feat(python): Add DataFrame.style namespace

docs/src/python/user-guide/misc/styling.py

@@ -0,0 +1,151 @@
+# --8<-- [start:dataframe]
+import polars as pl
+import polars.selectors as cs
+
+path = "docs/data/iris.csv"
+
+df = (
+    pl.scan_csv(path)
+    .group_by("species")
+    .agg(cs.starts_with("petal").mean().round(3))
+    .collect()
+)
+print(df)
+# --8<-- [end:dataframe]
+
+# --8<-- [start:structure-header]
+df.style.tab_header(title="Iris Data", subtitle="Mean measurement values per species")
+# --8<-- [end:structure-header]
+
+# --8<-- [start:structure-header-out]
+print(
+    df.style.tab_header(
+        title="Iris Data", subtitle="Mean measurement values per species"
+    ).as_raw_html()
+)
+# --8<-- [end:structure-header-out]
+
+
+# --8<-- [start:structure-stub]
+df.style.tab_stub(rowname_col="species")
+# --8<-- [end:structure-stub]
+
+# --8<-- [start:structure-stub-out]
+print(df.style.tab_stub(rowname_col="species").as_raw_html())
+# --8<-- [end:structure-stub-out]
+
+# --8<-- [start:structure-spanner]
+(
+    df.style.tab_spanner("Petal", cs.starts_with("petal")).cols_label(
+        petal_length="Length", petal_width="Width"
+    )
+)
+# --8<-- [end:structure-spanner]
+
+# --8<-- [start:structure-spanner-out]
+print(
+    df.style.tab_spanner("Petal", cs.starts_with("petal"))
+    .cols_label(petal_length="Length", petal_width="Width")
+    .as_raw_html()
+)
+# --8<-- [end:structure-spanner-out]
+
+# --8<-- [start:format-number]
+df.style.fmt_number("petal_width", decimals=1)
+# --8<-- [end:format-number]
+
+
+# --8<-- [start:format-number-out]
+print(df.style.fmt_number("petal_width", decimals=1).as_raw_html())
+# --8<-- [end:format-number-out]
+
+
+# --8<-- [start:style-simple]
+from great_tables import loc, style
+
+df.style.tab_style(
+    style.fill("yellow"),
+    loc.body(
+        rows=pl.col("petal_length") == pl.col("petal_length").max(),
+    ),
+)
+# --8<-- [end:style-simple]
+
+# --8<-- [start:style-simple-out]
+from great_tables import loc, style
+
+print(
+    df.style.tab_style(
+        style.fill("yellow"),
+        loc.body(
+            rows=pl.col("petal_length") == pl.col("petal_length").max(),
+        ),
+    ).as_raw_html()
+)
+# --8<-- [end:style-simple-out]
+
+
+# --8<-- [start:style-bold-column]
+from great_tables import loc, style
+
+df.style.tab_style(
+    style.text(weight="bold"),
+    loc.body(columns="species"),
+)
+# --8<-- [end:style-bold-column]
+
+# --8<-- [start:style-bold-column-out]
+from great_tables import loc, style
+
+print(
+    df.style.tab_style(
+        style.text(weight="bold"),
+        loc.body(columns="species"),
+    ).as_raw_html()
+)
+# --8<-- [end:style-bold-column-out]
+
+# --8<-- [start:full-example]
+from great_tables import loc, style
+
+(
+    df.style.tab_header(
+        title="Iris Data", subtitle="Mean measurement values per species"
+    )
+    .tab_stub(rowname_col="species")
+    .cols_label(petal_length="Length", petal_width="Width")
+    .tab_spanner("Petal", cs.starts_with("petal"))
+    .fmt_number("petal_width", decimals=2)
+    .tab_style(
+        style.fill("yellow"),
+        loc.body(
+            rows=pl.col("petal_length") == pl.col("petal_length").max(),
+        ),
+    )
+)
+# --8<-- [end:full-example]
+
+# --8<-- [start:full-example-out]
+from great_tables import loc, style
+
+print(
+    df.style.tab_header(
+        title="Iris Data", subtitle="Mean measurement values per species"
+    )
+    .tab_stub(rowname_col="species")
+    .cols_label(petal_length="Length", petal_width="Width")
+    .tab_spanner("Petal", cs.starts_with("petal"))
+    .fmt_number("petal_width", decimals=2)
+    .tab_style(
+        style.fill("yellow"),
+        loc.body(
+            rows=pl.col("petal_length") == pl.col("petal_length").max(),
+        ),
+    )
+    .tab_style(
+        style.text(weight="bold"),
+        loc.body(columns="species"),
+    )
+    .as_raw_html()
+)
+# --8<-- [end:full-example-out]

