使用布尔逻辑合并和过滤一个数据框的多列

Question

目标：将买入/卖出/中性/错误指标输出到单个 df[column]，同时过滤掉“假”值。指标基于以下数据框列，然后用布尔语句制定：

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的当前输出

buy  sell  Neutral Error
False False False ERROR
BUY False False False
False SELL False False
False False NEUTRAL False

df 的期望输出

Indicator
ERROR
BUY
SELL
NEUTRAL

尝试和方法： 第一种方法：合并所有买入/卖出/中性/错误列并尝试删除“假”值。 Dataframe 在出错之前只迭代一次。

df['sma_10_indic']=[df['buy'].astype(str)+df['sell'].astype(str)+df['neutral'].astype(str)+df['error'].astype(str)].drop("False")

我尝试过 if & elif 的子程序，例如： 此方法在第一个索引之前也会出错

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'])

我不确定前方的道路，我已经在这条路上用头撞墙了大约 14 个小时，前方没有清晰的道路。我还尝试创建另一个单独的数据框并通过 concat 合并它们，但由于布尔值而没有运气。我对 python 和 pandas/dataframes 比较陌生，所以请耐心等待。提前谢谢你！

Answer 1

使用numpy.select:

m1 = df['close'] > df['sma_10']
m2 = df['close'] < df['sma_10']
m3 = df['close'] == df['sma_10']

df['Indicator'] = np.select([m1, m2, m3], ['BUY','SELL','NEUTRAL'], 'ERROR')