TA貢獻(xiàn)1871條經(jīng)驗(yàn) 獲得超13個(gè)贊
您可以groupby按自定義使用Series：
df = pd.DataFrame({'a': [1, 1, -1, 1, -1, -1]})
print (df)
a
0 1
1 1
2 -1
3 1
4 -1
5 -1
print ((df.a != df.a.shift()).cumsum())
0 1
1 1
2 2
3 3
4 4
5 4
Name: a, dtype: int32
for i, g in df.groupby([(df.a != df.a.shift()).cumsum()]):
print (i)
print (g)
print (g.a.tolist())
a
0 1
1 1
[1, 1]
2
a
2 -1
[-1]
3
a
3 1
[1]
4
a
4 -1
5 -1
[-1, -1]
TA貢獻(xiàn)1817條經(jīng)驗(yàn) 獲得超6個(gè)贊
使用groupby從itertools來自杰斯數(shù)據(jù)
from itertools import groupby[ list(group) for key, group in groupby(df.a.values.tolist())]Out[361]: [[1, 1], [-1], [1], [-1, -1]]
舉報(bào)