Hist Plot¶

Plot univariate or bivariate histograms to show distributions of datasets.

plot: 'histplot'

Plot-Specific Parameters

hue (str, list, numpy.ndarray, pandas.core.indexes.base.Index, or None, default: None): Semantic variable that is mapped to determine the color of plot elements.
weights (str, list, numpy.ndarray, pandas.core.indexes.base.Index, or None, default: None): If provided, weight the contribution of the corresponding data points towards the count in each bin by these factors.
stat (str, default: ‘count’): Aggregate statistic to compute in each bin. - ‘count’, to show the number of observations in each bin. - ‘frequency’, to show the number of observations divided by the bin width. - ‘probability’ or ‘proportion’, to normalize such that bar heights sum to 1. - ‘percent’, to normalize such that bar heights sum to 100. - ‘density’, to normalize such that the total area of the histogram equals 1.
bins (str, float, or list, default: ‘auto’): Generic bin parameter that can be the name of a reference rule, the number of bins, or the breaks of the bins. Passed to numpy.histogram_bin_edges().
binwidth (float, pair of float, or None, default: None): Width of each bin, overrides bins but can be used with binrange.
binrange (pair of float, a pair of pairs, or None, default: None): Lowest and highest value for bin edges; can be used either with bins or binwidth. Defaults to data extremes.
discrete (bool or None, default: None): If True, default to binwidth=1 and draw the bars so that they are centered on their corresponding data points. This avoids ‘gaps’ that may otherwise appear when using discrete (integer) data.
cumulative (bool, default: False): If True, plot the cumulative counts as bins increase.
common_bins (bool, default: True): If True, use the same bins when semantic variables produce multiple plots. If using a reference rule to determine the bins, it will be computed with the full dataset.
common_norm (bool, default: True): If True and using a normalized statistic, the normalization will apply over the full dataset. Otherwise, normalize each histogram independently.
multiple (str, default: ‘layer’): Approach to resolving multiple elements when semantic mapping creates subsets. Only relevant with univariate data.
element (str, default: ‘bars’): Visual representation of the histogram statistic. Only relevant with univariate data.
fill (bool, default: True): If True, fill in the space under the histogram. Only relevant with univariate data.
shrink (float, default: 1): Scale the width of each bar relative to the binwidth by this factor. Only relevant with univariate data.
kde (bool, default: False): If True, compute a kernel density estimate to smooth the distribution and show on the plot as (one or more) line(s). Only relevant with univariate data.
kde_kws (dict or None, default: None): Parameters that control the KDE computation, as in kdeplot().
line_kws (dict or None, default: None): Parameters that control the KDE visualization, passed to matplotlib.axes.Axes.plot().
thresh (float or None, default: 0): Cells with a statistic less than or equal to this value will be transparent. Only relevant with bivariate data.
pthresh (float or None, default: None): Like thresh, but a value in the range 0 until 1 such that cells with aggregate counts (or other statistics, when used) up to this proportion of the total will be transparent.
pmax (float or None, default: None): A value in the range 0 until 1 that sets that saturation point for the colormap at a value such that cells below is constistute this proportion of the total count (or other statistic, when used).
cbar (bool, default: False): If True, add a colorbar to annotate the color mapping in a bivariate plot. Note: Does not currently support plots with a hue variable well.
cbar_ax (matplotlib.axes.Axes or None, default: None): Pre-existing axes for the colorbar.
cbar_kws (dict or None, default: None): Additional parameters passed to matplotlib.figure.Figure.colorbar().
palette (str, list, matplotlib.colors.Colormap, or None, default: None): Method for choosing the colors to use when mapping the hue semantic. String values are passed to color_palette(). List values imply categorical mapping, while a colormap object implies numeric mapping.
hue_order (list or None, default: None): Specify the order of processing and plotting for categorical levels of the hue semantic.
hue_norm (tuple, matplotlib.colors.Normalize, or None, default: None): Either a pair of values that set the normalization range in data units or an object that will map from data units into a 0 until 1 interval. Usage implies numeric mapping.
color (matplotlib.colors or None, default: None): Single color specification for when hue mapping is not used. Otherwise, the plot will try to hook into the matplotlib property cycle.
alpha (float or None, default: None): Proportional opacity of the points.
legend (bool, default: True): If False, suppress the legend for semantic variables.
zorder (int or None, default: None): Axes order. The default drawing order for axes is patches, lines, text for each plot order.

Example 1

from grplot import plot2d
import grplot_seaborn as gs
gs.set_theme(context='notebook', style='darkgrid', palette='deep')

tips = gs.load_dataset('tips')
ax = plot2d(plot='histplot',
            df=tips,
            x='total_bill',
            xsep='.c',
            ysep='.',
            ytext='h',
            statdesc={'total_bill': 'general'},
            xtick_add='Rp(_)',
            title='Histogram Count vs total_bill',
            alpha=0.75,
            kde=True)

Histogram with KDE overlay and statistical summary

Example 2

from grplot import plot2d
import grplot_seaborn as gs
gs.set_theme(context='notebook', style='darkgrid', palette='deep')


tips = gs.load_dataset('tips')
ax = plot2d(plot='histplot',
            df=tips,
            x='total_bill',
            hue='sex',
            xsep='.c',
            ysep='.',
            statdesc={'total_bill':'general'},
            xtick_add='Rp(_)',
            ytext='h',
            title='Histogram Count vs total_bill',
            multiple='stack',
            kde=True,
            alpha=0.75)