Sum only certain rows in a given column of pandas dataframe

I think need DataFrame.iloc for select rows by positions with get_indexer for positions of columns by names:

#data borrowed from Akshay Nevrekar answer, but changed index values
data = {'x':[1,2,3,4,5], 
        'y':[2,5,7,9,11], 
        'z':[2,6,7,3,4]}
df=pd.DataFrame(data, index=list('abcde'))
print (df)
   x   y  z
a  1   2  2
b  2   5  6
c  3   7  7
d  4   9  3
e  5  11  4

a = df.iloc[:3, df.columns.get_indexer(['x','z'])].sum()

What is same as:

a = df.iloc[:3, [0,2]].sum()

print (a)
x     6
z    15
dtype: int64

Detail:

print (df.iloc[:3, df.columns.get_indexer(['x','z'])])
   x  z
a  1  2
b  2  6
c  3  7

If want only one column use get_loc for position:

b = df.iloc[:3, df.columns.get_loc('x')].sum()

What is same as:

b = df.iloc[:3, 0].sum()

print (b)
6

Detail:

print (df.iloc[:3, df.columns.get_loc('x')])
a    1
b    2
c    3
Name: x, dtype: int64

You need something like this:

import pandas as pd
data = {'x':[1,2,3,4,5], 'y':[2,5,7,9,11], 'z':[2,6,7,3,4]}
df=pd.DataFrame(data)

Use list of columns along with rows:

df.loc[0:310][['x','z']].sum()

output:

x    15
z    22
dtype: int64