识别出现在特定年份而不是另一组年份的记录

Question

我正在尝试根据 ID 和年份标记行，如果 ID 出现在 [2017,2018,2019] 年份并且 未出现 [2020,2021, 2022] 然后需要将其标记为 1 else 0.

df1 = pd.DataFrame({'ID': ['AX1', 'Ax1', 'AX1','AX1','AX1','AX1','AX2','AX2','AX2','AX3','AX3','AX4','AX4','AX4'],'year':[2017,2018,2019,2020,2021,2022,2019,2020,2022,2019,2020,2017,2018,2019]})

     ID  year
0   AX1  2017
1   Ax1  2018
2   AX1  2019
3   AX1  2020
4   AX1  2021
5   AX1  2022
6   AX2  2019
7   AX2  2020
8   AX2  2022
9   AX3  2019
10  AX3  2020
11  AX4  2017
12  AX4  2018
13  AX4  2019

预期输出：

     ID  year  label
0   AX1  2017      0
1   Ax1  2018      0
2   AX1  2019      0
3   AX1  2020      0
4   AX1  2021      0
5   AX1  2022      0
6   AX2  2019      0
7   AX2  2020      0
8   AX2  2022      0
9   AX3  2019      0
10  AX3  2020      0
11  AX4  2017      1
12  AX4  2018      1
13  AX4  2019      1

在上面的例子中ID:AX4被标记为1，因为它是唯一出现在第一组年份[2017,2018,2019]而没有出现的ID第二组[2020,2021,2022].

如何实现？

Answer 1

使用

df1 = pd.DataFrame({'ID': ['AX1', 'AX1', 'AX1','AX1','AX1','AX1','AX2','AX2','AX2','AX3','AX3','AX4','AX4','AX4'],'year':[2017,2018,2019,2020,2021,2022,2019,2020,2022,2019,2020,2017,2018,2019]})
# find group level labels by checking if all of 2017-19 and none of 2020-22 exist for each ID
gr_lbl = df1.groupby('ID')['year'].apply(lambda g: {2017,2018,2019}.issubset(g) and not bool({2020,2021,2022}.intersection(g)))*1
# map group level labels to ID
df1['labels'] = df1.ID.map(gr_lbl)
df1

Answer 2

import pandas as pd

df1 = pd.DataFrame({'ID': ['AX1', 'Ax1', 'AX1','AX1','AX1','AX1','AX2','AX2','AX2','AX3','AX3','AX4','AX4','AX4'],'year':[2017,2018,2019,2020,2021,2022,2019,2020,2022,2019,2020,2017,2018,2019]})

include = set()
exclude = set()

for ID, year in zip(df1['ID'], df1['year']):
    if year in [2017,2018,2019]:
        include.add(ID.upper())
    if year in [2020,2021,2022]:
        exclude.add(ID.upper())
        
df1['label'] = [int(x.upper() in include - exclude) for x in df1['ID']]

print(df1)

Answer 3

通过聚合 set 创建 Series，然后通过 set.issubset 进行比较，最后映射输出到新列：

y1 = set([2017,2018,2019])
y2 = set([2020,2021,2022])

s = df1.groupby('ID')['year'].agg(set)
df1['label'] = df1['ID'].map((s.map(y1.issubset) & ~s.map(y2.issubset)).astype(int))
print (df1)
     ID  year  label
0   AX1  2017      0
1   Ax1  2018      0
2   AX1  2019      0
3   AX1  2020      0
4   AX1  2021      0
5   AX1  2022      0
6   AX2  2019      0
7   AX2  2020      0
8   AX2  2022      0
9   AX3  2019      0
10  AX3  2020      0
11  AX4  2017      1
12  AX4  2018      1
13  AX4  2019      1

详情:

print (df1.groupby('ID')['year'].agg(set))
ID
AX1    {2017, 2019, 2020, 2021, 2022}
AX2                {2019, 2020, 2022}
AX3                      {2019, 2020}
AX4                {2017, 2018, 2019}
Ax1                            {2018}
Name: year, dtype: object()

print ((s.map(y1.issubset) & ~s.map(y2.issubset)).astype(int))
ID
AX1    0
AX2    0
AX3    0
AX4    1
Ax1    0
Name: year, dtype: int32

识别出现在特定年份而不是另一组年份的记录

Identify records that are present in particular set of years and not in the another set of years

python

dataframe

python-3.x

pandas