用groupby计算数据帧上的累积移动平均

Question

我可以解决这个问题，但不是用一种Python的方式。给定以下数据帧：

   time  rssi  key1  key2  CMA
0  0.021 -71   P     A     NaN
1  0.022 -60   Q     A     NaN
2  0.025 -56   P     B     NaN
3  0.12  -70   Q     B     NaN
4  0.167 -65   P     A     NaN
5  0.210 -55   P     B     NaN
6  0.211 -74   Q     A     NaN
7  0.213 -62   Q     B     NaN
...

逐行计算RSSI的累计移动平均值（CMA），将该值放入RSSI平均值列。迭代时间越长，但按key1，key2分组。这相当于要计算四个CMA：(P,A)、(P,B)、(Q,A)、(Q,B)。最后，计算的CMA应放入CMA列。你知道吗

---

注1：我知道RSSI平均值不能用这个公式计算，我不在乎。你知道吗

注2:CMA公式为avg(n) = (avg(n-1) * (n-1) + value(n))/n

示例1:

定义groupby()策略。你知道吗

   time  rssi  key1  key2  CMA
0  0.021 -71   P     A     NaN <<-- first value can stay NaN or be default to rssi (i.e. -71)
4  0.167 -65   P     A     -68
...

示例2:

期望输出

   time  rssi  key1  key2  CMA
0  0.021 -71   P     A     NaN
1  0.022 -60   Q     A     NaN
2  0.025 -56   P     B     NaN
3  0.12  -70   Q     B     NaN
4  0.167 -65   P     A     -68
5  0.210 -55   P     B     -55.5
6  0.211 -74   Q     A     -67
7  0.213 -62   Q     B     -66
...

---

到目前为止，这是我能想到的

import pandas as pd
import numpy as np
df = pd.DataFrame()
df['time'] = [0.021,0.022,0.025,0.12,0.167,0.210,0.211,0.213]
df['rssi'] = [-71,-60,-56,-70,-65,-55,-74,-62]
df['key1'] = ['P','Q','P','Q','P','P','Q','Q']
df['key2'] = ['A','A','B','B','A','B','A','B']
df["CMA"] = np.nan

for key, grp in df.groupby(['key1', 'key2']):
    i = 0
    old_index = 0
    for index, row in grp.iterrows():
        if i == 0:
            # allowed alternative
            df.at[index,'CMA'] = grp.at[index,'rssi']
            old_index = index
        else:
            df.at[index,'CMA'] = ((df.at[old_index,'CMA'] * i) + df.at[index,'rssi']) / (i+1)
            old_index = index
        i += 1

print df

很管用，但很难看。必须有一个不那么痛苦的方式来实现同样的一个更Python的方式。如何在不显式设置该列的每个单元格值的情况下改进这一点？

Answer 1

您可以使用reset_index执行groupby().expanding().mean()：

df['CMA'] = (df.groupby(['key1','key2'], 
                        as_index=False)['rssi']
               .expanding(min_periods=2).mean()
               .reset_index(level=0, drop=True)
            )

输出：

    time  rssi key1 key2   CMA
0  0.021   -71    P    A   NaN
1  0.022   -60    Q    A   NaN
2  0.025   -56    P    B   NaN
3  0.120   -70    Q    B   NaN
4  0.167   -65    P    A -68.0
5  0.210   -55    P    B -55.5
6  0.211   -74    Q    A -67.0
7  0.213   -62    Q    B -66.0