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I have a dataframe and want to insert a new column for it. However, I have to insert this column multiple times, each time for some rows. E.g.

 source feature 1 feature 2 0 a xxx xxx 1 b xxx xxx 2 c xxx xxx 3 a xxx xxx

I want to insert a feature 3. However, for different sources, I have to do it separately.

First, I got the index of df['source'] ==a , and a list of their values E.g.

 index_for_a = [0,3] values_for_a = [2, 4] source feature 1 feature 2 feature_3 0 a xxx xxx 2 1 b xxx xxx 2 c xxx xxx 3 a xxx xxx 4

Is there any API I can use to insert the value for multiple specified rows? I found one dataframe.insert(). However, it only works for one specific location so I have to iterate on the rows. Is there any more efficient way to do so?

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Use DataFrame.loc for set new values, if no match get missing values:

index_for_a = [0,3] values_for_a = [2, 4] df.loc[index_for_a, 'feature_3'] = values_for_a

One possible idea for multiple values:

index_for_a = [0,3] values_for_a = [2, 4] index_for_b = [1,2] values_for_b = [5, 6] d = dict(zip(index_for_a + index_for_b, values_for_a +values_for_b)) print (d) {0: 2, 3: 4, 1: 5, 2: 6} df['new'] = df.index.map(d) print (df) source feature 1 feature 2 feature_3 new 0 a xxx xxx 2.0 2 1 b xxx xxx NaN 5 2 c xxx xxx NaN 6 3 a xxx xxx 4.0 4
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