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+---
+title: "Style Table Body with `mask=` in `loc.body()`"
+html-table-processing: none
+author: Jerry Wu
+date: 2025-01-24
+freeze: true
+jupyter: python3
+format:
+  html:
+    code-summary: "Show the Code"
+---
+
+In Great Tables `0.16.0`, we introduced the `mask=` parameter in `loc.body()`, enabling users to apply conditional styling to rows on a per-column basis more efficiently when working with a Polars DataFrame. This post will demonstrate how it works and compare it with the "old-fashioned" approach:
+
+* **Leveraging the `mask=` parameter in `loc.body()`:** Use Polars expressions for streamlined styling.
+* **Utilizing the `locations=` parameter in `GT.tab_style()`:** Pass a list of `loc.body()` objects.
+
+Let’s dive in.
+
+### Preparations
+We'll use the built-in dataset `gtcars` to create a Polars DataFrame. Next, we'll select the columns `mfr`, `drivetrain`, `year`, and `hp` to create a small pivoted table named `df_mini`. Finally, we'll pass `df_mini` to the `GT` object to create a table named `gt`, using `drivetrain` as the `rowname_col=` and `mfr` as the `groupname_col=`, as shown below:
+```{python}
+# | code-fold: true
+import polars as pl
+from great_tables import GT, loc, style
+from great_tables.data import gtcars
+from polars import selectors as cs
+
+year_cols = ["2014", "2015", "2016", "2017"]
+df_mini = (
+    pl.from_pandas(gtcars)
+    .filter(pl.col("mfr").is_in(["Ferrari", "Lamborghini", "BMW"]))
+    .sort("drivetrain")
+    .pivot(on="year", index=["mfr", "drivetrain"], values="hp", aggregate_function="mean")
+    .select(["mfr", "drivetrain", *year_cols])
+)
+
+gt = GT(df_mini).tab_stub(rowname_col="drivetrain", groupname_col="mfr").opt_stylize(color="cyan")
+gt
+```
+
+The numbers in the cells represent the average horsepower for each combination of `mfr` and `drivetrain` for a specific year.
+
+### Leveraging the `mask=` parameter in `loc.body()`
+The `mask=` parameter in `loc.body()` accepts a Polars expression that evaluates to a boolean result for each cell.
+
+Here’s how we can use it to achieve the two goals:
+
+* Highlight the cell text in red if the column datatype is numerical and the cell value exceeds 650.
+* Fill the background color as lightgrey if the cell value is missing in the last two columns (`2016` and `2017`).
+
+```{python}
+(
+    gt.tab_style(
+        style=style.text(color="red"),
+        locations=loc.body(mask=cs.numeric().gt(650))
+    ).tab_style(
+        style=style.fill(color="lightgrey"),
+        locations=loc.body(mask=pl.nth(-2, -1).is_null()),
+    )
+)
+```
+
+In this example:
+
+* `cs.numeric()` targets numerical columns, and `.gt(650)` checks if the cell value is greater than 650.
+* `pl.nth(-2, -1)` targets the last two columns, and `.is_null()` identifies missing values.
+
+Did you notice that we can use Polars selectors and expressions to dynamically identify columns at runtime? This is definitely a killer feature when working with pivoted operations.
+
+The `mask=` parameter acts as a syntactic sugar, streamlining the process and removing the need to loop through columns manually.
+
+::: {.callout-warning collapse="false"}
+## Using `mask=` Independently
+`mask=` should not be used in combination with the `columns` or `rows` arguments. Attempting to do so will raise a `ValueError`.
+:::
+
+### Utilizing the `locations=` parameter in `GT.tab_style()`
+A more "old-fashioned" approach involves passing a list of `loc.body()` objects to the `locations=` parameter in `GT.tab_style()`:
+```{python}
+# | eval: false
+(
+    gt.tab_style(
+        style=style.text(color="red"),
+        locations=[loc.body(columns=col, rows=pl.col(col).gt(650))
+                   for col in year_cols],
+    ).tab_style(
+        style=style.fill(color="lightgrey"),
+        locations=[loc.body(columns=col, rows=pl.col(col).is_null())
+                   for col in year_cols[-2:]],
+    )
+)
+```
+
+This approach, though functional, demands additional effort:
+
+* Explicitly preparing the column names in advance.
+* Specifying the `columns=` and `rows=` arguments for each `loc.body()` in the loop.
+
+While effective, it is less efficient and more verbose compared to the first approach.
+
+### Wrapping up
+With the introduction of the `mask=` parameter in `loc.body()`, users can now style the table body in a more vectorized-like manner, akin to using `df.apply()` in Pandas, enhancing the overall user experience.
+
+We extend our gratitude to [@igorcalabria](https://github.com/igorcalabria) for suggesting this feature in [#389](https://github.com/posit-dev/great-tables/issues/389) and providing an insightful explanation of its utility. A special thanks to [@henryharbeck](https://github.com/henryharbeck) for providing the second approach.
+
+We hope you enjoy this new functionality as much as we do! Have ideas to make Great Tables even better? Share them with us via [GitHub Issues](https://github.com/posit-dev/great-tables/issues). We're always amazed by the creativity of our users! See you, until the next great table.