Pandas Slice Rows Based On Joint Condition

consider the below dataframe -df one two three four five six seven eight 0 0.1 1.1 2.2 3.3 3.6 4.1 0.0 0.0 1 0.1 2.1 2.3 3.2 3.7 4.3 0.0

Solution 1:

You can do the following in short:

df.eq(3.2).any(axis=1) & ~df.isin([0.1, 1.2]).any(axis=1)

Or here more explicitly:

contains = df.eq(3.2).any(axis=1)
not_contains = ~df.isin([0.1,1.2]).any(axis=1)

print(df[contains & not_contains])
   one    two    three    four    five    six    seven    eight
52.13.20.00.00.00.00.00.062.12.33.24.30.00.00.00.0

Solution 2:

For performance, specially since you mentioned large dataset and if you are looking to exclude just two numbers, here's one approach with array data -

a = df.values
df_out = df.iloc[(a == 3.2).any(1) & (((a!=0.1) & (a!=1.2)).all(1))]

Sample run -

In [43]: a = df.values

In [44]: df.iloc[(a ==3.2).any(1) & (((a!=0.1) & (a!=1.2)).all(1))]
Out[44]: 
   one  two  three  four  five  six  seven  eight
52.13.20.00.0000062.12.33.24.30000

Solution 3:

You could just combine the conditions.

>>>df[(df == 3.2).any(1) & ~df.isin([0.1, 1.2]).any(1)]

   one  two  three  four  five  six   seven  eight
5  2.1  3.2    0.0   0.0   0.0   0.0    0.0    0.0
6  2.1  2.3    3.2   4.3   0.0   0.0    0.0    0.0