Python的熊貓CSV 「類型錯誤：<lambda>（）恰恰1個參數（5給出）」

Question

我有一個格式化的CSV：

"Year","Month","Day","Hour","Minute","Direct","Diffuse","D_Global","D_IR","U_Global","U_IR","Zenith" 
2001,3,1,0,1,0.28,84.53,83.53,224.93,76.67,228.31,80.031 
2001,3,1,0,2,0.15,84.24,83.25,224.76,76.54,228.62,80.059 
2001,3,1,0,3,0.16,84.63,83.43,225.62,76.76,229.06,80.087 
2001,3,1,0,4,0.20,85.20,83.99,226.56,77.15,228.96,80.115 

然後我的腳本：

df1 = pd.read_csv(input_file, 
     sep = ",", 
     parse_dates = {'Date': [0,1,2,3,4]}, 
     date_parser = lambda x: pd.to_datetime(x, format="%Y %m %d %H %M"), 
     index_col = ['Date']) 

錯誤我得到的是：

Traceback (most recent call last): 
    File "convertCSVtoNC.py", line 70, in <module> 
    openFile(sys.argv[1:]) 
    File "convertCSVtoNC.py", line 30, in openFile 
    df2 = createDataFrame(input_file, counter) 
    File "convertCSVtoNC.py", line 43, in createDataFrame 
    index_col = ['Date']) 
... 
TypeError: <lambda>() takes exactly 1 argument (5 given) 

運行該腳本罰款302個以前的輸入，一個例子是格式：

"Year","Month","Day","Hour","Minute","Direct","Diffuse","D_Global","D_IR","U_Global","U_IR","Zenith" 
1976,1,1,0,3,-999.00,-999.00,-999.00,-999.00,-999.00,-999.00,95.751 
1976,1,1,0,6,-999.00,-999.00,-999.00,-999.00,-999.00,-999.00,95.839 
1976,1,1,0,9,-999.00,-999.00,-999.00,-999.00,-999.00,-999.00,95.930 
1976,1,1,0,12,-999.00,-999.00,-999.00,-999.00,-999.00,-999.00,96.023 

任何想法爲什麼？

Answer 1

它正常工作對我來說：

df1 = pd.read_csv(file_name, parse_dates={'Date':[0,1,2,3,4]}, 
        date_parser=lambda x: pd.to_datetime(x, format='%Y %m %d %H %M'), 
        index_col=['Date'])) 


In [215]: df1 
Out[215]: 
        Direct Diffuse D_Global D_IR U_Global U_IR Zenith 
Date 
2001-03-01 00:01:00 0.28 84.53  83.53 224.93  76.67 228.31 80.031 
2001-03-01 00:02:00 0.15 84.24  83.25 224.76  76.54 228.62 80.059 
2001-03-01 00:03:00 0.16 84.63  83.43 225.62  76.76 229.06 80.087 
2001-03-01 00:04:00 0.20 85.20  83.99 226.56  77.15 228.96 80.115 

PS我使用熊貓0.19.1

Answer 2

原來有我的輸入CSV文件的末尾幾個換行符。我想這是有道理的，因爲我的lambda函數將整行作爲輸入。也許在未來的lambda相關問題中尋找一些東西。