Modifying pandas dataframe through function in python

Question

I tried to modify the dataframe through function and return the modified dataframe. Somehow, it is not reflected. In the below code, I pass a dataframe 'ding' to function 'test' and create a new column 'C' and return the modified dataframe. I expected the test_ding df to have 3 columns but could see only two columns. Any help is highly appreciated.

s1 = pd.Series([1,3,5,6,8,10,1,1,1,1,1,1])
s2 = pd.Series([4,5,6,8,10,1,7,1,6,5,4,3])

ding=pd.DataFrame({'A':s1,'B':s2})

def test(ding):
    ding.C=ding.A+ding.B
    return ding

test_ding=test(ding)

Back2Basics · Accepted Answer · 2017-05-11 22:10:11Z

5

ding.C doesn't exist and can't be accessed like an attribute. You need to change the line to

ding['C']=ding.A+ding.B

You can create an column like ding['A'] = blah which then turns into an attribute of the DataFrame. But you can't go the other way around.

answered May 11, 2017 at 22:10

Back2Basics

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Scott Boston · Accepted Answer · 2017-05-11 22:13:16Z

1

Let's try this:

s1 = pd.Series([1,3,5,6,8,10,1,1,1,1,1,1])
s2 = pd.Series([4,5,6,8,10,1,7,1,6,5,4,3])

ding=pd.DataFrame({'A':s1,'B':s2})

def test(ding):
    ding = ding.assign(C=ding.A+ding.B)
    return ding

test_ding=test(ding)

print(test_ding)

Output:

     A   B   C
0    1   4   5
1    3   5   8
2    5   6  11
3    6   8  14
4    8  10  18
5   10   1  11
6    1   7   8
7    1   1   2
8    1   6   7
9    1   5   6
10   1   4   5
11   1   3   4

answered May 11, 2017 at 22:13

Scott Boston

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