sme-dataprep
Educational data-preprocessing utilities for the Software Matemático y Estadístico coursework. The package includes dataset management, missing-value handling, normalization, standardization, discretization, association metrics, AUC/ROC helpers, and visualizations.
Installation
Install the package from PyPI:
python -m pip install sme-dataprep
In a Jupyter notebook, you can run:
%pip install sme-dataprep
Quick start
The distribution is named sme-dataprep; import its API from dataprepkit:
import pandas as pd
from dataprepkit import dataset_summary, normalize_variable
data = pd.DataFrame({"score": [10, 20, 30], "group": ["a", "a", "b"]})
print(dataset_summary(data))
scaled_score = normalize_variable(data["score"])
The project source and additional examples are available in the GitHub repository.
1 maintainer
Metadata
1 maintainer
Release files for sme-dataprep 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sme_dataprep-0.1.0.tar.gz | 16.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sme_dataprep-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 37.4 kB
Release files / sme_dataprep-0.1.0.tar.gz
| Download URL | sme_dataprep-0.1.0.tar.gz |
|---|---|
| Size | 16.9 kB |
| Tags | Source |
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Release files / sme_dataprep-0.1.0-py3-none-any.whl
| Download URL | sme_dataprep-0.1.0-py3-none-any.whl |
|---|---|
| Size | 20.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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