sons dataStory

The following graphs are the codes for the figures included in Chapter 5 of the e-book Statistics with Python . import numpy as np import pandas as pd from scipy import stats from sklearn.preprocessing import StandardScaler import yfinance as yf import matplotlib.pyplot as plt import seaborn as sns sns.set_style("darkgrid") #fig511 st=pd.Timestamp(2024, 4,20) et=pd.Timestamp(2024, 5, 30) da1=yf.download('GOOGL', st, et)["Close"] da2=yf.download('MSFT', st, et)["Close"] da1=da1.iloc[:,0].pct_change()[1:]*100 da2=da2.iloc[:,0].pct_change()[1:]*100 da=pd.DataFrame([da1, da2], index=['data1', 'data2']).T da.index=range(len(da1)) mu1, sd1, n1=np.mean(da1), np.std(da1, ddof=1), len(da1) mu2, sd2, n2=np.mean(da2), np.std(da2, ddof=1), len(da2) s_p=np.sqrt(((n1-1)*sd1**2+(n2-1)*sd2**2)/(n1+n2-2)) se=s_p*np.sqrt((1/n1+1/n2)) df=n1+n2-2 mu=mu1-mu2 testStatic=((mu1-mu2)-0)/se x=np.linspace(-3, 3, 500) p=stats.t.pdf(x, df) l=stats.t...