Python 如何计算滑动窗口结果并将其写入csv？

我有一个csv数据文件，如下所示：

我想得到这样的结果：

我还想将结果保存到csv文件中

到目前为止，我尝试使用滑动窗口：

def window(iterable, size=3):
    i = iter(iterable)
    win = []
    for e in range(0, size):
        win.append(next(i))
    yield win
    for e in i:
        win = win[1:] + [e]
        yield win
        
X = [188,122,222,222,222,222,222,222,222,222]
count = 0
for w in window(X):
    print(w)
    count = count + 1
    print(count)
    if count == 10:
        break

---

一个选项是创建自己的滚动聚合函数：

def rolling_agg_list(src_frame, cols, window):
    frame = src_frame[cols]
    d = {v: list for v in cols}
    values = [frame.iloc[i:i + window].agg(d)
              for i in range(len(frame) - window + 1)]
    return pd.DataFrame(values, columns=cols)


new_df = rolling_agg_list(df, ['X', 'Y', 'Z'], 3)

要写入CSV，只需调用新的数据帧：

new_df.to_csv('out.csv', index=False)

示例

df

：

import numpy as np
import pandas as pd

np.random.seed(5)
df = pd.DataFrame({
    'ID': np.arange(1, 11),
    'X': np.random.randint(118, 122, size=10),
    'Y': np.random.random(size=10) * 2 - 1,
    'Z': np.random.random(size=10) * 2 - 1
})

   ID    X         Y         Z
0   1  121  0.223488 -0.451827
1   2  120  0.531816 -0.171530
2   3  121  0.036836 -0.407840
3   4  119 -0.406399  0.257576
4   5  120 -0.624558  0.159676
5   6  120 -0.838517  0.199858
6   7  118  0.476881 -0.468362
7   8  119 -0.117382 -0.430628
8   9  118 -0.683380 -0.492824
9  10  118  0.759874 -0.344872

                       X                                                                 Y                                                                  Z
0  [121.0, 120.0, 121.0]   [0.22348772580529142, 0.5318157129606311, 0.036835975745886484]  [-0.4518270760155507, -0.17152996183789737, -0.40784013453270407]
1  [120.0, 121.0, 119.0]   [0.5318157129606311, 0.036835975745886484, -0.4063989968475561]  [-0.17152996183789737, -0.40784013453270407, 0.25757581775896665]
2  [121.0, 119.0, 120.0]  [0.036835975745886484, -0.4063989968475561, -0.6245575426774967]    [-0.40784013453270407, 0.25757581775896665, 0.1596756203790901]
3  [119.0, 120.0, 120.0]   [-0.4063989968475561, -0.6245575426774967, -0.8385174624702503]     [0.25757581775896665, 0.1596756203790901, 0.19985839324997512]
4  [120.0, 120.0, 118.0]      [-0.6245575426774967, -0.8385174624702503, 0.47688059239794]     [0.1596756203790901, 0.19985839324997512, -0.4683617649289855]
5  [120.0, 118.0, 119.0]     [-0.8385174624702503, 0.47688059239794, -0.11738155420809382]    [0.19985839324997512, -0.4683617649289855, -0.4306282387172724]
6  [118.0, 119.0, 118.0]     [0.47688059239794, -0.11738155420809382, -0.6833802645746976]     [-0.4683617649289855, -0.4306282387172724, -0.492823588452425]
7  [119.0, 118.0, 118.0]   [-0.11738155420809382, -0.6833802645746976, 0.7598740624025577]     [-0.4306282387172724, -0.492823588452425, -0.3448721046225318]

df

：

import numpy as np
import pandas as pd

np.random.seed(5)
df = pd.DataFrame({
    'ID': np.arange(1, 11),
    'X': np.random.randint(118, 122, size=10),
    'Y': np.random.random(size=10) * 2 - 1,
    'Z': np.random.random(size=10) * 2 - 1
})

   ID    X         Y         Z
0   1  121  0.223488 -0.451827
1   2  120  0.531816 -0.171530
2   3  121  0.036836 -0.407840
3   4  119 -0.406399  0.257576
4   5  120 -0.624558  0.159676
5   6  120 -0.838517  0.199858
6   7  118  0.476881 -0.468362
7   8  119 -0.117382 -0.430628
8   9  118 -0.683380 -0.492824
9  10  118  0.759874 -0.344872

                       X                                                                 Y                                                                  Z
0  [121.0, 120.0, 121.0]   [0.22348772580529142, 0.5318157129606311, 0.036835975745886484]  [-0.4518270760155507, -0.17152996183789737, -0.40784013453270407]
1  [120.0, 121.0, 119.0]   [0.5318157129606311, 0.036835975745886484, -0.4063989968475561]  [-0.17152996183789737, -0.40784013453270407, 0.25757581775896665]
2  [121.0, 119.0, 120.0]  [0.036835975745886484, -0.4063989968475561, -0.6245575426774967]    [-0.40784013453270407, 0.25757581775896665, 0.1596756203790901]
3  [119.0, 120.0, 120.0]   [-0.4063989968475561, -0.6245575426774967, -0.8385174624702503]     [0.25757581775896665, 0.1596756203790901, 0.19985839324997512]
4  [120.0, 120.0, 118.0]      [-0.6245575426774967, -0.8385174624702503, 0.47688059239794]     [0.1596756203790901, 0.19985839324997512, -0.4683617649289855]
5  [120.0, 118.0, 119.0]     [-0.8385174624702503, 0.47688059239794, -0.11738155420809382]    [0.19985839324997512, -0.4683617649289855, -0.4306282387172724]
6  [118.0, 119.0, 118.0]     [0.47688059239794, -0.11738155420809382, -0.6833802645746976]     [-0.4683617649289855, -0.4306282387172724, -0.492823588452425]
7  [119.0, 118.0, 118.0]   [-0.11738155420809382, -0.6833802645746976, 0.7598740624025577]     [-0.4306282387172724, -0.492823588452425, -0.3448721046225318]

