python - Pandas .read_csv() : Can't convert 'int' object to str implicitly

Question

导入 .txt 文件时，出现以下错误:

Can't convert 'int' object to str implicitly

我按如下方式导入我的 .txt 文件:

activityHeaders = ['Type', 'AccountID', 'ConID', 'SecurityID', 'Symbol', 'BBTicker', 'Currency', 'BaseCurrency', 'TradeDate', 'SettleDate', 'TransactionType', 'Quantity', 'UnitPrice', 'GrossAmount', 'SECFee', 'Commission', 'NetInBase', 'FXRatetoBase', 'Other1', 'Other2', 'Description']

dfActivity = pd.read_csv(activityFileUrl, skiprows=[1], header=activityHeaders, error_bad_lines=False)

我的 .txt 文件如下所示:

"H","I000000","Activity","20100407","16:02:38","20100329","1.0"
"D","I000000","","","","","CAD","EUR","20100329","20100329","WITH","0","0","-14.88","0","0","0","-14.88","-10.8158","",,"CASH TRANSFER (INTERNAL)"
"D","I000000","","","","","AUD","EUR","20100328","20100328","ADJ","0","0","4","0","0","0","4","2.7211","",,"CLIENT FEE (U000001, Commission)"
"D","U000001","37036548","DE000A0F6MD5","PRA","STK","EUR","EUR","20100329","20100331","SELL","-300","7.776","-2332.8","0","-6","0","2326.8","2326.8","405346125","FI","TRADE PRAKTIKER BAU-UND HEIMWERK A"

我不明白 int 可能来自哪里。请注意，我使用 skiprows 和 error_bad_lines 跳过了第一行和最后一行。我也将 header 设置为 None，它返回了同样的错误。

Answer 1

如果需要列表 activityHeaders 的列名称，则需要将 header 更改为 names。

df = pd.read_csv(StringIO(temp), names=activityHeaders, skiprows=1, error_bad_lines=False)
print (df)
      Type   AccountID         ConID SecurityID Symbol BBTicker Currency  \
D  I000000         NaN           NaN        NaN    NaN      CAD      EUR   
D  I000000         NaN           NaN        NaN    NaN      AUD      EUR   
D  U000001  37036548.0  DE000A0F6MD5        PRA    STK      EUR      EUR   

   BaseCurrency  TradeDate SettleDate                 ...                  \
D      20100329   20100329       WITH                 ...                   
D      20100328   20100328        ADJ                 ...                   
D      20100329   20100331       SELL                 ...                   

   Quantity  UnitPrice  GrossAmount  SECFee  Commission  NetInBase  \
D     0.000     -14.88            0       0           0     -14.88   
D     0.000       4.00            0       0           0       4.00   
D     7.776   -2332.80            0      -6           0    2326.80   

   FXRatetoBase       Other1  Other2                         Description  
D      -10.8158          NaN     NaN            CASH TRANSFER (INTERNAL)  
D        2.7211          NaN     NaN    CLIENT FEE (U000001, Commission)  
D     2326.8000  405346125.0      FI  TRADE PRAKTIKER BAU-UND HEIMWERK A  

[3 rows x 21 columns]

此外，如果不需要跳过第二行，请省略 skiprows:

df = pd.read_csv(StringIO(temp), names=activityHeaders, error_bad_lines=False)
print (df)
  Type AccountID     ConID    SecurityID    Symbol  BBTicker Currency  \
0    H   I000000  Activity      20100407  16:02:38  20100329      1.0   
1    D   I000000       NaN           NaN       NaN       NaN      CAD   
2    D   I000000       NaN           NaN       NaN       NaN      AUD   
3    D   U000001  37036548  DE000A0F6MD5       PRA       STK      EUR   

  BaseCurrency   TradeDate  SettleDate     ...      Quantity  UnitPrice  \
0          NaN         NaN         NaN     ...           NaN        NaN   
1          EUR  20100329.0  20100329.0     ...           0.0      0.000   
2          EUR  20100328.0  20100328.0     ...           0.0      0.000   
3          EUR  20100329.0  20100331.0     ...        -300.0      7.776   

   GrossAmount  SECFee  Commission  NetInBase  FXRatetoBase     Other1  \
0          NaN     NaN         NaN        NaN           NaN        NaN   
1       -14.88     0.0         0.0        0.0        -14.88   -10.8158   
2         4.00     0.0         0.0        0.0          4.00     2.7211   
3     -2332.80     0.0        -6.0        0.0       2326.80  2326.8000   

        Other2  Description  
0          NaN          NaN  
1          NaN          NaN  
2          NaN          NaN  
3  405346125.0           FI  

[4 rows x 21 columns]