# idendrogram
idendrogram helps you create nicer, interactive visualizations of hierarchical clustering trees (a.k.a. dendrograms) from clustering outputs generated by your preferred hierarchical clustering library (SciPy, Scikit-learn or HDBSCAN) in your preferred python visualization library (Altair, Plotly or Matplotlib)
It also supports bi-directional Streamlit integration via a custom D3-powered component.
<img src='docs/gallery/custom-radii.png' width=500>
<img src='docs/gallery/streamlit-integration.gif' width=500>
## Installation
To use the main package:
```pip install idendrogram```
To use the bi-directional Streamlit component:
```pip install idendrogram idendrogram-streamlit-component```
## Basic usage
```python
import idendrogram
import scipy.cluster.hierarchy as sch
from idendrogram.targets.altair import to_altair
#cluster the data
linkage_matrix = sch.linkage(
data['data'], method='single', metric='euclidean'
)
threshold = 0.8
flat_clusters = sch.fcluster(
linkage_matrix, t=threshold, criterion='distance'
)
#wrap clustering outputs / parameters into a container
cl_data = idendrogram.ClusteringData(
linkage_matrix = linkage_matrix,
cluster_assignments = flat_clusters
)
#pass to idendrogram and visualize
idd = idendrogram.idendrogram()
idd.set_cluster_info(cl_data)
dendrogram = idd.create_dendrogram(truncate_mode='level', p=10)
to_altair(dendrogram=dendrogram, height=200, width=629)
```
For more, see docs at https://kamicollo.github.io/idendrogram/
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"description": "# idendrogram\n\nidendrogram helps you create nicer, interactive visualizations of hierarchical clustering trees (a.k.a. dendrograms) from clustering outputs generated by your preferred hierarchical clustering library (SciPy, Scikit-learn or HDBSCAN) in your preferred python visualization library (Altair, Plotly or Matplotlib)\n\nIt also supports bi-directional Streamlit integration via a custom D3-powered component.\n\n<img src='docs/gallery/custom-radii.png' width=500>\n<img src='docs/gallery/streamlit-integration.gif' width=500>\n\n## Installation\n\nTo use the main package:\n\n```pip install idendrogram```\n\nTo use the bi-directional Streamlit component:\n\n```pip install idendrogram idendrogram-streamlit-component``` \n\n## Basic usage \n\n```python\nimport idendrogram\nimport scipy.cluster.hierarchy as sch\nfrom idendrogram.targets.altair import to_altair\n\n#cluster the data\nlinkage_matrix = sch.linkage(\n data['data'], method='single', metric='euclidean'\n)\nthreshold = 0.8\nflat_clusters = sch.fcluster(\n linkage_matrix, t=threshold, criterion='distance'\n)\n\n#wrap clustering outputs / parameters into a container\ncl_data = idendrogram.ClusteringData(\n linkage_matrix = linkage_matrix, \n cluster_assignments = flat_clusters\n)\n\n#pass to idendrogram and visualize\nidd = idendrogram.idendrogram()\nidd.set_cluster_info(cl_data)\ndendrogram = idd.create_dendrogram(truncate_mode='level', p=10)\nto_altair(dendrogram=dendrogram, height=200, width=629)\n```\n\nFor more, see docs at https://kamicollo.github.io/idendrogram/\n",
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