How do I create a "not" filter in python for pandas

Question

I have this large dataframe I've imported into pandas and I want to chop it down via a filter. Here is my basic sample code:

import pandas as pd
import numpy as np
from pandas import Series, DataFrame

df = DataFrame({'A':[12345,0,3005,0,0,16455,16454,10694,3005],'B':[0,0,0,1,2,4,3,5,6]})

df2= df[df["A"].map(lambda x: x > 0) & (df["B"] > 0)]

Basically this displays bottom 4 results which is semi-correct. But I need to display everything BUT these results. So essentially, I'm looking for a way to use this filter but in a "not" version if that's possible. So if column A is greater than 0 AND column B is greater than 0 then we want to disqualify these values from the dataframe. Thanks

Answer 1

No need for map function call on Series "A".

Apply De Morgan's Law:

"not (A and B)" is the same as "(not A) or (not B)"

df2 = df[~(df.A > 0) | ~(df.B > 0)]

Answer 2

There is no need for the map implementation. You can just reverse the arguments like ...

df.ix[(df.A<=0)|(df.B<=0),:]

Or use boolean indexing without ix:

df[(df.A<=0)|(df.B<=0)]