ฝ์ž… ๋ถˆ๊ฐ€- ํ•œ ๊ฐ€์ง€ ๋ฐ์ดํ„ฐ ํƒ€์ž…๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ pandasNumPy๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋งŒ๋“ค์–ด์ง„ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฐ์ดํ„ฐ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ, ๊ฐ€๊ณตํ•˜๊ธฐ, ๋ถ„์„ํ•˜๊ธฐ, ์‹œ๊ฐํ™”ํ•˜๊ธฐ ๋“ฑ ํ‘œ ํ˜•ํƒœ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๊ฐ„ํŽธํ•˜๊ฒŒ ๋‹ค๋ฃจ๊ณ  ์‹ถ์„ ๋•Œ ์‚ฌ์šฉ 4๊ฐœ์˜ ํ–‰(๋กœ์šฐ) 3๊ฐœ์˜ ์—ด(์ปฌ๋Ÿผ)0,1,2,3 = ๋กœ์šฐ์˜ ์ด๋ฆ„/์ธ๋ฑ์Šค  DataFrame์„ ๋งŒ๋“œ๋Š” ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•import pandas as pdimport numpy as npdict_df = pd.DataFrame({ 'category': ['skirt', 'sweater', 'coat', 'jeans'], 'quantity': [10, 15, 6.. 2025. 2. 25.
[ํŒŒ์ด์ฌ] Matplotlib Matplotlib๊ณผ ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”ํŒŒ์ด์ฌ๊ณผ ๋„˜ํŒŒ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฐ์ดํ„ฐ๋ฅผ ์‹œ๊ฐํ™”ํ•˜๋Š” ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ๋‹ค์–‘ํ•œ ๊ทธ๋ž˜ํ”„ ๊ทธ๋ ค๋ณด๊ธฐ ์‹ค์Šตimport numpy as npimport matplotlib.pyplot as pltsales_array = np.array([10, 13, 8, 15, 6, 11, 4])category_array = ['skirt', 't-shirt', 'dress', 'sweater', 'coat', 'jeans', 'shoes']# ์—ฌ๊ธฐ์— ์ฝ”๋“œ๋ฅผ ์ž‘์„ฑํ•˜์„ธ์š”.plt.bar(category_array, sales_array)plt.show()๊ฒฐ๊ณผ ๊ทธ๋ž˜ํ”„ ๊ฐ„๋‹จํ•˜๊ฒŒ ๊พธ๋ฏธ๊ธฐ ๊ทธ์™ธ ์˜ต์…˜ matplotlib.markers — Matplotlib 3.10.0 documentationmatplotlib.mark.. 2025. 2. 25.
[ํŒŒ์ด์ฌ] NumPy NumPy๋ž€?Numerical Python ์ˆ˜์น˜์ ์ธ ์—ฐ์‚ฐ์— ์ตœ์ ํ™”๋œ ํŒŒ์ด์ฌ ๋„๊ตฌ NumPy์™€ array ์ธ๋ฑ์‹ฑ๊ณผ ์Šฌ๋ผ์ด์‹ฑ: 1์ฐจ์› array ์‹ค์Šตimport numpy as npbitcoin_array = np.array([970, 1180, 1072, 1348, 2286, 2481, 2875, 4703, 4339, 6468, 10234, 14156])# ์—ฌ๊ธฐ์— ์ฝ”๋“œ๋ฅผ ์ž‘์„ฑํ•˜์„ธ์š”.bitcoin_array[6:9]#๊ฒฐ๊ณผ#array([2875, 4703, 4339]) ์ธ๋ฑ์‹ฑ๊ณผ ์Šฌ๋ผ์ด์‹ฑ: 2์ฐจ์› array ์‹ค์Šตimport numpy as npbitcoin_array = np.array([[970, 1180, 1072, 1348, 2286, 2481, .. 2025. 2. 25.
