Replacing NaN values in a column from a second column

Question

I would like to replace NaN values in Target with the corresponding Node value. My data is:

 Node  Target   Color
node1   node7   Red
node1   node9   Red
node3   node5   Green
node1   node3   Red
node3   node1   Red
node5   NaN     Yellow

I would need to have:

   Node  Target     Color
    node1   node7   Red
    node1   node9   Red
    node3   node5   Green
    node1   node3   Red
    node3   node1   Red
    node5   node5   Yellow # here the replacement

I think that a possible solution could be using an if statement to check if a node has Target equal to NaN: if yes, then it would be possible to assign itself as target.

df["Target"] = df["Target"].fillna(df["Node"])

user15270287
– user15270287

2021-04-09 20:54:50 +00:00
Commented Apr 9, 2021 at 20:54 — user15270287
– user15270287, Commented Apr 9, 2021 at 20:54

smci · Accepted Answer · 2021-04-09 21:04:26Z

Yes, df.fillna(value, ...) will allow the value (replacement) arg to be a Series (column), not just a constant:

df['Target'] = df['Target'].fillna(df['Node'])

Note this is better than if...else logic because it does one vectorized assignment to the entire dataframe, as the testcase below shows.

Alternative, if df.fillna() hadn't allowed us to do this:

You could also use df.where() on your column of interest, where the df.where(... other) arg is your replacement column, df['Node']:

df['Target'] = df['Target'].where(df['Target'].notna(), df['Node'])

Note also how we use logical indexing, using df['Target'].notna() to get us a mask/ logical vector of rows where Target is/isn't NA.

Better testcase:

import pandas as pd
from io import StringIO

df = """ Node  Target   Color
node1   node7   Red
node1   node9   Red
node2   NaN     Brown
node3   node5   Green
node1   node3   Red
node3   node1   Red
node5   NaN     Yellow"""

df = pd.read_csv(StringIO(df), sep=r'\s+')

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