#Day 2+学习笔记+07/16
transform
nvda=pd.read_csv("data/NVDA.csv",index_col=0,parse_dates='"Date"])
nvda.index[0].weekday()
key=lambda x:x.year
nvda.groupby(key).agg([np.mean,np.std])
zscore=lambda x:(x-x.mean())/x.std()
transformed=nvda.groupby(key).transform(zscore)
%matplotlib inline
nvda.plot()
transformed["Adj Close"].plot(grid=True,figsize=(10.8,7.6))
compare=pd.DataFrame({"Original Adj Close":nvda["Adj Close"], "Transformed Adj Close":transformed["Adj Close"]})
compare.plot(grid=True,figsize=(10.8,7.5))
year_key=lambda x:x.year
month_key=lambda x:x.month
nvda.groupby([year_key,month_key]).sum()
nvda.groupby([year_key,month_key]).agg([np.mean,np.std])
monthly_nvda=nvda.groupby([year_key,month_key]).last()
index=[ str(i[0]) + "-" + str(i[1]) for i in monthly_nvda.index.values ]
index=pd.PeriodIndex(index, freq="M")
monthly_nvda.index=index
monthly_nvda["Adj Close"].plot()
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