plotnine.scale_shape_identity

scale_shape_identity(
    *,
    name=None,
    breaks=True,
    limits=None,
    labels=True,
    expand=None,
    guide=None,
    na_value=np.nan,
    aesthetics=(),
    drop=True,
    na_translate=True
)

No shape scaling

Parameter Attributes

name: str | None = None

The name of the scale. It is used as the label of the axis or the title of the guide. Suitable defaults are chosen depending on the type of scale.

breaks: DiscreteBreaksUser = True

List of major break points. Or a callable that takes a tuple of limits and returns a list of breaks. If True, automatically calculate the breaks.

limits: DiscreteLimitsUser = None

Limits of the scale. These are the categories (unique values) of the variables. If is only a subset of the values, those that are left out will be treated as missing data and represented with a na_value.

labels: ScaleLabelsUser = True

Labels at the breaks. Alternatively, a callable that takes an array_like of break points as input and returns a list of strings.

expand: ( tuple[float, float] | tuple[float, float, float, float] | None ) = None

Multiplicative and additive expansion constants that determine how the scale is expanded. If specified must be of length 2 or 4. Specifically the values are in this order:

(mul, add)
(mul_low, add_low, mul_high, add_high)

For example,

(0, 0) - Do not expand.
(0, 1) - Expand lower and upper limits by 1 unit.
(1, 0) - Expand lower and upper limits by 100%.
(0, 0, 0, 0) - Do not expand, as (0, 0).
(0, 0, 0, 1) - Expand upper limit by 1 unit.
(0, 1, 0.1, 0) - Expand lower limit by 1 unit and upper limit by 10%.
(0, 0, 0.1, 2) - Expand upper limit by 10% plus 2 units.

If not specified, suitable defaults are chosen.

guide: Literal["legend"] | None = None

na_value: Any = np.nan

If na_translate=True, what aesthetic value should be assigned to the missing values. This parameter does not apply to position scales where nan is always placed on the right.

aesthetics: Sequence[ScaledAestheticsName] = ()

Aesthetics affected by this scale. These are defined by each scale and the user should probably not change them. Have fun.

drop: bool = True

Whether to drop unused categories from the scale

na_translate: bool = True

If True translate missing values and show them. If False remove missing values.

Examples

from plotnine import (
    aes,
    annotate,
    coord_equal,
    element_rect,
    element_text,
    facet_wrap,
    geom_point,
    geom_segment,
    geom_text,
    geom_tile,
    ggplot,
    lims,
    scale_shape_identity,
    theme,
    theme_void,
)

import polars as pl
import numpy as np

Scatter Plot Shapes

List the shapes and prepare dataframe with what is required to display them.

filled_shapes = (
    "o",  # circle
    "^",  # triangle up
    "s",  # square
    "D",  # Diamond
    "v",  # triangle down
    "*",  # star
    "p",  # pentagon
    "8",  # octagon
    "<",  # triangle left
    "h",  # hexagon1
    ">",  # triangle right
    "H",  # hexagon1
    "d",  # thin diamond
)

unfilled_shapes = (
    "+",  # plus
    "x",  # x
    ".",  # point
    "1",  # tri_down
    "2",  # tri_up
    "3",  # tri_left
    "4",  # tri_right
    ",",  # pixel
    "_",  # hline
    "|",  # vline
    0,  # tickleft
    1,  # tickright
    2,  # tickup
    3,  # tickdown
    4,  # caretleft
    5,  # caretright
    6,  # caretup
    7,  # caretdown
)

n1 = len(filled_shapes)
n2 = len(unfilled_shapes)
ncols = 10
shapes = filled_shapes + unfilled_shapes

df = pl.DataFrame({
    "x": np.hstack([np.arange(n1) % ncols, np.arange(n2) % ncols]),
    "y": 4 - 1.5 * np.hstack([np.arange(n1) // ncols, np.arange(n2) // ncols]),
    "shape": pl.Series(shapes, dtype=object),
    "shape_kind": np.repeat(["Filled Shapes", "Unfilled Shapes"], (n1, n2))
})

df

shape: (31, 4)

x	y	shape	shape_kind
i64	f64	object	str
0	4.0	o	"Filled Shapes"
1	4.0	^	"Filled Shapes"
2	4.0	s	"Filled Shapes"
3	4.0	D	"Filled Shapes"
4	4.0	v	"Filled Shapes"
…	…	…	…
3	2.5	3	"Unfilled Shapes"
4	2.5	4	"Unfilled Shapes"
5	2.5	5	"Unfilled Shapes"
6	2.5	6	"Unfilled Shapes"
7	2.5	7	"Unfilled Shapes"

# Gallery, points

def double_quote_strings(series):
    return [
        '"{}"'.format(s) if isinstance(s, str) else "{}".format(s)
        for s in series
    ]

(
    ggplot(df, aes("x", "y"))
    
    # Background
    + geom_tile(aes(width=0.9, height=0.9), fill="#F8F8F8")
    
    # Cross-grid to help see the centering of the shapes
    + geom_segment(aes(x="x-0.45", xend="x+0.45", yend="y"), color="Crimson", alpha=0.05)
    + geom_segment(aes(xend="x", y="y-0.45", yend="y+0.45"), color="Crimson", alpha=0.05)
    
    # The shape
    + geom_point(aes(shape="shape"), fill="#E5C8D6", size=8)

    # Specification value for the shape
    + geom_text(
        aes(y="y+.5", label="double_quote_strings(shape)"),
        color="darkblue",
        va="bottom",
    )

    # Separate the shapes by their kind
    + facet_wrap("shape_kind", ncol=1)

    # Make things pretty
    + lims(y=(2, 5))
    + scale_shape_identity()
    + coord_equal()
    + theme_void()
    + theme(
        plot_background=element_rect(fill="white"),
        strip_text=element_text(size=12)
    )
)

Source: Scatter Plot Shapes