우선, 잡플래닛 리뷰에 접근 가능한 아이디가 필요합니다.USER_EMAIL = "___" : ID 입력하기USER_PW = "___" : PW 입력하기COMPANY_ID = "___" : 수집하고자하는 회사의 URL ID 확인하기 import timeimport pandas as pdfrom bs4 import BeautifulSoupfrom selenium import webdriverfrom selenium.webdriver.common.by import Byfrom selenium.webdriver.chrome.service import Servicefrom selenium.webdriver.support.ui import WebDriverWaitfrom selenium.webdriver.sup..

4.2 이산분포4.2.1 베르누이 분포와 이항분포import numpy as npfrom scipy import stats as stfrom matplotlib import pyplot as pltst.binom.pmf(n=10,p=0.3,k=[0,1,2,3,4,5])# 예제 4.2.1 (b)# x값이 (1,3,6)일 경우 각 값까지의 누적확률 구하기np.round(st.binom.cdf(n=10,p=0.3,k=[1,3,6]),3)# 예제 4.2.1 (c)# 0.2, 0.5, 0.8의 누적확률을 갖는 확률변수값 구하기st.binom.ppf(n=10,p=0.3,q=[0.2,0.5,0.8])# 예제 4.2.1 (d)# 이항분포 B(10, 0.3)를 따르는 난수 5개 얻기st.binom.rvs(n=10,p=0..

import numpy as npfrom scipy import stats as stfrom matplotlib import pyplot as pltmu=0sigma=1x=np.linspace(mu-4*sigma,mu+4*sigma,100) # 표준정규분포 x값의 범위 -4 ~ +4y=st.norm.pdf(x,mu,sigma) # 정규분포에 의한 확률밀도함수의 값xx=np.linspace(mu-4*sigma,mu-1*sigma,100) # P(Z​mu=0sigma=1x=np.linspace(mu-4*sigma,mu+4*sigma,100) # 표준정규분포 x값의 범위 -4 ~ +4y=st.norm.pdf(x,mu,sigma) # 정규분포에 의한 확률밀도함수의 값xx=np.linspace(mu-1*sigma..

import pandas as pdimport numpy as npimport seaborn as snsfrom matplotlib import pyplot as pltactivities=['eat','sleep','work','play']slices=[3,7,8,6]colors=['r','y','g','b']plt.pie(slices,labels=activities, colors=colors, startangle=90, shadow=True, explode=(0,0,0.1,0), radius=1.2, autopct='%1.1f%%')plt.legend()plt.show()​color='cornflowerblue'points=np.ones(5)text_style=dict(horizo..

결측값import pandas as pdimport numpy as np# 결측값# 필요 데이터셋 생성b=np.arange(12).reshape(4,3)df=pd.DataFrame(b, columns=['X1','X2','X3'])df# 결측값 만들기df.loc[1,['X1','X3']]=np.nandf.loc[2,'X2']=np.nandf.loc[3,'X2']=np.nandfdf_0=df.fillna(0) # fillna()로 결측값 채우기df_0df_missing=df.fillna('missing') # 문자로도 채울수 있음df_missingdf_f=df.fillna(method='ffill') # .fillna(method='ffill') 위에 열의 값으로 채워줌df_fd..

import numpy as npimport pandas as pdimport scipy as spimport math as m# 2장 연습문제 1#연습문제 aprint('a :', 1+2*(3+4))#연습문제 bprint('b :',1+1/2+1/3+1/4+1/5+1/6)#연습문제 cprint('c :',m.sqrt((4+3)*(2+5)))#연습문제 dprint('d :', ( (1+2)/(4+5) )**3 )#연습문제 eprint('e :',122+12*23)#연습문제 fprint('f :',math.factorial(10))#연습문제 gprint('g :',math.sqrt( (6**2+4) ))#연습문제 hprint('h :',m.sin(np.pi/3) + m.cos(np.pi/6))#연습문제 i..

import numpy as npimport pandas as pdimport scipy as sp# 연습문제 3# 행렬과 벡터 정의A = np.array([[1, -1, 4], [-1, 1, 3], [4, 3, 2]])B = np.array([[3, -2, 4], [-2, 1, 0], [4, 0, 5]])x = np.array([1, -2, 4]).reshape(3, 1)y = np.array([3, 2, 1]).reshape(3, 1)# (a)print(A + B)# (b)print(A.T) # .T : 전치행렬 구하기# (c)print(np.dot(np.dot(x.T, A), y)) # np.dot : 내적 구하기# (d)print(np.dot(x.T, x))# (e)print(np.dot(np.d..

import numpy as npimport math as mimport pandas as pd# 2장 연습문제 1#연습문제 aprint(1+2*(3+4))#연습문제 bprint(1+1/2+1/3+1/4+1/5+1/6)#연습문제 cmath.sqrt((4+3)*(2+5))#연습문제 dprint( ( (1+2)/(4+5) )**3 )#연습문제 eprint(122+12*23)#연습문제 fmath.factorial(10)#연습문제 gmath.sqrt( (6**2+4) )#연습문제 hm.sin(np.pi/3) + m.cos(np.pi/6)#연습문제 im.log10(24)+m.log(10)#연습문제 jm.sin(np.pi/4)#연습문제 km.cos(np.pi/3)#연습문제 lprint((1+2+3)/(4+5+6))..