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I have next DataFrame in Pandas:

data1 = pd.DataFrame(data=[[1, 10, 100], [2,2,200],[3,3,300], [4,40,400]],
                     columns=['A', 'B', 'C'])

Here it is:

    A   B   C
0   1   10  100
1   2   2   200
2   3   3   300
3   4   40  400

What I want to do: find rows, where 'A' == 'B' and replace for this rows column 'C' value.

So what I want to get:

    A   B   C
0   1   10  100
1   2   2   -1
2   3   3   -1
3   4   40  400

What I already tried:

data1[data1['A']==data1['B']]

So I find necessary rows. Now I try to replace values in this rows:

data1[data1['A']==data1['B']]['C'] = -1

But data1 is the same! Looks like this difficult chain indexing goes wrong or all this operation returns copy of dataframe. But I can't save it to new dataframe, because I used = in last command, I just can't write newdf = data1[...] = -1. I found also replace function:

data1.replace(data1[data1['A']==data1['B']], "-1")

But it replace all values in row, when I need only last column:

    A   B   C
0   1   10  100
1   -1  -1  -1
2   -1  -1  -1
3   4   40  400

P.S. I know I can do it by using for loop. But I try to find better (more elegant) solution.

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4 Answers 4

Reset to default
2

use DataFrame.loc!

mask = data1['A'] == data1['B']
data1.loc[mask, 'C'] = -1
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1 Comment

It works! The same solution as stackoverflow.com/a/49558169/4960953
1

You can using pandas mask

data1.C=data1.C.mask(data1.A==data1.B,-1)
data1
Out[371]: 
   A   B    C
0  1  10  100
1  2   2   -1
2  3   3   -1
3  4  40  400
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Comments

1 
data1.loc[data1.A==data1.B,'C']='-1'
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1 Comment

That works perfectly!
1

df['C'] = np.where(df.A == df.B, -1, df.C)

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3 Comments

Are you sure it is correct answer? I tried it and get just last column (but yep with correct values).
You will need to reindex, e.g. df['C'] =
Can you please add an explanation? This by itself only helps the OP so much.

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