目標(biāo)：將買入/賣出/中性/錯(cuò)誤指標(biāo)輸出到單個(gè) df[column]，同時(shí)過濾掉“假”值。指標(biāo)基于以下數(shù)據(jù)框列，然后用布爾語句制定：df['sma_10'] = pd.DataFrame(ta.SMA(df['close'], timeperiod=10), dtype=np.float, columns=['close'])      df['buy'] = pd.DataFrame(df['close'] > df['sma_10'], columns=['buy'])   df['buy'] = df['buy'].replace({True: 'BUY'})        df['sell'] = pd.DataFrame(df['close'] < df['sma_10'], columns=['sell'])     df['sell'] = df['sell'].replace({True: 'SELL'})         df['neutral'] = pd.DataFrame(df['close'] == df['sma_10'], columns=['neutral'])       df['neutral'] = df['neutral'].replace({True: 'NEUTRAL'})        df['error'] = pd.DataFrame((df['buy'] == False) & (df['sell'] == False) & (df['neutral'] == False), columns=['Error'])      df['error'] = df['error'].replace({True: 'ERROR'})df的當(dāng)前輸出buy  sell  Neutral ErrorFalse False False ERRORBUY False False FalseFalse SELL False FalseFalse False NEUTRAL Falsedf 的期望輸出IndicatorERRORBUYSELLNEUTRAL嘗試和方法：第一種方法：合并所有買入/賣出/中性/錯(cuò)誤列并嘗試刪除“假”值。Dataframe 在出錯(cuò)之前只迭代一次。df['sma_10_indic']=[df['buy'].astype(str)+df['sell'].astype(str)+df['neutral'].astype(str)+df['error'].astype(str)].drop("False")我嘗試了 if & elif 的子程序，例如：此方法也在第一個(gè)索引之前出錯(cuò)df['buy'] = pd.DataFrame(df['close'] > df['sma_10'])df['sell'] = pd.DataFrame(df['close'] < df['sma_10'])df['neutral'] = pd.DataFrame(df['close'] == df['sma_10'])error = ((buy == False) and (sell == False) and (neutral == False))if (df['buy'] == "True"):   df['sma_10_indic'] = pd.DataFrame("BUY",columns=['indicator'])elif (df['sell'] == "True"):   df['sma_10_indic'] = pd.DataFrame("SELL",columns=['indicator'])elif (df['neutral'] == "True"):   df['sma_10_indic'] = pd.DataFrame("NEUTRAL",columns=['indicator'])elif (error == True):   df['sma_10_indic'] = pd.DataFrame("ERROR",columns=['indicator'])我不確定前方的道路，我已經(jīng)在這條路上撞墻了大約 14 個(gè)小時(shí)，沒有明確的道路。我還嘗試創(chuàng)建另一個(gè)單獨(dú)的數(shù)據(jù)框并通過 concat 合并它們，但由于布爾值而沒有運(yùn)氣。我對 python 和 pandas/dataframes 比較陌生，所以請耐心等待。先感謝您！