pandas: get value based on column of indexes

Question

I have a pd Dataframe like this:

df = pd.DataFrame({'val':[0.1,0.2,0.3,None,None],'parent':[None,None,None,0,2]})

   parent  val
0     NaN  0.1
1     NaN  0.2
2     NaN  0.3
3     0.0  NaN
4     2.0  NaN

where parent represents an index within the pandas df. I want to create a new column that has either the value, or the value of the parent.

that would look like this:

   parent  val  val_full
0     NaN  0.1       0.1
1     NaN  0.2       0.2
2     NaN  0.3       0.3
3     0.0  NaN       0.1
4     2.0  NaN       0.3

This is a fairly large dataframe (10k+ rows), so something efficient would be preferable. How can I do this without using something like .iterrows()?

Answer 1

In your case do

df['new'] = df.val
df.loc[df.new.isna(),'new'] = df.loc[df.parent.dropna().values,'val'].values
df
Out[289]: 
   val  parent  new
0  0.1     NaN  0.1
1  0.2     NaN  0.2
2  0.3     NaN  0.3
3  NaN     0.0  0.1
4  NaN     2.0  0.3

Or try fillna with replace

df['new'] = df.val.fillna(df.parent.replace(df.val))
Out[290]: 
0    0.1
1    0.2
2    0.3
3    0.1
4    0.3
Name: val, dtype: float64