[英]Concatenating data frames pandas
I would like to historical close prices from the yfinance module and create a data frame with a column with these closing prices for each of the tickers stored in the Holdings list.我想从 yfinance 模块中获取历史收盘价,并创建一个数据框,其中有一列包含存储在 Holdings 列表中的每个代码的收盘价。 I can do everything except creating that data frame at the end.除了最后创建该数据框外,我可以做任何事情。 Can someone please help?:有人可以帮忙吗?:
Holdings = ['RL', 'AMC', 'BYND', 'BRK-B',
'BBY', 'AYX', 'AAPL', 'KO',
'FB', 'RACE', 'INTC', 'PFE',
'CRM', 'WFC', 'JPM', 'GOOG']
Hist_Holdings = []
for symbol in Holdings:
Ticker = yf.Ticker(symbol)
Hist = Ticker.history(period = "6mo", interval = "1d")
Hist = Hist['Close']
Hist.columns = [symbol]
Hist_Holdings.append(Hist)
The desired data frame format is not known, but the following code will concatenate the stocks you want to get with spaces.所需的数据框格式未知,但以下代码会将您想要获取的股票与空格连接起来。 It is fast and returns the data in a data frame format.它速度很快,并以数据帧格式返回数据。 The code below specifies only the closing price.下面的代码仅指定收盘价。
import yfinance as yf
import datetime
now_ = datetime.datetime.today()
start = datetime.datetime(now_.year, now_.month - 6, now_.day + 1)
end = datetime.datetime(now_.year, now_.month, now_.day - 1)
Holdings = 'RL AMC BYND BRK-B BBY AYX AAPL KO FB RACE INTC PFE CRM WFC JPM GOOG'
data = yf.download(Holdings, start=start, end=end)['Close']
AAPL AMC AYX BBY BRK-B BYND CRM FB GOOG INTC JPM KO PFE RACE RL WFC
Date
2020-06-12 84.699997 5.89 141.130005 77.760002 181.210007 144.740005 175.110001 228.580002 1413.180054 59.330002 99.870003 45.599998 32.020874 167.889999 74.769997 27.969999
2020-06-15 85.747498 5.80 142.940002 80.010002 181.550003 154.000000 178.610001 232.500000 1419.849976 60.099998 101.250000 46.299999 31.650854 169.699997 73.739998 28.209999
2020-06-16 88.019997 5.56 145.690002 83.470001 182.300003 151.940002 180.479996 235.649994 1442.719971 60.400002 102.059998 46.770000 31.688805 169.690002 76.419998 28.520000
2020-06-17 87.897499 5.42 150.990005 83.239998 180.860001 156.339996 181.399994 235.529999 1451.119995 60.490002 99.480003 46.580002 31.840607 169.809998 74.480003 27.450001
2020-06-18 87.932503 5.63 160.779999 82.300003 180.729996 158.199997 187.660004 235.940002 1435.959961 60.080002 98.940002 46.990002 31.537003 168.580002 73.940002 27.549999
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