How to create n new Column based on a categorical variable which holds values of another column in that

Question

So This is the dataframe.

the attribute 'age' has 6 discrete values, therefore I want to add 6more columns which holds values from attribute 'suicides_no". and removing 'age' attribute. So the final DF will look like:

Albania 1987  female    0    14    4    6    0     1
Albania 1987   male     0    21    9    16   1     1

Note: Taking suicides_no.values and reshaping it won't help as the total entries are not divisible by 6

Can you explain how would you like to create these new columns? — CezarySzulc, Sep 27 '20 at 10:14
such that the dataframe will be reduced by 1/6 of its total size(rows) — Nastik, Sep 27 '20 at 10:16
Does this answer your question? [How to pivot a dataframe in Pandas?](https://stackoverflow.com/questions/28337117/how-to-pivot-a-dataframe-in-pandas) — Soumendra Mishra, Sep 27 '20 at 12:00

Mayank Porwal · Accepted Answer · 2020-09-27T10:35:45.470

1

You can try using pivot:

suicide_df.pivot(index=['country', 'year', 'sex'], columns='age', values='suicides_no').reset_index()

edited Sep 27 '20 at 10:35

answered Sep 27 '20 at 10:15

Mayank Porwal

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Shukriya. reset_index after that gave the desired results – Nastik Sep 27 '20 at 10:22
Your'e welcome. Updated my answer to include `reset_index()`. – Mayank Porwal Sep 27 '20 at 10:23

How to create n new Column based on a categorical variable which holds values of another column in that

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