CODE EXAMPLE FOR PYTHON

remove outliers in dataframe

# Solution is based on this article: 
# http://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm

import pandas as pd
import numpy as np

def remove_outliers_from_series(series):
    q1 = series.quantile(0.25)
    q3 = series.quantile(0.75)
    intraquartile_range = q3 - q1
    fence_low  = q1 - 1.5 * intraquartile_range
    fence_high = q3 + 1.5 * intraquartile_range
    return series[(series > fence_low) & (series < fence_high)]


def remove_outliers_from_dataframe(self, df, col):
    q1 = df[col].quantile(0.25)
    q3 = df[col].quantile(0.75)
    intraquartile_range = q3 - q1
    fence_low  = q1 - 1.5 * intraquartile_range
    fence_high = q3 + 1.5 * intraquartile_range
    return df.loc[(df[col] > fence_low) & (df[col] < fence_high)]


def remove_outliers_from_np_array(self, arr):
    q1 = np.percentile(arr, 25)
    q3 = np.percentile(arr, 75)
    intraquartile_range = q3 - q1
    fence_low  = q1 - 1.5 * intraquartile_range
    fence_high = q3 + 1.5 * intraquartile_range
    return arr[(arr > fence_low) & (arr < fence_high)]


def remove_outliers_from_python_list(self, _list):
    arr = np.array(_list)
    return list(remove_outliers_from_np_array(arr))


def remove_outliers(*args, **kwargs):
        if isinstance(args[0], pd.DataFrame):
            return remove_outliers_from_dataframe(*args, **kwargs)
        elif isinstance(args[0], pd.Series):
            return remove_outliers_from_series(*args, **kwargs)
        elif isinstance(args[0], np.ndarray):
            return remove_outliers_from_np_array(*args, **kwargs)
        elif isinstance(args[0], list):
            return remove_outliers_from_python_list(*args, **kwargs)
        else:
            raise TypeError(f'{type(args[0])} is not supported.')