我有一个像这样的数据框:
df_1 = pd.DataFrame({
'ID' : ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C'],
'VAL' : ['shoes', 'flowers', 'chairs', 'apples', 'dice', 'shoes', 'apples',
'curtain', 'sand', 'socks', 'necklacs', 'tables', 'dishes', 'apples'],
'SEQ' : [0, 1, 2, 3, 4, 0, 1, 2, 3, 0, 1, 2, 3, 4]
})
ID VAL SEQ
0 A shoes 0
1 A flowers 1
2 A chairs 2
3 A apples 3
4 A dice 4
5 B shoes 0
6 B apples 1
7 B curtain 2
8 B sand 3
9 C socks 0
10 C necklacs 1
11 C tables 2
12 C dishes 3
13 C apples 4
我想将行切片到一个值,例如,将每个ID
组中的所有行切片到apple
:
Out[110]:
ID VAL SEQ
0 A shoes 0
1 A flowers 1
2 A chairs 2
3 A apples 3
4 B shoes 0
5 B apples 1
6 C socks 0
7 C necklacs 1
8 C tables 2
9 C dishes 3
10 C apples 4
idxmax
,groupby
,concat
pd.concat([
d.loc[:d.VAL.eq('apples').idxmax()]
for _, d in df_1.groupby('ID')
])
ID VAL SEQ
0 A shoes 0
1 A flowers 1
2 A chairs 2
3 A apples 3
5 B shoes 0
6 B apples 1
9 C socks 0
10 C necklacs 1
11 C tables 2
12 C dishes 3
13 C apples 4
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