Plot markers with flexible keyword arguments.
*args (x or y, x) – The data passed as positional or keyword arguments. Interpreted as follows:
If only x coordinates are passed, try to infer the y coordinates from the Series or DataFrame indices or the DataArray coordinates. Otherwise, the y coordinates are np.arange(0, x.shape[0]).
If the x coordinates are a 2D array, plot each column of data in succession (except where each column of data represents a statistical distribution, as with boxplot, violinplot, or when using means=True or medians=True).
If any arguments are pint.Quantity, auto-add the pint unit registry to matplotlib’s unit registry using setup_matplotlib. A pint.Quantity embedded in an xarray.DataArray is also supported.
s, size, ms, markersize (float or array-like or unit-spec, optional) – The marker size area(s). If this is an array matching the shape of x and y, the units are scaled by smin and smax. If this contains unit string(s), it is processed by units and represents the width rather than area.
c, color, colors, mc, markercolor, markercolors, fc, facecolor, facecolors (array-like or color-spec, optional) – The marker color(s). If this is an array matching the shape of x and y, the colors are generated using cmap, norm, vmin, and vmax. Otherwise, this should be a valid matplotlib color.
smin, smax (float, optional) – The minimum and maximum marker size area in units points ** 2. Ignored if absolute_size is True. Default value for smin is 1 and for smax is the square of rc['lines.markersize'] = 6.0.
area_size (bool, default: True) – Whether the marker sizes s are scaled by area or by radius. The default True is consistent with matplotlib. When absolute_size is True, the s units are points ** 2 if area_size is True and points if area_size is False.
absolute_size (bool, default: True or False) – Whether s should be taken to represent “absolute” marker sizes in units points or points ** 2 or “relative” marker sizes scaled by smin and smax. Default is True if s is scalar and False if s is array-like or smin or smax were passed.
vmin, vmax (float, optional) – The minimum and maximum color scale values used with the norm normalizer. If discrete is False these are the absolute limits, and if discrete is True these are the approximate limits used to automatically determine levels or values lists at “nice” intervals. If levels or values were already passed as lists, these are ignored, and vmin and vmax are set to the minimum and maximum of the lists. If robust was passed, the default vmin and vmax are some percentile range of the data values. Otherwise, the default vmin and vmax are the minimum and maximum of the data values.
data (dict-like, optional) – A dict-like dataset container (e.g., DataFrame or Dataset). If passed, each data argument can optionally be a string key and the arrays used for plotting are retrieved with data[key]. This is a native matplotlib feature.
autoformat (bool, default: rc.autoformat = True) – Whether the x axis labels, y axis labels, axis formatters, axes titles, legend titles, and colorbar labels are automatically configured when a Series, DataFrame, DataArray, or Quantity is passed to the plotting command. Formatting of pint.Quantity unit strings is controlled by rc.unitformat = 'L'.
cmap (colormap-spec, default: rc['cmap.sequential'] = 'Fire' or rc['cmap.diverging'] = 'BuRd') – The colormap specifer, passed to the Colormap constructor function. If rc['cmap.autodiverging'] is True and the normalization range contains negative and positive values then rc['cmap.diverging'] is used. Otherwise rc['cmap.sequential'] is used.
c, color, colors (color-spec or sequence of color-spec, optional) – The color(s) used to create a DiscreteColormap. If not passed, cmap is used.
norm (norm-spec, default: Normalize or DivergingNorm) – The data value normalizer, passed to the Norm constructor function. If discrete is True then 1) this affects the default level-generation algorithm (e.g. norm='log' builds levels in log-space) and 2) this is passed to DiscreteNorm to scale the colors before they are discretized (if norm is not already a DiscreteNorm). If rc['cmap.autodiverging'] is True and the normalization range contains negative and positive values then DivergingNorm is used. Otherwise Normalize is used.
extend ({'neither', 'both', 'min', 'max'}, default: 'neither') – Direction for drawing colorbar “extensions” indicating out-of-bounds data on the end of the colorbar.
discrete (bool, default: rc['cmap.discrete'] = None) – If False, then DiscreteNorm is not applied to the colormap. Instead, for non-contour plots, the number of levels will be roughly controlled by rc['cmap.lut']. This has a similar effect to using levels=large_number but it may improve rendering speed. Default is True only for contouring commands like contourf and pseudocolor commands like pcolor.
sequential, diverging, cyclic, qualitative (bool, default: None) – Boolean arguments used if cmap is not passed. Set these to True to use the default rc['cmap.sequential'], rc['cmap.diverging'], rc['cmap.cyclic'], and rc['cmap.qualitative'] colormaps. The diverging option also applies DivergingNorm as the default continuous normalizer.
