What is cluster map?

What is cluster map?#

Plot a matrix dataset as a hierarchically-clustered heatmap.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline

# laod flight data set
iris = sns.load_dataset("iris")
iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

# Plot a clustered heatmap:
species = iris.pop("species")
sns.clustermap(iris)

<seaborn.matrix.ClusterGrid at 0x163cdf926d0>

## Use a different similarity metric:
sns.clustermap(iris_data,metric = 'correlation')

<seaborn.matrix.ClusterGrid at 0x1be276a14a8>

# Use a different clustering method:
sns.clustermap(iris,method = 'single')

<seaborn.matrix.ClusterGrid at 0x163ce3245e0>

# Use a different colormap and ignore outliers in colormap limits:

sns.clustermap(iris, cmap="mako", robust=True)

<seaborn.matrix.ClusterGrid at 0x163ca247070>

# Change the size of the figure:
sns.clustermap(iris, figsize=(6, 7))

<seaborn.matrix.ClusterGrid at 0x163cfcc79d0>

# Plot one of the axes in its original organization:

sns.clustermap(iris, col_cluster=False)

<seaborn.matrix.ClusterGrid at 0x163d0288250>

# Add colored labels:

lut = dict(zip(species.unique(), "rbg"))
row_colors = species.map(lut)
g = sns.clustermap(iris, row_colors=row_colors)

# Standardize the data within the columns:

sns.clustermap(iris, standard_scale=1)

<seaborn.matrix.ClusterGrid at 0x163d05bfa60>

# Normalize the data within the rows:

sns.clustermap(iris, z_score=0)

<seaborn.matrix.ClusterGrid at 0x163d0f2ae20>