How do you plot a dendrogram in python?

This example plots the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy.

import numpy as np

from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram
from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering


def plot_dendrogram[model, **kwargs]:
    # Create linkage matrix and then plot the dendrogram

    # create the counts of samples under each node
    counts = np.zeros[model.children_.shape[0]]
    n_samples = len[model.labels_]
    for i, merge in enumerate[model.children_]:
        current_count = 0
        for child_idx in merge:
            if child_idx 1\] original observation are labeled with the number of observations they contain in parentheses.
no_plotbool, optional
When True, the final rendering is not performed. This is useful if only the data structures computed for the rendering are needed or if matplotlib is not available.
no_labelsbool,
optional
When True, no labels appear next to the leaf nodes in the rendering of the dendrogram.
leaf_rotationdouble, optional
Specifies the angle [in degrees] to rotate the leaf labels. When unspecified, the rotation is based on the number of nodes in the dendrogram [default is 0].

  

leaf_font_sizeint, optional
Specifies the font size [in points] of the leaf labels. When
unspecified, the size based on the number of nodes in the dendrogram.
leaf_label_funclambda or function, optional
When leaf_label_func is a callable function, for each leaf with cluster index \[k < 2n-1\]. The function is expected to return a string with the label for the leaf.
Indices \[k < n\] correspond to original observations while indices \[k \geq n\] correspond to non-singleton clusters.
For example, to label singletons with their node id and non-singletons with their id, count, and inconsistency coefficient, simply do:

  

# First define the leaf label function.
def llf[id]:
    if id >> from scipy.cluster import hierarchy
>>> import matplotlib.pyplot as plt
A very basic example:
>>> ytdist = np.array[[662., 877., 255., 412., 996., 295., 468., 268.,
...                    400., 754., 564., 138., 219., 869., 669.]]
>>> Z = hierarchy.linkage[ytdist, 'single']
>>> plt.figure[]
>>> dn = hierarchy.dendrogram[Z]
Now, plot in given axes, improve the color scheme and use both vertical and horizontal orientations:
>>> hierarchy.set_link_color_palette[['m', 'c', 'y', 'k']]
>>> fig, axes = plt.subplots[1, 2, figsize=[8, 3]]
>>> dn1 = hierarchy.dendrogram[Z, ax=axes[0], above_threshold_color='y',
...                            orientation='top']
>>> dn2 = hierarchy.dendrogram[Z, ax=axes[1],
...                            above_threshold_color='#bcbddc',
...                            orientation='right']
>>> hierarchy.set_link_color_palette[None]  # reset to default after use
>>> plt.show[]