Outlier Detection and Treatment with ODC

OilDataCleaner (ODC) is a Python module designed for detecting anomalies in time series data related to the oil industry. With its data cleaning techniques, ODC is an essential tool for anyone working with oil data. Whether you're a researcher, analyst, or engineer, ODC can help you identify unusual patterns and outliers in your data, enabling you to make more informed decisions.

Installation

To use this module:

1. Clone or download this repository to your local machine.

2. Install the required packages by running the following command in your command prompt or terminal:

pip install -r requirements.txt

This will install all the necessary dependencies for using this module.

3. Import the necessary packages and the DetectOutliers class in your Python script:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from odc import DetectOutliers

4. Load your dataset using pandas and create an instance of the DetectOutliers class:

df = pd.read_csv("F_14.csv", parse_dates=["DATEPRD"], index_col="DATEPRD")
clean = DetectOutliers(df)

5. Detect outliers in the desired variable:

# Detecting outliers in the 'ON_STREAM_HRS' variable
ON_STREAM_HRS_outliers = clean.detect_outliers_in_time('ON_STREAM_HRS', 'BORE_OIL_VOL')
print("Outliers detected in ON_STREAM_HRS variable:\n", ON_STREAM_HRS_outliers)

Output:

Outliers detected in ON_STREAM_HRS variable:
 DATEPRD
2010-10-31    25.00000
2012-09-15     0.95833
2013-10-27    24.30833
2014-10-26    25.00000
Name: ON_STREAM_HRS, dtype: float64

6. Plot the outliers:

clean.plot_outliers(ON_STREAM_HRS_outliers)

Output:

7. Treat the outliers:

df['ON_STREAM_HRS'] = clean.treat_outliers_in_time()

8. Check that the outliers were treated:

outliers_after_treatment = clean.detect_outliers_in_time('ON_STREAM_HRS', 'BORE_OIL_VOL')
print("Outliers detected in ON_STREAM_HRS variable after treatment:\n", outliers_after_treatment)

Output:

Outliers detected in ON_STREAM_HRS variable after treatment:
 No outliers detected.

For more examples of using this module, please refer to the data_cleaning.ipynb notebook in this repository.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are always welcome!

See contributing.md for ways to get started.

Contact

If you have any questions or encounter any issues running odc module, please feel free to open an issue. I'll be happy to help!