Reclassification by column name in pandas

Question

I would like to apply a test to a pandas dataframe, and create flags in a corresponding dataframe based on the test results. I've gotten this far:

import numpy as np
import pandas as pd


matrix = pd.DataFrame({'a': [1, 11, 2, 3, 4], 'b': [5, 6, 22, 8, 9]})
flags = pd.DataFrame(np.zeros(matrix.shape), columns=matrix.columns)
flag_values = pd.Series({"a": 100, "b": 200})

flags[matrix > 10] = flag_values 

but this raises the error

ValueError: Must specify axis=0 or 1

Where can I specify the axis in this situation? Is there a better way to accomplish this?

Edit:

The result I'm looking for in this example for "flags" is

a    b
0    0
100  0
0    200
0    0
0    0

Answer 1

You could define flags = (matrix > 10) * flag_values:

In [35]: (matrix > 10) * flag_values
Out[35]: 
     a    b
0    0    0
1  100    0
2    0  200
3    0    0
4    0    0

This relies on True having numeric value 1 and False having numeric value 0. It also relies on Pandas' nifty automatic alignment of DataFrames (and Series) based on labels before performing arithmetic operations.

Answer 2

mask with mul

flags.mask(matrix > 10,1).mul(flag_values,axis=1)

Out[566]: 
       a      b
0    0.0    0.0
1  100.0    0.0
2    0.0  200.0
3    0.0    0.0
4    0.0    0.0