小编noi*_*dea的帖子

我试图连接以下内容:

DF1

    price   side    timestamp
timestamp           
2016-01-04 00:01:15.631331072   0.7286  2   1451865675631331
2016-01-04 00:01:15.631399936   0.7286  2   1451865675631400
2016-01-04 00:01:15.631860992   0.7286  2   1451865675631861
2016-01-04 00:01:15.631866112   0.7286  2   1451865675631866

和

DF2

    bid bid_size    offer   offer_size
timestamp               
2016-01-04 00:00:31.331441920   0.7284  4000000 0.7285  1000000
2016-01-04 00:00:53.631324928   0.7284  4000000 0.7290  4000000
2016-01-04 00:01:03.131234048   0.7284  5000000 0.7286  4000000
2016-01-04 00:01:12.131444992   0.7285  1000000 0.7286  4000000
2016-01-04 00:01:15.631364096   0.7285  4000000 0.7290  4000000

同

 data = pd.concat([df1,df2], axis=1)  

但我得到了以下输出:

InvalidIndexError                         Traceback (most recent call last)
<ipython-input-38-2e88458f01d7> in <module>()
----> 1 …

我正在尝试将列标题添加到我已使用Pandas解析为数据帧的csv文件中。

dfTrades = pd.read_csv('pnl1.txt',delim_whitespace=True,header=None,);
dfTrades = dfTrades.drop(dfTrades.columns[[3,4,6,8,10,11,13,15,17,18,25,27,29,32]], axis=1)     # Note: zero indexed
dfTrades = dfTrades.set_index([dfTrades.index]);
df = pd.DataFrame(dfTrades,columns=['TradeDate',
                                      'TradeTime',
                                      'CumPnL',
                                      'DailyCumPnL',
                                      'RealisedPnL',
                                      'UnRealisedPnL',
                                      'CCYCCY',
                                      'CCYCCYPnLDaily',
                                      'Position',
                                      'CandleOpen',
                                      'CandleHigh',
                                      'CandleLow',
                                      'CandleClose',
                                      'CandleDir',
                                      'CandleDirSwings',
                                      'TradeAmount',
                                      'Rate',
                                      'PnL/Trade',
                                      'Venue',
                                      'OrderType',
                                      'OrderID'
                                      'Code']);


print df

数据的结构为：

01/10/2015 05:47.3  190 190 -648 838 EURNOK -648 0  0 611   -1137   -648 H 2     -1000000   9.465   -648    INTERNAL    IOC 287 AS

熊猫的回报是：

  TradeDate  TradeTime  CumPnL  DailyCumPnL  RealisedPnL  UnRealisedPnL  \
0            NaN        NaN     NaN          NaN          NaN            NaN   ... …