# Pandas: sum DataFrame rows for given columns

## Question:

I have the following DataFrame:

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

df
Out :
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.

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.

You can just `sum` and set param `axis=1` to sum the rows, this will ignore none numeric columns:

``````In :

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)
df
Out:
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 :

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

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

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)
``````

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 ‘:’

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: Following syntax helped me when I have columns in sequence

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

The shortest and simplest way here is to use

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

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
``````
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