How to fill column values from dataframe column names based on condition?

Question

I have a dataframe which contains these columns - maxlevel, level1id, level2id, level3id.

I need to populate a new column - newcol_value based on the value from maxlevel column.

If maxlevel = 1, populate the newcol_value with level1id

If maxlevel = 2, populate the newcol_value with level2id

If maxlevel = 3, populate the newcol_value with level3id

I tried this.

id_dict = {1:'level1id', 2:'Level2ID', 3:'level3id', 4:'level4id', 5:'level5id'}
df['id'] = df[id_dict[df['maxlevel']]]

But as the df['maxlevel] is giving series, I'm getting the following error.

TypeError: 'Series' objects are mutable, thus they cannot be hashed

How can I fill the newcol_value values based on the value in maxlevel column?

Possible duplicate of [Replace values in a pandas series via dictionary efficiently](https://stackoverflow.com/questions/49259580/replace-values-in-a-pandas-series-via-dictionary-efficiently) — Ben.T, Aug 12 '19 at 18:11

BENY · Accepted Answer · 2019-08-12T18:49:08.547

1

You should using lookup

d={'level1id':1, 'Level2ID':2, 'level3id':3, 'level4id':4, 'level5id':5}


df=df.rename(columns=d)
df['New']=df.lookup(df.index,df.maxlevel)

edited Aug 12 '19 at 18:49

answered Aug 12 '19 at 18:11

BENY

No. What it did instead was just updated with `column_name`. Not with the column value. **Expected:** value in `level1id` for that row. **Actual:** `level1id` – Underoos Aug 12 '19 at 18:38
@SukumarRdjf try replace then – BENY Aug 12 '19 at 18:42
No. it's still giving the same output. – Underoos Aug 12 '19 at 18:44
@SukumarRdjf ah using the lookup , sorry overlook the question – BENY Aug 12 '19 at 18:49

1 Answers1