Filling non-numeric values in Pandas based on another column

Question

I have missing non-numeric data in a pandas dataframe, is there a way of replacing the NaN with the value from a different row when another column matches? E.g:

tdf = pandas.DataFrame({
    "id": [np.nan, 22, 22, 45, 45, 81],
    "item": ["apple", "apple", "apple", "orange", "orange", "banana"]
})

    id  item
0   NaN apple
1   22  apple
2   22  apple
3   45  orange
4   45  orange
5   81  banana

So I would want to replace the id in the first row with 22 because the item is the same as row 1 or 2.

EdChum · Accepted Answer · 2015-07-15 15:44:33Z

3

You can groupby on 'item' and pass param as_index=False and then call bfill to fill the NaN values backwards:

In [424]:

tdf.groupby('item', as_index=False)..bfill()
Out[424]:
   id    item
0  22   apple
1  22   apple
2  22   apple
3  45  orange
4  45  orange
5  81  banana

answered Jul 15, 2015 at 15:44

EdChum

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1 Comment

galinden Over a year ago

Thank you! Exactly what I was looking for but didn't come across this possibility yet.

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