pandas read_csv预期列数错误，且CSV文件不完整

Question

我有一个csv文件，其中包含几百行和26列，但最后几列仅在几行中有一个值，它们朝向文件的中间或结尾。当我尝试使用read_csv（）读取它时，出现以下错误。“ValueError：期望有23列，在第64行中有26列”

我看不到在哪里明确说明文件中的列数，或者如何确定文件认为应该包含多少列。转储在下面

In [3]:

infile =open(easygui.fileopenbox(),"r")
pledge = read_csv(infile,parse_dates='true')


---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-b35e7a16b389> in <module>()
      1 infile =open(easygui.fileopenbox(),"r")
      2 
----> 3 pledge = read_csv(infile,parse_dates='true')


C:\Python27\lib\site-packages\pandas-0.8.1-py2.7-win32.egg\pandas\io\parsers.pyc in read_csv(filepath_or_buffer, sep, dialect, header, index_col, names, skiprows, na_values, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding, squeeze)
    234         kwds['delimiter'] = sep
    235 
--> 236     return _read(TextParser, filepath_or_buffer, kwds)
    237 
    238 @Appender(_read_table_doc)

C:\Python27\lib\site-packages\pandas-0.8.1-py2.7-win32.egg\pandas\io\parsers.pyc in _read(cls, filepath_or_buffer, kwds)
    189         return parser
    190 
--> 191     return parser.get_chunk()
    192 
    193 @Appender(_read_csv_doc)

C:\Python27\lib\site-packages\pandas-0.8.1-py2.7-win32.egg\pandas\io\parsers.pyc in get_chunk(self, rows)
    779             msg = ('Expecting %d columns, got %d in row %d' %
    780                    (col_len, zip_len, row_num))
--> 781             raise ValueError(msg)
    782 
    783         data = dict((k, v) for k, v in izip(self.columns, zipped_content))

ValueError: Expecting 23 columns, got 26 in row 64

Answer 1

您可以使用names参数。例如，如果您有这样的csv文件：

1,2,1
2,3,4,2,3
1,2,3,3
1,2,3,4,5,6

并尝试阅读它，您会收到错误消息

>>> pd.read_csv(r'D:/Temp/tt.csv')
Traceback (most recent call last):
...
Expected 5 fields in line 4, saw 6

但是，如果传递names参数，则会得到结果：

>>> pd.read_csv(r'D:/Temp/tt.csv', names=list('abcdef'))
   a  b  c   d   e   f
0  1  2  1 NaN NaN NaN
1  2  3  4   2   3 NaN
2  1  2  3   3 NaN NaN
3  1  2  3   4   5   6

希望能帮助到你。