Metadata-Version: 2.1
Name: sensor_dataset
Version: 0.0.1
Summary: Put a description
Home-page: https://github.com/kikejimenez/sensor_dataset/tree/master/
Author: Enrique Jimenez
Author-email: physieira@gmail.com
License: Apache Software License 2.0
Description: # Outlier Detection
        > Detect and filter outliers.
        
        
        > [Documentation and Code can be found on Github]()
        
        ## Install
        
        `pip install sensor_dataset`
        
        ## Z-SCORE Normalization
        
        > Normalize data with Z-SCORE
        
        ```python
        from sensor_dataset.outlier_detection import ZSCORE
        ```
        
        Get a normalized Koalas dataframe for the sensor dataset and fig objects by calling:
        
        ```python
        kdf, figs = ZSCORE()
        
        figs['NORMAL'].write_image("images/zscore_normal.png")
        figs['RECOVERING'].write_image("images/zscore_recovering.png")
        figs['BROKEN'].write_image("images/zscore_broken.png")
        ```
        
        <img src="https://raw.githubusercontent.com/kikejimenez/sensor_dataset/master/nbs/images/zscore_normal.png" width="400" height="300" style="max-width: 400px">
        <img src="https://raw.githubusercontent.com/kikejimenez/sensor_dataset/master/nbs/images/zscore_recovering.png" width="400" height="300" style="max-width: 400px">
        <img src="https://raw.githubusercontent.com/kikejimenez/sensor_dataset/master/nbs/images/zscore_broken.png" width="400" height="300" style="max-width: 400px">
        
        When running on a notebook you may show an interactive plot by using:
        ```python
        fig.show()
        ```
        
        ## IQR
        
        > Filter data using IQR
        
        ```python
        from sensor_dataset.outlier_detection import IQR
        
        kdf, figs = IQR()
        
        figs['NORMAL'].write_image("images/iqr_normal.png")
        figs['RECOVERING'].write_image("images/iqr_recovering.png")
        figs['BROKEN'].write_image("images/iqr_broken.png")
        ```
        
        <img src="https://raw.githubusercontent.com/kikejimenez/sensor_dataset/master/nbs/images/iqr_normal.png" width="400" height="300" style="max-width: 400px">
        <img src="https://raw.githubusercontent.com/kikejimenez/sensor_dataset/master/nbs/images/iqr_recovering.png" width="400" height="300" style="max-width: 400px">
        <img src="https://raw.githubusercontent.com/kikejimenez/sensor_dataset/master/nbs/images/iqr_broken.png" width="400" height="300" style="max-width: 400px">
        
Keywords: outlier detection statistics
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
