Efficient method to add values in a pandas df

Question

I am trying to determine a more efficient to add specific values in a pandas df.

For the df below, I want to add the integers in Value for each X + Y in Area. So, for every X, I want to add that to the following Y.

import pandas as pd

d = ({
    'Area' : ['X','Y','Z','X','Y','Z'],                                     
    'Value' : [10,11,20,21,30,31],                                     
     })

df = pd.DataFrame(data=d)

If there's not many values I can go through manually as per the following:

x = df.iloc[0] + df.iloc[1]

But if the df is quite large, this becomes inefficient.

Intended Output:

21
51

jezrael · Accepted Answer · 2019-03-04 09:36:12Z

1

Filter by boolean indexing to Series, create default index and Series.add:

s1 = df.loc[df['Area'].eq('X'), 'Value'].reset_index(drop=True)
s2 = df.loc[df['Area'].eq('Y'), 'Value'].reset_index(drop=True)

s = s1.add(s2)
print (s)
0    21
1    51
dtype: int64

Advantage of solution is not important ordering of X and Y values.

edited Mar 4, 2019 at 9:36

answered Mar 4, 2019 at 9:30

jezrael

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Comments

anky · Accepted Answer · 2019-03-04 09:24:34Z

1

create a mask with X and Y and groupby on every 2 indexes and sum(), use:

m=df[df.Area.isin(['X','Y'])].reset_index(drop=True)
print(m.groupby(m.index//2)['Value'].sum())

Output

   0    21
   1    51

edited Mar 4, 2019 at 9:24

answered Mar 4, 2019 at 9:21

anky

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