請問為什麼迴圈跑到第二圈的時候都會報錯Length mismatch: Expected axis has 0 elements, new values have 2 elements
,並且print(data_slected)都會說empty dataframe呢??
已經弄了一個下午還是找不到哪裡有問題..

這些程式碼是要整理PTT_運動內衣_所有資料short.csv檔案，抓取['Nike','Shock_Absorber','addidas','UA','Triumph','Mollifix','Uniqlo','Calvin_Klein','UT','AB']這些字在csv檔出現過幾次並做成圖表

a=['Nike','Shock_Absorber','addidas','UA','Triumph','Mollifix','Uniqlo','Calvin_Klein','UT','AB']
# 匯入原始爬蟲資料
data=pd.read_csv('PTT_運動內衣_所有資料short.csv') #開啟檔案

for i in a:
    # 將重複與空白訊息去除
    data.drop_duplicates()
    data.dropna(inplace=True)
    #資料欄位整合
    data["所有文"]=data["標題"]+data["內文"]
    data['所有文'].replace('Nike','nike')
    #下條件找出符合條件的資料，再資料欄位整合，所有文字變成一個大字串
    data_slected=data[data["所有文"].str.contains(i)]
    theSTR=str(data_slected["所有文"].sum())
    #資料清理，無意義字元去除
    #無意義字元
    removeword = ['span','class','f3','https','imgur','h1','_   blank','href','rel','nofollow','target','cdn','cgi','b4','jpg','hl','b1','f5','f4','goo.gl','f2','email','map','f1','f6','__cf___','data','bbs''html','cf','f0','b2','b3','b5','b6','原文內容','原文連結','作者''標題','時間','看板','<','>','，','。','？','—','閒聊','・','/',
'','=','\"','\n','」','「','！','[',']','：','‧','╦','╔','╗','║','╠','╬','╬',':','╰','╩','╯','╭','╮','│','╪','─','《','》','_','.','、','（','）','　','*','※','~','○','”','“','～','@','＋','\r','▁',')','(','','═','?',',','!','…','&',';','『','』','#','＝','\l']
    # 移除無意義字元列
    for word in removeword:
        theSTR = theSTR.replace(word,'')
    #-------------Jieba自動切詞-------------
    jieba.set_dictionary('dict.txt.big')
    words=jieba.lcut(theSTR, cut_all=False)
    #計算各詞數量
    words_voice=[]
    for j in words:
        words_voice.append(words.count(j))
    words_voice_df=pd.DataFrame(zip(words,words_voice))       
    words_voice_df.columns=["字詞","聲量"]
    
    #問題:如何應用停用詞stopwords.txt, 去除無意義的斷詞字詞
    with open('stopwords.txt','r',encoding='utf-8-sig') as f:
        stops=f.read().split('\n')
    wordslist=[]
    words_voice=[]
    for j in words:
        if j not in stops:
            wordslist.append(j)
            words_voice.append(words.count(j))
    # 各詞數量匯總
    words_voice_df=pd.DataFrame(zip(wordslist,words_voice))       
    words_voice_df.columns=["字詞","聲量"]
    # 將自己常用的詞加入字典
    jieba.load_userdict('user_dict.txt')
    keywords_top=jieba.analyse.extract_tags(theSTR,topK=5, withWeight=True) #基于TF-IDF算法進行關鍵詞抽取
    keywords_top
    keywords_top_DF = pd.DataFrame(keywords_top)
    keywords_top_DF.columns=["字詞","聲量"]