我使用pandas来读取非常大的csv文件，这些文件也是gzip压缩文件。 我解压缩到大约30-50GB的csv文件。 我将文件分块并处理/操作它们。 最后将相关数据添加到我压缩的HDF5文件
它工作正常，但速度慢，因为我每天必须处理一个文件并且有几年的数据（600TB未压缩的csv）
购买更多ram可以避免分块并加快进程速度64GB / 128GB吗？ 但是这会让熊猫变得缓慢而笨拙吗？ 我是否正确地说，切换到C ++可以加快进程，但我仍然受到读取过程的困扰，并且不得不以块的形式处理数据。 最后有没有人对处理这个问题的最佳方法有任何想法。
顺便说一下，一旦完成工作，我就不必再回头再处理数据，所以只想让它在合理的时间内工作，所以写一些并行流程可能很好但是经验有限的东西我需要一段时间来构建它，所以不要这样做，除非这是唯一的选择。
更新。我认为看代码会更容易。无论如何，我不相信代码特别慢。我认为技术/方法可能是。
def txttohdf(path, contract):
#create dataframes for trade and quote
dftrade = pd.DataFrame(columns = ["datetime", "Price", "Volume"])
dfquote = pd.DataFrame(columns = ["datetime", "BidPrice", "BidSize","AskPrice", "AskSize"])
#create an hdf5 file with high compression and table so we can append
hdf = pd.HDFStore(path + contract + '.h5', complevel=9, complib='blosc')
hdf.put('trade', dftrade, format='table', data_columns=True)
hdf.put('quote', dfquote, format='table', data_columns=True)
#date1 = date(start).strftime('%Y%m%d')
#date2 = date(end).strftime('%Y%m%d')
#dd = [date1 + timedelta(days=x) for x in range((date2-date1).days + 1)]
#walkthrough directories
for subdir, dir, files in os.walk(path):
for file in files:
#check if contract has name
#print(file)
#create filename from directory and file
filename = os.path.join(subdir, file)
#read in csv
if filename.endswith('.gz'):
df = pd.read_csv(gzip.open(filename),header=0,iterator=True,chunksize = 10000, low_memory =False, names = ['RIC','Date','Time','GMTOffset','Type','ExCntrbID','LOC','Price','Volume','MarketVWAP','BuyerID','BidPrice','BidSize','NoBuyers','SellerID','AskPrice','AskSize','NoSellers','Qualifiers','SeqNo','ExchTime','BlockTrd','FloorTrd','PERatio','Yield','NewPrice','NewVol','NewSeqNo','BidYld','AskYld','ISMABidYld','ISMAAskYld','Duration','ModDurtn','BPV','AccInt','Convexity','BenchSpd','SwpSpd','AsstSwpSpd','SwapPoint','BasePrice','UpLimPrice','LoLimPrice','TheoPrice','StockPrice','ConvParity','Premium','BidImpVol','AskImpVol','ImpVol','PrimAct','SecAct','GenVal1','GenVal2','GenVal3','GenVal4','GenVal5','Crack','Top','FreightPr','1MnPft','3MnPft','PrYrPft','1YrPft','3YrPft','5YrPft','10YrPft','Repurch','Offer','Kest','CapGain','Actual','Prior','Revised','Forecast','FrcstHigh','FrcstLow','NoFrcts','TrdQteDate','QuoteTime','BidTic','TickDir','DivCode','AdjClose','PrcTTEFlag','IrgTTEFlag','PrcSubMktId','IrgSubMktId','FinStatus','DivExDate','DivPayDate','DivAmt','Open','High','Low','Last','OpenYld','HighYld','LowYld','ShortPrice','ShortVol','ShortTrdVol','ShortTurnnover','ShortWeighting','ShortLimit','AccVolume','Turnover','ImputedCls','ChangeType','OldValue','NewValue','Volatility','Strike','Premium','AucPrice','Auc Vol','MidPrice','FinEvalPrice','ProvEvalPrice','AdvancingIssues','DecliningIssues','UnchangedIssues','TotalIssues','AdvancingVolume','DecliningVolume','UnchangedVolume','TotalVolume','NewHighs','NewLows','TotalMoves','PercentageChange','AdvancingMoves','DecliningMoves','UnchangedMoves','StrongMarket','WeakMarket','ChangedMarket','MarketVolatility','OriginalDate','LoanAskVolume','LoanAskAmountTradingPrice','PercentageShortVolumeTradedVolume','PercentageShortPriceTradedPrice','ForecastNAV','PreviousDaysNAV','FinalNAV','30DayATMIVCall','60DayATMIVCall','90DayATMIVCall','30DayATMIVPut','60DayATMIVPut','90DayATMIVPut','BackgroundReference','DataSource','BidSpread','AskSpread','ContractPhysicalUnits','Miniumumquantity','NumberPhysicals','ClosingReferencePrice','ImbalanceQuantity','FarClearingPrice','NearClearingPrice','OptionAdjustedSpread','ZSpread','ConvexityPremium','ConvexityRatio','PercentageDailyReturn','InterpolatedCDSBasis','InterpolatedCDSSpread','ClosesttoMaturityCDSBasis','SettlementDate','EquityPrice','Parity','CreditSpread','Delta','InputVolatility','ImpliedVolatility','FairPrice','BondFloor','Edge','YTW','YTB','SimpleMargin','DiscountMargin','12MonthsEPS','UpperTradingLimit','LowerTradingLimit','AmountOutstanding','IssuePrice','GSpread','MiscValue','MiscValueDescription'])
#parse date time this is quicker than doing it while we read it in
