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We also have a quick-reference cheatsheet (new!) to help you get started!

3D Clustering with Alpha Shapes

```python deletable=true editable=true import plotly.plotly as py import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/alpha_shape.csv') df.head()

scatter = dict( mode = "markers", name = "y", type = "scatter3d", x = df['x'], y = df['y'], z = df['z'], marker = dict( size=2, color="rgb(23, 190, 207)" ) ) clusters = dict( alphahull = 7, name = "y", opacity = 0.1, type = "mesh3d", x = df['x'], y = df['y'], z = df['z'] ) layout = dict( title = '3d point clustering', scene = dict( xaxis = dict( zeroline=False ), yaxis = dict( zeroline=False ), zaxis = dict( zeroline=False ), ) ) fig = dict( data=[scatter, clusters], layout=layout )

Use py.iplot() for IPython notebook

py.iplot(fig, filename='3d point clustering') 

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#### Reference
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See https://plot.ly/python/reference/#mesh3d for more information regarding subplots!
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```python deletable=true editable=true
from IPython.display import display, HTML

display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))

! pip install git+https://github.com/plotly/publisher.git --upgrade

import publisher
publisher.publish(
    '3d-clusters.ipynb', 'python/3d-point-clustering/', 'Python 3D Clustering | plotly',
    'How to cluster points in 3d with alpha shapes in plotly and Python',
    title= '3D Point Clustering in Python | plotly',
    name = '3d Clustering',
    has_thumbnail='true', thumbnail='thumbnail/3d-clusters.jpg',
    language='python',
    display_as='3d_charts', order=14,
    ipynb= '~notebook_demo/74')

```python deletable=true editable=true

```

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