根据Jerald Achaibar给出的答案，我们可以检测混合Dytpes警告，并且只在警告发生时使用较慢的python引擎:

import warnings

# Force mixed datatype warning to be a python error so we can catch it and reattempt the 
# load using the slower python engine
warnings.simplefilter('error', pandas.errors.DtypeWarning)
try:
    df = pandas.read_csv(path, sep=sep, encoding=encoding)
except pandas.errors.DtypeWarning:
    df = pandas.read_csv(path, sep=sep, encoding=encoding, engine="python")

它为我工作与low_memory = False同时导入一个数据帧。这就是所有对我有效的改变:

df = pd.read_csv('export4_16.csv',low_memory=False)

在处理一个巨大的csv文件(600万行)时，我也遇到过类似的问题。我有三个问题:

文件包含奇怪字符(使用编码修复) 未指定数据类型(使用dtype属性修复) 使用上面的方法，我仍然面临一个问题，这与无法基于文件名定义的file_format有关(使用try ..除了. .)

    df = pd.read_csv(csv_file,sep=';', encoding = 'ISO-8859-1',
                     names=['permission','owner_name','group_name','size','ctime','mtime','atime','filename','full_filename'],
                     dtype={'permission':str,'owner_name':str,'group_name':str,'size':str,'ctime':object,'mtime':object,'atime':object,'filename':str,'full_filename':str,'first_date':object,'last_date':object})
    
    try:
        df['file_format'] = [Path(f).suffix[1:] for f in df.filename.tolist()]
    except:
        df['file_format'] = ''

根据Jerald Achaibar给出的答案，我们可以检测混合Dytpes警告，并且只在警告发生时使用较慢的python引擎:

import warnings

# Force mixed datatype warning to be a python error so we can catch it and reattempt the 
# load using the slower python engine
warnings.simplefilter('error', pandas.errors.DtypeWarning)
try:
    df = pandas.read_csv(path, sep=sep, encoding=encoding)
except pandas.errors.DtypeWarning:
    df = pandas.read_csv(path, sep=sep, encoding=encoding, engine="python")

这对我很管用!

dashboard_df = pd.read_csv(p_file, sep=';', error_bad_lines=False, index_col=False, dtype='unicode')

这对我很管用!

file = pd.read_csv('example.csv', engine='python')