为什么不能分配多个与原始 pandas df 中名称不同的列？

Question

     odds_ft_home_team_win  odds_ft_draw  odds_ft_away_team_win
0                 0.850212      0.100281               0.049506
1                 0.081114      0.146371               0.772515
2                 0.486790      0.266734               0.246476
3                 0.355737      0.301008               0.343255
4                 0.294952      0.299490               0.405559

This is a dataframe prob_odds I want to assign to my previous data-frame df .

df.loc[:,["w","d","a"]]=prob_odds

As you can see I want the new columns to have a different name than in the original dataframe. This returns a data-frame that has the columns "w","d","a" with NA values. If I change the names of the columns in the prob_odds Data-Frame to match the names in the df the assignment works. Why is that? What is the way to go around this so that you can assign new columns without having to rename the old ones beforehand.

Answer 1

You can, just without .loc (because .loc would search for existing indexes and columns to operate on):

N = 10
df = pd.DataFrame({'a': 1 * np.ones(N), 'b': 2 * np.ones(N)})
df[['c', 'd']] = pd.DataFrame({'x': 3 * np.ones(N), 'y': 4 * np.ones(N)})

df

Output:

     a    b    c    d
0  1.0  2.0  3.0  4.0
1  1.0  2.0  3.0  4.0
2  1.0  2.0  3.0  4.0
3  1.0  2.0  3.0  4.0
4  1.0  2.0  3.0  4.0
5  1.0  2.0  3.0  4.0
6  1.0  2.0  3.0  4.0
7  1.0  2.0  3.0  4.0
8  1.0  2.0  3.0  4.0
9  1.0  2.0  3.0  4.0

PS To match your naming, df[["w","d","a"]] = prob_odds should work