我有一个如下所示的数据框:
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
...
我需要的是获取每种唯一类型的最新日期的行,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
下面的代码块就是这样做的
idx = df.groupby(['user_id','type'])['date'].transform(max) == df['date']
df[idx]
现在,我不能做的是为每种类型(A
、B
等)获取具有最大类型值的行,以便最终数据帧看起来像这样。
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,依此类推。请注意,某些行没有分类类型(no_ctgrzd
),无论如何都应包含在最终数据框中。请不要犹豫,纠正任何你认为不合理的部分,比如标题:)。谢谢!
答案 0 :(得分:3)
正是您的方法 - 只需得出您分组所依据的价值。
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]
idx | user_id | 输入 | 日期 | |
---|---|---|---|---|
0 | 0 | 123 | A1 | 2021-01-15 00:00:00 |
2 | 3 | 123 | B2 | 2021-01-11 00:00:00 |
3 | 4 | 123 | not_ctrgzd | 2021-01-10 00:00:00 |
4 | 5 | 124 | A2 | 2021-02-11 00:00:00 |
6 | 7 | 124 | B1 | 2021-02-11 00:00:00 |
7 | 8 | 125 | not_ctrgzd | 2021-01-31 00:00:00 |
8 | 9 | 126 | A1 | 2021-02-02 00:00:00 |
答案 1 :(得分:2)
你可以用 pd.CategoricalDtype 这样做:
#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
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