Pandas - drop all rows with 0 in at least two columns

Question

I have some DataFrame:

df = pd.DataFrame({'name': ['apple1', 'apple2', 'apple3', 'apple4', 'orange1', 'orange2', 'orange3', 'orange4'], 
                   'A': [0, 0, 0, 0, 0, 0 ,0, 0], 
                  'B': [0.10, -0.15, 0.25, -0.55, 0.50, -0.51, 0.70, 0], 
                  'C': [0, 0, 0.25, -0.55, 0.50, -0.51, 0.70, 0.90],
                  'D': [0.10, -0.15, 0.25, 0, 0.50, -0.51, 0.70, 0.90]})
df
    name    A   B   C   D
0   apple1  0   0.10    0.00    0.10
1   apple2  0   -0.15   0.00    -0.15
2   apple3  0   0.25    0.25    0.25
3   apple4  0   -0.55   -0.55   0.00
4   orange1 0   0.50    0.50    0.50
5   orange2 0   -0.51   -0.51   -0.51
6   orange3 0   0.70    0.70    0.70
7   orange4 0   0.00    0.90    0.90

I'd like to drop all rows that have two or more zeros in columns A,B,C,D.

This DataFrame has other columns that have zeros; I only want to check for zeros in columns A,B,C,D.

Cactus Philosopher · Accepted Answer · 2020-01-14 03:51:23Z

4

You can use .eq to check if dataframe is equal to 0 and then take sum on axis=1 and return a boolean series by checking if the sum is greater than or equal to 2 (ge):

df[~df[['A','B','C','D']].eq(0).sum(1).ge(2)]

    name    A   B   C   D
2   apple3  0   0.25    0.25    0.25
4   orange1 0   0.50    0.50    0.50
5   orange2 0   -0.51   -0.51   -0.51
6   orange3 0   0.70    0.70    0.70

edited Jan 14, 2020 at 3:51

Cactus Philosopher

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answered Jan 14, 2020 at 3:40

anky

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2 Comments

Cactus Philosopher Over a year ago

This works. Only comment would be to remove these rows: df[~df[['A','B','C','D']].eq(0).sum(1).ge(2)]

anky Over a year ago

@BuffaloCollector ahh okay, yes :) Thank you I missed the part where they had to be dropped

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