https://github.com/datamole-ai/edvart
An open-source Python library for Data Scientists & Data Analysts designed to simplify the exploratory data analysis process. Using Edvart, you can explore data sets and generate reports with minimal coding.
https://github.com/datamole-ai/edvart
analysis data-analysis data-science data-visualization data-viz eda exploration exploratory-data-analysis exploratory-data-analysis-eda plots python
Last synced: 4 months ago
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An open-source Python library for Data Scientists & Data Analysts designed to simplify the exploratory data analysis process. Using Edvart, you can explore data sets and generate reports with minimal coding.
- Host: GitHub
- URL: https://github.com/datamole-ai/edvart
- Owner: datamole-ai
- License: mit
- Created: 2023-07-21T08:44:20.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2026-01-29T09:16:32.000Z (4 months ago)
- Last Synced: 2026-01-30T00:33:07.286Z (4 months ago)
- Topics: analysis, data-analysis, data-science, data-visualization, data-viz, eda, exploration, exploratory-data-analysis, exploratory-data-analysis-eda, plots, python
- Language: Python
- Homepage: https://datamole-ai.github.io/edvart/
- Size: 25.3 MB
- Stars: 60
- Watchers: 2
- Forks: 8
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# Edvart
> [!WARNING]
> This project is no longer maintained.
Edvart is an open-source Python library designed to simplify and streamline
your exploratory data analysis (EDA) process.
## Key Features
* **One-line Reports**: Generate a comprehensive set of pandas DataFrame
visualizations using a single Python statement.
Edvart supports:
- Data overview,
- Univariate analysis,
- Bivariate analysis,
- Multivariate analysis,
- Grouped analysis,
- Time series analysis.
* **Customizable Reports**: Produce, iterate, and style detailed reports
in Jupyter notebooks and HTML formats.
* **Flexible API**: From high-level simplicity in a single line of code
to detailed control, choose the API level that fits your needs.
* **Interactive Visualizations**: Many of the visualizations are interactive
and can be used to explore the data in detail.
## One-line Report
## Installation
Edvart is available on PyPI and can be installed using pip:
```bash
pip install edvart
```
## Usage
See the notebook
[examples/report-example.ipynb](https://nbviewer.org/github/datamole-ai/edvart/blob/main/examples/report-example.ipynb)
for an example report on a tabular dataset or
[examples/time-series-report-example.ipynb](https://nbviewer.org/github/datamole-ai/edvart/blob/main/examples/time-series-report-example.ipynb)
for an example report on a time-series dataset.
See the [Usage section](https://datamole-ai.github.io/edvart/usage.html) of the documentation
for more information.
### Creating a Default Report
```python
import edvart
# Load a dataset to a pandas DataFrame
dataset = edvart.example_datasets.dataset_titanic()
# Create a default report
report = edvart.DefaultReport(dataset)
# Show the report in the current Jupyter notebook
report.show()
# Export the report to an HTML file
report.export_html("report.html")
# Export the code generating the report to a Jupyter notebook
report.export_notebook("report.ipynb")
```
## User Documentation
The user documentation is available at https://datamole-ai.github.io/edvart/.
## License
Edvart is licensed under the [MIT
license](https://opensource.org/license/mit/). See the LICENSE file for more
details.
## Contact
Edvart has a [Gitter room](https://app.gitter.im/#/room/#edvart:gitter.im)
for development-related and general discussions.
## How to Contribute
See [CONTRIBUTING.md](CONTRIBUTING.md).