lets_plot.geom_histogram(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, bins=None, binwidth=None, center=None, boundary=None, **other_args)

Displays a 1d distribution by dividing variable mapped to x axis into bins and counting the number of observations in each bin.


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 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.

statstr, default=’bin’

The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

positionstr or FeatureSpec, default=’stack’

Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

show_legendbool, default=True

False - do not show legend for this layer.


Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.


Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

binsint, default=30

Number of bins. Overridden by binwidth.


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.


Specifies x-value to align bin centers to.


Specifies x-value to align bin boundary (i.e. point berween bins) to.


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.


Geom object specification.


geom_histogram() displays a 1d distribution by dividing variable mapped to x axis into bins and counting the number of observations in each bin.

Computed variables:

  • ..count.. : number of points with x-axis coordinate in the same bin.

geom_histogram() understands the following aesthetics mappings:

  • x : x-axis value (this values will produce cases or bins for bars).

  • y : y-axis value, default: ‘..count..’. Alternatively: ‘..density..’.

  • alpha : transparency level of a layer. Understands numbers between 0 and 1.

  • color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

  • fill : color of geometry filling.

  • size : lines width.

  • weight : used by ‘bin’ stat to compute weighted sum instead of simple count.


1import numpy as np
2from lets_plot import *
5data = {'x': np.random.normal(size=1000)}
6ggplot(data, aes(x='x')) + geom_histogram()