python - Pandas 数据框过滤多个列和行

给定具有以下格式的数据框：

TEST_ID | ATOMIC_NUMBER | COMPOSITION_PERCENT | POSITION
1       | 28            | 49.84               | 0
1       | 22            | 50.01               | 0
1       | 47            | 0.06                | 1
2       | 22            | 49.84               | 0
2       | 47            | 50.01               | 1
3       | 28            | 49.84               | 0
3       | 22            | 50.01               | 0
3       | 47            | 0.06                | 0

我只想选择POSITION 0中ATOMIC_NUMBER为22 AND 28的测试，不多不少。所以我想要一个返回的过滤器：

TEST_ID | ATOMIC_NUMBER | COMPOSITION_PERCENT | POSITION
1       | 28            | 49.84               | 0
1       | 22            | 50.01               | 0
1       | 47            | 0.06                | 1

编辑：我正在尝试将此逻辑从SQL转换为python。这是SQL代码：

select * from compositions
where compositions.test_id in (

  select a.test_id from (

    select test_id from compositions
    where test_id in (
      select test_id from (
        select * from COMPOSITIONS where position == 0 )
      group by test_id
      having count(test_id) = 2 )
    and atomic_number = 22) a

  join (

    select test_id from compositions
    where test_id in (
      select test_id from (
        select * from COMPOSITIONS where position == 0 )
      group by test_id
      having count(test_id) = 2 )
    and atomic_number = 28) b

  on a.test_id = b.test_id )

最佳答案

您可以创建一个布尔序列来捕获test_id，然后使用该序列对df进行索引。

s = df[df['POSITION'] == 0].groupby('TEST_ID').apply(lambda x: ((x['ATOMIC_NUMBER'].count() == 2 ) & (sorted(x['ATOMIC_NUMBER'].values.tolist()) == [22,28])).all())

test_id = s[s].index.tolist()

df[df['TEST_ID'].isin(test_id)]

    TEST_ID ATOMIC_NUMBER   COMPOSITION_PERCENT POSITION
0   1       28              49.84               0
1   1       22              50.01               0
2   1       47              0.06                1

关于python - Pandas 数据框过滤多个列和行，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/52635026/