matplotlib.pyplot.hexbin#

matplotlib.pyplot.hexbin(x, y, C=None, *, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='face', reduce_C_function=<function mean>, mincnt=None, marginals=False, colorizer=None, data=None, **kwargs)[source]#

Make a 2D hexagonal binning plot of points x, y.

If C is None, the value of the hexagon is determined by the number of points in the hexagon. Otherwise, C specifies values at the coordinate (x[i], y[i]). For each hexagon, these values are reduced using reduce_C_function.

Parameters:

x, yarray-like

The data positions. x and y must be of the same length.

Carray-like, optional

If given, these values are accumulated in the bins. Otherwise, every point has a value of 1. Must be of the same length as x and y.

gridsizeint or (int, int), default: 100

If a single int, the number of hexagons in the x-direction. The number of hexagons in the y-direction is chosen such that the hexagons are approximately regular.

Alternatively, if a tuple (nx, ny), the number of hexagons in the x-direction and the y-direction. In the y-direction, counting is done along vertically aligned hexagons, not along the zig-zag chains of hexagons; see the following illustration.

(Source code, 2x.png, png)

To get approximately regular hexagons, choose \(n_x = \sqrt{3}\,n_y\).

bins'log' or int or sequence, default: None

Discretization of the hexagon values.

If None, no binning is applied; the color of each hexagon directly corresponds to its count value.
If 'log', use a logarithmic scale for the colormap. Internally, \(log_{10}(i+1)\) is used to determine the hexagon color. This is equivalent to norm=LogNorm().
If an integer, divide the counts in the specified number of bins, and color the hexagons accordingly.
If a sequence of values, the values of the lower bound of the bins to be used.

xscale{'linear', 'log'}, default: 'linear'

Use a linear or log10 scale on the horizontal axis.

yscale{'linear', 'log'}, default: 'linear'

Use a linear or log10 scale on the vertical axis.

mincntint >= 0, default: None

If not None, only display cells with at least mincnt number of points in the cell.

marginalsbool, default: False

If marginals is True, plot the marginal density as colormapped rectangles along the bottom of the x-axis and left of the y-axis.

extent4-tuple of float, default: None

The limits of the bins (xmin, xmax, ymin, ymax). The default assigns the limits based on gridsize, x, y, xscale and yscale.

If xscale or yscale is set to 'log', the limits are expected to be the exponent for a power of 10. E.g. for x-limits of 1 and 50 in 'linear' scale and y-limits of 10 and 1000 in 'log' scale, enter (1, 50, 1, 3).

Returns:

PolyCollection

A PolyCollection defining the hexagonal bins.

PolyCollection.get_offsets contains a Mx2 array containing the x, y positions of the M hexagon centers in data coordinates.
PolyCollection.get_array contains the values of the M hexagons.

If marginals is True, horizontal bar and vertical bar (both PolyCollections) will be attached to the return collection as attributes hbar and vbar.

Other Parameters:

cmapstr or Colormap, default: rcParams["image.cmap"] (default: 'viridis')

The Colormap instance or registered colormap name used to map scalar data to colors.

normstr or Normalize, optional

The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using cmap. By default, a linear scaling is used, mapping the lowest value to 0 and the highest to 1.

If given, this can be one of the following:

An instance of Normalize or one of its subclasses (see Colormap normalization).
A scale name, i.e. one of "linear", "log", "symlog", "logit", etc. For a list of available scales, call matplotlib.scale.get_scale_names(). In that case, a suitable Normalize subclass is dynamically generated and instantiated.

vmin, vmaxfloat, optional

When using scalar data and no explicit norm, vmin and vmax define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use vmin/vmax when a norm instance is given (but using a str norm name together with vmin/vmax is acceptable).

alphafloat between 0 and 1, optional

The alpha blending value, between 0 (transparent) and 1 (opaque).

linewidthsfloat, default: None

If None, defaults to rcParams["patch.linewidth"] (default: 1.0).

edgecolors{'face', 'none', None} or color, default: 'face'

The color of the hexagon edges. Possible values are:

'face': Draw the edges in the same color as the fill color.
'none': No edges are drawn. This can sometimes lead to unsightly unpainted pixels between the hexagons.
None: Draw outlines in the default color.
An explicit color.

reduce_C_functioncallable, default: numpy.mean

The function to aggregate C within the bins. It is ignored if C is not given. This must have the signature:

def reduce_C_function(C: array) -> float

Commonly used functions are:

numpy.mean: average of the points
numpy.sum: integral of the point values
numpy.amax: value taken from the largest point

By default will only reduce cells with at least 1 point because some reduction functions (such as numpy.amax) will error/warn with empty input. Changing mincnt will adjust the cutoff, and if set to 0 will pass empty input to the reduction function.

colorizerColorizer or None, default: None

The Colorizer object used to map color to data. If None, a Colorizer object is created from a norm and cmap.

dataindexable object, optional

If given, the following parameters also accept a string s, which is interpreted as data[s] if s is a key in data:

x, y, C

**kwargsPolyCollection properties

All other keyword arguments are passed on to PolyCollection:

Property	Description
`agg_filter`	a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
`alpha`	array-like or float or None
`animated`	bool
`antialiased` or `aa` or `antialiaseds`	bool or list of bools
`array`	array-like or None
`capstyle`	`CapStyle` or {'butt', 'projecting', 'round'}
`clim`	(vmin: float, vmax: float)
`clip_box`	`BboxBase` or None
`clip_on`	bool
`clip_path`	Patch or (Path, Transform) or None
`cmap`	`Colormap` or str or None
`color`	color or list of RGBA tuples
`edgecolor` or `ec` or `edgecolors`	color or list of color or 'face'
`facecolor` or `facecolors` or `fc`	color or list of color
`figure`	`Figure` or `SubFigure`
`gid`	str
`hatch`	{'/', '\', '\|', '-', '+', 'x', 'o', 'O', '.', '*'}
`hatch_linewidth`	unknown
`in_layout`	bool
`joinstyle`	`JoinStyle` or {'miter', 'round', 'bevel'}
`label`	object
`linestyle` or `dashes` or `linestyles` or `ls`	str or tuple or list thereof
`linewidth` or `linewidths` or `lw`	float or list of floats
`mouseover`	bool
`norm`	`Normalize` or str or None
`offset_transform` or `transOffset`	`Transform`
`offsets`	(N, 2) or (2,) array-like
`path_effects`	list of `AbstractPathEffect`
`paths`	list of array-like
`picker`	None or bool or float or callable
`pickradius`	float
`rasterized`	bool
`sizes`	`numpy.ndarray` or None
`sketch_params`	(scale: float, length: float, randomness: float)
`snap`	bool or None
`transform`	`Transform`
`url`	str
`urls`	list of str or None
`verts`	list of array-like
`verts_and_codes`	unknown
`visible`	bool
`zorder`	float

Examples using `matplotlib.pyplot.hexbin`#

Hexagonal binned plot

matplotlib.pyplot.hexbin#

Examples using matplotlib.pyplot.hexbin#

Examples using `matplotlib.pyplot.hexbin`#