Pandas fillna values with numpy array

Question

I have a dataframe as such, it has other columns but this one is important:

df = pd.DataFrame({'A': ['foo','bar','baz','foo','bar','bar','baz','foo']})

I'm trying to create another column then add array values to the new column that match the A column.

New Column: df['B'] = np.nan

Array: arr = np.array([5,3,9])

Attempts

I'm wanting to assign the array to all foo in column A

df['B'] = np.where(df['A']=='foo',arr,np.nan) # ValueError: operands could not be 
                                              # broadcast together with shapes 
                                              # (8,) (3,) () 

I also tried:

df['B'][df['A']=='foo'].values = arr # AttributeError: can't set attribute

Finally,

df['B'] = df['B'][df['A']=='foo'].map(arr) # TypeError: 'numpy.ndarray' object is not callable

Expected output

     A   B
0  foo   5
1  bar NaN
2  baz NaN
3  foo   3
4  bar NaN
5  bar NaN
6  baz NaN
7  foo   9

Answer 1

If you're sure that arr is the same length as the number of times 'foo' appears, you can use the following to set the values:

df.loc[df['A'] == 'foo', 'B'] = arr

This is a bit like df['B'][df['A']=='foo'] = arr (close to one of the methods you've tried), but avoids chained assignment (which can lead to values not being set correctly, or at all).