I am trying to transform the age columns of a pandas dataframe by applying apply function. how to make below code work or is there a more pythonic way way to do this.
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, ' ')))
i would use cut() function for that:
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
I have found one more way to do this but thanks MaxU your answers works too
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))))
)
I wonder which one is more efficient?
timeit - so you'll see which one is more efficient ;)