stat_summary_bin

stat_summary_bin(mapping=None, *, data=None, geom=None, position=None, show_legend=None, inherit_aes=None, sampling=None, tooltips=None, orientation=None, fun=None, fun_min=None, fun_max=None, quantiles=None, bins=None, binwidth=None, center=None, boundary=None, color_by=None, fill_by=None, **other_args)

Display a distribution by dividing variable mapped to x axis into bins and applying aggregation functions to each bin.

Parameters:

mappingFeatureSpec

Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or Pandas or Polars DataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

geomstr, default=’pointrange’

The geometry to display the summary stat for this layer, as a string.

positionstr or FeatureSpec, default=’identity’

Position adjustment. Either a position adjustment name: ‘dodge’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).

show_legendbool, default=True

False - do not show legend for this layer.

inherit_aesbool, default=True

False - do not combine the layer aesthetic mappings with the plot shared mappings.

samplingFeatureSpec

Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).

tooltipslayer_tooltips

Result of the call to the layer_tooltips() function. Specify appearance, style and content. Set tooltips=’none’ to hide tooltips from the layer.

orientationstr, default=’x’

Specify the axis that the layer’s stat and geom should run along. Possible values: ‘x’, ‘y’.

fun{‘count’, ‘sum’, ‘mean’, ‘median’, ‘min’, ‘max’, ‘lq’, ‘mq’, ‘uq’}, default=’mean’

Name of function computing stat variable ‘..y..’. Names ‘lq’, ‘mq’, ‘uq’ corresponds to lower, middle and upper quantiles, default=[0.25, 0.5, 0.75].

fun_min{‘count’, ‘sum’, ‘mean’, ‘median’, ‘min’, ‘max’, ‘lq’, ‘mq’, ‘uq’}, default=’min’

Name of function computing stat variable ‘..ymin..’. Names ‘lq’, ‘mq’, ‘uq’ corresponds to lower, middle and upper quantiles, default=[0.25, 0.5, 0.75].

fun_max{‘count’, ‘sum’, ‘mean’, ‘median’, ‘min’, ‘max’, ‘lq’, ‘mq’, ‘uq’}, default=’max’

Name of function computing stat variable ‘..ymax..’. Names ‘lq’, ‘mq’, ‘uq’ corresponds to lower, middle and upper quantiles, default=[0.25, 0.5, 0.75].

quantileslist of float, default=[0.25, 0.5, 0.75]

A list of probabilities defining the quantile functions ‘lq’, ‘mq’ and ‘uq’. Must contain exactly 3 values between 0 and 1.

binsint, default=30

Number of bins. Overridden by binwidth.

binwidthfloat

The width of the bins. The default is to use bin widths that cover the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data.

centerfloat

Specify x-value to align bin centers to.

boundaryfloat

Specify x-value to align bin boundary (i.e. point between bins) to.

color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’

Define the color aesthetic for the geometry.

fill_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’fill’

Define the fill aesthetic for the geometry.

other_args

Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns:

LayerSpec

Geom object specification.

Notes

Computed variables:

• ..y.. : result of calculating of fun.

• ..ymin.. : result of calculating of fun_min.

• ..ymax.. : result of calculating of fun_max.

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To hide axis tooltips, set ‘blank’ or the result of element_blank() to the axis_tooltip or axis_tooltip_x parameter of the theme().

Examples

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 100
np.random.seed(42)
x = np.random.uniform(size=n)
y = np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    stat_summary_bin()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 100
np.random.seed(42)
x = np.random.uniform(size=n)
y = np.random.normal(size=n)
g = np.random.choice(["A", "B"], size=n)
ggplot({'x': x, 'y': y, 'g': g}, aes(x='x', y='y', fill='g')) + \
    stat_summary_bin(geom='crossbar', bins=6, fatten=5, position='dodge')

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 100
np.random.seed(42)
x = np.random.uniform(size=n)
y = np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    stat_summary_bin(fun='mq', fun_min='lq', fun_max='uq', geom='crossbar', \
                     bins=11, width=1, quantiles=[.05, .5, .95], boundary=0) + \
    geom_point()