Source code for vivainsights.identify_outlier
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
"""
This function takes in a selected metric and uses the z-score (number of standard deviations) to identify outliers across time. There are applications in this for identifying weeks with abnormally low collaboration activity, e.g. holidays. Time as a grouping variable can be overridden with the `group_var` argument.
"""
import pandas as pd
[docs]
def identify_outlier(data: pd.DataFrame, group_var = "MetricDate", metric = "Collaboration_hours"):
"""
Name
-----
identify_outlier
Description
-----------
This function takes in a selected metric and uses the
z-score (number of standard deviations) to identify outliers
across time. There are applications in this for identifying
weeks with abnormally low collaboration activity, e.g. holidays.
Time as a grouping variable can be overridden with the `group_var`
argument.
Parameters
----------
data : pandas dataframe
A Standard Person Query dataset in the form of a pandas dataframe.
group_var : str
A string with the name of the grouping variable. Default: `MetricDate`.
metric : str
A string containing the name of the metric (e.g., "Collaboration_hours")
Returns
-------
A dataframe with `MetricDate` (if grouping variable is not set),
the metric, and the corresponding z-score.
Example
-------
>>> import vivainsights as vi
>>> pq_data = vi.load_pq_data()
>>> vi.identify_outlier(pq_data, group_var = "MetricDate", metric = "Collaboration_hours")
"""
try:
# Group by the grouping variable and calculate the mean of the metric
main_table = data.groupby(group_var).agg({metric: "mean"})
# Calculate the z-score of the metric
main_table["zscore"] = (main_table[metric] - main_table[metric].mean()) / main_table[metric].std()
return main_table
except:
# Check for the error in the input data
requirements = [group_var, "PersonId", metric]
# Check if the required variables are present
for i in requirements:
if i not in data.columns:
raise ValueError("The required variable {} is not present in the dataframe.".format(i))
# Check to see if the metric column is numeric
if not pd.api.types.is_numeric_dtype(data[requirements[2]]):
raise ValueError("The metric column {} is not numeric.".format(requirements[2]))