Python DataReader - 更新新信息

如何解决Python DataReader - 更新新信息

import pandas as pd
from pandas_datareader import data as wb

tickers = ["MMM","ABT","ABBV","ABMD","ACN","ATVI","ADBE","AMD","AAP","AES","AFL","A","APD","AKAM","ALK","ALB","ARE","ALXN","ALGN","ALLE","LNT","ALL","GOOGL","GOOG","MO","AMZN","AMCR","AEE","AAL","AEP","AXP","AIG","AMT","AWK","AMP","ABC","AME","AMGN","APH","ADI","ANSS","ANTM","AON","AOS","APA","AAPL","AMAT","APTV","ADM","ANET","AJG","AIZ","T","ATO","ADSK","ADP","AZO","AVB","AVY","BKR","BLL","BAC","BK","BAX","BDX","BBY","BIO","BIIB","BLK","BA","BKNG","BWA","BXP","BSX","BMY","Avgo","BR","CHRW","COG","CDNS","CZR","CPB","COF","CAH","KMX","ccl","CARR","CTLT","CAT","CBOE","CBRE","CDW","CE","CNC","CNP","CERN","CF","SCHW","CHTR","CVX","CMG","CB","CHD","CI","CINF","CTAS","CSCO","C","CFG","CTXS","CLX","cme","CMS","KO","CTSH","CL","CMCSA","CMA","CAG","cop","ED","STZ","COO","CPRT","GLW","CTVA","COST","CCI","CSX","CMI","CVS","DHI","DHR","DRI","DVA","DE","DAL","XRAY","DVN","dxcM","FANG","DLR","DFS","disCA","disCK","disH","DG","DLTR","D","DPZ","DOV","DOW","DTE","DUK","DRE","DD","dxc","EMN","ETN","EBAY","ECL","EIX","EW","EA","EMR","ENPH","ETR","EOG","EFX","EQIX","EQR","ESS","EL","ETSY","EVRG","ES","RE","EXC","EXPE","EXPD","EXR","XOM","FFIV","FB","FAST","FRT","FDX","FIS","FITB","FE","FRC","FISV","FLT","FLIR","FMC","F","FTNT","FTV","FBHS","FOXA","FOX","BEN","FCX","GPS","GRMN","IT","GNRC","GD","GE","GIS","GM","GPC","GILD","GL","GPN","GS","GWW","HAL","HBI","HIG","HAS","HCA","PEAK","HSIC","HSY","HES","HPE","HLT","HFC","HOLX","HD","HON","HRL","HST","HWM","HPQ","HUM","HBAN","HII","IEX","IDXX","INFO","ITW","ILMN","INCY","IR","INTC","ICE","IBM","IP","IPG","IFF","INTU","ISRG","IVZ","IPGP","IQV","IRM","JKHY","J","JBHT","SJM","JNJ","JCI","JPM","JNPR","KSU","K","KEY","KEYS","KMB","KIM","KMI","KLAC","KHC","KR","LB","LHX","LH","LRCX","LW","LVS","LEG","LDOS","LEN","LLY","LNC","LIN","LYV","LKQ","LMT","L","LOW","LUMN","LYB","MTB","MRO","MPC","MKTX","MAR","MMC","MLM","MAS","MA","MKC","MXIM","MCD","MCK","MDT","MRK","MET","MTD","MGM","MCHP","MU","MSFT","MAA","MHK","TAP","MDLZ","MPWR","MNST","MCO","MS","MOS","MSI","MSCI","NDAQ","NTAP","NFLX","NWL","NEM","NWSA","NWS","NEE","NLSN","NKE","NI","NSC","NTRS","NOC","NLOK","NCLH","NOV","NRG","NUE","NVDA","NVR","NXPI","ORLY","OXY","ODFL","OMC","OKE","ORCL","OTIS","PCAR","PKG","PH","PAYX","PAYC","PYPL","PENN","PNR","PBCT","PEP","PKI","PRGO","PFE","PM","PSX","PNW","PXD","PNC","POOL","PPG","PPL","PFG","PG","PGR","PLD","PRU","PEG","PSA","PHM","PVH","QRVO","PWR","QCOM","DGX","RL","RJF","RTX","O","REG","REGN","RF","RSG","RMD","RHI","ROK","ROL","ROP","ROST","RCL","SPGI","CRM","SBAC","SLB","STX","SEE","SRE","Now","SHW","SPG","SWKS","SNA","SO","LUV","SWK","SBUX","STT","STE","SYK","SIVB","SYF","SNPS","SYY","TMUS","TROW","TTWO","TPR","TGT","TEL","TDY","TFX","TER","TSLA","TXN","TXT","TMO","TJX","TSCO","TT","TDG","TRV","TRMB","TFC","TWTR","TYL","TSN","UDR","ULTA","USB","UAA","UA","UNP","UAL","UNH","UPS","URI","UHS","UNM","VLO","VAR","VTR","VRSN","VRSK","VZ","VRTX","VFC","VIAC","VTRS","V","VNO","VMC","WRB","WAB","WMT","WBA","dis","WM","WAT","WEC","WFC","WELL","WST","WDC","WU","WRK","WY","WHR","WMB","WLTW","WYNN","XEL","XLNX","XYL","YUM","ZBRA","ZBH","ZION","ZTS"]

financial_data = pd.DataFrame()
for t in tickers:
    financial_data[t] = wb.DataReader(t,data_source='yahoo',start = '1995-1-1')["Adj Close"]
financial_data.to_excel("Financial Data.xlsx")

我正在使用 Datareader 收集一些股票信息。我正在获取大量信息（从 1995 年到 2021 年），然后将其导出到 Excel。我想知道是否有办法，比如说明天，来加速信息的更新，而不是在 Python 上从头到尾运行整个脚本，因为我明天的目标只是在整个 Excel 文件。如果我只是执行脚本，它将覆盖 Excel 文件 + 添加新的信息行。这似乎非常无效，我想知道是否有办法“告诉脚本”我只是在寻找明天的信息，而不是“告诉它”从 1995 年开始再次抓取我的信息。

谢谢。

解决方法

我不知道 Pandas 到底是如何工作的，但我会说它可以进行延迟快速加载，而且这在计算上并不是很昂贵。代价高昂的是对每个加载的数据进行操作。然后我认为，在您的情况下，如果数据按日期按递增顺序排序，则将名为 timestamp_toStart 的变量第一次初始化为 '1995-1-1' 就足够了，在此之后，在第一次执行之后更新为上次读取日期的最后一个值。您可以将此值保存在一个文件中，并在每次重新运行脚本时重新读取和加载它。

financial_data = pd.DataFrame()
#load timestamp_toStart from the file here 
for t in tickers:
  financial_data[t] = wb.DataReader(t,data_source='yahoo',start = timestamp_toStart)["Adj Close"]
  timestamp = wb.DataReader(t,start = timestamp_toStart)["MMM"] #Not Sure about the correct syntax
timestamp_toStart = timestamp
#Save in a file timestamp_toStart
financial_data.to_excel("Financial Data.xlsx")