[ํŒŒ์ด์ฌ] ๋ฆฌ์ŠคํŠธ/์‚ฌ์ „๊ณผ for ๋ฐ˜๋ณต๋ฌธ ๋ฆฌ์ŠคํŠธ๋ฆฌ์ŠคํŠธ์™€ ์š”์†Œ, ์ธ๋ฑ์‹ฑ# ๋ฆฌ์ŠคํŠธ (list)numbers = [2, 3, 5, 7, 11, 13]names = ['์œค์ˆ˜',' ํ˜œ๋ฆฐ', 'ํƒœํ˜ธ', '์˜ํ›ˆ']print(numbers)print(names)# [2, 3, 5, 7, 11, 13]# ['์œค์ˆ˜', ' ํ˜œ๋ฆฐ', 'ํƒœํ˜ธ', '์˜ํ›ˆ']#์ธ๋ฑ์‹ฑ (indexing)print(names[1]) #ํ˜œ๋ฆฐprint(numbers[1] + numbers[3]) #10num_1 = numbers[1]num_3 = numbers[3]print(num_1 + num_3) #10#๋งˆ์ด๋„ˆ์Šค ์ธ๋ฑ์‹ฑprint(numbers[-1]) #13print(numbers[-6]) #2#๋ฆฌ์ŠคํŠธ ์Šฌ๋ผ์ด์‹ฑ (list slicing)print(numbers[0:4]) #[2,.. 2025. 2. 24.
[ํŒŒ์ด์ฌ] while ๋ฐ˜๋ณต๋ฌธ๊ณผ ์กฐ๊ฑด๋ฌธ while ๋ฐ˜๋ณต๋ฌธ ๊ฐœ๋…while ๋ฐ˜๋ณต๋ฌธ์˜ ๊ตฌ์กฐwhile ์กฐ๊ฑด ๋ถ€๋ถ„ : ์ˆ˜ํ–‰ ๋ถ€๋ถ„ while ๋ฐ˜๋ณต๋ฌธ ๋ฌธ๋ฒ•์กฐ๊ฑด ๋ถ€๋ถ„ : ๋ถˆ๋ฆฐ ๊ฐ’์œผ๋กœ ๊ณ„์‚ฐ๋˜๋Š” ์‹์ˆ˜ํ–‰ ๋ถ€๋ถ„ : ๋ฐ˜๋ณต์ ์œผ๋กœ ์‹คํ–‰ํ•˜๊ณ  ์‹ถ์€ ๋ช…๋ น์กฐ๊ฑด์ด False๋ฉด ์ข…๋ฃŒi = 1while i  while ๋ฐ˜๋ณต๋ฌธ ์‹ค์Šตi = 2while (i 2468...949698100 if๋ฌธ ๊ฐœ๋…if๋ฌธ ๊ตฌ์กฐif ์กฐ๊ฑด ๋ถ€๋ถ„ : ์ˆ˜ํ–‰ ๋ถ€๋ถ„ if๋ฌธ ๋ฌธ๋ฒ•์กฐ๊ฑด ๋ถ€๋ถ„ : ๋ถˆ๋ฆฐ ๊ฐ’์œผ๋กœ ๊ณ„์‚ฐ๋˜๋Š” ์‹์ˆ˜ํ–‰ ๋ถ€๋ถ„ : ๋ฐ˜๋ณต์ ์œผ๋กœ ์‹คํ–‰ํ•˜๊ณ  ์‹ถ์€ ๋ช…๋ นtemp = 8if temp temp = 16if temp  elif๋ฌธif ์กฐ๊ฑด : ์‹คํ–‰elif ์กฐ๊ฑด : ์‹คํ–‰elif ์กฐ๊ฑด : ์‹คํ–‰else : ์‹คํ–‰ ์‹ค์Šตdef print_grade(midterm_score, final_score): .. 2025. 2. 19.
[ํŒŒ์ด์ฌ] ์ถ”์ƒํ™” ๋” ์•Œ์•„๋ณด๊ธฐ ๋ณ€์ˆ˜ ์ œ๋Œ€๋กœ ์ดํ•ดํ•˜๊ธฐ"=" ์ง€์ • ์—ฐ์‚ฐ์žname = "๊น€ํ˜„์Šน"x = 7x = x + 1 # x = 7 + 1print(x) #8 ํ•จ์ˆ˜์˜ ์‹คํ–‰ ์ˆœ์„œdef hello(): print("hello") print("welcome") print("ํ•จ์ˆ˜ ํ˜ธ์ถœ ์ „")hello()print("ํ•จ์ˆ˜ ํ˜ธ์ถœ ํ›„")#ํ•จ์ˆ˜ ํ˜ธ์ถœ ์ „# hello# welcome# ํ•จ์ˆ˜ ํ˜ธ์ถœ ํ›„ return๋ฌธ ์ œ๋Œ€๋กœ ์ดํ•ดํ•˜๊ธฐdef square(x) : print("ํ•จ์ˆ˜ ์‹œ์ž‘") return x * x print("ํ•จ์ˆ˜ ๋") #dead code print(square(3))print("hello world")# ํ•จ์ˆ˜ ์‹œ์ž‘# 9# hello world return๊ณผ print์˜ ์ฐจ์ดdef print_s(x) : .. 2025. 2. 19.
