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If I have a dataframe that looks like this:

       Vintage   CprTarget
0         2017    9.908900
1         2015    9.172600
2         2017    9.993500
3         2018    8.985600
4         2015   12.190200
...        ...         ...
20707     2020    5.559933
20708     2015   12.866399
20709     2019   17.982506
20710     2016   12.098302
20711     2015   11.390324

And Vintage is type int64 how could I convert that column to instead be years before now? So instead it would look like:

       Age  CprTarget
0        5   9.908900
1        7   9.172600
2        5   9.993500
3        4   8.985600
4        7  12.190200
...    ...        ...
20707    2   5.559933
20708    7  12.866399
20709    3  17.982506
20710    6  12.098302
20711    7  11.390324

I know I can use today = date.today().year to get the year now, but how could I grab the year from the Vintage column to transform it to Age (and change it's name to Age while we're at it).

wjandrea
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Hefe
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2 Answers2

2
df['age'] = 2022 - df['Vintage'] 
df

or

df['age'] = pd.Timestamp.today().year - df['Vintage'] 
df

       Vintage  CprTarget  age
0         2017   9.908900  5
1         2015   9.172600  7
2         2017   9.993500  5
3         2018   8.985600  4
4         2015  12.190200  7
20707     2020   5.559933  2
20708     2015  12.866399  7
20709     2019  17.982506  3
20710     2016  12.098302  6
20711     2015  11.390324  7
Naveed
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0

Let us do

df['age'] = df.Vintage.rsub(pd.Timestamp.today().year)
BENY
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