py_outliers_utils.outliers

Module Contents

Functions

outlier_identifier(dataframe, columns=None, identifier='IQR', return_df=False)

A function that identify and summarize the count and range of based on the method the user choose
py_outliers_utils.outliers.outlier_identifier(dataframe, columns=None, identifier='IQR', return_df=False)[source]

A function that identify and summarize the count and range of based on the method the user choose :param dataframe: The target dataframe where the function is performed. :type dataframe: pandas.core.frame.DataFrame :param columns: The target columns where the function needed to be performed. Default is None, the function will check all columns :type columns: list, default=None :param identifier: The method of identifying outliers.

  • if “Z_score” : Use z-test with threshold of 3

  • if “IQR” : Use IQR (Inter Quantile range) to identify outliers (default)

Parameters

return_df (bool, default=False) – Can be set to True if want output as dataframe identified with outliers in rows

Returns

  • pandas.core.frame.DataFrame

  • (a dataframe with the summary of the outlier identified by the method) if return_df = False ,

  • (a dataframe with additional column having if row has outlier or not) if return_df = True

Examples

>>> import pandas as pd

>>> df = pd.DataFrame({
>>>    'SepalLengthCm' : [5.1, 4.9, 4.7, 5.5, 5.1, 50, 54, 5.0, 5.2, 5.3, 5.1],
>>>    'SepalWidthCm' : [1.4, 1.4, 20, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1],
>>>    'PetalWidthCm' : [0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5]
>>> })
>>> outlier_identifier(df)
                    SepalLengthCm SepalWidthCm PetalWidthCm
outlier_count                  2            1            1
outlier_percentage        18.18%        9.09%        9.09%
mean                       13.63         3.19         0.77
median                       5.1          1.5          0.4
std                        18.99         5.59         1.41
lower_range                  NaN          NaN          NaN
upper_range         (50.0, 54.0)         20.0          5.0