这里有两个玩家的数据帧示例以及预期输出的说明:
+--------+------------+-----------------------------------+
| Player | Result | Winning ratio (historical) |
+--------+------------+-----------------------------------+
| K2000 | Lose | 0% #first game so no hist |
| K2000 | Lose | 0% #0 game winned on 1 contested |
| K2000 | Win | 0% #0 game winned on 2 contested |
| K2000 | Not ranked | 33% #1 game winned on 3 contested |
| K2000 | Lose | 25% #and so on. |
| K2000 | Win | 20% |
| K2000 | Win | 33% |
| Kssis | Win | 0% |
| Kssis | Win | 100% |
| Kssis | Not ranked | 100% |
| Kssis | Lose | 66% |
| Kssis | Win | 50% |
+--------+------------+-----------------------------------+
为了获得它，我做了以下操作
df['sucess'] = df.apply(lambda row: 1 if row['result'] == 'Win' else 0, axis = 1)
df['nb_of_contests'] = df.apply(lambda row: 1 , axis = 1)
#gives
+--------+------------+--------+----------------+
| Player | Result | Sucess | Nb_of_contests |
+--------+------------+--------+----------------+
| K2000 | Lose | 0 | 1 |
| K2000 | Lose | 0 | 1 |
| K2000 | Win | 1 | 1 |
| K2000 | Not ranked | 0 | 1 |
| K2000 | Lose | 0 | 1 |
| K2000 | Win | 1 | 1 |
| K2000 | Win | 1 | 1 |
| Kssis | Win | 1 | 1 |
| Kssis | Win | 1 | 1 |
| Kssis | Not ranked | 0 | 1 |
| Kssis | Lose | 0 | 1 |
| Kssis | Win | 1 | 1 |
+--------+------------+--------+----------------+
#then the sums cumulated
cumul = df.groupby('Player')['sucess','nb_of_contests'].cumsum()
#cumul gives
+--------+------------+--------+----------------+
| Player | Result | Sucess | Nb_of_contests |
+--------+------------+--------+----------------+
| K2000 | Lose | 0 | 1 |
| K2000 | Lose | 0 | 2 |
| K2000 | Win | 1 | 3 |
| K2000 | Not ranked | 0 | 4 |
| K2000 | Lose | 0 | 5 |
| K2000 | Win | 2 | 6 |
| K2000 | Win | 3 | 7 |
| Kssis | Win | 1 | 1 |
| Kssis | Win | 2 | 2 |
| Kssis | Not ranked | 0 | 3 |
| Kssis | Lose | 0 | 4 |
| Kssis | Win | 3 | 5 |
+--------+------------+--------+----------------+
#then compute the ratio
winning_ratio = cumul['sucess']/cumul['nb_of_contests']
#finnaly shift
gb_winning_ratio = winning_ratio.groupby('Player') #in order to shift inside group, because cumul is a dataframe not a groupby object.
winning_ratio_shifted = gb_winning_ratio.shift(1)
那么，有没有更简单的方法呢？因为这里我认为这是可以简化的，但我没有足够的技能来改进它。因此，请毫不犹豫地给出深入的解释。我首先想掌握它。
Pandas 版本:0.23.4 Python 版本:3.7.4
最佳答案
通知:
避免:
ValueError: cannot reindex from a duplicate axis
创建默认RangeIndex:
df = df.reset_index(drop=True)
然后使用:
df['sucess'] = (df['Result'] == 'Win').astype(int)
df['nb_of_contests'] = 1
cumul = df.groupby('Player')['sucess','nb_of_contests'].cumsum()
winning_ratio = cumul['sucess'].div(cumul['nb_of_contests'])
winning_ratio_shifted = winning_ratio.groupby(df['Player']).shift().fillna(0)
print (winning_ratio_shifted)
0 0.000000
1 0.000000
2 0.000000
3 0.333333
4 0.250000
5 0.200000
6 0.333333
7 0.000000
8 1.000000
9 1.000000
10 0.666667
11 0.500000
dtype: float64
或者您可以使用 DataFrame.assign 的一行解决方案每组带有链 cumsum 和 shift:
winning_ratio_shifted = (df.assign(sucess = (df['Result'] == 'Win').astype(int),
nb_of_contests = 1)
.groupby('Player')['sucess','nb_of_contests']
.apply(lambda x: x.cumsum().shift())
.assign(new=lambda x: x['sucess'] / x['nb_of_contests'])['new']
.fillna(0)
)
print (winning_ratio_shifted)
1 0.000000
2 0.000000
3 0.333333
4 0.250000
5 0.200000
6 0.333333
7 0.000000
8 1.000000
9 1.000000
10 0.666667
11 0.500000
Name: new, dtype: float64
关于python - 在值(value)条件下，有没有更快的方法来计算带有 pandas 的 groupby 对象的历史比率？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58913240/