Filtering out outliers in Pandas dataframe with rolling median

Question

I am trying to filter out some outliers from a scatter plot of GPS elevation displacements with dates

I\'m trying to use df.rolling to compute a median and standard

Answer 1

Just filter the dataframe

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

This is my take on creating a median filter:

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

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

Answer 3

There might well be a more pandastic way to do this - this is a bit of a hack, relying on a sorta manual way of mapping the original df's index to each rolling window. (I picked size 6). The records up and until row 6 are associated with the first window; row 7 is the second window, and so on.

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")]