CODEWITHSUNDEEP
pythonpandasdataframe
Pauline
Pauline

Reputation: 3836

Sum of several columns from a pandas dataframe

So say I have the following table:

In [2]: df = pd.DataFrame({'a': [1,2,3], 'b':[2,4,6], 'c':[1,1,1]})

In [3]: df
Out[3]: 
   a  b  c
0  1  2  1
1  2  4  1
2  3  6  1

I can sum a and b that way:

In [4]: sum(df['a']) + sum(df['b'])
Out[4]: 18

However this is not very convenient for larger dataframe, where you have to sum multiple columns together.

Is there a neater way to sum columns (similar to the below)? What if I want to sum the entire DataFrame without specifying the columns?

In [4]: sum(df[['a', 'b']]) #that will not work!
Out[4]: 18
In [4]: sum(df) #that will not work!
Out[4]: 21

Upvotes: 15

Views: 40403

Answers (2)

Fermin Pitol
Fermin Pitol

Reputation: 486

Maybe you are looking something like this:

df["result"] = df.apply(lambda row: row['a' : 'c'].sum(),axis=1)

Upvotes: 1

jezrael
jezrael

Reputation: 862481

I think you can use double sum - first DataFrame.sum create Series of sums and second Series.sum get sum of Series:

print (df[['a','b']].sum())
a     6
b    12
dtype: int64

print (df[['a','b']].sum().sum())
18

You can also use:

print (df[['a','b']].sum(axis=1))
0    3
1    6
2    9
dtype: int64

print (df[['a','b']].sum(axis=1).sum())
18

Thank you pirSquared for another solution - convert df to numpy array by values and then sum:

print (df[['a','b']].values.sum())
18

print (df.sum().sum())
21

Upvotes: 23

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