python - 忽略 GroupBy 中 Max 上的重复项 - Pandas

Question

我读过这个关于分组和获取最大值的帖子:Apply vs transform on a group object .

如果您的 max 对一个组来说是唯一的，它会完美地工作并且很有帮助，但我遇到了一个问题，即忽略组中的重复项，获取唯一项的最大值，然后将其放回 DataSeries。

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

预期输出:

 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

供引用:

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

这不是最巧妙的解决方案，但它确实有效。我们的想法是首先确定每年出现的唯一值，然后仅对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)