机器学习去除停用词问题

Question

分词之后 去除停用词运行结果出问题了 有人知道是什么原因吗？

import pandas as pd  # pandas模块用于读取和处理数据
import jieba  # jieba模块用于对短信内容进行分词
from sklearn.feature_extraction.text import CountVectorizer  # 文本特征抽取
from sklearn.model_selection import train_test_split  # 划分训练集和测试集

def swindle():
    # 读取数据和处理数据
    data = pd.read_table("swindle_text.txt", sep="\t", header=None, nrows=10000, names=["标签", "短信内容"])
    new_data = []
    # 进行分词
    data['分词后数据'] = data["短信内容"].apply(lambda x: ' '.join(jieba.cut(x)))

    # 去除停用词
    # stpwrdpath = "/Users/mustafa-de/PycharmProjects/pythonProject/mechine_swindle/stop_words.txt"
    with open('stop_words.txt', 'rb') as f:
        stopword = f.read().decode('utf-8')  # 停用词提取
        stpwrdlst = stopword.splitlines()  # 将停用词表转换为list

        for word in data:
            if word not in stpwrdlst:
                new_data.append(word)
    print(new_data)

    # 提取特征

    # 分割训练集和测试集

    return None


if __name__ == "__main__":
    swindle()

Answer 1

data是个表啊
for word in data:
word是个行吗？