docs/user-guide/misc/styling.md

@@ -0,0 +1,65 @@
+# Styling
+
+Data in a Polars `DataFrame` can be styled for presentation use the `DataFrame.style` property. This returns a `GT` object from [Great Tables](https://posit-dev.github.io/great-tables/articles/intro.html), which enables structuring, formatting, and styling for table display.
+
+{{code_block('user-guide/misc/styling','dataframe',[])}}
+
+```python exec="on" result="text" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:dataframe"
+```
+
+## Structure: add header title
+
+{{code_block('user-guide/misc/styling','structure-header',[])}}
+
+```python exec="on" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:structure-header-out"
+```
+
+## Structure: add row stub
+
+{{code_block('user-guide/misc/styling','structure-stub',[])}}
+
+```python exec="on" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:structure-stub-out"
+```
+
+## Structure: add column spanner
+
+{{code_block('user-guide/misc/styling','structure-spanner',[])}}
+
+```python exec="on" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:structure-spanner-out"
+```
+
+## Format: limit decimal places
+
+{{code_block('user-guide/misc/styling','format-number',[])}}
+
+```python exec="on" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:format-number-out"
+```
+
+## Style: highlight max row
+
+{{code_block('user-guide/misc/styling','style-simple',[])}}
+
+```python exec="on" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:style-simple-out"
+```
+
+## Style: bold species column
+
+{{code_block('user-guide/misc/styling','style-bold-column',[])}}
+
+```python exec="on" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:style-bold-column-out"
+```
+
+## Full example
+
+{{code_block('user-guide/misc/styling','full-example',[])}}
+
+```python exec="on" session="user-guide/misc/styling"
+--8<-- "python/user-guide/misc/styling.py:full-example-out"
+```

mkdocs.yml

@@ -81,6 +81,7 @@ nav:
     - Misc:
       - user-guide/misc/multiprocessing.md
       - user-guide/misc/visualization.md
+      - user-guide/misc/styling.md
       - user-guide/misc/comparison.md
 
   - API reference: api/index.md

py-polars/docs/source/reference/dataframe/index.rst

@@ -17,6 +17,7 @@ This page gives an overview of all public DataFrame methods.
    modify_select
    miscellaneous
    plot
+   style
 
 .. currentmodule:: polars
 

py-polars/docs/source/reference/dataframe/style.rst

@@ -0,0 +1,7 @@
+=====
+Style
+=====
+
+.. currentmodule:: polars
+
+.. autoproperty:: DataFrame.style

py-polars/polars/dataframe/frame.py

@@ -76,12 +76,14 @@
     UInt64,
 )
 from polars.dependencies import (
+    _GREAT_TABLES_AVAILABLE,
     _HVPLOT_AVAILABLE,
     _PANDAS_AVAILABLE,
     _PYARROW_AVAILABLE,
     _check_for_numpy,
     _check_for_pandas,
     _check_for_pyarrow,
+    great_tables,
     hvplot,
     import_optional,
 )
@@ -112,6 +114,7 @@
     import jax
     import numpy.typing as npt
     import torch
+    from great_tables import GT
     from hvplot.plotting.core import hvPlotTabularPolars
     from xlsxwriter import Workbook
 
@@ -607,6 +610,62 @@ def plot(self) -> hvPlotTabularPolars:
         hvplot.post_patch()
         return hvplot.plotting.core.hvPlotTabularPolars(self)
 
