zscore-anomaly-detector 0.1.2

A Python package to detect anomalies using Z-Scores

Zscore Anomaly Detector

** Zscore Anomaly Detector** is a Python package designed to detect anomalies in numerical datasets using Z-Score analysis. This package identifies outliers by calculating the Z-Score for each numerical column and flags data points that deviate significantly from the mean. The package can handle mixed datasets containing numerical, categorical, and object types.

Installation

You can install the package using pip. Run the following command:

pip install zscore-anomaly-detector


## Usage
Here is an example of how to use the ZScore Anomaly Detector package with a realistic dataset containing both numerical and categorical columns:

import pandas as pd

from zscore_anomaly.zscore_anomaly_detector import ZScoreAnomalyDetector

## Sample Dataset

data = pd.DataFrame({
    'Age': [25, 32, 47, 51, 62, 35, 27, 100, 29, 38],  # Numeric
    'Salary': [50000, 54000, 58000, 62000, 65000, 52000, 51000, 200000, 53000, 56000],  # Numeric
    'Department': ['HR', 'IT', 'Finance', 'HR', 'IT', 'Finance', 'HR', 'IT', 'Finance', 'HR'],  # Categorical
    'Has_Debt': [True, False, True, True, False, False, True, True, False, True],  # Boolean
    'City': ['New York', 'San Francisco', 'Los Angeles', 'New York', 'San Francisco', 'Los Angeles', 'New York', 'San Francisco', 'Los Angeles', 'New York'],  # Object
})


#### Initialize the ZscoreAnomalyDetector

detector = ZScoreAnomalyDetector(threshold=2)  # The user can specify the threshold value for detecting anomalies. By default, the threshold is set to 3 if not provided.



#### Create a DataFrame that Includes Anomalies Marked

df_with_anomalies = detector.create_dataframe_with_anomalies(data)


#### Style the DataFrame to Highlight Anomalies in Red

styled_df = detector.style_dataframe(df_with_anomalies)


#### Display the Styled DataFrame

styled_df


Explanation
Age and Salary are numeric columns where Z-Scores will be calculated to detect anomalies.

Department is a categorical column, and City is an object column. These will not be included in Z-Score calculations, but they remain in the dataset.

Has_Debt is a boolean column.

This example shows how to detect anomalies in the numeric columns (Age and Salary) while leaving the non-numeric columns intact.

Output

After running the above code, the DataFrame will display anomalies detected in the numeric columns. Anomalies will be highlighted in red if used in a Jupyter notebook or a similar environment that supports DataFrame styling.