Python - Iterating through Dataframe and creating new column based on two other columns and string

Question

This should be simple, but for some reason I am not understanding where I am going wrong.

I have a sample dataframe:

df = pd.DataFrame({'name':('Nick', 'Nick', 'Nick', 'David'), 'num':(1, 2, 3, 4)})

I want to create a new column called link where if the value in name is 'Nick', then the link value would be some text + the num column value.

This is the code I am currently using:

df['link'] = np.where(df.name == "Nick","https://" + str(df.num), '')

But instead of the first row being:

0, Nick, 1, "https://1"

It is:

0, Nick, 1, "https://0    1\n1    2\n2    3\n3    4\nName: num, dtype: int64"

Which means it is using the whole num column, rather the row.

Any idea what I am doing wrong? And on a side note, I have to do this for millions of rows, any suggestions of the most efficient way of doing it?

mozway · Accepted Answer · 2022-07-07 11:50:33Z

1

Use df.num.astype(str), not str(df.num):

df['link'] = np.where(df.name=="Nick", "https://" + df.num.astype(str), '')

output:

    name  num       link
0   Nick    1  https://1
1   Nick    2  https://2
2   Nick    3  https://3
3  David    4

Why?

df.num.astype(str) converts each item to a string:

0    1
1    2
2    3
3    4
Name: num, dtype: object

str(df.num) converts to the string representation of the Series object, which gets applied to all rows by broadcasting:

'0    1\n1    2\n2    3\n3    4\nName: num, dtype: int64'

edited Jul 7, 2022 at 11:50

answered Jul 7, 2022 at 11:48

mozway

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1 Comment

Nicholas Over a year ago

THANK YOU!. Very quick too! I am guessing I was turning the column into a string in my version, whereas you are saying use the column where each value is now a string. Super!

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