Other tips: tabs, subsets, and maps

This page shows how to combine Quarto tabsets with graphs, use data subsets in your figures, and add a simple map example.

Tabs with a graph

Use Quarto’s panel tabset so readers can switch between views. Put the opening ::: {.panel-tabset} before the tabs, then each ### Tab name starts a new tab; close with :::.

Below, the first tab shows a full-dataset scatter plot; the next tabs show the same plot by region (one subset per tab). All use wbpyplot with the Plotly backend.

from wbpyplot import wb_plot
import pandas as pd
import numpy as np

gapminder = pd.read_csv("Gapminder Data.csv")
continents = gapminder["continent"].unique().tolist()
# Use one year for a cleaner scatter
gm = gapminder[gapminder["year"] == 2007].copy()

@wb_plot(
    title="Life expectancy vs GDP (2007)",
    subtitle="All countries",
    note=[("Source:", "Gapminder.")],
    palette="wb_categorical",
    legend_title="Region",
    width=600,
    height=800,
    backend="plotly",
)
def scatter_full(fig, gm, continents):
    for cont in continents:
        mask = gm["continent"] == cont
        fig.add_scatter(
            x=gm.loc[mask, "gdpPercap"],
            y=gm.loc[mask, "lifeExp"],
            name=cont,
            mode="markers",
        )
    fig.update_layout(xaxis_title="GDP per capita", yaxis_title="Life expectancy")

scatter_full(gm, continents)

Subset the data with gm[gm["continent"] == "Africa"] and plot in its own tab.

subset_africa = gm[gm["continent"] == "Africa"]

@wb_plot(
    title="Life expectancy vs GDP (2007)",
    subtitle="Africa",
    note=[("Source:", "Gapminder.")],
    width=600,
    height=500,
    backend="plotly",
)
def scatter_africa(fig, subset_africa):
    fig.add_scatter(
        x=subset_africa["gdpPercap"],
        y=subset_africa["lifeExp"],
        name="Africa",
        mode="markers",
    )
    fig.update_layout(xaxis_title="GDP per capita", yaxis_title="Life expectancy")

scatter_africa(subset_africa)

Same idea for Europe: filter, then plot in a tab.

subset_europe = gm[gm["continent"] == "Europe"]

@wb_plot(
    title="Life expectancy vs GDP (2007)",
    subtitle="Europe",
    note=[("Source:", "Gapminder.")],
    width=600,
    height=400,
    backend="plotly",
)
def scatter_europe(fig, subset_europe):
    fig.add_scatter(
        x=subset_europe["gdpPercap"],
        y=subset_europe["lifeExp"],
        name="Europe",
        mode="markers",
    )
    fig.update_layout(xaxis_title="GDP per capita", yaxis_title="Life expectancy")

scatter_europe(subset_europe)

Subsets in one figure

You can also plot multiple subsets in a single figure by filtering and passing each subset to the same plotting logic (e.g. one series per continent). The main wbplot-style examples do this: one scatter with all continents, each continent a different color. Subsetting is just df[df["col"] == value] or df[df["year"].between(2000, 2010)]; then use the subset in your fig.add_scatter or ax.scatter calls.

Subplots (nrows, ncols)

Use nrows and ncols in @wb_plot to create a grid of subplots (Matplotlib backend). The decorator creates a figure with nrows × ncols axes and passes an array axs to your function: use axs[0], axs[1], … for a single row, or axs[0, 0], axs[0, 1], axs[1, 0], axs[1, 1] for a 2×2 grid. You can iterate with for ax in axs.flat:.

Below, the same scatter (GDP vs life expectancy, 2007) is split by continent: one facet per continent using nrows=2, ncols=3. Each panel shows one continent’s data; the sixth panel is hidden because there are five continents.

@wb_plot(
    title="Life expectancy vs GDP (2007) by continent",
    subtitle="One facet per continent (nrows=2, ncols=3)",
    note=[("Source:", "Gapminder.")],
    nrows=2,
    ncols=3,
    width=1000,
    height=800,
)
def scatter_by_continent(fig, axs, gm, continents):
    for i, cont in enumerate(continents):
        ax = axs.flat[i]
        mask = gm["continent"] == cont
        ax.scatter(gm.loc[mask, "gdpPercap"], gm.loc[mask, "lifeExp"])
        ax.set_xlabel("GDP per capita")
        ax.set_ylabel("Life expectancy")
        ax.set_title(cont)
    # Hide the unused 6th panel (5 continents, 6 panels)
    axs.flat[len(continents)].set_visible(False)

scatter_by_continent(gm, continents)

Colours and palettes

You can pass palette= to @wb_plot to control colors. Available palettes include:

Categorical (discrete groups, e.g. scatter/bar by category):

wb_categorical — general purpose (up to 9 colors)
wb_region — World Bank regions (WLD, NAC, LCN, SAS, MEA, ECS, EAS, SSF, …)
wb_income — HIC, UMC, LMC, LIC
wb_gender — male, female, diverse
wb_urbanisation — rural, urban
wb_age, wb_binary, wb_total, wb_pillars, etc.

Sequential (ordered values, good for maps and choropleths):

wb_seq_bad_to_good — light (bad) → dark blue (good)
wb_seq_good_to_bad — light blue → dark red
wb_seq_monochrome_blue, wb_seq_monochrome_green, wb_seq_monochrome_red, wb_seq_monochrome_yellow, wb_seq_monochrome_purple — single-hue gradients

Diverging (positive/negative around a midpoint):

wb_div_default, wb_div_alt, wb_div_neutral

For maps: use a sequential palette (e.g. wb_seq_bad_to_good or wb_seq_monochrome_blue) and pass it to @wb_plot(palette="wb_seq_monochrome_blue", ...). For choropleths, the decorator builds a continuous colormap from the palette. You can discretize with palette_bins (e.g. palette_bins=5 or palette_bins=[50, 60, 70, 80, 90]) and palette_bin_mode ("linear" or "quantile").