[FIXED] Python dataframe drop negative values in multiple columns

Issue

I want drop negative values in some columns of the dataframe. My code:

ad = pd.DataFrame({'A':[-1,2,3,4],'B':[5,-6,7,8],'C':[1,-2,0,1]})

    A   B   C
0   -1  5   1
1   2   -6  -2
2   3   7   0
3   4   8   -1

I want to drop negative value rows in A and B columns

Expected result:

ad = 
    A   B   C
1   3   7   0
2   4   8   -1

Present solution:

ad = ad[ad[['A','B']]>0]
     A       B       C
0   NaN     5.0     NaN
1   2.0     NaN     NaN
2   3.0     7.0     NaN
3   4.0     8.0     NaN

ad.dropna(how='any',inplace=True)
ad = []

Update: I tried the below-accepted answer. Also, I figured out a numpy-based solution.

import pandas as pd
import numpy as np
ad = pd.DataFrame({'A':[-1,2,3,4],'B':[5,-6,7,8],'C':[1,-2,0,1]})
print(ad[np.logical_and.reduce(ad[['A','B']]>0,axis=1)])
%timeit ad[np.logical_and.reduce(ad[['A','B']]>0,axis=1)]
   A  B  C
2  3  7  0
3  4  8  1
1000 loops, best of 5: 795 µs per loop

print(ad[(ad[['A','B']] > 0).all(1)])
%timeit ad[(ad[['A','B']] > 0).all(1)]

   A  B  C
2  3  7  0
3  4  8  1
1000 loops, best of 5: 979 µs per loop

Solution

With all you can check whether all elements in a row or column are true. You can use this in a filter on a subset of columns:

import pandas as pd
ad = pd.DataFrame({'A':[-1,2,3,4],'B':[5,-6,7,8],'C':[1,-2,0,-1]})
ad[(ad[['A','B']] > 0).all(1)]

Output:

	A	B	C
2	3	7	0
3	4	8	-1

Answered By - RJ Adriaansen