所以我尝试从文件夹中读取所有csv文件，然后将它们连接起来创建一个大csv（所有文件的结构都相同），保存并再次读取。所有这些都是用熊猫完成的。读取时出错。我附上下面的代码和错误。
import pandas as pd
import numpy as np
import glob
path =r'somePath' # use your path
allFiles = glob.glob(path + "/*.csv")
frame = pd.DataFrame()
list_ = []
for file_ in allFiles:
df = pd.read_csv(file_,index_col=None, header=0)
list_.append(df)
store = pd.concat(list_)
store.to_csv("C:workDATARaw_data\store.csv", sep=',', index= False)
store1 = pd.read_csv("C:workDATARaw_data\store.csv", sep=',')
错误：-
CParserError Traceback (most recent call last)
<ipython-input-48-2983d97ccca6> in <module>()
----> 1 store1 = pd.read_csv("C:workDATARaw_data\store.csv", sep=',')
C:UsersarmsharmAppDataLocalContinuumAnacondalibsite-packagespandasioparsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
472 skip_blank_lines=skip_blank_lines)
473
--> 474 return _read(filepath_or_buffer, kwds)
475
476 parser_f.__name__ = name
C:UsersarmsharmAppDataLocalContinuumAnacondalibsite-packagespandasioparsers.pyc in _read(filepath_or_buffer, kwds)
258 return parser
259
--> 260 return parser.read()
261
262 _parser_defaults = {
C:UsersarmsharmAppDataLocalContinuumAnacondalibsite-packagespandasioparsers.pyc in read(self, nrows)
719 raise ValueError('skip_footer not supported for iteration')
720
--> 721 ret = self._engine.read(nrows)
722
723 if self.options.get('as_recarray'):
C:UsersarmsharmAppDataLocalContinuumAnacondalibsite-packagespandasioparsers.pyc in read(self, nrows)
1168
1169 try:
-> 1170 data = self._reader.read(nrows)
1171 except StopIteration:
1172 if nrows is None:
pandasparser.pyx in pandas.parser.TextReader.read (pandasparser.c:7544)()
pandasparser.pyx in pandas.parser.TextReader._read_low_memory (pandasparser.c:7784)()
pandasparser.pyx in pandas.parser.TextReader._read_rows (pandasparser.c:8401)()
pandasparser.pyx in pandas.parser.TextReader._tokenize_rows (pandasparser.c:8275)()
pandasparser.pyx in pandas.parser.raise_parser_error (pandasparser.c:20691)()
CParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file.
我也尝试过使用csv阅读器：-
import csv
with open("C:workDATARaw_data\store.csv", 'rb') as f:
reader = csv.reader(f)
l = list(reader)
错误：-
Error Traceback (most recent call last)
<ipython-input-36-9249469f31a6> in <module>()
1 with open('C:workDATARaw_data\store.csv', 'rb') as f:
2 reader = csv.reader(f)
----> 3 l = list(reader)
Error: new-line character seen in unquoted field - do you need to open the file in universal-newline mode?
我发现了这个错误，原因是Panda用作行终止符的数据中有一些回车"\r"，就好像它是"\n"一样。我想我应该在这里发帖，因为这可能是出现这个错误的常见原因。
我找到的解决方案是在read_csv函数中添加lineterminator='n'，如下所示：
df_clean = pd.read_csv('test_error.csv',
lineterminator='n')
如果您使用的是python，并且它是一个大文件，您可以使用engine='python'如下，并且应该工作。
df = pd.read_csv( file_, index_col=None, header=0, engine='python' )
不是答案，但注释太长（不涉及代码格式）
当你在csv模块中读取它时，它会断开，你至少可以找到错误发生的行：
import csv
with open(r"C:workDATARaw_datastore.csv", 'rb') as f:
reader = csv.reader(f)
linenumber = 1
try:
for row in reader:
linenumber += 1
except Exception as e:
print (("Error line %d: %s %s" % (linenumber, str(type(e)), e.message)))
然后在store.csv中查看这一行发生了什么。
将目录更改为CSV
Corpus = pd.read_csv(r"C:UsersDellDesktopDataset.csv",encoding='latin-1')
问题来自excel文件的格式。我们从菜单中选择"另存为选项"，并将格式从xls更改为csv，然后它肯定会起作用。
在我的案例中，解决方案是根据以下答案指定utf-16的编码：https://stackoverflow.com/a/64516600/9836333
pd.read_csv("C:workDATARaw_datastore.csv", sep=',', encoding='utf-16')