数据集的可重现代码:
df = {'player' : ['a','a','a','a','a','a','a','a','a','b','b','b','b','b','b','b','b','b','c','c','c','c','c','c','c','c','c'],
'week' : ['1','1','1','2','2','2','3','3','3','1','1','1','2','2','2','3','3','3','1','1','1','2','2','2','3','3','3'],
'category': ['RES','VIT','MATCH','RES','VIT','MATCH','RES','VIT','MATCH','RES','VIT','MATCH','RES','VIT','MATCH','RES','VIT','MATCH','RES','VIT','MATCH','RES','VIT','MATCH','RES','VIT','MATCH'],
'energy' : [75,54,87,65,24,82,65,42,35,25,45,87,98,54,82,75,54,87,65,24,82,65,42,35,25,45,98] }
df = pd.DataFrame(data= df)
df = df[['player', 'week', 'category','energy']]
我需要找到“对于每个球员,找到他能量最大的那一周,并显示该周的所有类别、能量值”
所以我所做的是:
1.设置玩家和星期为索引
2.迭代索引找到能量的最大值并返回它 值
df = df.set_index(['player', 'week'])
for index, row in df1.iterrows():
group = df1.ix[df1['energy'].idxmax()]
获得的输出:
category energy
player week
b 2 RES 98
2 VIT 54
2 MATCH 82
此获得的输出是整个数据集中的最大能量,我希望每个玩家在该周的所有其他类别及其能量中的最大值。
预期输出:
我试过按照评论中的建议使用 groupby 方法,
df.groupby(['player','week'])['energy'].max().groupby(level=['player','week'])
得到的输出是:
energy category
player week
a 1 87 VIT
2 82 VIT
3 65 VIT
b 1 87 VIT
2 98 VIT
3 87 VIT
c 1 82 VIT
2 65 VIT
3 98 VIT
最佳答案
找到每个玩家的最大能量周,然后为该玩家选择该周并将所有玩家的结果连接起来。
max_energy_idx = df.groupby('player')['energy'].idxmax() # 2, 12, 26
max_energy_weeks = df['week'].iloc[max_energy_idx] # '1', '2', '3'
players = sorted(df['player'].unique()) # 'a', 'b', 'c'
result = pd.concat(
[df.loc[(df['player'] == player) & (df['week'] == max_enery_week), :]
for player, max_enery_week in zip(players, max_energy_weeks)]
)
>>> result
player week category energy
0 a 1 RES 75
1 a 1 VIT 54
2 a 1 MATCH 87
12 b 2 RES 98
13 b 2 VIT 54
14 b 2 MATCH 82
24 c 3 RES 25
25 c 3 VIT 45
26 c 3 MATCH 98
如果需要,您可以在结果上设置索引:
result = result.set_index(['player', 'week'])
关于python - 在多索引数据框中查找列的最大值并返回其所有值,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/49906335/