Iterate through two columns and match values from different rows in pandas

Question

My pandas DataFrame looks like this:

            ID                      NAME  PARENT_ID
0            1                    Fruits          0
1            2                   Bananas          1
2            3                    Apples          1
3            4                   Peaches          1
4            5                     Pears          1

I would like to iterate through rows and map PARENT_ID to actual NAME values, creating a new column called PARENT_NAME, like this:

            ID                      NAME  PARENT_ID   PARENT
0            1                    Fruits          0      NaN
1            2                   Bananas          1   Fruits
2            3                    Apples          1   Fruits
3            4                   Peaches          1   Fruits
4            5                     Pears          1   Fruits

What would be the best way to achieve this?

`df['PARENT'] = df['PARENT_ID'].map(df.set_index('ID')['NAME'])` — user3483203, Sep 04 '19 at 18:08
@user3483203 Thank you so much! Was racking my brain over this for a day — Wolfgang, Sep 04 '19 at 18:28
@user3483203: nice short way. Why don't you create an answer for that? — jottbe, Sep 04 '19 at 18:31
This needs to be closed as a dupe of a question on [pandas] map column ; ythere are [1216 such questions](https://stackoverflow.com/search?q=%5Bpandas%5D+map+column+is%3Aquestion) — smci, Sep 05 '19 at 01:13

score 1 · Accepted Answer · answered Sep 05 '19 at 06:41

1

As per @user3483203 's answer:

df['PARENT'] = df['PARENT_ID'].map(df.set_index('ID')['NAME'])

answered Sep 05 '19 at 06:41

Wolfgang

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Iterate through two columns and match values from different rows in pandas

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