N – Shorthand for levels.
levels (int or sequence of float, default: rc['cmap.levels'] = 11) – The number of level edges or a sequence of level edges. If the former, locator is used to generate this many level edges at “nice” intervals. If the latter, the levels should be monotonically increasing or decreasing (note decreasing levels fail with contour plots).
values (int or sequence of float, default: None) – The number of level centers or a sequence of level centers. If the former, locator is used to generate this many level centers at “nice” intervals. If the latter, levels are inferred using edges. This will override any levels input.
robust (bool, float, or 2-tuple, default: rc['cmap.robust'] = False) – If True and vmin or vmax were not provided, they are determined from the 2nd and 98th data percentiles rather than the minimum and maximum. If float, this percentile range is used (for example, 90 corresponds to the 5th to 95th percentiles). If 2-tuple of float, these specific percentiles should be used. This feature is useful when your data has large outliers.
inbounds (bool, default: rc['cmap.inbounds'] = True) – If True and vmin or vmax were not provided, when axis limits have been explicitly restricted with set_xlim or set_ylim, out-of-bounds data is ignored. See also rc['cmap.inbounds'] and rc['axes.inbounds'].
locator (locator-spec, default: matplotlib.ticker.MaxNLocator) – The locator used to determine level locations if levels or values were not already passed as lists. Passed to the Locator constructor. Default is MaxNLocator with levels integer levels.
locator_kw (dict-like, optional) – Keyword arguments passed to matplotlib.ticker.Locator class.
symmetric (bool, default: False) – If True, the normalization range or discrete colormap levels are symmetric about zero.
positive (bool, default: False) – If True, the normalization range or discrete colormap levels are positive with a minimum at zero.
negative (bool, default: False) – If True, the normaliation range or discrete colormap levels are negative with a minimum at zero.
nozero (bool, default: False) – If True, 0 is removed from the level list. This is mainly useful for single-color contour plots.
cycle (cycle-spec, optional) – The cycle specifer, passed to the Cycle constructor. If the returned cycler is unchanged from the current cycler, the axes cycler will not be reset to its first position. To disable property cycling and just use black for the default color, use cycle=False, cycle='none', or cycle=() (analogous to disabling ticks with e.g. xformatter='none'). To restore the default property cycler, use cycle=True.
lw, linewidth, linewidths, mew, markeredgewidth, markeredgewidths (float or sequence, optional) – The marker edge width(s).
edgecolors, markeredgecolor, markeredgecolors (color-spec or sequence, optional) – The marker edge color(s).
mean, means (bool, default: False) – Whether to plot the means of each column for 2D x coordinates. Means are calculated with numpy.nanmean. If no other arguments are specified, this also sets barstd=True (and boxstd=True for violin plots).
median, medians (bool, default: False) – Whether to plot the medians of each column for 2D x coordinates. Medians are calculated with numpy.nanmedian. If no other arguments arguments are specified, this also sets barstd=True (and boxstd=True for violin plots).
barstd, barstds (bool, float, or 2-tuple of float, optional) – Valid only if mean or median is True. Standard deviation multiples for thin error bars with optional whiskers (i.e., caps). If scalar, then +/- that multiple is used. If True, the default standard deviation range of +/-3 is used.
barpctile, barpctiles (bool, float, or 2-tuple of float, optional) – Valid only if mean or median is True. As with barstd, but instead using percentiles for the error bars. If scalar, that percentile range is used (e.g., 90 shows the 5th to 95th percentiles). If True, the default percentile range of 0 to 100 is used.
bardata (array-like, optional) – Valid only if mean and median are False. If shape is 2 x N, these are the lower and upper bounds for the thin error bars. If shape is N, these are the absolute, symmetric deviations from the central points.
boxes (bool, default: None) – Shorthand for boxstd, boxstds.
