获取多个最大值

Question

我有一个像这样的数据框架:

indx   user_id     type        date
0      123          A Level-1  2021-01-15
1      123          A Level-1  2021-01-10
2      123          A Level-2  2021-01-10
3      123          B Level-2  2021-01-11
4      123          not_ctrgzd 2021-01-10
5      124          A Level-2  2021-02-11
6      124          B Level-1  2021-01-21
7      124          B Level-1+ 2021-02-11
8      125          not_ctrgzd 2021-01-31
9      126          A Level-1  2021-02-02
...

我需要的是获得每个唯一类型的最近日期的行，即

indx   user_id     type        date
0      123          A Level-1  2021-01-15
2      123          A Level-2  2021-01-10
3      123          B Level-2  2021-01-11
4      123          not_ctrgzd 2021-01-10
5      124          A Level-2  2021-02-11
6      124          B Level-1  2021-01-21
7      124          B Level-1+ 2021-02-11
8      125          not_ctrgzd 2021-01-31
9      126          A Level-1  2021-02-02

下面的代码块在做

idx = df.groupby(['user_id','type'])['date'].transform(max) == df['date']
df[idx]

现在，我不能做的是获得每个类型(A,B等)的最大类型值的行，以便最终，数据框看起来像这样。

indx   user_id     type        date
2      123          A Level-2  2021-01-10
3      123          B Level-2  2021-01-11
4      123          not_ctrgzd 2021-01-10
5      124          A Level-2  2021-02-11
7      124          B Level-1+ 2021-02-11
8      125          not_ctrgzd 2021-01-31
9      126          A Level-1  2021-02-02

因为B Level-1+大于B Level-1, A Level-2大于A Level-1等等。请注意，有些行没有分类类型(no_ctgrzd)，无论如何都应包括在最终数据框中。请不要犹豫，纠正任何不合理的部分，如标题:)。谢谢!

Answer 1

正是您的方法-只需导出您分组的值。

idx = df.groupby(['user_id',
np.where(df.type.str.match("[A,B][1,2]"), df.type.str.replace(r"([A-B])[1,2]",r"1-", regex=True), df.type)]
)['date'].transform(max) == df['date']
df[idx]

2021-01-15就是2021-01-11就是2021-01-10就是2021-02-11就是2021-02-11就是2021-01-31就是2021-02-02就是

对于pd可以这样做。CategoricalDtype:

#Create a catoregy and order for type
catTypeDtype = pd.CategoricalDtype(['1','1+','2'], ordered=True)
#Split the type into two helper columns to sort on category
df[['t1','t2']] = df['type'].str.extract('(?P<t1>[AB]|(?:.*))(?P<t2>.*)')
#change dtype from string to categorical
df['t2'] = df['t2'].astype(catTypeDtype)
#Sort dataframe on categorical data and date
dfs = df.sort_values(['t2','date'], ascending=[False, False])
#Groupby and take the first record after sorting
df_out = dfs.groupby(['user_id','t1'], group_keys=False, as_index=False).first()
.drop(['t1','t2'], axis=1)
df_out 

输出:

user_id  indx        type        date
0      123     2          A2  2021-01-10
1      123     3          B2  2021-01-11
2      123     4  not_ctrgzd  2021-01-10
3      124     5          A2  2021-02-11
4      124     6          B2  2021-01-21
5      125     8  not_ctrgzd  2021-01-31
6      126     9          A1  2021-02-02

---

使用新数据更新

catTypeDtype = pd.CategoricalDtype(['1','1+','2'], ordered=True)
df[['t1','t2']] = df['type'].str.extract('(?P<t1>[AB]|(?:.*))(?:sLevel-)?(?P<t2>.*)')
# df
df['t2'] = df['t2'].astype(catTypeDtype)
dfs = df.sort_values(['t2','date'], ascending=[False, False])
df_out = dfs.groupby(['user_id','t1'], group_keys=False, as_index=False).first()
.drop(['t1','t2'], axis=1)

输出:

user_id  indx        type        date
0      123     2   A Level-2  2021-01-10
1      123     3   B Level-2  2021-01-11
2      123     4  not_ctrgzd  2021-01-10
3      124     5   A Level-2  2021-02-11
4      124     7  B Level-1+  2021-02-11
5      125     8  not_ctrgzd  2021-01-31
6      126     9   A Level-1  2021-02-02