seaborn.heatmap#

seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)#

Plot rectangular data as a color-encoded matrix.

This is an Axes-level function and will draw the heatmap into the currently-active Axes if none is provided to the ax argument. Part of this Axes space will be taken and used to plot a colormap, unless cbar is False or a separate Axes is provided to cbar_ax.

Parameters:

datarectangular dataset: 2D dataset that can be coerced into an ndarray. If a Pandas DataFrame is provided, the index/column information will be used to label the columns and rows.
vmin, vmaxfloats, optional: Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments.
cmapmatplotlib colormap name or object, or list of colors, optional: The mapping from data values to color space. If not provided, the default will depend on whether center is set.
centerfloat, optional: The value at which to center the colormap when plotting divergent data. Using this parameter will change the default cmap if none is specified.
robustbool, optional: If True and vmin or vmax are absent, the colormap range is computed with robust quantiles instead of the extreme values.
annotbool or rectangular dataset, optional: If True, write the data value in each cell. If an array-like with the same shape as data, then use this to annotate the heatmap instead of the data. Note that DataFrames will match on position, not index.
fmtstr, optional: String formatting code to use when adding annotations.
annot_kwsdict of key, value mappings, optional: Keyword arguments for matplotlib.axes.Axes.text() when annot is True.
linewidthsfloat, optional: Width of the lines that will divide each cell.
linecolorcolor, optional: Color of the lines that will divide each cell.
cbarbool, optional: Whether to draw a colorbar.
cbar_kwsdict of key, value mappings, optional: Keyword arguments for matplotlib.figure.Figure.colorbar().
cbar_axmatplotlib Axes, optional: Axes in which to draw the colorbar, otherwise take space from the main Axes.
squarebool, optional: If True, set the Axes aspect to “equal” so each cell will be square-shaped.
xticklabels, yticklabels“auto”, bool, list-like, or int, optional: If True, plot the column names of the dataframe. If False, don’t plot the column names. If list-like, plot these alternate labels as the xticklabels. If an integer, use the column names but plot only every n label. If “auto”, try to densely plot non-overlapping labels.
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.
axmatplotlib Axes, optional: Axes in which to draw the plot, otherwise use the currently-active Axes.
kwargsother keyword arguments: All other keyword arguments are passed to matplotlib.axes.Axes.pcolormesh().

Returns:

axmatplotlib Axes: Axes object with the heatmap.

See also

clustermap: Plot a matrix using hierarchical clustering to arrange the rows and columns.

Examples

Pass a DataFrame to plot with indices as row/column labels:

glue = sns.load_dataset("glue").pivot("Model", "Task", "Score")
sns.heatmap(glue)

/var/folders/qk/cdrdfhfn5g554pnb30pp4ylr0000gn/T/ipykernel_77613/2862412127.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  glue = sns.load_dataset("glue").pivot("Model", "Task", "Score")

Use annot to represent the cell values with text:

sns.heatmap(glue, annot=True)

Control the annotations with a formatting string:

sns.heatmap(glue, annot=True, fmt=".1f")

Use a separate dataframe for the annotations:

sns.heatmap(glue, annot=glue.rank(axis="columns"))

Add lines between cells:

sns.heatmap(glue, annot=True, linewidth=.5)

Select a different colormap by name:

sns.heatmap(glue, cmap="crest")

Or pass a colormap object:

sns.heatmap(glue, cmap=sns.cubehelix_palette(as_cmap=True))

Set the colormap norm (data values corresponding to minimum and maximum points):

sns.heatmap(glue, vmin=50, vmax=100)

Use methods on the matplotlib.axes.Axes object to tweak the plot:

ax = sns.heatmap(glue, annot=True)
ax.set(xlabel="", ylabel="")
ax.xaxis.tick_top()