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HiPlot - High dimensional Interactive Plotting [![CircleCI](https://circleci.com/gh/facebookresearch/hiplot/tree/main.svg?style=svg\u0026circle-token=c89b6825078e174cf35bdc18e4ad4a16e28876f9)](https://circleci.com/gh/facebookresearch/hiplot/tree/main)\n\n\n![Logo](https://raw.githubusercontent.com/facebookresearch/hiplot/main/hiplot/static/logo.png)\n\n[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat\u0026labelColor=005BBB)](https://opensource.fb.com/support-ukraine)\n[![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)\n[![PyPI download month](https://img.shields.io/pypi/dm/hiplot.svg)](https://pypi.python.org/pypi/hiplot/) [![PyPI version](https://img.shields.io/pypi/v/hiplot.svg)](https://pypi.python.org/pypi/hiplot/) [![docs](https://img.shields.io/badge/docs-passing-brightgreen.svg)](https://facebookresearch.github.io/hiplot/index.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/hiplot/blob/main/examples/HiPlotColabExample.ipynb)\n\n\nHiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.\n\n### [Try a demo now with sweep data](https://facebookresearch.github.io/hiplot/_static/demo/ml1.csv.html) or [upload your CSV](https://facebookresearch.github.io/hiplot/_static/hiplot_upload.html)  or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/hiplot/blob/main/examples/HiPlotColabExample.ipynb)\n\nThere are several modes to HiPlot:\n- As a web-server (if your data is a CSV for instance)\n- In a jupyter notebook (to visualize python data), or in [Streamlit apps](https://facebookresearch.github.io/hiplot/tuto_streamlit.html)\n- In CLI to render standalone HTML\n\n\n```bash\npip install -U hiplot  # Or for conda users: conda install -c conda-forge hiplot\n```\n\nIf you have a jupyter notebook, you can get started with something as simple as:\n\n```python\nimport hiplot as hip\ndata = [{'dropout':0.1, 'lr': 0.001, 'loss': 10.0, 'optimizer': 'SGD'},\n        {'dropout':0.15, 'lr': 0.01, 'loss': 3.5, 'optimizer': 'Adam'},\n        {'dropout':0.3, 'lr': 0.1, 'loss': 4.5, 'optimizer': 'Adam'}]\nhip.Experiment.from_iterable(data).display()\n```\n\n### [See the live result](https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html)\n![Result](https://raw.githubusercontent.com/facebookresearch/hiplot/main/assets/notebook.png)\n\n## Links\n\n* Blog post: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/\n* Documentation: https://facebookresearch.github.io/hiplot/index.html\n* Pypi package: https://pypi.org/project/hiplot/\n* Conda package: https://anaconda.org/conda-forge/hiplot\n* NPM package: https://www.npmjs.com/package/hiplot\n* Examples: https://github.com/facebookresearch/hiplot/tree/main/examples\n\n\n## Citing\n\n```bibtex\n@misc{hiplot,\n    author = {Haziza, D. and Rapin, J. and Synnaeve, G.},\n    title = {{Hiplot, interactive high-dimensionality plots}},\n    year = {2020},\n    publisher = {GitHub},\n    journal = {GitHub repository},\n    howpublished = {\\url{https://github.com/facebookresearch/hiplot}},\n}\n```\n\n## Credits\nInspired by and based on code from [Kai Chang](http://bl.ocks.org/syntagmatic/3150059), [Mike Bostock](http://bl.ocks.org/1341021) and [Jason Davies](http://bl.ocks.org/1341281).\n\nExternal contributors (*please add your name when you submit your first pull request*):\n- [louismartin](https://github.com/louismartin)\n- [GoldenCorgi](https://github.com/GoldenCorgi)\n- [callistachang](https://github.com/callistachang)\n\n\n## 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