DataProfileViewer
Data profile viewer is compatible with Jupyter Notebooks. Supports the metadata format generated by datamart-profiler library.
Install via pip
pip install data-profile-viewer
pip install datamart-profiler
Demo
In Jupyter Notebook:
import DataProfileViewer
data = DataProfileViewer.get_lifeexpectancy_data()
DataProfileViewer.plot_data_summary(data)
Data Profile Exploration
import DataProfileViewer
import datamart_profiler
In a jupyter notebook, load the data
data_path = 'lifeexpectancydata.csv'
metadata = datamart_profiler.process_dataset(data_path, include_sample=True, plots=True)
and then plot it using:
DataProfileViewer.plot_data_summary(metadata)
Refine Data Profiler Results
You might want to correct/refine the type information, or provide additional annotations for the columns. To do so, use the code:
DataProfileViewer.plot_edit_profiler(metadata)
To retrieve the updated metadata, use the code:
updatedMetadata = DataProfileViewer.get_exported_metadata(data_path)
1 maintainer
Metadata
1 maintainer
Release files for data-profile-viewer 0.2.7
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| data-profile-viewer-0.2.7.tar.gz | 1.5 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| data_profile_viewer-0.2.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.0 MB
Release files / data-profile-viewer-0.2.7.tar.gz
| Download URL | data-profile-viewer-0.2.7.tar.gz |
|---|---|
| Size | 1.5 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / data_profile_viewer-0.2.7-py3-none-any.whl
| Download URL | data_profile_viewer-0.2.7-py3-none-any.whl |
|---|---|
| Size | 1.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Security for data-profile-viewer 0.2.7
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