CODEWITHSUNDEEP
pandas
Patrik
Patrik

Reputation: 75

Unable to get Value from based on conditions in pandas

I have a dataset in this form:

col_1     col_2    col_3    col_4     col_5

0          0        0        0         Nan
0          1        Nan      1         1
1          0        1        0         Nan
0          0        0        0         0

Now, I want to create a new column such that for any particular row, if any of the column values is 1, then the output should be 1.. For example, in above dataset, the new column will have second and third row as 1...

SO, I have tried this approach:

if ((df['col_1]==1) | (df['col_2']==1) | (df['col_3']==1) | (df['col_4']==1) | (df['col_5']==1)):
    df['new_column']=1
else:
    df['new_column']=0

This code was giving me error.

So, I tried a different approach.

lists = ['col_1','col_2','col_3','col_4','col_5']
for i in lists:
   if(df[i]==1):
       df['new_column]==1
    else:
        df['new_column']==0

This code is again giving me wrong values...

Can someone please help me in solving it as I am beginner in pandas and stuck in this problem.

Upvotes: 0

Views: 27

Answers (2)

G.G
G.G

Reputation: 765

df1.assign(col11=df1.apply(lambda ss:(ss==1).any().astype(int),axis=1))

  col_1  col_2 col_3  col_4 col_5  col11
0      0      0     0      0   Nan      0
1      0      1   Nan      1     1      1
2      1      0     1      0   Nan      1
3      0      0     0      0     0      0

Upvotes: 0

mozway
mozway

Reputation: 260300

Use comparison to 1 (with eq), aggregate with any per row (axis=1), then perform boolean indexing:

df.loc[df.eq(1).any(axis=1), 'new_col'] = 1

output:

   col_1  col_2 col_3  col_4 col_5  new_col
0      0      0     0      0   Nan      NaN
1      0      1   Nan      1     1      1.0
2      1      0     1      0   Nan      1.0
3      0      0     0      0     0      NaN

If you prefer a 0/1 output:

df['new'] = df.eq(1).any(1).astype(int)

output:

   col_1  col_2 col_3  col_4 col_5  new
0      0      0     0      0   Nan    0
1      0      1   Nan      1     1    1
2      1      0     1      0   Nan    1
3      0      0     0      0     0    0

Upvotes: 2

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