python - Multiplying multiple columns in a DataFrame -
i'm trying multiply n columns in dataframe n columns in same dataframe, , divide results single column. i'm having trouble first part, see example below.
import pandas pd numpy import random foo = pd.dataframe({'a':random.rand(10), 'b':random.rand(10), 'c':random.rand(10), 'n':random.randint(1,100,10), 'x':random.rand(10), 'y':random.rand(10), 'z':random.rand(10), }) foo[['a','b','c']].multiply(foo[['x','y','z']], axis=0).divide(foo['n'], axis=0) what i'm trying @ column-wise multiplication (i.e. a*x, b*y, c*z) result not n column matrix 2n one, columns i'm trying multiply added dataframe, , entries have nan values, so:
b c x y z 0 nan nan nan nan nan nan 1 nan nan nan nan nan nan 2 nan nan nan nan nan nan 3 nan nan nan nan nan nan 4 nan nan nan nan nan nan 5 nan nan nan nan nan nan 6 nan nan nan nan nan nan 7 nan nan nan nan nan nan 8 nan nan nan nan nan nan 9 nan nan nan nan nan nan what's going on here, , how do column-wise multiplication?
this work using values columns x, y, z , n, perhaps see issue is:
>>> (foo[['a','b','c']] .multiply(foo[['x','y','z']].values) .divide(foo['n'].values, axis=0)) b c 0 0.000452 0.004049 0.010364 1 0.004716 0.001566 0.012881 2 0.001488 0.000296 0.004415 3 0.000269 0.001168 0.000327 4 0.001386 0.008267 0.012048 5 0.000084 0.009588 0.003189 6 0.000099 0.001063 0.006493 7 0.009958 0.035766 0.012618 8 0.001252 0.000860 0.000420 9 0.006422 0.005013 0.004108 the result indexed on columns a, b, c. unclear resulting columns should be, why getting nans.
appending function above .values give result desire, replace index , columns.
>>> (foo[['a','b','c']] .multiply(foo[['x','y','z']].values) .divide(foo['n'].values, axis=0)).values array([[ 4.51754797e-04, 4.04911292e-03, 1.03638836e-02], [ 4.71588457e-03, 1.56556402e-03, 1.28805803e-02], [ 1.48820116e-03, 2.95700572e-04, 4.41516179e-03], [ 2.68791866e-04, 1.16836123e-03, 3.27217820e-04], [ 1.38648301e-03, 8.26692582e-03, 1.20482313e-02], [ 8.38762247e-05, 9.58768066e-03, 3.18903965e-03], [ 9.94132918e-05, 1.06267623e-03, 6.49315435e-03], [ 9.95764539e-03, 3.57657737e-02, 1.26179014e-02], [ 1.25210929e-03, 8.59735215e-04, 4.20124326e-04], [ 6.42175897e-03, 5.01250179e-03, 4.10783492e-03]])
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