PlotSpec

class PlotSpec(data, mapping, scales, layers, metainfo_list=[], is_livemap=False, crs_initialized=False, crs=None, **kwargs)

A class of the initial plot object.

Do not use this class explicitly.

Instead, you should construct its objects with functions ggplot(), corr_plot(…).points().build() etc.

__init__(data, mapping, scales, layers, metainfo_list=[], is_livemap=False, crs_initialized=False, crs=None, **kwargs)

Initialize self.

get_plot_shared_data()

Extract the data shared by all layers.

Returns:

dict or DataFrame

Object data.

Examples

from lets_plot import *
LetsPlot.setup_html()
p = ggplot({'x': [0], 'y': [0]}, aes('x', 'y'))
p += geom_point(data={'x': [1], 'y': [1]})
p.get_plot_shared_data()

{'x': [0], 'y': [0]}

has_layers() → bool

Check if the PlotSpec object has at least one layer.

Returns:

bool

True if object has layers.

Examples

from lets_plot import *
LetsPlot.setup_html()
p = ggplot()
print(p.has_layers())
p += geom_point(x=0, y=0)
print(p.has_layers())

False
True

__add__(other)

Allow to add different specs to the PlotSpec object.

Examples

from lets_plot import *
LetsPlot.setup_html()
p = ggplot({'x': [0, 1, 2], 'y': [0, 1, 2]}, aes('x', 'y'))
l = layer('point', mapping=aes(color='x'))
s = scale_color_discrete()
t = theme(axis_title='blank')
p + l + s + t

as_dict()

Return the dictionary of all properties of the object with as_dict() applied recursively to all subproperties of FeatureSpec type.

Returns:

dict

Dictionary of properties.

Examples

from lets_plot import *
LetsPlot.setup_html()
p = ggplot({'x': [0], 'y': [0]}) + geom_point(aes('x', 'y'))
p.as_dict()

{'data': {'x': [0], 'y': [0]},
 'mapping': {},
 'data_meta': {'series_annotations': [{'type': 'int', 'column': 'x'},
   {'type': 'int', 'column': 'y'}]},
 'kind': 'plot',
 'scales': [],
 'layers': [{'geom': 'point',
   'mapping': {'x': 'x', 'y': 'y'},
   'data_meta': {}}],
 'metainfo_list': []}

show()

Draw a plot.

Examples

from lets_plot import *
LetsPlot.setup_html()
p = ggplot() + geom_point(x=0, y=0)
p.show()

to_svg(path=None) → str

Export the plot in SVG format.

Parameters:

selfPlotSpec

Plot specification to export.

pathstr, file-like object, default=None

Сan be either a string specifying a file path or a file-like object. If a string is provided, the result will be exported to the file at that path. If a file-like object is provided, the result will be exported to that object. If None is provided, the result will be returned as a string.

Returns:

str

Absolute pathname of created file, SVG content as a string or None if a file-like object is provided.

Examples

import numpy as np
import io
from lets_plot import *
from IPython import display
LetsPlot.setup_html()
x = np.random.randint(10, size=100)
p = ggplot({'x': x}, aes(x='x')) + geom_bar()
file_like = io.BytesIO()
p.to_svg(file_like)
display.SVG(file_like.getvalue())

to_html(path=None, iframe: bool | None = None) → str

Export the plot in HTML format.

Parameters:

selfPlotSpec

Plot specification to export.

pathstr, file-like object, default=None

Сan be either a string specifying a file path or a file-like object. If a string is provided, the result will be exported to the file at that path. If a file-like object is provided, the result will be exported to that object. If None is provided, the result will be returned as a string.

iframebool, default=False

Whether to wrap HTML page into a iFrame.

Returns:

str

Absolute pathname of created file, HTML content as a string or None if a file-like object is provided.

Examples

import numpy as np
import io
from lets_plot import *
LetsPlot.setup_html()
x = np.random.randint(10, size=100)
p = ggplot({'x': x}, aes(x='x')) + geom_bar()
file_like = io.BytesIO()
p.to_html(file_like)

to_png(path, scale: float | None = None, w=None, h=None, unit=None, dpi=None) → str

Export a plot to a file or to a file-like object in PNG format.

