facet_grid – plotnine {{< var version >}}

facet_grid(
    rows=None,
    cols=None,
    *,
    margins=False,
    scales="fixed",
    space="fixed",
    shrink=True,
    labeller="label_value",
    as_table=True,
    drop=True
)

Wrap 1D Panels onto 2D surface

Parameters

rows : Optional[str | Sequence[str]] = None

Variable expressions along the rows of the facets/panels. Each expression is evaluated within the context of the dataframe.

cols : Optional[str | Sequence[str]] = None

Variable expressions along the columns of the facets/panels. Each expression is evaluated within the context of the dataframe.

margins : bool | Sequence[str] = False

variable names to compute margins for. True will compute all possible margins.

space : Literal["fixed", "free", "free_x", "free_y"] | FacetSpaceRatios = "fixed"

Control the size of the x or y sides of the panels. The size also depends to the scales parameter.

If a string, it should be one of ['fixed', 'free', 'free_x', 'free_y'].

If a dict, it indicates the relative facet size ratios such as:

{"x": [1, 2], "y": [3, 1, 1]}

This means that in the horizontal direction, the second panel will be twice the length of the first. In the vertical direction the top facet will be the 3 times longer then the second and third facets.

Note that the number of dimensions in the list must equal the number of facets that will be produced.

shrink : bool = True

Whether to shrink the scales to the output of the statistics instead of the raw data.

labeller : Literal["label_value", "label_both", "label_context"] = "label_value"

How to label the facets. A string value if it should be one of ["label_value", "label_both", "label_context"].

as_table : bool = True

If True, the facets are laid out like a table with the highest values at the bottom-right. If False the facets are laid out like a plot with the highest value a the top-right

drop : bool = True

If True, all factor levels not used in the data will automatically be dropped. If False, all factor levels will be shown, regardless of whether or not they appear in the data.