Pandas Dataframe中应用python lambda func的方法

Question

我试图通过应用函数来转换熊猫的年龄列。如何使下面的代码工作，或者有更多的仿生方式来做到这一点。

cps=(cps.assign(Age_grp_T=cps['age'].apply(lambda x:{x>=71:'Tradionalists',
                                                  71>x>=52:'Baby Boomers',
                                                  52>x>=46:'Generation X',
                                                  46>x>=16:'Millennials'}.get(x, ' ')))

Answer 1

为此，我将使用切()函数：

In [663]: labels=[' ','Millennials','Generation X','Baby Boomers','Tradionalists']

In [664]: a['category']  = pd.cut(a['age'], bins=[1, 16,46,52,71, 200],labels=labels)

In [665]: a
Out[665]:
    age      category
0    29   Millennials
1    65  Baby Boomers
2    68  Baby Boomers
3    18   Millennials
4    29   Millennials
5    58  Baby Boomers
6    15
7    67  Baby Boomers
8    21   Millennials
9    17   Millennials
10   19   Millennials
11   39   Millennials
12   64  Baby Boomers
13   70  Baby Boomers
14   33   Millennials
15   27   Millennials
16   54  Baby Boomers
17   60  Baby Boomers
18   23   Millennials
19   65  Baby Boomers
20   63  Baby Boomers
21   36   Millennials
22   53  Baby Boomers
23   29   Millennials
24   66  Baby Boomers

Answer 2

我又找到了一种方法，但是谢谢MaxU，你的答案也很有效

cps=(cps.assign(Age_grp_T=np.where(cps['age']>=71,"Tradionalists",
                      np.where(np.logical_and(71>cps['age'],cps['age']>=52),"Baby Boomers",
                      np.where(np.logical_and(52>cps['age'],cps['age']>=46),"Generation X",
                      np.where(np.logical_and(46>cps['age'],cps['age']>=16),"Millennials",-99))))
           )

我想知道哪一个更有效率？