本文整理汇总了Python中Parser.Parser.getValueFromDf方法的典型用法代码示例。如果您正苦于以下问题：Python Parser.getValueFromDf方法的具体用法？Python Parser.getValueFromDf怎么用？Python Parser.getValueFromDf使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Parser.Parser的用法示例。
在下文中一共展示了Parser.getValueFromDf方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: all
# 需要导入模块: from Parser import Parser [as 别名]
# 或者: from Parser.Parser import getValueFromDf [as 别名]
for fullpath in fullpaths:
output = {}
keyStats = {}
regex = 'market_time.....,\s(...)\s(..),\s(....)'
ticker = p.getTickerFromFullPath(fullpath, 'forward/data/', '.html')
forwardDate, _ = p.getDateFromMarketTime(fullpath)
fwdUnixTime, date = p.getDateFromMarketTime(fullpath)
# pull key stats from Yahoo screen
for feature in p.features:
value = p.searchSourceForFeature(fullpath, feature)
value = p.cleanup(value)
keyStats[feature] = value
# Pull stock price and s&p adjusted close on date and forward date and clean missing values
price = p.getValueFromDf(p.stock_df, ticker.upper(), p.oneYearAgo(fwdUnixTime))
priceFwd = p.getValueFromDf(p.stock_df, ticker.upper(),forwardDate)
sp500 = p.getValueFromDf(p.sp500_df, 'Adjusted Close', p.oneYearAgo(fwdUnixTime))
sp500Fwd = p.getValueFromDf(p.sp500_df, 'Adjusted Close', forwardDate)
# clean up
p.setDefaultIfNone(price, fwdUnixTime)
p.setDefaultIfNone(priceFwd, fwdUnixTime)
p.setDefaultIfNone(sp500, fwdUnixTime)
p.setDefaultIfNone(sp500Fwd, fwdUnixTime)
# calculate returns and alphas
stockReturn = p.getReturn(price, priceFwd)
sp500Return = p.getReturn(sp500, sp500Fwd)
if all([stockReturn, sp500Return]):示例2: all
# 需要导入模块: from Parser import Parser [as 别名]
# 或者: from Parser.Parser import getValueFromDf [as 别名]
output_df = pd.DataFrame()
for fullpath in fullpaths:
output = {}
keyStats = {}
ticker = p.getTickerFromFullPath(fullpath, '_KeyStats/', '/.*')
dateStamp, unixTime = p.getDateStampAndUnixTime(os.path.basename(fullpath))
# pull key stats from Yahoo screen
for feature in p.features:
value = p.searchSourceForFeature(fullpath, feature)
value = p.cleanup(value)
keyStats[feature] = value
# Pull stock price and s&p adjusted close on date and forward date and clean missing values
price = p.getValueFromDf(p.stock_df, ticker.upper(), unixTime)
priceIn1y = p.getValueFromDf(p.stock_df, ticker.upper(), p.oneYearLater(unixTime))
sp500 = p.getValueFromDf(p.sp500_df, 'Adjusted Close', unixTime)
sp500In1y = p.getValueFromDf(p.sp500_df, 'Adjusted Close', p.oneYearLater(unixTime))
# clean up
p.setDefaultIfNone(price, unixTime)
p.setDefaultIfNone(priceIn1y, unixTime)
p.setDefaultIfNone(sp500, unixTime)
p.setDefaultIfNone(sp500In1y, unixTime)
# calculate returns and alphas
stockReturn = p.getReturn(price, priceIn1y)
sp500Return = p.getReturn(sp500, sp500In1y)
if all([stockReturn, sp500Return]):