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Lets-Plot for Python

v4.3.2

lets_plot.geom_histogram#

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

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

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 a position adjustment name: ‘dodge’, ‘dodgev’, ‘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.

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.

labelslayer_labels

Result of the call to the layer_labels() function. Specify style and content of the annotations.

orientationstr, default=’x’

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

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

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.

  • ..binwidth.. : width of each bin.

geom_histogram() understands the following aesthetics mappings:

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

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

  • alpha : transparency level of a layer. Accept values between 0 and 1.

  • color (colour) : color of the geometry lines. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

  • fill : fill color. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

  • size : line width.

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

Examples

1import numpy as np
2from lets_plot import *
3LetsPlot.setup_html()
4np.random.seed(42)
5data = {'x': np.random.normal(size=1000)}
6ggplot(data, aes(x='x')) + geom_histogram()


1import numpy as np
2from lets_plot import *
3LetsPlot.setup_html()
4np.random.seed(42)
5data = {'x': np.random.gamma(2.0, size=1000)}
6ggplot(data, aes(x='x')) + \
7    geom_histogram(aes(color='x', fill='x'), bins=50)


 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4np.random.seed(42)
 5x = np.random.normal(scale=3, size=1000)
 6y = 2 * (np.round(x) % 2) - 1
 7ggplot({'x': x, 'y': y}) + \
 8    geom_histogram(aes(x='x', weight='y'), \
 9                   center=0, binwidth=1, \
10                   color='black', fill='gray', size=1)
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