滚动平均 pandas 数据框的所有值 [英] Rolling average all values of pandas DataFrame

问题描述

我有一个pandas DataFrame，我想滚动计算所有值的平均值:对于所有列，对于滚动窗口中的所有观察值.

I have a pandas DataFrame and I want to calculate on a rolling basis the average of all the value: for all the columns, for all the observations in the rolling window.

我有一个带循环的解决方案，但感觉效率很低.请注意，我的数据中可以包含NaNs，因此根据窗口形状计算总和和跳水将是不安全的(因为我需要nanmean).

I have a solution with loops but feels very inefficient. Note that I can have NaNs in my data, so calculating the sum and diving by the shape of the window would not be safe (as I want a nanmean).

还有更好的方法吗?

设置

import numpy as np
import pandas as pd

np.random.seed(1)

df = pd.DataFrame(np.random.randint(0, 10, size=(10, 2)), columns=['A', 'B'])

df[df>5] = np.nan  # EDIT: add nans

我的尝试

n_roll = 2

df_stacked = df.values
roll_avg = {}
for idx in range(n_roll, len(df_stacked)+1):
    roll_avg[idx-1] = np.nanmean(df_stacked[idx - n_roll:idx, :].flatten())

roll_avg = pd.Series(roll_avg)
roll_avg.index = df.index[n_roll-1:]
roll_avg = roll_avg.reindex(df.index)

所需结果

roll_avg
Out[33]: 
0         NaN
1    5.000000
2    1.666667
3    0.333333
4    1.000000
5    3.000000
6    3.250000
7    3.250000
8    3.333333
9    4.000000

谢谢！

推荐答案

这是一个NumPy解决方案，其中滑动窗口已关闭view_as_windows-

Here's one NumPy solution with sliding windows off view_as_windows -

from skimage.util.shape import view_as_windows

# Setup o/p array
out = np.full(len(df),np.nan)

# Get sliding windows of length n_roll along axis=0
w = view_as_windows(df.values,(n_roll,1))[...,0]

# Assign nan-ignored mean values computed along last 2 axes into o/p
out[n_roll-1:] = np.nanmean(w, (1,2))

使用views的内存效率-

In [62]: np.shares_memory(df,w)
Out[62]: True

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