IdentifyOutliers 0.0.2

IdentifyOutliers provides a combined functionality of scaling data using the standard scaler approach while also removing outliers based on a z-score threshold for panda's projects.

IdentifyOutliers

A Python package for efficient scaling and outlier handling of pandas DataFrames using the standard scaler approach, .

IdentifyOutliers is designed to provide a seamless experience in preprocessing pandas DataFrames by ensuring data normalization and outlier handling in one step.

Features

• Data Scaling: Utilizes the standard scaler method for data normalization.
• Outlier Detection: Provides an option to set a z-score threshold for outlier detection.
• Multiple Outputs: Returns the original data, the scaled data without outliers, a separate DataFrame for detected outliers, and scaled outliers.

Installation

Install the package using pip:

pip install IdentifyOutliers

Usage

import pandas as pd
from IdentifyOutliers.CustomZscoreScaler import CustomZscoreScaler

# Sample DataFrame
data = {
    'A': [1, 2, 3, 100, 5],
    'B': [5, 6, 7, 8, 500]
}
df = pd.DataFrame(data)

# Initialize the scaler with a z-score threshold (default is 3.0)
scaler = CustomZscoreScaler(threshold=3.0)

# Transform the data
df_no_outliers, df_scaled_no_outliers, df_outliers, df_scaled_outliers = scaler.transform(df)

# Print the results
print(df_no_outliers)
#    A  B
# 0  1  5
# 1  2  6
# 2  3  7

print(df_scaled_no_outliers)
#           A         B
# 0 -0.544672 -0.507592
# 1 -0.518980 -0.502526
# 2 -0.493288 -0.497461

print(df_outliers)
#      A    B
# 3  100    8
# 4    5  500

print(df_outliers)
#      A    B
# 3  100    8
# 4    5  500

print(df_scaled_outliers)
#           A         B
# 3  1.998845 -0.492395
# 4 -0.441904  1.999974

Parameters

threshold: The z-score threshold for outlier detection. Data points exceeding threshold standard deviations away from the mean are considered outliers. The default value is 3.0.

Contributions

Contributions are welcome! Please create an issue or submit a pull request.

License

This project is licensed under the [MIT License] (https://github.com/amithpdn/IdentifyOutliers/blob/master/LICENSE.TXT).