使用 pandas 高效计算时间特征

Question

我有以下 .csv 文件：

Match_idx,Date,Player_1,Player_2,Player_1_wins
0,2020-01-01,p1,p2,1
1,2020-01-02,p2,p3,0
2,2020-01-03,p3,p1,1
3,2020-01-04,p4,p1,1

我想计算更多列以获得以下输出 .csv 文件：

Match_idx,Date,Player_1,Player_2,Player_1_wins,Player_1_winrate,Player_2_winrate,Player_1_matches,Player_2_matches,Head_to_head
0,2020-01-01,p1,p2,1,0,0,0,0,0,''
1,2020-01-02,p2,p3,0,0,0,1,0,0,''
2,2020-01-03,p3,p1,1,1,1,1,1,0,''
3,2020-01-04,p4,p1,1,0,1/2,0,2,0,''
4,2020-01-05,p1,p3,0,1/2,2/2,3,2,'0'
5,2020-01-06,p3,p1,1,1/3,3/3,4,3,'11'

每一列的语义：

• Match_idx，Date，Player_1，Player_2：直截了当
• Player_1_wins：Player_1 赢了比赛吗？ 1 : 0

这些列将被保留，我想添加这些：

• Player_1_winrate : number_of_wins_for_player_1_before_this_one / number_of_matches_played_by_player_1_before_this_one

• Player_2_winrate : 与上面的 player_2 相同

• Player_1_matches : number_of_matches_played_by_player_1_before_this_one

• Player_2_matches : 与上面的 player_2 相同

• Head_to_head ：Player_1 和 Player_2 之间先前匹配的结果。编码为 {'0' 和 '1'} 的字符串，如果 Player_1 赢得比赛，则为 '1'，否则为 '0'。

我做了什么

我正在使用 pandas 库来操作这个文件。我一直在考虑的幼稚方法如下：选择每场比赛，输或赢，由球员参加，并按日期排序。之后，对于胜率功能，将以下两个函数应用于一场比赛。

def get_matches_won_before_by_player(df: pd.DataFrame, player: str, before: str):
    mask_player_won = (
        ((df['Player_1_wins'] == 1) & (df['Player_1'] == player)) | 
        ((df['Player_1_wins'] == 0) & (df['Player_2'] == player))
    )

    req = df[(df['Date'] < before) & mask_player_won]
    req.sort_values(by='Date', inplace=True)
    return req

def get_matches_played_before_by_player(df: pd.DataFrame, player: str, before: str):
    mask_player_played = (
        (df['Player_1'] == player) | 
        (df['Player_2'] == player)
    )

    req = df[(df['Date'] < before) & mask_player_played]
    req.sort_values(by='Date', inplace=True)
    return req

我可以将该逻辑应用于每场比赛，但这将涉及为每场比赛运行这些函数，这是非常非常无效的。

我想做的事

如何仅使用给定比赛中每位球员的最后一场比赛有效地计算我的特征？例如，更新每个玩家的胜率可以通过以下逻辑来完成：

1. 将每列初始化为 0。
2. 更新胜率如下：(M/M+1) + (W/N+1)，M当前胜率，N当前比赛场数，W = 1 if玩家获胜，否则为 0。

非常感谢组织此类过程的任何帮助或想法。

Answer 1

我尝试对系列进行操作，以便解决方案快速运行。我将在代码中通过 cmets 进行解释。

# to return head to head
strp1gw = ""
def get_head_to_head(s):
    global strp1gw
    strp1gw +=str(s)
    return strp1gw

(
    df = df
    .assign(
        # this is player 1 all wins before but to avoid creating extra columns I named it as Player_1_winrate to replace it with rate as you dont need cumulative sum of wins
        Player_1_winrate = lambda x: x['Player_1_wins'].cumsum(),
        # if player 1 played?
        Player_1_matches = lambda x: np.where((x['Player_1'] =='p1') | (x['Player_2'] == 'p1'),1,0)
    )
    # this is number of matches played by player 1 before this one
    .assign(Player_1_matches = lambda x: x['Player_1_matches'].cumsum())
    # the player 1 winrate
    .assign(Player_1_winrate = lambda x: x['Player_1_winrate']/x['Player_1_matches'])
    # same for player 2 but you didnt mention how to compute Player_2_wins
    .assign(
        Player_2_winrate = lambda x: x['Player_2_wins'].cumsum(),
        Player_2_matches = lambda x: np.where((x['Player_1'] =='p2') | (x['Player_2'] == 'p2'),1,0)
    )
    .assign(Player_2_matches = lambda x: x['Player_2_matches'].cumsum())
    .assign(Player_2_winrate = lambda x: x['Player_2_winrate']/x['Player_2_matches'])
    # to apply function to get head to head value
    .assign(Head_to_head=lambda x: x['Player_1_wins'].apply(lambda s: get_head_to_head(s)))
)