seaborn.clustermap¶

seaborn.clustermap(data, *, pivot_kws=None, method='average', metric='euclidean', z_score=None, standard_scale=None, figsize=(10, 10), cbar_kws=None, row_cluster=True, col_cluster=True, row_linkage=None, col_linkage=None, row_colors=None, col_colors=None, mask=None, dendrogram_ratio=0.2, colors_ratio=0.03, cbar_pos=(0.02, 0.8, 0.05, 0.18), tree_kws=None, **kwargs)¶

Plot a matrix dataset as a hierarchically-clustered heatmap.

Parameters

data2D array-like: Rectangular data for clustering. Cannot contain NAs.
pivot_kwsdict, optional: If data is a tidy dataframe, can provide keyword arguments for pivot to create a rectangular dataframe.
methodstr, optional: Linkage method to use for calculating clusters. See scipy.cluster.hierarchy.linkage() documentation for more information.
metricstr, optional: Distance metric to use for the data. See scipy.spatial.distance.pdist() documentation for more options. To use different metrics (or methods) for rows and columns, you may construct each linkage matrix yourself and provide them as {row,col}_linkage.
z_scoreint or None, optional: Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores for the rows or the columns. Z scores are: z = (x - mean)/std, so values in each row (column) will get the mean of the row (column) subtracted, then divided by the standard deviation of the row (column). This ensures that each row (column) has mean of 0 and variance of 1.
standard_scaleint or None, optional: Either 0 (rows) or 1 (columns). Whether or not to standardize that dimension, meaning for each row or column, subtract the minimum and divide each by its maximum.
figsizetuple of (width, height), optional: Overall size of the figure.
cbar_kwsdict, optional: Keyword arguments to pass to cbar_kws in heatmap(), e.g. to add a label to the colorbar.
{row,col}_clusterbool, optional: If True, cluster the {rows, columns}.
{row,col}_linkagenumpy.ndarray, optional: Precomputed linkage matrix for the rows or columns. See scipy.cluster.hierarchy.linkage() for specific formats.
{row,col}_colorslist-like or pandas DataFrame/Series, optional: List of colors to label for either the rows or columns. Useful to evaluate whether samples within a group are clustered together. Can use nested lists or DataFrame for multiple color levels of labeling. If given as a pandas.DataFrame or pandas.Series, labels for the colors are extracted from the DataFrames column names or from the name of the Series. DataFrame/Series colors are also matched to the data by their index, ensuring colors are drawn in the correct order.
maskbool array or DataFrame, optional: If passed, data will not be shown in cells where mask is True. Cells with missing values are automatically masked. Only used for visualizing, not for calculating.
{dendrogram,colors}_ratiofloat, or pair of floats, optional: Proportion of the figure size devoted to the two marginal elements. If a pair is given, they correspond to (row, col) ratios.
cbar_postuple of (left, bottom, width, height), optional: Position of the colorbar axes in the figure. Setting to None will disable the colorbar.
tree_kwsdict, optional: Parameters for the matplotlib.collections.LineCollection that is used to plot the lines of the dendrogram tree.
kwargsother keyword arguments: All other keyword arguments are passed to heatmap().

Returns

ClusterGrid: A ClusterGrid instance.

See also

heatmap: Plot rectangular data as a color-encoded matrix.

Notes

The returned object has a savefig method that should be used if you want to save the figure object without clipping the dendrograms.

To access the reordered row indices, use: clustergrid.dendrogram_row.reordered_ind

Column indices, use: clustergrid.dendrogram_col.reordered_ind

Examples

Plot a clustered heatmap:

>>> import seaborn as sns; sns.set_theme(color_codes=True)
>>> iris = sns.load_dataset("iris")
>>> species = iris.pop("species")
>>> g = sns.clustermap(iris)

Change the size and layout of the figure:

>>> g = sns.clustermap(iris,
...                    figsize=(7, 5),
...                    row_cluster=False,
...                    dendrogram_ratio=(.1, .2),
...                    cbar_pos=(0, .2, .03, .4))

Add colored labels to identify observations:

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

Use a different colormap and adjust the limits of the color range:

>>> g = sns.clustermap(iris, cmap="mako", vmin=0, vmax=10)

Use a different similarity metric:

>>> g = sns.clustermap(iris, metric="correlation")

Use a different clustering method:

>>> g = sns.clustermap(iris, method="single")

Standardize the data within the columns:

>>> g = sns.clustermap(iris, standard_scale=1)

Normalize the data within the rows:

>>> g = sns.clustermap(iris, z_score=0, cmap="vlag")