检测DataFrame中Subject字段是否含'OCC'报错：'str'对象无'str'属性

错误原因 #

使用iterrows()遍历DataFrame时，row['Subject']返回的是单个Python字符串对象，而.str.contains()是pandas Series（列）的专属方法，不能直接调用在普通字符串上，这就是报错的根源。

解决方案 #

方案1：修改循环内的判断逻辑（用Python原生字符串方法） #

直接用Python原生的in关键字判断字符串是否包含目标子串，同时修正原代码中错误的条件写法：

#messages extracted from pst into data frame
message_df = pd.DataFrame(message_data, columns = ['Subject', 'Body', 'Read Flag'])

#want to check if subject in each of the mail contains OCC word or not
for index, row in message_df.iterrows():
    print("\n For mail # ", index, "Subject is: ", row['Subject'],"\n Body is: ",  row['Body'],"\n Read Flag is: ",  row['Read Flag'], "\n")
    
    # 改用原生字符串判断
    if "OCC" in row['Subject']:
        print("Subject contains OCC: ", row['Subject'])

方案2：用pandas矢量化操作（推荐，效率更高） #

iterrows()逐行遍历效率较低，尤其是数据量大时，推荐直接用pandas的列操作筛选目标行：

#messages extracted from pst into data frame
message_df = pd.DataFrame(message_data, columns = ['Subject', 'Body', 'Read Flag'])

# 筛选Subject包含'OCC'的行，na=False处理空值避免报错
occ_mails = message_df[message_df['Subject'].str.contains("OCC", na=False)]

# 遍历筛选结果打印
for _, row in occ_mails.iterrows():
    print("Subject contains OCC: ", row['Subject'])

# 也可以直接打印筛选后的整个DataFrame
# print(occ_mails)

内容的提问来源于stack exchange，提问作者Nikhil Gohad