用滚动中值过滤熊猫数据框中的异常值

Question

我正在尝试从具有日期的GPS高程位移散点图中过滤掉一些离群值

我正在尝试使用df.rolling计算每个窗口的中位数和标准偏差，如果该点大于3个标准偏差，则将其删除。

但是，我想不出一种方法来遍历该列并比较所计算的中位数滚动。

这里是我到目前为止的代码

import pandas as pd
import numpy as np

def median_filter(df, window):
    cnt = 0
    median = df['b'].rolling(window).median()
    std = df['b'].rolling(window).std()
    for row in df.b:
      #compare each value to its median




df = pd.DataFrame(np.random.randint(0,100,size=(100,2)), columns = ['a', 'b'])

median_filter(df, 10)

我如何遍历并比较每个点并将其删除？

Answer 1

仅过滤数据框

df['median']= df['b'].rolling(window).median()
df['std'] = df['b'].rolling(window).std()

#filter setup
df = df[(df.b <= df['median']+3*df['std']) & (df.b >= df['median']-3*df['std'])]

Answer 2

可能会有更泛泛的方法来执行此操作-这有点麻烦，它依赖于一种将原始df的索引映射到每个滚动窗口的手动方法。 （我选了6号）。直到第6行的记录与first窗口关联；第7行是第二个窗口，依此类推。

n = 100
df = pd.DataFrame(np.random.randint(0,n,size=(n,2)), columns = ['a','b'])

## set window size
window=6
std = 1  # I set it at just 1; with real data and larger windows, can be larger

## create df with rolling stats, upper and lower bounds
bounds = pd.DataFrame({'median':df['b'].rolling(window).median(),
'std':df['b'].rolling(window).std()})

bounds['upper']=bounds['median']+bounds['std']*std
bounds['lower']=bounds['median']-bounds['std']*std

## here, we set an identifier for each window which maps to the original df
## the first six rows are the first window; then each additional row is a new window
bounds['window_id']=np.append(np.zeros(window),np.arange(1,n-window+1))

## then we can assign the original 'b' value back to the bounds df
bounds['b']=df['b']

## and finally, keep only rows where b falls within the desired bounds
bounds.loc[bounds.eval("lower<b<upper")]

Answer 3

这是我创建中值过滤器的方法：

def median_filter(num_std=3):
    def _median_filter(x):
        _median = np.median(x)
        _std = np.std(x)
        s = x[-1]
        return (s >= _median - num_std * _std and s <= _median + num_std * _std)
    return _median_filter

df.y.rolling(window).apply(median_filter(num_std=3), raw=True)