Outlier Detection
Detect and filter outliers.
Install
pip install sensor_dataset
Z-SCORE Normalization
Normalize data with Z-SCORE
from sensor_dataset.outlier_detection import ZSCORE
Get a normalized Koalas dataframe for the sensor dataset and fig objects by calling:
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")
When running on a notebook you may show an interactive plot by using:
fig.show()
IQR
Filter data using IQR
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")
1 maintainer
Metadata
1 maintainer
Release files for sensor-dataset 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sensor_dataset-0.0.1.tar.gz | 11.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sensor_dataset-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 20.8 kB
Release files / sensor_dataset-0.0.1.tar.gz
| Download URL | sensor_dataset-0.0.1.tar.gz |
|---|---|
| Size | 11.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / sensor_dataset-0.0.1-py3-none-any.whl
| Download URL | sensor_dataset-0.0.1-py3-none-any.whl |
|---|---|
| Size | 9.2 kB |
| Tags | Python 3 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
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twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.56.2 CPython/3.8.6
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