Pandas: sum DataFrame rows for given columns

Question

I have the following DataFrame:

In [1]:
df = pd.DataFrame({'a': [1, 2, 3],
                   'b': [2, 3, 4],
                   'c': ['dd', 'ee', 'ff'],
                   'd': [5, 9, 1]})

df
Out [1]:
   a  b   c  d
0  1  2  dd  5
1  2  3  ee  9
2  3  4  ff  1

I would like to add a column 'e' which is the sum of columns 'a', 'b' and 'd'.

Going across forums, I thought something like this would work:

df['e'] = df[['a', 'b', 'd']].map(sum)

But it didn't.

I would like to know the appropriate operation with the list of columns ['a', 'b', 'd'] and df as inputs.

Answer 1

You can just sum and set axis=1 to sum the rows, which will ignore non-numeric columns; from pandas 2.0+ you also need to specify numeric_only=True.

In [91]:

df = pd.DataFrame({'a': [1,2,3], 'b': [2,3,4], 'c':['dd','ee','ff'], 'd':[5,9,1]})
df['e'] = df.sum(axis=1, numeric_only=True)
df
Out[91]:
   a  b   c  d   e
0  1  2  dd  5   8
1  2  3  ee  9  14
2  3  4  ff  1   8

If you want to just sum specific columns then you can create a list of the columns and remove the ones you are not interested in:

In [98]:

col_list= list(df)
col_list.remove('d')
col_list
Out[98]:
['a', 'b', 'c']
In [99]:

df['e'] = df[col_list].sum(axis=1)
df
Out[99]:
   a  b   c  d  e
0  1  2  dd  5  3
1  2  3  ee  9  5
2  3  4  ff  1  7

sum docs

Answer 2

If you have just a few columns to sum, you can write:

df['e'] = df['a'] + df['b'] + df['d']

This creates new column e with the values:

   a  b   c  d   e
0  1  2  dd  5   8
1  2  3  ee  9  14
2  3  4  ff  1   8

For longer lists of columns, EdChum's answer is preferred.

Answer 3

Create a list of column names you want to add up.

df['total']=df.loc[:,list_name].sum(axis=1)

If you want the sum for certain rows, specify the rows using ':'

Answer 4

This is a simpler way using iloc to select which columns to sum:

df['f']=df.iloc[:,0:2].sum(axis=1)
df['g']=df.iloc[:,[0,1]].sum(axis=1)
df['h']=df.iloc[:,[0,3]].sum(axis=1)

Produces:

   a  b   c  d   e  f  g   h
0  1  2  dd  5   8  3  3   6
1  2  3  ee  9  14  5  5  11
2  3  4  ff  1   8  7  7   4

I can't find a way to combine a range and specific columns that works e.g. something like:

df['i']=df.iloc[:,[[0:2],3]].sum(axis=1)
df['i']=df.iloc[:,[0:2,3]].sum(axis=1)

Answer 5

You can simply pass your dataframe into the following function:

def sum_frame_by_column(frame, new_col_name, list_of_cols_to_sum):
    frame[new_col_name] = frame[list_of_cols_to_sum].astype(float).sum(axis=1)
    return(frame)

Example:

I have a dataframe (awards_frame) as follows:

...and I want to create a new column that shows the sum of awards for each row:

Usage:

I simply pass my awards_frame into the function, also specifying the name of the new column, and a list of column names that are to be summed:

sum_frame_by_column(awards_frame, 'award_sum', ['award_1','award_2','award_3'])

Result:

Answer 6

You can use the function aggragate or agg:

df[['a','b','d']].agg('sum', axis=1)

The advantage of agg is that you can use multiple aggregation functions:

df[['a','b','d']].agg(['sum', 'prod', 'min', 'max'], axis=1)

Output:

   sum  prod  min  max
0    8    10    1    5
1   14    54    2    9
2    8    12    1    4

Answer 7

Following syntax helped me when I have columns in sequence

awards_frame.values[:,1:4].sum(axis =1)

Answer 8

The shortest and simplest way here is to use

df.eval('e = a + b + d')