在我的例子中，一个文件具有USC-2 LE BOM编码，根据notepad++。 对于python，它是encoding="utf_16_le"。

希望，这能帮助别人更快地找到答案。

在传递给熊猫之前检查编码。它会让你慢下来，但是……

with open(path, 'r') as f:
    encoding = f.encoding 

df = pd.read_csv(path,sep=sep, encoding=encoding)

在python 3.7中

试试这个:

import pandas as pd
with open('filename.csv') as f:
    data = pd.read_csv(f)

看起来它会处理编码，而不会通过参数显式地表示它

这个问题困扰了我一段时间，我想我应该发布这个问题，因为它是第一个搜索结果。将encoding="iso-8859-1"标签添加到pandas read_csv中不起作用，任何其他编码也不起作用，一直给出UnicodeDecodeError。

如果将文件句柄传递给pd.read_csv()，则需要将encoding属性放在打开的文件上，而不是在read_csv中。事后看来很明显，但这是一个需要追查的微妙错误。

请尝试添加

import pandas as pd
df = pd.read_csv('file.csv', encoding='unicode_escape')

这将有所帮助。为我工作。另外，请确保使用了正确的分隔符和列名。

为了快速加载文件，可以从只加载1000行开始。

Pandas允许指定编码，但不允许忽略错误，不允许自动替换违规字节。因此，没有一种适合所有情况的方法，而是根据实际用例使用不同的方法。

You know the encoding, and there is no encoding error in the file. Great: you have just to specify the encoding: file_encoding = 'cp1252' # set file_encoding to the file encoding (utf8, latin1, etc.) pd.read_csv(input_file_and_path, ..., encoding=file_encoding) You do not want to be bothered with encoding questions, and only want that damn file to load, no matter if some text fields contain garbage. Ok, you only have to use Latin1 encoding because it accept any possible byte as input (and convert it to the unicode character of same code): pd.read_csv(input_file_and_path, ..., encoding='latin1') You know that most of the file is written with a specific encoding, but it also contains encoding errors. A real world example is an UTF8 file that has been edited with a non utf8 editor and which contains some lines with a different encoding. Pandas has no provision for a special error processing, but Python open function has (assuming Python3), and read_csv accepts a file like object. Typical errors parameter to use here are 'ignore' which just suppresses the offending bytes or (IMHO better) 'backslashreplace' which replaces the offending bytes by their Python’s backslashed escape sequence: file_encoding = 'utf8' # set file_encoding to the file encoding (utf8, latin1, etc.) input_fd = open(input_file_and_path, encoding=file_encoding, errors = 'backslashreplace') pd.read_csv(input_fd, ...)