给出以下数据框
open high low close volume
0 74.090 74.144 74.089 74.136 0.000012
1 74.110 74.143 74.009 74.072 0.000419
2 74.074 74.190 74.063 74.081 0.000223
3 74.100 74.244 74.085 74.182 0.000429
4 74.194 74.222 74.164 74.199 0.000090
5 74.198 74.265 74.181 74.213 0.000071
6 74.223 74.244 74.120 74.174 0.000124
7 74.181 74.229 74.132 74.161 0.000087
8 74.164 74.337 74.126 74.324 0.000299
9 74.303 74.407 74.302 74.400 0.000185
10 74.408 74.440 74.373 74.409 0.000163
11 74.437 74.438 74.399 74.418 0.000208
12 74.428 74.464 74.385 74.385 0.000231
如何在整个数据帧中高效循环，并在每一行获取（在新数据帧中）前5行（包括当前行）？
答案 0 :(得分：5)
如果您想要效率，请使用numpy的步幅
import pandas as pd
import numpy as np
from numpy.lib.stride_tricks import as_strided as stride
sr, sc = v.strides
data = stride(v, (v.shape[1], v.shape[0] - 4, 5), (sc, sr, sr))
pn5 = pd.Panel(data, df.columns, df.index[4:], pd.RangeIndex(5))
df5 = pn5.to_frame()
df5.head(10)
open high low close volume
major minor
4 0 74.090 74.144 74.089 74.136 0.000012
1 74.110 74.143 74.009 74.072 0.000419
2 74.074 74.190 74.063 74.081 0.000223
3 74.100 74.244 74.085 74.182 0.000429
4 74.194 74.222 74.164 74.199 0.000090
5 0 74.110 74.143 74.009 74.072 0.000419
1 74.074 74.190 74.063 74.081 0.000223
2 74.100 74.244 74.085 74.182 0.000429
3 74.194 74.222 74.164 74.199 0.000090
4 74.198 74.265 74.181 74.213 0.000071
示例处理
def process(df):
return df.loc[df.name].tail(2)
print(df5.groupby(level=0).apply(process))
open high low close volume
major minor
4 3 74.100 74.244 74.085 74.182 0.000429
4 74.194 74.222 74.164 74.199 0.000090
5 3 74.194 74.222 74.164 74.199 0.000090
4 74.198 74.265 74.181 74.213 0.000071
6 3 74.198 74.265 74.181 74.213 0.000071
4 74.223 74.244 74.120 74.174 0.000124
7 3 74.223 74.244 74.120 74.174 0.000124
4 74.181 74.229 74.132 74.161 0.000087
8 3 74.181 74.229 74.132 74.161 0.000087
4 74.164 74.337 74.126 74.324 0.000299
9 3 74.164 74.337 74.126 74.324 0.000299
4 74.303 74.407 74.302 74.400 0.000185
10 3 74.303 74.407 74.302 74.400 0.000185
4 74.408 74.440 74.373 74.409 0.000163
11 3 74.408 74.440 74.373 74.409 0.000163
4 74.437 74.438 74.399 74.418 0.000208
12 3 74.437 74.438 74.399 74.418 0.000208
4 74.428 74.464 74.385 74.385 0.000231
设置
df = pd.DataFrame([
[74.09, 74.14399999999999, 74.089, 74.13600000000001, 1.2e-05],
[74.11, 74.143, 74.009, 74.072, 0.00041900000000000005],
[74.074, 74.19, 74.063, 74.081, 0.000223],
[74.1, 74.244, 74.085, 74.182, 0.000429],
[74.194, 74.222, 74.164, 74.199, 9e-05],
[74.19800000000001, 74.265, 74.181, 74.21300000000001, 7.099999999999999e-05],
[74.223, 74.244, 74.12, 74.17399999999999, 0.000124],
[74.181, 74.229, 74.132, 74.161, 8.7e-05],
[74.164, 74.337, 74.126, 74.324, 0.000299],
[74.303, 74.407, 74.30199999999999, 74.4, 0.000185],
[74.408, 74.44, 74.373, 74.40899999999999, 0.00016299999999999998],
[74.437, 74.438, 74.399, 74.418, 0.00020800000000000001],
[74.428, 74.464, 74.385, 74.385, 0.000231]
], columns=['open', 'high', 'low', 'close', 'volume'])