Adding new column to dataframe with value based on existing columns

Question

How would I go through a dataframe and add a new column containing values based on whether the existing columns have the same values for each row?

For example in the following Dataframe I want to add a new column that contains 1 in the rows where Col1 and Col2 contain 1s and 0 if they do not all contain 1.

The output that I would want is

Col1 Col2 Col3
1    1    1
1    1    1
1    0    0 
0    0    0 
0    1    0
1    1    1
1    1    1

Ideally this would be scalable for more columns in the future (new column would only contain 1 if all columns contain 1)

ansev · Accepted Answer · 2020-10-29 12:29:34Z

1

if there are only 0 and 1 you try with Series.mul

df['Col3'] = df['Col1'].mul(df['Col2'])

edited Oct 29, 2020 at 12:29

answered Oct 29, 2020 at 12:24

ansev

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3 Comments

ansev Over a year ago

check now:) @ jezrael

jezrael Over a year ago

Super, now working very nice for 2 columns fileld by 1 or 0

Grayrigel Over a year ago

@ansev Creative solution! +1

jezrael · Accepted Answer · 2020-10-29 12:30:20Z

1

If need check if all columns are 1 use DataFrame.all with casting to integers, working if data are only 1 and 0:

df['col3'] = df.all(axis=1).astype(int)

If need test only 1, working for any data use DataFrame.eq for ==:

df['col3'] = df.eq(1).all(axis=1).astype(int)

If want select columns for check add subset:

cols = ['Col1', 'Col2']

df['col3'] = df[cols].all(axis=1).astype(int)

Or:

df['col3'] = df[cols].eq(1).all(axis=1).astype(int)

edited Oct 29, 2020 at 12:30

answered Oct 29, 2020 at 12:23

jezrael

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