Docs: examples page -> gt-extras example

.github/workflows/ci-docs.yaml

@@ -14,6 +14,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
       - uses: actions/setup-python@v5
         with:
           python-version: "3.10"

.github/workflows/ci-tests.yaml

@@ -20,6 +20,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
@@ -44,6 +46,8 @@ jobs:
     runs-on: windows-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
@@ -59,6 +63,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
@@ -83,6 +89,8 @@ jobs:
 
       #
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
@@ -108,6 +116,8 @@ jobs:
     if: github.event_name == 'release'
     steps:
       - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
       - uses: actions/setup-python@v2
         with:
           python-version: "3.10"

docs/examples/index.qmd

@@ -7,6 +7,7 @@ format:
   html:
     code-fold: true
     code-summary: "Show the Code"
+toc: false
 ---
 
 :::::: {.column-page}
@@ -419,3 +420,198 @@ coffee_table
 :::::
 ::::::
 
+## Ecosystem
+
+:::::: {.column-page}
+::::: {.grid}
+
+:::{.g-col-lg-6 .g-col-12 .shrink-example}
+
+<a
+  href="https://posit-dev.github.io/gt-extras/examples/with-code.html#gini_coefficient_table"
+  target="_blank"
+  >See the code (gt-extras site) ⬀</a>
+
+
+```{python}
+# | echo: false
+import polars as pl
+from great_tables import GT, html
+import gt_extras as gte
+
+pre_tax_col = "gini_market__age_total"
+post_tax_col = "gini_disposable__age_total"
+
+# Read the data
+df = pl.read_csv(
+    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-05/income_inequality_raw.csv",
+    schema={
+        "Entity": pl.String,
+        "Code": pl.String,
+        "Year": pl.Int64,
+        post_tax_col: pl.Float64,
+        pre_tax_col: pl.Float64,
+        "population_historical": pl.Int64,
+        "owid_region": pl.String,
+    },
+    null_values=["NA", ""],
+)
+
+# Propogate the region field to all rows of that country
+df = (
+    df.sort("Entity")
+    .group_by("Entity", maintain_order=True)
+    .agg(
+        [
+            pl.col("Code"),
+            pl.col("Year"),
+            pl.col(post_tax_col),
+            pl.col(pre_tax_col),
+            pl.col("population_historical"),
+            # Most important action happens here
+            pl.col("owid_region").fill_null(strategy="backward"),
+        ]
+    )
+    .explode(
+        [
+            "Code",
+            "Year",
+            post_tax_col,
+            pre_tax_col,
+            "population_historical",
+            "owid_region",
+        ]
+    )
+)
+
+# Drop rows where there is a null in either pre-tax or post-tax cols
+df = df.drop_nulls(
+    subset=(
+        pl.col(post_tax_col),
+        pl.col(pre_tax_col),
+    )
+)
+
+# Compute the percent reduction in gini coefficient.
+df = df.with_columns(
+    ((pl.col(pre_tax_col) - pl.col(post_tax_col)) / pl.col(pre_tax_col) * 100)
+    .round(2)
+    .alias("gini_pct_change")
+)
+
+# Calculate 5-year benchmark (mean) of percent change for each country
+df = df.with_columns(
+    pl.col("gini_pct_change")
+    .rolling_mean(window_size=5)
+    .over(pl.col("Entity"))
+    .alias("gini_pct_benchmark_5yr")
+)
+
+# Select rows with large population in the year 2020, sorted by coefficient post-tax
+df = (
+    # Choose a smaller pop to include more countries
+    df.filter(pl.col("population_historical").gt(40000000))
+    .filter(pl.col("Year").eq(2020))
+    .sort(by=pl.col(post_tax_col))
+)
+
+
+# Scale population
+df = df.with_columns((pl.col("population_historical").log10()).alias("pop_log"))
+pop_min = df["pop_log"].min() / 1
+pop_max = df["pop_log"].max()
+
+# Set up gt-extras icons, scaling population to 1-10 range
+df = df.with_columns(
+    ((pl.col("pop_log") - pop_min) / (pop_max - pop_min) * 10 + 1)
+    .round(0)
+    .cast(pl.Int64)
+    .alias("pop_icons")
+)
+
+# Format original population value with commas
+df = df.with_columns(
+    pl.col("population_historical").map_elements(
+        lambda x: f"{int(x):,}" if x is not None else None, return_dtype=pl.String
+    )
+)
+
+# Apply gte.fa_icon_repeat to each entry in the pop_icons column
+df_with_icons = df.with_columns(
+    pl.col("pop_icons").map_elements(
+        lambda x: gte.fa_icon_repeat(name="person", repeats=int(x)),
+        return_dtype=pl.String,
+    )
+)
+
+# Generate the table, before gt-extras add-ons
+gt = (
+    GT(df_with_icons, rowname_col="Entity", groupname_col="owid_region")
+    .tab_header(
+        "Income Inequality Before and After Taxes in 2020",
+        "As measured by the Gini coefficient, where 0 is best and 1 is worst",
+    )
+    .cols_move("pop_icons", after=pre_tax_col)
+    .cols_align("left")
+    .cols_hide(["Year", "pop_log", "population_historical"])
+    .fmt_flag("Code")
+    .cols_label(
+        {
+            "Code": "",
+            "gini_pct_change": "Improvement Post Taxes",
+            "pop_icons": "Population",
+        }
+    )
+    .tab_source_note(
+        html(
+            """
+            <div>
+            <strong>Source:</strong> Data from <a href="https://github.com/rfordatascience/tidytuesday">#TidyTuesday</a> (2025-08-05).<br>
+                <div>
+                <strong>Dumbbell plot:</strong>
+                <span style="color:#106ea0;">Blue:</span> post-tax Gini coefficient
+                <span style="color:#e0b165;">Gold:</span> pre-tax Gini coefficient
+                <br>
+                </div>
+            <strong>Bullet plot:</strong> Percent reduction in Gini after taxes for each country, compared to its 5-year average benchmark.
+            </div>
+            """
+        )
+    )
+)
+
+# Apply the gt-extras functions via pipe
+(
+    gt.pipe(
+        gte.gt_plt_dumbbell,
+        col1=pre_tax_col,
+        col2=post_tax_col,
+        col1_color="#e0b165",
+        col2_color="#106ea0",
+        dot_border_color="transparent",
+        num_decimals=2,
+        width=240,
+        label="Pre-tax to Post-tax Coefficient",
+    )
+    .pipe(
+        gte.gt_plt_bullet,
+        "gini_pct_change",
+        "gini_pct_benchmark_5yr",
+        fill="#963d4c",
+        target_color="#3D3D3D",
+        bar_height=15,
+        width=200,
+    )
+    .pipe(
+        gte.gt_merge_stack,
+        col1="pop_icons",
+        col2="population_historical",
+    )
+    .pipe(gte.gt_theme_guardian)
+)
+```
+
+:::
+
+:::::
+::::::

pyproject.toml

@@ -73,6 +73,7 @@ dev-no-pandas = [
     "jupyter",
     "quartodoc>=0.11.0; python_version >= '3.9'",
     "griffe==0.38.1",
+    "gt-extras>=0.0.7",
     "polars",
     "pre-commit==2.15.0",
     "pyarrow",