Adding columns of different index

Question

I know this adds consecutive columns of same index:

df['C'] = df['A'] + df['B']

But how to add columns of different index:

Lets say I have a dataframe like this:

I need to create another column C which is the addition of current index row of column A(i.e. idx) and previous index row of column B(i.e. idx-1), such as:

df
   A   B   C  
0  9  40  (9)
1  1  70  (40 + 1)
2  5  80  (70 + 5)
3  8  30  (80 + 8)
4  7  50  (30 + 7)

final result should look like this:

df
   A   B  C  
0  9  40  9
1  1  70  41
2  5  80  75
3  8  30  88
4  7  50  37

Alex · Accepted Answer · 2016-07-18 19:12:33Z

4

You can use Series.shift.

df['C'] = df['A'] + df['B'].shift(1).fillna(0)

answered Jul 18, 2016 at 19:12

Alex

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jezrael · Accepted Answer · 2016-07-18 19:37:14Z

1

Amother solution with Series.add:

df['C'] = df['A'].add(df['B'].shift(), fill_value=0)
print (df)
   A   B     C
0  9  40   9.0
1  1  70  41.0
2  5  80  75.0
3  8  30  88.0
4  7  50  37.0

Timings:

In [2]: %timeit df['C'] = df['A'].add(df['B'].shift(), fill_value=0)
1000 loops, best of 3: 457 µs per loop

In [3]: %timeit df['C1'] = df['A'] + df['B'].shift(1).fillna(0)
1000 loops, best of 3: 544 µs per loop

If need function, you can add column names (similar use for another solution):

def f(a,b,c):
    df[c] = df[a].add(df[b].shift(), fill_value=0)
    return df

print (f('A','B','C'))
  A   B     C
0  9  40   9.0
1  1  70  41.0
2  5  80  75.0
3  8  30  88.0
4  7  50  37.0

edited Jul 18, 2016 at 19:37

answered Jul 18, 2016 at 19:14

jezrael

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2 Comments

DevEx Over a year ago

Elegant solutions. Thanks. Is there a way to put this in a function, such that there wont be a need to explicitly state the column names?

jezrael Over a year ago

I am not sure if understand you correctly, but I try add solution.

BrenBarn · Accepted Answer · 2016-07-18 19:12:56Z

0

Use shift:

df['C'] = df.A + df.B.shift().fillna(0)

answered Jul 18, 2016 at 19:12

BrenBarn

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