I have a multi-indexed pandas dataframe that looks like this (snippet):
Smad3_pS423/425_customer 0 1 0.664263
2 0.209911
3 0.099809
5 1 0.059652
2 0.190174
3 0.138850
a-Tubulin 0 1 0.072436
2 0.068282
3 0.087989
5 1 0.083960
2 0.076102
3 0.068119
The output of df.index is (with the labels
bit shortened for viewing purposes):
MultiIndex(levels=[[u'Customer_Col1A2', u'Smad2_pS465/467 customer', u'Smad3_pS423/425_customer', u'Smad4_customer', u'Smad7_customer', u'a-Tubulin'], [u'0', u'10', u'120', u'180', u'20', u'240', u'30', u'300', u'45', u'5', u'60', u'90'], [u'1', u'2', u'3']],
labels=[[2, 2, 2, 2, 2, 2, 2, ... more_labels...]],
names=[u'Antibody', u'Time', u'Repeats'])
My question is, what is the best way to divide the a-tubulin
data entry by the Smad3_pS423/425_customer
entry?
One cumbersome method is:
ab=[]
for i in self.data.index.get_level_values('Antibody'):
ab.append(i)
antibodies= list(set(ab))
for i in antibodies:
print self.data.loc[i]/self.HK
But this doesn't seem like the pandas
way of doing this. Does anybody know of an easier way to do this? (I suspect pandas
might have built in a one liner to do this). Thanks
How about just:
df.ix['a-Tubulin'] / df.ix['Smad3_pS423/425_customer']
3
1 2
0 1 0.109047
2 0.325290
3 0.881574
5 1 1.407497
2 0.400170
3 0.490594
Here's the df dataframe I used, that you can load with df = pd.read_clipboard(sep=',', index_col=[0,1,2])
0,1,2,3
Smad3_pS423/425_customer,0,1,0.664263
Smad3_pS423/425_customer,0,2,0.20991100000000001
Smad3_pS423/425_customer,0,3,0.09980900000000001
Smad3_pS423/425_customer,5,1,0.059652
Smad3_pS423/425_customer,5,2,0.190174
Smad3_pS423/425_customer,5,3,0.13885
a-Tubulin,0,1,0.072436
a-Tubulin,0,2,0.06828200000000001
a-Tubulin,0,3,0.087989
a-Tubulin,5,1,0.08396
a-Tubulin,5,2,0.076102
a-Tubulin,5,3,0.068119
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