我有一个这样的数据框：
id year Q
1 2017 1
2 2017 2
3 2018 1
4 2018 2
5 2018 3
并希望创建另一个名为D的列：
id year Q Desc
1 2017 1 '2017 first Quarter'
2 2017 2 '2017 second Quarter'
3 2018 1 '2018 first Quarter'
4 2018 2 '2018 second Quarter'
5 2018 3 '2017 third Quarter'
但是np.where似乎只接受2个参数（如果为true和false）。因此，我们的想法是拥有一个可以处理多种可能性的np.where句子
答案 0 :(得分：2)
大致上，使用np.select
df['Desc'] = df.year.astype(str) + ' ' + np.select([df.Q==x for x in [1,2,3,4]], ['first', 'second', 'third', 'quarter']) + ' quarter'
答案 1 :(得分：2)
IIUC，您只需要将Q列map移至character，然后使用字符串总和
df['Desc']=df.year.astype(str)+' '+ df.Q.map({1:'first',2:'second',3:'third',4:'fourth'})+' Quarter'
df
Out[26]:
id year Q Desc
0 1 2017 1 2017 first Quarter
1 2 2017 2 2017 second Quarter
2 3 2018 1 2018 first Quarter
3 4 2018 2 2018 second Quarter
4 5 2018 3 2018 third Quarter
答案 2 :(得分：0)
使用+和pandas.DataFrame.apply：
d={1:'first',2:'second',3:'third',4:'fourth'}
df['Desc']=(df['year'].astype(str)+' '+df['Q'].astype(str)+' Quarter').apply(lambda x: ' '.join(d[int(i)] if len(i)==1 else i for i in x.split()))
print(df)
输出：
id year Q Desc
0 1 2017 1 2017 first Quarter
1 2 2017 2 2017 second Quarter
2 3 2018 1 2018 first Quarter
3 4 2018 2 2018 second Quarter
4 5 2018 3 2018 third Quarter