Compare two columns using pandas 2

Question

I'm comparing two columns in a dataframe (A & B). I have a method that works (C5). It came from this question: Compare two columns using pandas

I wondered why I couldn't get the other methods (C1 - C4) to give the correct answer:

df = pd.DataFrame({'A': [1,1,1,1,1,2,2,2,2,2],
                   'B': [1,1,1,1,1,1,0,0,0,0]})

#df['C1'] = 1 [df['A'] == df['B']]

df['C2'] = df['A'].equals(df['B'])

df['C3'] = np.where((df['A'] == df['B']),0,1)

def fun(row):
    if ['A'] == ['B']:
        return 1
    else:
        return 0
df['C4'] = df.apply(fun, axis=1)

df['C5'] = df.apply(lambda x : 1 if x['A'] == x['B'] else 0, axis=1)

score 3 · Answer 1 · answered Feb 01 '19 at 09:45

3

IIUC you need this:

def fun(row):
if row['A'] == row['B']:
    return 1
else:
    return 0

answered Feb 01 '19 at 09:45

anky

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score 3 · Accepted Answer · answered Feb 01 '19 at 09:45

Use:

df = pd.DataFrame({'A': [1,1,1,1,1,2,2,2,2,2],
                   'B': [1,1,1,1,1,1,0,0,0,0]})

So for C1 and C2 need compare columns by == or eq for boolean mask and then convert it to integers - True, False to 1,0:

df['C1'] = (df['A'] == df['B']).astype(int)
df['C2'] = df['A'].eq(df['B']).astype(int)

Here is necessary change order 1,0 - for match condition need 1:

df['C3'] = np.where((df['A'] == df['B']),1,0)

In function is not selected values of Series, missing row:

def fun(row):
    if row['A'] == row['B']:
        return 1
    else:
        return 0
df['C4'] = df.apply(fun, axis=1)

Solution is correct:

df['C5'] = df.apply(lambda x : 1 if x['A'] == x['B'] else 0, axis=1)
print (df)
   A  B  C1  C2  C3  C4  C5
0  1  1   1   1   1   1   1
1  1  1   1   1   1   1   1
2  1  1   1   1   1   1   1
3  1  1   1   1   1   1   1
4  1  1   1   1   1   1   1
5  2  1   0   0   0   0   0
6  2  0   0   0   0   0   0
7  2  0   0   0   0   0   0
8  2  0   0   0   0   0   0
9  2  0   0   0   0   0   0

Compare two columns using pandas 2

2 Answers2