[ํŒŒ์ด์ฌ] ์ž๋ฃŒํ˜• ๋” ์•Œ์•„๋ณด๊ธฐ ์ˆซ์žํ˜•์ •์ˆ˜ํ˜•์ •์ˆ˜ํ˜•๋ผ๋ฆฌ ๊ณ„์‚ฐํ•˜๋ฉด ๊ฒฐ๊ณผ๊ฐ’์˜ ํƒ€์ž…์€ ์ •์ˆ˜ํ˜•์œผ๋กœ ๋‚˜์˜จ๋‹ค.#๋ง์…ˆprint(4 + 7) # 11#๋บ„์…ˆprint(2 - 4) # -2#๊ณฑ์…ˆprint(5 * 3) # 15#๋‚˜๋จธ์ง€print(7 % 3) # 1#๊ฑฐ๋“ญ์ œ๊ณฑprint(2 ** 3) # 8 ์†Œ์ˆ˜ํ˜•์†Œ์ˆ˜ํ˜•๋ผ๋ฆฌ ๊ณ„์‚ฐํ•˜๊ฑฐ๋‚˜, ์†Œ์ˆ˜ํ˜•+์ •์ˆ˜ํ˜•์œผ๋กœ ๊ณ„์‚ฐํ•˜๋ฉด ๊ฒฐ๊ณผ๊ฐ’์˜ ํƒ€์ž…์€ ์†Œ์ˆ˜ํ˜•์œผ๋กœ ๋‚˜์˜จ๋‹ค.#๋ง์…ˆprint(4.0 + 7.0) # 11.0#๋บ„์…ˆprint(2.0 - 4.0) # -2.0#๊ณฑ์…ˆprint(5.0 * 3.0) # 15.0#๋‚˜๋จธ์ง€print(7.0 % 3.0) # 1.0#๊ฑฐ๋“ญ์ œ๊ณฑprint(2.0 ** 3.0) # 8.0 ๋‚˜๋ˆ„๊ธฐ์ •์ˆ˜ํ˜•๋ผ๋ฆฌ ๋‚˜๋ˆ„๋“ , ์†Œ์ˆ˜ํ˜•๋ผ๋ฆฌ ๋‚˜๋ˆ„๋“  ๋‚˜๋ˆ„๊ธฐ์˜ ๊ฒฐ๊ณผ๊ฐ’์˜ ํƒ€์ž…์€ ์†Œ์ˆ˜ํ˜•์œผ๋กœ ๋‚˜์˜จ๋‹ค.#๋‚˜๋ˆ„๊ธฐprint(7/2) #3.5print(6/2.. 2025. 2. 19.
[ํŒŒ์ด์ฌ] ํŒŒ์ด์ฌ ์ฒซ๊ฑธ์Œ ์ฝ”๋ฉ˜ํŠธํŒŒ์ด์ฌ์€ ์ฃผ์„ ๋Œ€์‹  #์ฝ”๋ฉ˜ํŠธ๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค.#์ฝ”๋ฉ˜ํŠธ  ํŒŒ์ด์ฌ์˜ ๋ฐ์ดํ„ฐ ํƒ€์ž…- ์ •์ˆ˜ํ˜• integer- ์†Œ์ˆ˜ํ˜• floating point- ๋ฌธ์ž์—ด string- ๋ถˆ๋ฆฐ boolean ์ถ”์ƒํ™” Abstraction- ๋ณ€์ˆ˜ Variable- ํ•จ์ˆ˜ Function- ๊ฐ์ฒด Object ๋ณ€์ˆ˜ ์—ฐ์Šตburger = 4990fries = 1490drink = 1250print(burger*3 + fries*2 + drink*5)#์‹คํ–‰๊ฒฐ๊ณผ 24200 ํ•จ์ˆ˜ ์—ฐ์Šต#์ •์˜def hello(): print("hello") print("welcome") #ํ˜ธ์ถœhello() #print : ๋‚ด์žฅํ•จ์ˆ˜#์‹คํ–‰๊ฒฐ๊ณผ : #hello #welcome ํŒŒ๋ผ๋ฏธํ„ฐ#์ •์˜def hello(name): print("hello") pr.. 2025. 2. 18.