boxstd, boxstds, boxpctile, boxpctiles, boxdata (optional) – As with barstd, barpctile, and bardata, but for thicker error bars representing a smaller interval than the thin error bars. If boxstds is True, the default standard deviation range of +/-1 is used. If boxpctiles is True, the default percentile range of 25 to 75 is used (i.e., the interquartile range). When “boxes” and “bars” are combined, this has the effect of drawing miniature box-and-whisker plots.
capsize (float, default: rc['errorbar.capsize'] = 3.0) – The cap size for thin error bars in points.
barz, barzorder, boxz, boxzorder (float, default: 2.5) – The “zorder” for the thin and thick error bars.
barc, barcolor, boxc, boxcolor (color-spec, default: rc['boxplot.whiskerprops.color'] = 'black') – Colors for the thin and thick error bars.
barlw, barlinewidth, boxlw, boxlinewidth (float, default: rc['boxplot.whiskerprops.linewidth'] = 1.0) – Line widths for the thin and thick error bars, in points. The default for boxes is 4 times rc['boxplot.whiskerprops.linewidth'].
boxm, boxmarker (bool or marker-spec, default: 'o') – Whether to draw a small marker in the middle of the box denoting the mean or median position. Ignored if boxes is False.
boxms, boxmarkersize (size-spec, default: (2 * boxlinewidth) ** 2) – The marker size for the boxmarker marker in points ** 2.
boxmc, boxmarkercolor, boxmec, boxmarkeredgecolor (color-spec, default: 'w') – Color, face color, and edge color for the boxmarker marker.
shadestd, shadestds, shadepctile, shadepctiles, shadedata (optional) – As with barstd, barpctile, and bardata, but using shading to indicate the error range. If shadestds is True, the default standard deviation range of +/-2 is used. If shadepctiles is True, the default percentile range of 10 to 90 is used.
fadestd, fadestds, fadepctile, fadepctiles, fadedata (optional) – As with shadestd, shadepctile, and shadedata, but for an additional, more faded, secondary shaded region. If fadestds is True, the default standard deviation range of +/-3 is used. If fadepctiles is True, the default percentile range of 0 to 100 is used.
shadec, shadecolor, fadec, fadecolor (color-spec, default: None) – Colors for the different shaded regions. The parent artist color is used by default.
shadez, shadezorder, fadez, fadezorder (float, default: 1.5) – The “zorder” for the different shaded regions.
shadea, shadealpha, fadea, fadealpha (float, default: 0.4, 0.2) – The opacity for the different shaded regions.
shadelw, shadelinewidth, fadelw, fadelinewidth (float, default: rc['patch.linewidth'] = 0.6.) – The edge line width for the shading patches.
shdeec, shadeedgecolor, fadeec, fadeedgecolor (float, default: 'none') – The edge color for the shading patches.
shadelabel, fadelabel (bool or str, optional) – Labels for the shaded regions to be used as separate legend entries. To toggle labels “on” and apply a default label, use e.g. shadelabel=True. To apply a custom label, use e.g. shadelabel='label'. Otherwise, the shading is drawn underneath the line and/or marker in the legend entry.
inbounds (bool, default: rc['axes.inbounds'] = True) – Whether to restrict the default y (x) axis limits to account for only in-bounds data when the x (y) axis limits have been locked. See also rc['axes.inbounds'] and rc['cmap.inbounds'].
label, value (float or str, optional) – The single legend label or colorbar coordinate to be used for this plotted element. Can be numeric or string. This is generally used with 1D positional arguments.
labels, values (sequence of float or sequence of str, optional) – The legend labels or colorbar coordinates used for each plotted element. Can be numeric or string, and must match the number of plotted elements. This is generally used with 2D positional arguments.
colorbar (bool, int, or str, optional) – If not None, this is a location specifying where to draw an inset or outer colorbar from the resulting object(s). If True, the default rc['colorbar.loc'] = 'right' is used. If the same location is used in successive plotting calls, object(s) will be added to the existing colorbar in that location (valid for colorbars built from lists of artists). Valid locations are shown in in colorbar.
colorbar_kw (dict-like, optional) – Extra keyword args for the call to colorbar.
legend (bool, int, or str, optional) – Location specifying where to draw an inset or outer legend from the resulting object(s). If True, the default rc['legend.loc'] = 'best' is used. If the same location is used in successive plotting calls, object(s) will be added to existing legend in that location. Valid locations are shown in legend.
legend_kw (dict-like, optional) – Extra keyword args for the call to legend.
**kwargs – Passed to scatter.
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