for chunk in df:
chunk['datetime'] = chunk.apply(lambda row: datetime.datetime.strptime(row['Date']+ ':' + row['Time'],'%d-%b-%Y:%H:%M:%S.%f'), axis=1)
#df = df[~df.comment.str.contains('ALIAS')]
#drop uneeded columns inc date and time
chunk = chunk.drop(['Date','Time','GMTOffset','ExCntrbID','LOC','MarketVWAP','BuyerID','NoBuyers','SellerID','NoSellers','Qualifiers','SeqNo','ExchTime','BlockTrd','FloorTrd','PERatio','Yield','NewPrice','NewVol','NewSeqNo','BidYld','AskYld','ISMABidYld','ISMAAskYld','Duration','ModDurtn','BPV','AccInt','Convexity','BenchSpd','SwpSpd','AsstSwpSpd','SwapPoint','BasePrice','UpLimPrice','LoLimPrice','TheoPrice','StockPrice','ConvParity','Premium','BidImpVol','AskImpVol','ImpVol','PrimAct','SecAct','GenVal1','GenVal2','GenVal3','GenVal4','GenVal5','Crack','Top','FreightPr','1MnPft','3MnPft','PrYrPft','1YrPft','3YrPft','5YrPft','10YrPft','Repurch','Offer','Kest','CapGain','Actual','Prior','Revised','Forecast','FrcstHigh','FrcstLow','NoFrcts','TrdQteDate','QuoteTime','BidTic','TickDir','DivCode','AdjClose','PrcTTEFlag','IrgTTEFlag','PrcSubMktId','IrgSubMktId','FinStatus','DivExDate','DivPayDate','DivAmt','Open','High','Low','Last','OpenYld','HighYld','LowYld','ShortPrice','ShortVol','ShortTrdVol','ShortTurnnover','ShortWeighting','ShortLimit','AccVolume','Turnover','ImputedCls','ChangeType','OldValue','NewValue','Volatility','Strike','Premium','AucPrice','Auc Vol','MidPrice','FinEvalPrice','ProvEvalPrice','AdvancingIssues','DecliningIssues','UnchangedIssues','TotalIssues','AdvancingVolume','DecliningVolume','UnchangedVolume','TotalVolume','NewHighs','NewLows','TotalMoves','PercentageChange','AdvancingMoves','DecliningMoves','UnchangedMoves','StrongMarket','WeakMarket','ChangedMarket','MarketVolatility','OriginalDate','LoanAskVolume','LoanAskAmountTradingPrice','PercentageShortVolumeTradedVolume','PercentageShortPriceTradedPrice','ForecastNAV','PreviousDaysNAV','FinalNAV','30DayATMIVCall','60DayATMIVCall','90DayATMIVCall','30DayATMIVPut','60DayATMIVPut','90DayATMIVPut','BackgroundReference','DataSource','BidSpread','AskSpread','ContractPhysicalUnits','Miniumumquantity','NumberPhysicals','ClosingReferencePrice','ImbalanceQuantity','FarClearingPrice','NearClearingPrice','OptionAdjustedSpread','ZSpread','ConvexityPremium','ConvexityRatio','PercentageDailyReturn','InterpolatedCDSBasis','InterpolatedCDSSpread','ClosesttoMaturityCDSBasis','SettlementDate','EquityPrice','Parity','CreditSpread','Delta','InputVolatility','ImpliedVolatility','FairPrice','BondFloor','Edge','YTW','YTB','SimpleMargin','DiscountMargin','12MonthsEPS','UpperTradingLimit','LowerTradingLimit','AmountOutstanding','IssuePrice','GSpread','MiscValue','MiscValueDescription'], axis=1)
# convert to datetime explicitly and add nanoseconds to same time stamps
chunk['datetime'] = pd.to_datetime(chunk.datetime)
#nanoseconds = df.groupby(['datetime']).cumcount()
#df['datetime'] += np.array(nanoseconds, dtype='m8[ns]')
# drop empty prints and make sure all prices are valid
dfRic = chunk[(chunk["RIC"] == contract)]
if len(dfRic)>0:
print(dfRic)
if ~chunk.empty:
dft = dfRic[(dfRic["Type"] == "Trade")]
dft.dropna(subset = ["Volume"], inplace =True)
dft = dft.drop(["RIC","Type","BidPrice", "BidSize", "AskPrice", "AskSize"], axis=1)
dft = dft[(dft["Price"] > 0)]
# clean up bid and ask
dfq = dfRic[(dfRic["Type"] == "Quote")]
dfq.dropna(how = 'all', subset = ["BidSize","AskSize"], inplace =True)
dfq = dfq.drop(["RIC","Type","Price", "Volume"], axis=1)
dfq = dfq[(dfq["BidSize"] > 0) | (dfq["AskSize"] > 0)]
dfq = dfq.ffill()
else:
print("Empty")
#add to hdf and close if loop finished
hdf.append('trade', dft, format='table', data_columns=True)
hdf.append('quote', dfq, format='table', data_columns=True)
hdf.close()
答案 0 :(得分：1)
我认为你有很多可以优化的东西：
首先只读取您真正需要的列而不是阅读然后删除它们 - 使用usecols=list_of_needed_columns参数
增加你的chunksize - 尝试使用不同的值 - 我会从10**5开始
不要使用chunk.apply(...)来转换您的日期时间 - 它非常慢 - 请使用pd.to_datetime（column，format ='...'）
您可以在组合多个条件时更有效地过滤数据位，而不是逐步执行此操作：