Parameters:

selfPlotSpec

Plot specification to export.

pathstr, file-like object

Сan be either a string specifying a file path or a file-like object. If a string is provided, the result will be exported to the file at that path. If a file-like object is provided, the result will be exported to that object.

scalefloat

Scaling factor for raster output. Default value is 2.0.

wfloat, default=None

Width of the output image in units. Only applicable when exporting to PNG or PDF.

hfloat, default=None

Height of the output image in units. Only applicable when exporting to PNG or PDF.

unit{‘in’, ‘cm’, ‘mm’}, default=’in’

Unit of the output image. One of: ‘in’, ‘cm’, ‘mm’. Only applicable when exporting to PNG or PDF.

dpiint, default=300

Resolution in dots per inch. Only applicable when exporting to PNG or PDF.

Returns:

str

Absolute pathname of created file or None if a file-like object is provided.

Notes

• If w, h, unit, and dpi are all specified:

  • The plot’s pixel size (default or set by ggsize()) is ignored.

  • The output size is calculated using the specified w, h, unit, and dpi.

    • The plot is resized to fit the specified w x h area, which may affect the layout, tick labels, and other elements.

• If only dpi is specified:

  • The plot’s pixel size (default or set by ggsize()) is converted to inches using the standard display PPI of 96.

  • The output size is then calculated based on the specified DPI.

    • The plot maintains its aspect ratio, preserving layout, tick labels, and other visual elements.

    • Useful for printing - the plot will appear nearly the same size as on screen.

• If w, h are not specified:

  • The scale parameter is used to determine the output size.

    • The plot maintains its aspect ratio, preserving layout, tick labels, and other visual elements.

    • Useful for generating high-resolution images suitable for publication.

Examples

import numpy as np
import io
from lets_plot import *
from IPython import display
LetsPlot.setup_html()
x = np.random.randint(10, size=100)
p = ggplot({'x': x}, aes(x='x')) + geom_bar()
file_like = io.BytesIO()
p.to_png(file_like)
display.Image(file_like.getvalue())

to_pdf(path, scale: float | None = None, w=None, h=None, unit=None, dpi=None) → str

Export a plot to a file or to a file-like object in PDF format.

Parameters:

selfPlotSpec

Plot specification to export.

pathstr, file-like object

Сan be either a string specifying a file path or a file-like object. If a string is provided, the result will be exported to the file at that path. If a file-like object is provided, the result will be exported to that object.

scalefloat

Scaling factor for raster output. Default value is 2.0.

wfloat, default=None

Width of the output image in units. Only applicable when exporting to PNG or PDF.

hfloat, default=None

Height of the output image in units. Only applicable when exporting to PNG or PDF.

unit{‘in’, ‘cm’, ‘mm’}, default=’in’

Unit of the output image. One of: ‘in’, ‘cm’, ‘mm’. Only applicable when exporting to PNG or PDF.

dpiint, default=300

Resolution in dots per inch. Only applicable when exporting to PNG or PDF.

Returns:

str

Absolute pathname of created file or None if a file-like object is provided.

Notes

• If w, h, unit, and dpi are all specified:

  • The plot’s pixel size (default or set by ggsize()) is ignored.

  • The output size is calculated using the specified w, h, unit, and dpi.

    • The plot is resized to fit the specified w x h area, which may affect the layout, tick labels, and other elements.

• If only dpi is specified:

  • The plot’s pixel size (default or set by ggsize()) is converted to inches using the standard display PPI of 96.

  • The output size is then calculated based on the specified DPI.

    • The plot maintains its aspect ratio, preserving layout, tick labels, and other visual elements.

    • Useful for printing - the plot will appear nearly the same size as on screen.

• If w, h are not specified:

  • The scale parameter is used to determine the output size.

    • The plot maintains its aspect ratio, preserving layout, tick labels, and other visual elements.

    • Useful for generating high-resolution images suitable for publication.

Examples

import numpy as np
import io
import os
from lets_plot import *
from IPython import display
LetsPlot.setup_html()
n = 60
np.random.seed(42)
x = np.random.choice(list('abcde'), size=n)
y = np.random.normal(size=n)
p = ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + geom_jitter()
file_like = io.BytesIO()
p.to_pdf(file_like)