result_lst.append(y)
return result_lst
- # input_series = pd.Series()
- # for index, value in enumerate(input_list):
- # item_pd = pd.Series([value, ], index=[index, ])
- # input_series.append(item_pd)
- # output_series, _ = split_outliers(input_series, outlier_const=outlier_const,
- # window=window)
- # output_list = [y for x, y in output_series.items() if not np.isnan(y)]
- #
- # return output_list
-
def split_outliers(input_series, outlier_const=1.5, window=14):
"""Go through the input data and generate two pandas series:
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
+ low = q1 - iqr
item_pd = pd.Series([item_y, ], index=[item_x, ])
- if low <= item_y <= high:
+ if low <= item_y:
trimmed_data = trimmed_data.append(item_pd)
else:
outliers = outliers.append(item_pd)