Python 当使用if-else使用pandas操作数据帧时，我遇到值错误？

这是我写的代码，df是dataframe。 我正在使用python3，我对pandas是新手，我尝试过按位运算符以及关键字和或

if((df['Day_Perc_Change']>=-0.5) & (df['Day_Perc_Change']<=0.5)):

    df['Trend']="Slight or No Change"

elif((df['Day_Perc_Change']>=0.5) & (df['Day_Perc_Change']<=1)):

    df['Trend']="Slight Positive"

elif((df['Day_Perc_Change']>=-1) & (df['Day_Perc_Change']<=-0.5)):

    df['Trend']="Slight Negative"

elif((df['Day_Perc_Change']>=1) & (df['Day_Perc_Change']<=3)):

    df['Trend']="Positive"

elif((df['Day_Perc_Change']>=-3) & (df['Day_Perc_Change']<=-1)):

    df['Trend']="Negative"

elif((df['Day_Perc_Change']>=3) & (df['Day_Perc_Change']<=7)):

    df['Trend']='Among top gainers'

**这就是我得到的错误

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
 I have used both and as well as | but it is working.
Can anyone help me out?
​**

BAM，

np.where（）

，它也是矢量化的，性能很高

df['Trend'] = ''
df['Trend'] = np.where((df['Day_Perc_Change']>=-0.5) & (df['Day_Perc_Change']<=0.5), "Slight or No Change", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=0.5) & (df['Day_Perc_Change']<=1), "Slight Positive", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=-1) & (df['Day_Perc_Change']<=-0.5), "Slight Negative", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=1) & (df['Day_Perc_Change']<=3), "Positive", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=-3) & (df['Day_Perc_Change']<=-1), "Negative", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=3) & (df['Day_Perc_Change']<=7), "Among top gainers", df['Trend'])

df['Trend']='
df['Trend']=np.其中（（df['Day'u Perc_'u Change']>=-0.5）和（df['Day'u Perc_Change']=0.5）和（df['Day'u Perc_Change']=-1）和（df['Day'u Perc_Change']=-3）和（df['Day'u Perc_Change']=3）和（df['Day'Day'u Perc_Change'>，其中也是高性能的

df['Trend'] = ''
df['Trend'] = np.where((df['Day_Perc_Change']>=-0.5) & (df['Day_Perc_Change']<=0.5), "Slight or No Change", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=0.5) & (df['Day_Perc_Change']<=1), "Slight Positive", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=-1) & (df['Day_Perc_Change']<=-0.5), "Slight Negative", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=1) & (df['Day_Perc_Change']<=3), "Positive", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=-3) & (df['Day_Perc_Change']<=-1), "Negative", df['Trend'])
df['Trend'] = np.where((df['Day_Perc_Change']>=3) & (df['Day_Perc_Change']<=7), "Among top gainers", df['Trend'])

df['Trend']='
df['Trend']=np.where（（df['Day'u Perc_-Change']>=-0.5）和（df['Day'u Perc_-Change']=0.5）和（df['Day'u Perc_-Change']=-1）和（df['Day'u Perc_-Change']=3）和（df['Day'u Perc-Change'=3）有帮助吗？这回答了你的问题吗？