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, ...)

在我的案例中，我无法使用之前提供的任何方法来克服这个问题。将编码器类型更改为utf-8、utf-16、iso-8859-1或任何其他类型都不工作。

但是不用pd。read_csv(文件名，分隔符=';')，我用的是;

pd。Read_csv (open(filename， 'r')， delimiter=';')

一切似乎都很顺利。

这个答案似乎是CSV编码问题的万能答案。如果你的头文件出现了奇怪的编码问题，就像这样:

>>> f = open(filename,"r")
>>> reader = DictReader(f)
>>> next(reader)
OrderedDict([('\ufeffid', '1'), ... ])

然后在CSV文件的开头有一个字节顺序标记(BOM)字符。这个答案解决了这个问题:

Python读取csv - BOM嵌入到第一个键

解决方案是用encoding="utf-8-sig"加载CSV:

>>> f = open(filename,"r", encoding="utf-8-sig")
>>> reader = DictReader(f)
>>> next(reader)
OrderedDict([('id', '1'), ... ])

希望这能帮助到一些人。

尝试改变编码。 在我的例子中，encoding = "utf-16"起作用了。

df = pd.read_csv（“file.csv”，encoding='utf-16'）

你总是可以尝试先检测文件的编码，使用chardet或cchardet或charset-normalizer:

from pathlib import Path
import chardet

filename = "file_name.csv"
detected = chardet.detect(Path(filename).read_bytes())
# detected is something like {'encoding': 'utf-8', 'confidence': 0.99, 'language': ''}

encoding = detected.get("encoding")
assert encoding, "Unable to detect encoding, is it a binary file?"

df = pd.read_csv(filename, encoding=encoding)

最简单的解决方案:

import pandas as pd
df = pd.read_csv('file_name.csv', engine='python')

替代解决方案:

崇高的文本:

在Sublime文本编辑器或VS Code中打开csv文件。 以utf-8格式保存。 在sublime中，单击File -> Save with encoding -> UTF-8

VS代码:

在VSCode的底部栏中，您将看到标签UTF-8。点击它。弹出窗口会打开。单击“保存编码”。现在可以为该文件选择新的编码。

然后，您可以像往常一样读取您的文件:

import pandas as pd
data = pd.read_csv('file_name.csv', encoding='utf-8')

其他不同的编码类型有:

encoding = "cp1252"
encoding = "ISO-8859-1"