Question

我已经阅读了关于分组和获取max：Apply vs transform on a group object的帖子。

如果你的max对于一个组来说是唯一的，那么它可以很好地工作并且很有帮助但是我遇到了一个忽略组中重复项的问题，获得了最多的唯一项，然后将它放回到DataSeries中。

输入（名为df1）：

date       val
2004-01-01 0
2004-02-01 0
2004-03-01 0
2004-04-01 0
2004-05-01 0 
2004-06-01 0
2004-07-01 0
2004-08-01 0
2004-09-01 0
2004-10-01 0
2004-11-01 0
2004-12-01 0
2005-01-01 11
2005-02-01 11
2005-03-01 8
2005-04-01 5
2005-05-01 0 
2005-06-01 0
2005-07-01 2
2005-08-01 1
2005-09-01 0
2005-10-01 0
2005-11-01 3
2005-12-01 3

我的代码：

df1['peak_month'] = df1.groupby(df1.date.dt.year)['val'].transform(max) == df1['val']

我的输出：

date       val   max
2004-01-01 0     true #notice how all duplicates are true in 2004
2004-02-01 0     true
2004-03-01 0     true
2004-04-01 0     true
2004-05-01 0     true
2004-06-01 0     true
2004-07-01 0     true
2004-08-01 0     true
2004-09-01 0     true
2004-10-01 0     true
2004-11-01 0     true
2004-12-01 0     true
2005-01-01 11    true #notice how these two values 
2005-02-01 11    true #are the max values for 2005 and are true
2005-03-01 8     false
2005-04-01 5     false
2005-05-01 0     false 
2005-06-01 0     false
2005-07-01 2     false
2005-08-01 1     false
2005-09-01 0     false
2005-10-01 0     false
2005-11-01 3     false
2005-12-01 3     false

预期产出：

 date       val   max
2004-01-01 0     false #notice how all duplicates are false in 2004
2004-02-01 0     false #because they are the same and all vals are max
2004-03-01 0     false
2004-04-01 0     false
2004-05-01 0     false 
2004-06-01 0     false
2004-07-01 0     false
2004-08-01 0     false
2004-09-01 0     false
2004-10-01 0     false
2004-11-01 0     false
2004-12-01 0     false
2005-01-01 11    false #notice how these two values 
2005-02-01 11    false #are the max values for 2005 but are false
2005-03-01 8     true  #this is the second max val and is true
2005-04-01 5     false
2005-05-01 0     false 
2005-06-01 0     false
2005-07-01 2     false
2005-08-01 1     false
2005-09-01 0     false
2005-10-01 0     false
2005-11-01 3     false
2005-12-01 3     false

供参考：

df1 = pd.DataFrame({'val':[0, 0, 0, 0, 0 , 0, 0, 0, 0, 0, 0, 0, 11, 11, 8, 5, 0 , 0, 2, 1, 0, 0, 3, 3],
'date':['2004-01-01','2004-02-01','2004-03-01','2004-04-01','2004-05-01','2004-06-01','2004-07-01','2004-08-01','2004-09-01','2004-10-01','2004-11-01','2004-12-01','2005-01-01','2005-02-01','2005-03-01','2005-04-01','2005-05-01','2005-06-01','2005-07-01','2005-08-01','2005-09-01','2005-10-01','2005-11-01','2005-12-01',]})

Answer 1

不是最简单的解决方案，但它确实有效。我们的想法是首先确定每年出现的唯一值，然后根据这些唯一值进行转换。

# Determine the unique values appearing in each year.
df1['year'] = df1.date.dt.year
unique_vals = df1.drop_duplicates(subset=['year', 'val'], keep=False)

# Max transform on the unique values.
df1['peak_month'] = unique_vals.groupby('year')['val'].transform(max) == unique_vals['val']

# Fill NaN's as False, drop extra column.
df1['peak_month'].fillna(False, inplace=True)
df1.drop('year', axis=1, inplace=True)

在GroupBy中忽略Max上的重复 - Pandas

1 个答案: