return float(((nr2 - nr1) / nr1) * 100)
+
def remove_outliers(input_list, outlier_const=1.5, window=14):
"""Return list with outliers removed, using split_outliers.
:rtype: list of floats
"""
- 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
+ data = np.array(input_list)
+ upper_quartile = np.percentile(data, 75)
+ lower_quartile = np.percentile(data, 25)
+ iqr = (upper_quartile - lower_quartile) * outlier_const
+ quartile_set = (lower_quartile - iqr, upper_quartile + iqr)
+ result_lst = list()
+ for y in input_list:
+ if quartile_set[0] <= y <= quartile_set[1]:
+ result_lst.append(y)
+ return result_lst
def split_outliers(input_series, outlier_const=1.5, window=14):
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)
:param path: Path to files.
:param extension: Extension of files to process. If it is the empty string,
- all files will be processed.
+ all files will be processed.
:param full_path: If True, the files with full path are generated.
:type path: str
:type extension: str