将分组的熊猫数据应用回原始数据框

Question

I have the dataframe below that I am working with:

These are chess games which I am trying to group by game and then perform a function on each game based on the number of moves played in that game...

        game_id     move_number colour  avg_centi
0       03gDhPWr    1           white   NaN
1       03gDhPWr    2           black   37.0
2       03gDhPWr    3           white   61.0
3       03gDhPWr    4           black   -5.0
4       03gDhPWr    5           white   26.0
5       03gDhPWr    6           black   31.0
6       03gDhPWr    7           white   -2.0
... ... ... ... ...
110091  zzaiRa7s    34          black   NaN
110092  zzaiRa7s    35          white   NaN
110093  zzaiRa7s    36          black   NaN
110094  zzaiRa7s    37          white   NaN
110095  zzaiRa7s    38          black   NaN
110096  zzaiRa7s    39          white   NaN
110097  zzaiRa7s    40          black   NaN

Specifically, I am using pd.cut to create a new column, game_phase , which lists whether the given move was played in the opening, middlegame, or endgame.

I'm using the following code to achieve this. Note that each game must be partitioned into opening , middlegame , and endgame bins based on the total number of moves played in that game.

def define_move_phase(x):
    bins = (0, round(x['move_number'].max() * 1/3), round(x['move_number'].max() * 2/3), x['move_number'].max())    
    phases = ["opening", "middlegame", "endgame"]
    try:
        x.loc[:, 'phase'] = pd.cut(x['move_number'], bins, labels=phases)
    except ValueError:
        x.loc[:, 'phase'] = None
    print(x)

df.groupby('game_id').apply(define_move_phase)

The print statement in that function shows that the function is working on the individual groups (see below) but it does not apply the phase column back to the original dataframe.

     game_id  move_number colour  avg_centi    phase
0   03gDhPWr            1  white        NaN  opening
1   03gDhPWr            2  black       37.0  opening
2   03gDhPWr            3  white       61.0  opening
3   03gDhPWr            4  black       -5.0  opening
4   03gDhPWr            5  white       26.0  opening
5   03gDhPWr            6  black       31.0  opening
6   03gDhPWr            7  white       -2.0  opening
..       ...          ...    ...        ...      ...
54  03gDhPWr           55  white       58.0  endgame
55  03gDhPWr           56  black       26.0  endgame
56  03gDhPWr           57  white      116.0  endgame
57  03gDhPWr           58  black     2000.0  endgame
58  03gDhPWr           59  white        0.0  endgame
59  03gDhPWr           60  black        0.0  endgame
60  03gDhPWr           61  white        NaN  endgame

[61 rows x 5 columns]
     game_id  move_number colour  avg_centi    phase
0   03gDhPWr            1  white        NaN  opening
1   03gDhPWr            2  black       37.0  opening
2   03gDhPWr            3  white       61.0  opening
3   03gDhPWr            4  black       -5.0  opening
4   03gDhPWr            5  white       26.0  opening
5   03gDhPWr            6  black       31.0  opening
6   03gDhPWr            7  white       -2.0  opening
..       ...          ...    ...        ...      ...
54  03gDhPWr           55  white       58.0  endgame
55  03gDhPWr           56  black       26.0  endgame
56  03gDhPWr           57  white      116.0  endgame
57  03gDhPWr           58  black     2000.0  endgame
58  03gDhPWr           59  white        0.0  endgame
59  03gDhPWr           60  black        0.0  endgame
60  03gDhPWr           61  white        NaN  endgame

[61 rows x 5 columns]

etc...

I would like to apply the new phase columns back to the original dataframe or ungroup the grouped dataframes into one big dataframe again. What is the best way to go about doing that?

Answer 1

您的函数没有 return 语句