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:

Parameters:	data : rectangular 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, vmax : floats, optional Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments. cmap : matplotlib 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. center : float, optional The value at which to center the colormap when plotting divergant data. Using this parameter will change the default `cmap` if none is specified. robust : bool, optional If True and `vmin` or `vmax` are absent, the colormap range is computed with robust quantiles instead of the extreme values. annot : bool 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 raw data. fmt : string, optional String formatting code to use when adding annotations. annot_kws : dict of key, value mappings, optional Keyword arguments for `ax.text` when `annot` is True. linewidths : float, optional Width of the lines that will divide each cell. linecolor : color, optional Color of the lines that will divide each cell. cbar : boolean, optional Whether to draw a colorbar. cbar_kws : dict of key, value mappings, optional Keyword arguments for fig.colorbar. cbar_ax : matplotlib Axes, optional Axes in which to draw the colorbar, otherwise take space from the main Axes. square : boolean, 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. mask : boolean array or DataFrame, optional If passed, data will not be shown in cells where `mask` is True. Cells with missing values are automatically masked. ax : matplotlib Axes, optional Axes in which to draw the plot, otherwise use the currently-active Axes. kwargs : other keyword arguments All other keyword arguments are passed to `ax.pcolormesh`.
Returns:	ax : matplotlib Axes Axes object with the heatmap.

data : rectangular 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, vmax : floats, optional: Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments.
cmap : matplotlib 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.
center : float, optional: The value at which to center the colormap when plotting divergant data. Using this parameter will change the default cmap if none is specified.
robust : bool, optional: If True and vmin or vmax are absent, the colormap range is computed with robust quantiles instead of the extreme values.
annot : bool 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 raw data.
fmt : string, optional: String formatting code to use when adding annotations.
annot_kws : dict of key, value mappings, optional: Keyword arguments for ax.text when annot is True.
linewidths : float, optional: Width of the lines that will divide each cell.
linecolor : color, optional: Color of the lines that will divide each cell.
cbar : boolean, optional: Whether to draw a colorbar.
cbar_kws : dict of key, value mappings, optional: Keyword arguments for fig.colorbar.
cbar_ax : matplotlib Axes, optional: Axes in which to draw the colorbar, otherwise take space from the main Axes.
square : boolean, 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.
mask : boolean array or DataFrame, optional: If passed, data will not be shown in cells where mask is True. Cells with missing values are automatically masked.
ax : matplotlib Axes, optional: Axes in which to draw the plot, otherwise use the currently-active Axes.
kwargs : other keyword arguments: All other keyword arguments are passed to ax.pcolormesh.

Returns:

ax : matplotlib Axes: Axes object with the heatmap.

See also

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

Examples

Plot a heatmap for a numpy array:

>>> import numpy as np; np.random.seed(0)
>>> import seaborn as sns; sns.set()
>>> uniform_data = np.random.rand(10, 12)
>>> ax = sns.heatmap(uniform_data)

Change the limits of the colormap:

>>> ax = sns.heatmap(uniform_data, vmin=0, vmax=1)

Plot a heatmap for data centered on 0 with a diverging colormap:

>>> normal_data = np.random.randn(10, 12)
>>> ax = sns.heatmap(normal_data, center=0)

Plot a dataframe with meaningful row and column labels:

>>> flights = sns.load_dataset("flights")
>>> flights = flights.pivot("month", "year", "passengers")
>>> ax = sns.heatmap(flights)

Annotate each cell with the numeric value using integer formatting:

>>> ax = sns.heatmap(flights, annot=True, fmt="d")

Add lines between each cell:

>>> ax = sns.heatmap(flights, linewidths=.5)

Use a different colormap:

>>> ax = sns.heatmap(flights, cmap="YlGnBu")

Center the colormap at a specific value:

>>> ax = sns.heatmap(flights, center=flights.loc["January", 1955])

Plot every other column label and don’t plot row labels:

>>> data = np.random.randn(50, 20)
>>> ax = sns.heatmap(data, xticklabels=2, yticklabels=False)

Don’t draw a colorbar:

>>> ax = sns.heatmap(flights, cbar=False)

Use different axes for the colorbar:

>>> grid_kws = {"height_ratios": (.9, .05), "hspace": .3}
>>> f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws)
>>> ax = sns.heatmap(flights, ax=ax,
...                  cbar_ax=cbar_ax,
...                  cbar_kws={"orientation": "horizontal"})

Use a mask to plot only part of a matrix

>>> corr = np.corrcoef(np.random.randn(10, 200))
>>> mask = np.zeros_like(corr)
>>> mask[np.triu_indices_from(mask)] = True
>>> with sns.axes_style("white"):
...     ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True)