给定一个数据帧df: pd.Dataframe和来自df.index的索引子集selected_indexes，我如何使用应用于每个间隔selected_indexes[i], selected_indexes[i+1]的max操作符对df进行重采样？
例如，给定一个数据帧:
col
0 5
1 0
2 3
3 3
4 7
5 9
6 3
7 5
8 2
9 4
和 Select 索引"SELECTED_INDEX=[0，5，6，9]"，并在每个间隔之间的col列上应用最大值(假设我们保留终点而不包括起点)，我们应该得到:
col
0 5
5 9
6 3
9 5
例如，第9行由第7, 8, 9 \in (6, 9]行中的max(5, 2, 4)行组成.
selected_indexes = [0, 5, 6, 9]
group = (df.index.to_series().shift() # make groups
.isin(selected_indexes) # based on
.cumsum() # previous indices
)
# get max per group
out = df.groupby(group).max().set_axis(selected_indexes)
# or for many aggregations (see comments):
out = (df.groupby(group).agg({'col1': 'max', 'col2': 'min'})
.set_axis(selected_indexes)
)
输出:
col
0 5
5 9
6 3
9 5
你可能需要rolling.max分，而不是重新抽样:
out = df.loc[selected_indexes].rolling(3, center=True).max()
或者，如果您希望将±1应用于数据before选项:
out = df.rolling(3, center=True).max().loc[selected_indexes]
示例:
np.random.seed(0)
df = pd.DataFrame({'col': np.random.randint(0, 10, 10)})
selected_indexes = [1, 2, 3, 5, 6, 8, 9]
print(df)
col
0 5
1 0
2 3
3 3
4 7
5 9
6 3
7 5
8 2
9 4
out = df.loc[selected_indexes].rolling(3, center=True).max()
print(out)
col
1 NaN
2 3.0
3 9.0
5 9.0
6 9.0
8 4.0
9 NaN
out2 = df.rolling(3, center=True).max().loc[selected_indexes]
print(out2)
col
1 5.0
2 3.0
3 7.0
5 9.0
6 9.0
8 5.0
9 NaN