添加包含一个值的新列

Question

I have the following dataframe:

a = pd.DataFrame([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]], columns=['a','b','c'])
a
Out[234]: 
    a   b   c
0   1   2   3
1   4   5   6
2   7   8   9
3  10  11  12

I want to add a column with only the last row as the mean of the last 2 values of column c . Something like:

    a   b   c   d
0   1   2   3  NaN
1   4   5   6  NaN
2   7   8   9  NaN
3  10  11  12 mean(9,12)

I tried this but the first part gives an error:

a['d'].iloc[-1] = a.c.iloc[-2:].values.mean()

Answer 1

You can use .at to assign at a single row/column label pair:

ix = a.shape[0]
a.at[ix-1,'d'] = a.loc[ix-2:ix, 'c'].values.mean()

    a   b   c     d
0   1   2   3   NaN
1   4   5   6   NaN
2   7   8   9   NaN
3  10  11  12  10.5

---

Also note that chained indexing (what you're doing with aciloc[-2:] ) is explicitly discouraged in the docs, given that pandas sees these operations as separate events, namely two separate calls to __getitem__ , rather than a single call using a nested tuple of slices.

Answer 2

You may set d column beforehand (to ensure assignment):

In [100]: a['d'] = np.nan

In [101]: a['d'].iloc[-1] = a.c.iloc[-2:].mean()

In [102]: a
Out[102]: 
    a   b   c     d
0   1   2   3   NaN
1   4   5   6   NaN
2   7   8   9   NaN
3  10  11  12  10.5

Answer 3

We can use .loc , .iloc & np.mean

a.loc[a.index.max(), 'd'] = np.mean(a.iloc[-2:, 2])

    a   b   c     d
0   1   2   3   NaN
1   4   5   6   NaN
2   7   8   9   NaN
3  10  11  12  10.5

Or just using .loc and np.mean :

a.loc[a.index.max(), 'd'] = np.mean(a.loc[a.index.max()-1:, 'c'])

    a   b   c     d
0   1   2   3   NaN
1   4   5   6   NaN
2   7   8   9   NaN
3  10  11  12  10.5