1
我使用熊貓來讀取非常大的csv文件，這也是gzip。 我解壓縮到大約30-50GB的csv文件。 我分塊文件並處理/操作它們。 最後，相關數據添加到我壓縮購買內存，以避免30-50Gb加文件分塊
它工作正常，但速度很慢，因爲我要處理，每天一個文件，有幾個年的數據（600TB未壓縮的CSV）
能買HDF5文件更多內存是避免分塊和加速64GB/128GB的過程的好方法？ 但這會使熊貓變得緩慢而笨拙嗎？ 我是否正確地說切換到C++可以加速這個過程，但我仍然忍受着讀取過程，不得不以塊爲單位處理數據。 最後有沒有人有任何想法來處理這個最好的方法。
順便說一下，一旦工作完成，我不必回過頭去處理數據，所以想讓它在合理的時間內工作，所以寫了一些東西，並行過程可能不錯，但經驗有限那個領域需要我花些時間才能構建出來，所以寧願不去除非那是唯一的選擇。
更新。我認爲這會更容易看到代碼。無論如何，我不相信代碼特別慢。我認爲技術/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()
你能解釋什麼是緩慢的，爲什麼它慢？沒有更多的細節，很難猜測什麼有助於加快這一進程。 –
您應該嘗試分析和測量程序的性能，以確定哪些點最慢以及內存或CPU功耗是否是限制因素。這將有助於縮小特定更改對您的幫助。然後，您還可以將最慢的源代碼部分上傳到http://codereview.stackexchange.com/上的問題，並徵求關於提高其性能的建議。 – gfv
我會嘗試讀取塊壓縮的CSV格式，而不是首先解壓縮它們 - 這樣，您應該擁有更少的IO（通常是最慢的部分之一）。除此之外，擁有更多內存應該允許你擁有更大的塊，或者甚至可以在沒有塊的情況下完成，如果你的內存將近似於。比所產生的DF大兩倍。在同一臺服務器/計算機上進行並行處理（如果你的意思是DASK）會使開銷變得更糟。如果你需要一個真正的權力看看Apache PySpark SQL，但這將意味着更高的投資到Hadoop集羣 - 只是我的2美分... – MaxU