Put a description
Project description
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")
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sensor_dataset-0.0.1.tar.gz
(11.6 kB
view hashes)
Built Distribution
Close
Hashes for sensor_dataset-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e9a7c103986c002e95b9a45805ee2e1e2b4f2d4c8dfd8c74d4f4d0eab66bc06d |
|
MD5 | d74deef79f22bdacc2667f6eb76d34f9 |
|
BLAKE2b-256 | a6ecd1195aab3e77f43de374ab19e30b9b70237c9a49e574b22b4c8207bf03b2 |