A Bar Plot With 2 Variables

This notebook adapts the plotnine gallery example on grouped bar labels, using Polars for data preparation and the fluent API for plotting.

import polars as pl
import plotnine as p9
from plotnine import aes, ggplot

Visualizing a variable that contains nested, independent categories on a single plot can get crowded quickly. This example builds the chart up step by step.

Create the Data

df = pl.DataFrame(
    {
        "variable": [
            "gender",
            "gender",
            "age",
            "age",
            "age",
            "income",
            "income",
            "income",
            "income",
        ],
        "category": [
            "Female",
            "Male",
            "1-24",
            "25-54",
            "55+",
            "Lo",
            "Lo-Med",
            "Med",
            "High",
        ],
        "value": [60, 40, 50, 30, 20, 10, 25, 25, 40],
    }
).with_columns(
    pl.col("variable").cast(pl.Enum(["gender", "age", "income"])),
    pl.col("category").cast(
        pl.Enum(["Female", "Male", "1-24", "25-54", "55+", "Lo", "Lo-Med", "Med", "High"])
    ),
)

df

shape: (9, 3)

variable	category	value
enum	enum	i64
"gender"	"Female"	60
"gender"	"Male"	40
"age"	"1-24"	50
"age"	"25-54"	30
"age"	"55+"	20
"income"	"Lo"	10
"income"	"Lo-Med"	25
"income"	"Med"	25
"income"	"High"	40

Start With a Simple Bar Chart

(
    ggplot(df)
    .aes(x="variable", y="value", fill="category")
    .geom_col()
)

Dodge the Bars

(
    ggplot(df)
    .aes(x="variable", y="value", fill="category")
    .geom_col(position="dodge")
)

Label Each Category

dodge_text = p9.position_dodge(width=0.9)

(
    ggplot(df)
    .aes(x="variable", y="value", fill="category")
    .geom_col(position="dodge", show_legend=False)
    .geom_text(
        aes(y=-0.5, label="category"),
        position=dodge_text,
        color="gray",
        size=8,
        angle=45,
        va="top",
    )
    .lims(y=(-5, 60))
)

Add Value Labels

dodge_text = p9.position_dodge(width=0.9)

(
    ggplot(df)
    .aes(x="variable", y="value", fill="category")
    .geom_col(position="dodge", show_legend=False)
    .geom_text(
        aes(y=-0.5, label="category"),
        position=dodge_text,
        color="gray",
        size=8,
        angle=45,
        va="top",
    )
    .geom_text(
        aes(label="value"),
        position=dodge_text,
        size=8,
        va="bottom",
        format_string="{}%",
    )
    .lims(y=(-5, 60))
)

Final Polish

dodge_text = p9.position_dodge(width=0.9)
ccolor = "#555555"

(
    ggplot(df)
    .aes(x="variable", y="value", fill="category")
    .geom_col(position="dodge", show_legend=False)
    .geom_text(
        aes(y=-0.5, label="category"),
        position=dodge_text,
        color=ccolor,
        size=8,
        angle=45,
        va="top",
    )
    .geom_text(
        aes(label="value"),
        position=dodge_text,
        size=8,
        va="bottom",
        format_string="{}%",
    )
    .lims(y=(-5, 60))
    .add_theme(
        panel_background=p9.element_rect(fill="white"),
        axis_title_y=p9.element_blank(),
        axis_line_x=p9.element_line(color="black"),
        axis_line_y=p9.element_blank(),
        axis_text_y=p9.element_blank(),
        axis_text_x=p9.element_text(color=ccolor),
        axis_ticks_major_y=p9.element_blank(),
        panel_grid=p9.element_blank(),
        panel_border=p9.element_blank(),
    )
)