return float(((nr2 - nr1) / nr1) * 100)
-def remove_outliers(input_data, outlier_const):
- """
+def remove_outliers(input_list, outlier_const=1.5, window=14):
+ """Return list with outliers removed, using split_outliers.
- :param input_data: Data from which the outliers will be removed.
+ :param input_list: Data from which the outliers will be removed.
:param outlier_const: Outlier constant.
- :type input_data: list
+ :param window: How many preceding values to take into account.
+ :type input_list: list of floats
:type outlier_const: float
+ :type window: int
:returns: The input list without outliers.
- :rtype: list
+ :rtype: list of floats
"""
- data = np.array(input_data)
+ data = np.array(input_list)
upper_quartile = np.percentile(data, 75)
lower_quartile = np.percentile(data, 25)
iqr = (upper_quartile - lower_quartile) * outlier_const
return result_lst
-def find_outliers(input_data, outlier_const=1.5):
+def split_outliers(input_series, outlier_const=1.5, window=14):
"""Go through the input data and generate two pandas series:
- - input data without outliers
+ - input data with outliers replaced by NAN
- outliers.
The function uses IQR to detect outliers.
- :param input_data: Data to be examined for outliers.
+ :param input_series: Data to be examined for outliers.
:param outlier_const: Outlier constant.
- :type input_data: pandas.Series
+ :param window: How many preceding values to take into account.
+ :type input_series: pandas.Series
:type outlier_const: float
- :returns: Tuple: input data with outliers removed; Outliers.
- :rtype: tuple (trimmed_data, outliers)
+ :type window: int
+ :returns: Input data with NAN outliers and Outliers.
+ :rtype: (pandas.Series, pandas.Series)
"""
- upper_quartile = input_data.quantile(q=0.75)
- lower_quartile = input_data.quantile(q=0.25)
- iqr = (upper_quartile - lower_quartile) * outlier_const
- low = lower_quartile - iqr
- high = upper_quartile + iqr
+ list_data = list(input_series.items())
+ head_size = min(window, len(list_data))
+ head_list = list_data[:head_size]
trimmed_data = pd.Series()
outliers = pd.Series()
- for item in input_data.items():
- item_pd = pd.Series([item[1], ], index=[item[0], ])
- if low <= item[1] <= high:
+ for item_x, item_y in head_list:
+ item_pd = pd.Series([item_y, ], index=[item_x, ])
+ trimmed_data = trimmed_data.append(item_pd)
+ for index, (item_x, item_y) in list(enumerate(list_data))[head_size:]:
+ y_rolling_list = [y for (x, y) in list_data[index - head_size:index]]
+ y_rolling_array = np.array(y_rolling_list)
+ q1 = np.percentile(y_rolling_array, 25)
+ q3 = np.percentile(y_rolling_array, 75)
+ iqr = (q3 - q1) * outlier_const
+ low, high = q1 - iqr, q3 + iqr
+ item_pd = pd.Series([item_y, ], index=[item_x, ])
+ if low <= item_y <= high:
trimmed_data = trimmed_data.append(item_pd)
else:
- trimmed_data = trimmed_data.append(pd.Series([np.nan, ],
- index=[item[0], ]))
outliers = outliers.append(item_pd)
+ nan_pd = pd.Series([np.nan, ], index=[item_x, ])
+ trimmed_data = trimmed_data.append(nan_pd)
return trimmed_data, outliers