# CleanDat Python functions to facilitate the pre-processing of data to prepare them for ML tasks, especially suitable for data in a clinical context.
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Major functionalities include heuristic based data cleaning and feature engineering like: - Automatic detection of encoding strings (e.g. 1=m) and application of the corresponding encoding to un-encoded data of the corresponding column - Automatic detection of date strings of different formats (e.g. 2019-01-01, 01/01/2019, January 2022) and conversion to a unified format - Encoding of date strings into decomposed date features (e.g. year, month, day, weekday, etc.) - Heuristics for unification of different number formats, e.g. 1,000.00 vs. 1.000,00 or exponential notations like 1e3 vs 10x10^2 - Detection and replacement of inconsistent data values
# Setup
Install via pip:
pip install cleandat
Metadata
Release files for cleandat 0.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cleandat-0.0.3.tar.gz | 10.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cleandat-0.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.4 kB
Release files / cleandat-0.0.3.tar.gz
| Download URL | cleandat-0.0.3.tar.gz |
|---|---|
| Size | 10.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
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twine/4.0.2 CPython/3.9.18
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Release files / cleandat-0.0.3-py3-none-any.whl
| Download URL | cleandat-0.0.3-py3-none-any.whl |
|---|---|
| Size | 13.9 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
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