new_df

：

import numpy as np
import pandas as pd

np.random.seed(5)
df = pd.DataFrame({
    'ID': np.arange(1, 11),
    'X': np.random.randint(118, 122, size=10),
    'Y': np.random.random(size=10) * 2 - 1,
    'Z': np.random.random(size=10) * 2 - 1
})

   ID    X         Y         Z
0   1  121  0.223488 -0.451827
1   2  120  0.531816 -0.171530
2   3  121  0.036836 -0.407840
3   4  119 -0.406399  0.257576
4   5  120 -0.624558  0.159676
5   6  120 -0.838517  0.199858
6   7  118  0.476881 -0.468362
7   8  119 -0.117382 -0.430628
8   9  118 -0.683380 -0.492824
9  10  118  0.759874 -0.344872

                       X                                                                 Y                                                                  Z
0  [121.0, 120.0, 121.0]   [0.22348772580529142, 0.5318157129606311, 0.036835975745886484]  [-0.4518270760155507, -0.17152996183789737, -0.40784013453270407]
1  [120.0, 121.0, 119.0]   [0.5318157129606311, 0.036835975745886484, -0.4063989968475561]  [-0.17152996183789737, -0.40784013453270407, 0.25757581775896665]
2  [121.0, 119.0, 120.0]  [0.036835975745886484, -0.4063989968475561, -0.6245575426774967]    [-0.40784013453270407, 0.25757581775896665, 0.1596756203790901]
3  [119.0, 120.0, 120.0]   [-0.4063989968475561, -0.6245575426774967, -0.8385174624702503]     [0.25757581775896665, 0.1596756203790901, 0.19985839324997512]
4  [120.0, 120.0, 118.0]      [-0.6245575426774967, -0.8385174624702503, 0.47688059239794]     [0.1596756203790901, 0.19985839324997512, -0.4683617649289855]
5  [120.0, 118.0, 119.0]     [-0.8385174624702503, 0.47688059239794, -0.11738155420809382]    [0.19985839324997512, -0.4683617649289855, -0.4306282387172724]
6  [118.0, 119.0, 118.0]     [0.47688059239794, -0.11738155420809382, -0.6833802645746976]     [-0.4683617649289855, -0.4306282387172724, -0.492823588452425]
7  [119.0, 118.0, 118.0]   [-0.11738155420809382, -0.6833802645746976, 0.7598740624025577]     [-0.4306282387172724, -0.492823588452425, -0.3448721046225318]

---

CSV输出示例：

X,Y,Z
"[121.0, 120.0, 121.0]","[0.22348772580529142, 0.5318157129606311, 0.036835975745886484]","[-0.4518270760155507, -0.17152996183789737, -0.40784013453270407]"
"[120.0, 121.0, 119.0]","[0.5318157129606311, 0.036835975745886484, -0.4063989968475561]","[-0.17152996183789737, -0.40784013453270407, 0.25757581775896665]"
"[121.0, 119.0, 120.0]","[0.036835975745886484, -0.4063989968475561, -0.6245575426774967]","[-0.40784013453270407, 0.25757581775896665, 0.1596756203790901]"
"[119.0, 120.0, 120.0]","[-0.4063989968475561, -0.6245575426774967, -0.8385174624702503]","[0.25757581775896665, 0.1596756203790901, 0.19985839324997512]"
"[120.0, 120.0, 118.0]","[-0.6245575426774967, -0.8385174624702503, 0.47688059239794]","[0.1596756203790901, 0.19985839324997512, -0.4683617649289855]"
"[120.0, 118.0, 119.0]","[-0.8385174624702503, 0.47688059239794, -0.11738155420809382]","[0.19985839324997512, -0.4683617649289855, -0.4306282387172724]"
"[118.0, 119.0, 118.0]","[0.47688059239794, -0.11738155420809382, -0.6833802645746976]","[-0.4683617649289855, -0.4306282387172724, -0.492823588452425]"
"[119.0, 118.0, 118.0]","[-0.11738155420809382, -0.6833802645746976, 0.7598740624025577]","[-0.4306282387172724, -0.492823588452425, -0.3448721046225318]"

---

运行此操作时，我遇到构造错误。ValueError：传递值的形状是（1,8），索引暗示（3,8）“您使用的是什么版本？另外，您在提供的示例数据帧上遇到此错误，或者您向窗口函数传递了不同的数据帧？