plotnine.geom_step

geom_step(
    mapping=None,
    data=None,
    *,
    stat="identity",
    position="identity",
    na_rm=False,
    inherit_aes=True,
    show_legend=None,
    raster=False,
    direction="hv",
    **kwargs
)

Stepped connected points

Parameters

mapping : aes = None

Aesthetic mappings created with aes. If specified and inherit_aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic	Default value
x
y
alpha	`1`
color	`'black'`
group
linetype	`'solid'`
size	`0.5`

The bold aesthetics are required.

data : DataFrame = None

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

stat : str | stat = "identity"

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

position : str | position = "identity"

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rm : bool = False

If False, removes missing values with a warning. If True silently removes missing values.

inherit_aes : bool = True

If False, overrides the default aesthetics.

show_legend : bool | dict = None

Whether this layer should be included in the legends. None the default, includes any aesthetics that are mapped. If a bool, False never includes and True always includes. A dict can be used to exclude specific aesthetis of the layer from showing in the legend. e.g show_legend={'color': False}, any other aesthetic are included by default.

raster : bool = False

If True, draw onto this layer a raster (bitmap) object even ifthe final image is in vector format.

direction : Literal["hv", "vh", "mid"] = "hv"

horizontal-vertical steps, vertical-horizontal steps or steps half-way between adjacent x values.

**kwargs : Any = {}

Aesthetics or parameters used by the stat.

Examples


from plotnine import ggplot, aes, geom_step, geom_line, labs, theme, element_text
from plotnine.data import economics

Step plots

geom_step() connects points using ‘steps’ instead of a line.

# inspect the data
economics.head()

	date	pce	pop	psavert	uempmed	unemploy
0	1967-07-01	507.4	198712	12.5	4.5	2944
1	1967-08-01	510.5	198911	12.5	4.7	2945
2	1967-09-01	516.3	199113	11.7	4.6	2958
3	1967-10-01	512.9	199311	12.5	4.9	3143
4	1967-11-01	518.1	199498	12.5	4.7	3066

Plot a step plot using geom_plot(). Notice from the first point the line travels vertically then horizontally:

(
    ggplot(
        economics.iloc[:20],  # filter for first twenty rows (dates) to make steps more visible
        aes("date", "unemploy"),
    )
    + geom_step()  # step plot
    + labs(x="date", y="unemployment (,000)")  # label x & y-axis
    + theme(axis_text_x=element_text(angle=45))  # rotate x-axis text for readability
)

You can see how geom_path() (shown in pink) differs from geom_line() (black):

(
    ggplot(economics.iloc[:20], aes("date", "unemploy"))
    + geom_step(
        colour="#ff69b4",  # plot geom_step as the first layer - colour pink
        alpha=0.5,  # line transparency
        size=2.5,
    )  # line thickness
    + geom_line()  # plot geom_line as the second layer
    + labs(x="date", y="unemployment (,000)")
    + theme(axis_text_x=element_text(angle=45))
)

Rather than a line that travels vertically then horizontally, this order can be switched by specifying direction='vh' within geom_step(). Below direction='vh' is shown in black with the default direction='hv' shown in pink:

(
    ggplot(economics.iloc[:20], aes("date", "unemploy"))
    + geom_step(
        colour="#ff69b4",  # plot geom_path with default direction as the first layer - colour pink
        linetype="dashed",  # line type
        size=2,  # line thickness
        alpha=0.5,
    )  # line transparency
    + geom_step(direction="vh")  # plot geom_path with step order reversed
    + labs(x="date", y="unemployment (,000)")
    + theme(axis_text_x=element_text(angle=45))
)

Source: Step plots

Parameters

See Also

Examples

Step plots