Setting values in one dataframe from the boolean values in another

Question

I have a MWE that can be reproduced with the following code:

import pandas as pd
a = pd.DataFrame([[1,2],[3,4]], columns=['A', 'B'])
b = pd.DataFrame([[True,False],[False,True]], columns=['A', 'B'])

Which creates the following dataframes:

In [8]: a
Out[8]: 
   A  B
0  1  2
1  3  4

In [9]: b
Out[9]: 
       A      B
0   True  False
1  False   True

My question is, how can I change the values for dataframe A based on the boolean values in dataframe B?

Say for example if I wanted to make NAN values in dataframe A where there's an instance of False in dataframe B?

jezrael · Accepted Answer · 2016-10-04 13:41:37Z

1

If need replace False to NaN:

print (a[b])
     A    B
0  1.0  NaN
1  NaN  4.0

or:

print (a.where(b))
     A    B
0  1.0  NaN
1  NaN  4.0

and if need replace True to NaN:

print (a[~b])
     A    B
0  NaN  2.0
1  3.0  NaN

or:

print (a.mask(b))
     A    B
0  NaN  2.0
1  3.0  NaN

Also you can use where or mask with some scalar value:

print (a.where(b, 7))
   A  B
0  1  7
1  7  4

print (a.mask(b, 7))
   A  B
0  7  2
1  3  7

print (a.where(b, 'TEST'))
      A     B
0     1  TEST
1  TEST     4

edited Oct 4, 2016 at 13:41

answered Oct 4, 2016 at 13:32

jezrael

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6 Comments

BML91 Over a year ago

That does seem to work, thanks. Just to expand upon my question, is it possible to make all False values in dataframe B into the string 'TEST' in dataframe A? I'm interested to see how flexible I can be with this kind of mapping.

BML91 Over a year ago

Thanks, interesting this doesn't seem to work with multi-index dataframes.

jezrael Over a year ago

Hmmm, Dataframes with Multiindex have same indexes and same columns?

BML91 Over a year ago

Yes, or rather more specifically in my example, I have 2 dataframes, both have the same index (time series) and the same set of multi-level columns (7 levels). The above methods don't work with those.

jezrael Over a year ago

Then try add values print (a.mask(b.values))

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