我认为更好的方法是使用带有参数quoting=csv.QUOTE_NONE和error_bad_lines=False的函数read_csv。链接

import pandas as pd
import csv

test = pd.read_csv("output/Emails.csv", quoting=csv.QUOTE_NONE, error_bad_lines=False)

print (test.shape)
#(381422, 22)

但是一些数据（有问题的）将被跳过。
如果您想跳过电子邮件正文数据，您可以用途：

import pandas as pd
import csv

test = pd.read_csv(
    "output/Emails.csv",
    quoting=csv.QUOTE_NONE,
    sep=',',
    error_bad_lines=False,
    header=None,
    names=[
        "Id", "DocNumber", "MetadataSubject", "MetadataTo", "MetadataFrom",
        "SenderPersonId", "MetadataDateSent", "MetadataDateReleased",
        "MetadataPdfLink", "MetadataCaseNumber", "MetadataDocumentClass",
        "ExtractedSubject", "ExtractedTo", "ExtractedFrom", "ExtractedCc",
        "ExtractedDateSent", "ExtractedCaseNumber", "ExtractedDocNumber",
        "ExtractedDateReleased", "ExtractedReleaseInPartOrFull",
        "ExtractedBodyText", "RawText"])

print (test.shape)

#delete row with NaN in column MetadataFrom
test = test.dropna(subset=['MetadataFrom'])
#delete headers in data
test = test[test.MetadataFrom != 'MetadataFrom']