First, let me set the stage.
I start with a pandas
dataframe klmn
, that looks like this:
In [15]: klmn
Out[15]:
K L M N
0 0 a -1.374201 35
1 0 b 1.415697 29
2 0 a 0.233841 18
3 0 b 1.550599 30
4 0 a -0.178370 63
5 0 b -1.235956 42
6 0 a 0.088046 2
7 0 b 0.074238 84
8 1 a 0.469924 44
9 1 b 1.231064 68
10 2 a -0.979462 73
11 2 b 0.322454 97
Next I split klmn
into two dataframes, klmn0
and klmn1
, according to the value in the 'K' column:
In [16]: k0 = klmn.groupby(klmn['K'] == 0)
In [17]: klmn0, klmn1 = [klmn.ix[k0.indices[tf]] for tf in (True, False)]
In [18]: klmn0, klmn1
Out[18]:
( K L M N
0 0 a -1.374201 35
1 0 b 1.415697 29
2 0 a 0.233841 18
3 0 b 1.550599 30
4 0 a -0.178370 63
5 0 b -1.235956 42
6 0 a 0.088046 2
7 0 b 0.074238 84,
K L M N
8 1 a 0.469924 44
9 1 b 1.231064 68
10 2 a -0.979462 73
11 2 b 0.322454 97)
Finally, I compute the mean of the M
column in klmn0
, grouped by the value in the L
column:
In [19]: m0 = klmn0.groupby('L')['M'].mean(); m0
Out[19]:
L
a -0.307671
b 0.451144
Name: M
Now, my question is, how can I subtract m0
from the M
column of the klmn1
sub-dataframe, respecting the value in the L
column? (By this I mean that m0['a']
gets subtracted from the M
column of each row in klmn1
that has 'a'
in the L
column, and likewise for m0['b']
.)
One could imagine doing this in a way that replaces the the values in the M
column of klmn1
with the new values (after subtracting the value from m0
). Alternatively, one could imagine doing this in a way that leaves klmn1
unchanged, and instead produces a new dataframe klmn11
with an updated M
column. I'm interested in both approaches.
If you reset the index of your klmn1 dataframe to be that of the column L, then your dataframe will automatically align the indices with any series you subtract from it:
In [1]: klmn1.set_index('L')['M'] - m0
Out[1]:
L
a 0.777595
a -0.671791
b 0.779920
b -0.128690
Name: M
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