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, ...)

我正在更新这个旧线程。我找到了一个有效的解决方案，但需要打开每个文件。我在LibreOffice中打开我的csv文件，选择另存为>编辑过滤器设置。在下拉菜单中，我选择UTF8编码。然后我在data = pd.read_csv(r' c:\fullpathtofile\filename.csv'， sep = '，'， encoding="utf-8-sig")中添加了encoding="utf-8-sig"。

希望这能帮助到一些人。

Pandas不会通过更改编码样式自动替换违规字节。在我的例子中，将编码参数从encoding = "utf-8"更改为encoding = "utf-16"解决了这个问题。

在我的例子中，这适用于python 2.7:

data = read_csv(filename, encoding = "ISO-8859-1", dtype={'name_of_colum': unicode}, low_memory=False) 

对于python3，只有:

data = read_csv(filename, encoding = "ISO-8859-1", low_memory=False) 

with open('filename.csv') as f:
   print(f)

执行这段代码后，你会发现'filename.csv'的编码，然后执行如下代码

data=pd.read_csv('filename.csv', encoding="encoding as you found earlier"

好了

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, ...)