识别子组中的最大值并根据是否为最大值分配每行值的正确方法是什么？ 这是一个示例 df:
group subgroup
A 1
B 1
A 2
A 3
A 4
B 2
C 2
C 1
规则是:
if subgroup = max then result = 1
else subgroup = 2
结果将是:
group subgroup result
A 1 2
B 1 2
A 2 2
A 3 2
A 4 1
B 2 1
C 2 1
C 1 2
我现在这样做:
df['subgroup_max'] = df.groupby(['group'])['subgroup'].nunique()
df3['result'] = 2
df3.loc[df3['result'] == df3['subgroup_max'],'result'] = 1
好像效率不是很高。但是有更好的方法吗？
请您参考如下方法:
您可以使用 DataFrameGroupBy.idxmax对于每组 max 值的索引:
df['result'] = 2
idx = df.groupby(['group'])['subgroup'].idxmax()
df.loc[idx, 'result'] = 1
print (df)
group subgroup result
0 A 1 2
1 B 1 2
2 A 2 2
3 A 3 2
4 A 4 1
5 B 2 1
6 C 2 1
7 C 1 2
另一种解决方案 numpy.where和 Index.isin :
idx = df.groupby(['group'])['subgroup'].idxmax()
df['result'] = np.where(df.index.isin(idx), 1, 2)
print (df)
group subgroup result
0 A 1 2
1 B 1 2
2 A 2 2
3 A 3 2
4 A 4 1
5 B 2 1
6 C 2 1
7 C 1 2
idx = df.groupby(['group'])['subgroup'].idxmax()
df['result'] = (~df.index.isin(idx)).astype(int) + 1
print (df)
group subgroup result
0 A 1 2
1 B 1 2
2 A 2 2
3 A 3 2
4 A 4 1
5 B 2 1
6 C 2 1
7 C 1 2
但是如果每组有多个最大值并且需要为所有最大值分配值使用apply :
print (df)
group subgroup
0 A 4
1 B 1
2 A 2
3 A 3
4 A 4
5 B 2
6 C 2
7 C 1
mask = df.groupby(['group'])['subgroup'].apply(lambda x: x == x.max())
df['result'] = np.where(mask, 1, 2)
print (df)
group subgroup result
0 A 4 1
1 B 1 2
2 A 2 2
3 A 3 2
4 A 4 1
5 B 2 1
6 C 2 1
7 C 1 2
