I have this pandas DataFrame bellow:
Id Guild Test
0 5c5dc770f920209b94c3def3 72f92390/7f2e/4b41/b53b/393470619eca True
1 5c5dc7707d62f8b356457863 596f57d7/c8a9/4b14/aec1/18ef2b9fa940 None
2 5c5dc770974d1a6d38cffa3a 6a7ad94c/0511/4ef9/8b60/e05158cad03c False
3 5c5dc7709809c3452ae07d22 843d9c5f/1f53/4752/a905/0b1de73efab2 None
4 5c5dc7706c606a2118c4350b 9d63dcc5/1063/49b3/9a90/a854e7eb7398 None
When i tried to apply numpy.where:
pdf['Id'] = np.where(bool(pdf['Test']), pdf['Id'], None)
Also tried using numpy.equal:
pdf['Id'] = np.where(np.equal(pdf['Test'], None), None, pdf['Id'])
Throws me the error:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
My goal: Apply None to Id columns where Teste is not a valid boolean.
I've check these similar questions: link One Link Two
Thanks in advance.
The complication here is that your "boolean" column also has None values.
You can instead compare the "Test" column to True.
pdf['Id'] = np.where(pdf['Test'] == True, pdf['Id'], None)
pdf
Id Guild Test
0 5c5dc770f920209b94c3def3 72f92390/7f2e/4b41/b53b/393470619eca True
1 None 596f57d7/c8a9/4b14/aec1/18ef2b9fa940 None
2 None 6a7ad94c/0511/4ef9/8b60/e05158cad03c False
3 None 843d9c5f/1f53/4752/a905/0b1de73efab2 None
4 None 9d63dcc5/1063/49b3/9a90/a854e7eb7398 None
Or, assign using loc.
pdf.loc[pdf['Test'] != True, 'Id'] = None
pdf
Id Guild Test
0 5c5dc770f920209b94c3def3 72f92390/7f2e/4b41/b53b/393470619eca True
1 None 596f57d7/c8a9/4b14/aec1/18ef2b9fa940 None
2 None 6a7ad94c/0511/4ef9/8b60/e05158cad03c False
3 None 843d9c5f/1f53/4752/a905/0b1de73efab2 None
4 None 9d63dcc5/1063/49b3/9a90/a854e7eb7398 None