Clustered Heatmap

Seaborn basics

2 min read

Published Oct 7 2025

24
0
0
0
ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

seaborn.clustermap() creates a clustered heatmap — a combination of:

  • A heatmap (coloured grid of values), and
  • Hierarchical clustering on both rows and columns.

It automatically reorders rows and columns to group similar patterns together, and draws dendrograms (tree diagrams) showing the clustering structure.

It’s commonly used in:

  • Gene expression analysis
  • Feature correlation exploration
  • Pattern detection in any tabular numeric data

Syntax:

sns.clustermap(
    data,
    *,
    pivot_kws=None,
    method='average',
    metric='euclidean',
    z_score=None,
    standard_scale=None,
    figsize=None,
    cmap=None,
    center=None,
    robust=False,
    annot=None,
    fmt=".2g",
    annot_kws=None,
    linewidths=0,
    linecolor='white',
    cbar_pos=(0.02, 0.8, 0.05, 0.18),
    cbar_kws=None,
    mask=None,
    dendrogram_ratio=0.2,
    colors_ratio=0.03,
    cbar_pos_ratio=0.15,
    tree_kws=None,
    row_cluster=True,
    col_cluster=True,
    row_linkage=None,
    col_linkage=None,
    xticklabels=True,
    yticklabels=True,
    vmin=None,
    vmax=None,
    **kwargs
)

Parameters:

  • data = 2D data (DataFrame or matrix)
  • method = Clustering algorithm: "single", "complete", "average", "ward", etc.
  • metric = Distance measure: "euclidean", "correlation", "cityblock", etc.
  • z_score = Normalise rows (0) or columns (1) to z-scores
  • standard_scale = Scale rows (0) or columns (1) between 0 and 1
  • cmap = Colour map for the heatmap
  • center = Value to centre the colourmap
  • annot = Display numerical values in cells
  • row_cluster / col_cluster = Enable/disable clustering on rows or columns
  • row_linkage, col_linkage = Precomputed linkage matrices for custom clustering
  • xticklabels, yticklabels = Show/hide or customise labels
  • figsize = Size of the entire clustered plot
  • tree_kws = Arguments for dendrogram styling




Basic example

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
corr = iris.corr(numeric_only=True)

sns.clustermap(corr, cmap="coolwarm", annot=True)
plt.show()

Displays a correlation matrix clustered by similarity. Similar variables are grouped together with dendrograms on top and left.


seaborn clustermap plot basic example





Customise color palette

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
corr = iris.corr(numeric_only=True)

sns.clustermap(corr, cmap="viridis", annot=True)
plt.show()

You can use any Matplotlib or Seaborn colourmap ("mako", "crest", "rocket", etc.).


seaborn clustermap plot colourmap example





Normalise data (z-score)

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
corr = iris.corr(numeric_only=True)

sns.clustermap(corr, cmap="coolwarm", z_score=0)
plt.show()

Normalises data by rows to z-scores (mean = 0, std = 1). Use z_score=1 to normalise by columns.


seaborn clustermap plot z-score example





Disable clustering

Keeps the data order fixed along the chosen axis.


No Row Clustering:

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
corr = iris.corr(numeric_only=True)

sns.clustermap(corr, row_cluster=False, cmap="coolwarm")
plt.show()

seaborn clustermap plot no row example


No Column Clustering:

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
corr = iris.corr(numeric_only=True)

sns.clustermap(corr, col_cluster=False, cmap="coolwarm")
plt.show()

seaborn clustermap plot no col example





Change clustering method and distance

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
corr = iris.corr(numeric_only=True)

sns.clustermap(corr, method="ward", metric="euclidean", cmap="mako")
plt.show()

Controls how clusters are formed:

  • method: linkage type ("ward", "average", "complete", etc.)
  • metric: distance measure ("euclidean", "cosine", "correlation", etc.)

seaborn clustermap plot method example





Add annotations

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
corr = iris.corr(numeric_only=True)

sns.clustermap(corr, cmap="coolwarm", annot=True, fmt=".2f")
plt.show()

Displays the numeric values inside each cell. Great for small matrices (e.g., correlation heatmaps).


seaborn clustermap plot annotations example
© 2025 SimpleSteps.guide
AboutFAQPoliciesContact