CODEWITHSUNDEEP
pandas
peter_parker
peter_parker

Reputation: 133

Select rows based on condition in a set of columns

I have a df 

df = pd.DataFrame({'s1':[1,1],'s2':['-',1],'s3':[1,'-'], 's4':[0,1], 's5':[0,1], 's6':['-',1], 's7':[0,'-'], 's8':[0,0]})  

    s1  s2  s3  s4  s5  s6  s7  s8  
0   1   -   1   0   0   -   0   0  
1   1   1   -   1   1   1   -   0

I would like to select rows based on conditions in multiple sets of columns, such that
'1' is observed at least once in s1, s2, s3 or s4 and '1' is not observed in s5, s6, s7 or s8
OR
'1' is observed at least once in s5, s6 or s7 and '1' is not observed in s1, s2, s3, s4, s7 or s8

The result should be only the first row

Upvotes: 1

Views: 66

Answers (2)

Vaishali
Vaishali

Reputation: 38415

Define two sets of conditions and use boolean indexing,

cond1 = df[['s1', 's2', 's3','s4']].eq(1).any(1)
cond2 = df[['s5', 's6', 's7','s8']].ne(1).all(1)

df.loc[(cond1 & cond2)]


    s1  s2  s3  s4  s5  s6  s7  s8
0   1   -   1   0   0   -   0   0

Upvotes: 2

Dillon
Dillon

Reputation: 999

Here goes:

condition_1 = ((df.s1 == 1) | (df.s2 == 1) | (df.s3 == 1) | (df.s4 == 1)) & (df.s5 != 1)  & (df.s6 != 1) & (df.s7 != 1) & (df.s8 != 1)
condition_2 = ((df.s5 == 1) | (df.s6 == 1) | (df.s7 == 1)) & (df.s1 != 1) & (df.s2 != 1) & (df.s3 != 1) & (df.s4 != 1) & (df.s8 != 1)

df[condition_1 | condition_2].head(1)

output:

   s1 s2 s3  s4  s5 s6 s7  s8
0   1  -  1   0   0  -  0   0

Upvotes: 0

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