+    @property
+    @unstable()
+    def style(self) -> GT:
+        """
+        Create a Great Table for styling.
+
+        .. warning::
+            This functionality is currently considered **unstable**. It may be
+            changed at any point without it being considered a breaking change.
+
+        Polars does not implement styling logic itself, but instead defers to
+        the Great Tables package. Please see the `Great Tables reference <https://posit-dev.github.io/great-tables/reference/>`_
+        for more information and documentation.
+
+        Examples
+        --------
+        Import some styling helpers, and create example data:
+
+        >>> import polars.selectors as cs
+        >>> from great_tables import loc, style
+        >>> df = pl.DataFrame(
+        ...     {
+        ...         "site_id": [0, 1, 2],
+        ...         "measure_a": [5, 4, 6],
+        ...         "measure_b": [7, 3, 3],
+        ...     }
+        ... )
+
+        Emphasize the site_id as row names:
+
+        >>> df.style.tab_stub(rowname_col="site_id")  # doctest: +SKIP
+
+        Fill the background for the highest measure_a value row:
+
+        >>> df.style.tab_style(
+        ...     style.fill("yellow"),
+        ...     loc.body(rows=pl.col("measure_a") == pl.col("measure_a").max()),
+        ... )  # doctest: +SKIP
+
+        Put a spanner (high-level label) over measure columns:
+
+        >>> df.style.tab_spanner(
+        ...     "Measures", cs.starts_with("measure")
+        ... )  # doctest: +SKIP
+
+        Format measure_b values to two decimal places:
+
+        >>> df.style.fmt_number("measure_b", decimals=2)  # doctest: +SKIP
+
+        """
+        if not _GREAT_TABLES_AVAILABLE:
+            msg = "great_tables is required for `.style`"
+            raise ModuleNotFoundError(msg)
+
+        return great_tables.GT(self)
+
     @property
     def shape(self) -> tuple[int, int]:
         """

py-polars/polars/dependencies.py

@@ -11,6 +11,7 @@
 _DELTALAKE_AVAILABLE = True
 _FSSPEC_AVAILABLE = True
 _GEVENT_AVAILABLE = True
+_GREAT_TABLES_AVAILABLE = True
 _HVPLOT_AVAILABLE = True
 _HYPOTHESIS_AVAILABLE = True
 _NUMPY_AVAILABLE = True
@@ -152,6 +153,7 @@ def _lazy_import(module_name: str) -> tuple[ModuleType, bool]:
     import deltalake
     import fsspec
     import gevent
+    import great_tables
     import hvplot
     import hypothesis
     import numpy
@@ -175,6 +177,7 @@ def _lazy_import(module_name: str) -> tuple[ModuleType, bool]:
     # heavy/optional third party libs
     deltalake, _DELTALAKE_AVAILABLE = _lazy_import("deltalake")
     fsspec, _FSSPEC_AVAILABLE = _lazy_import("fsspec")
+    great_tables, _GREAT_TABLES_AVAILABLE = _lazy_import("great_tables")
     hvplot, _HVPLOT_AVAILABLE = _lazy_import("hvplot")
     hypothesis, _HYPOTHESIS_AVAILABLE = _lazy_import("hypothesis")
     numpy, _NUMPY_AVAILABLE = _lazy_import("numpy")
@@ -301,6 +304,7 @@ def import_optional(
     "deltalake",
     "fsspec",
     "gevent",
+    "great_tables",
     "hvplot",
     "numpy",
     "pandas",

py-polars/polars/meta/versions.py

@@ -69,6 +69,7 @@ def _get_dependency_info() -> dict[str, str]:
         "fastexcel",
         "fsspec",
         "gevent",
+        "great_tables",
         "hvplot",
         "matplotlib",
         "nest_asyncio",

py-polars/pyproject.toml

@@ -56,11 +56,12 @@ plot = ["hvplot >= 0.9.1"]
 pyarrow = ["pyarrow >= 7.0.0"]
 pydantic = ["pydantic"]
 sqlalchemy = ["sqlalchemy", "pandas"]
+style = ["great-tables >= 0.8.0"]
 timezone = ["backports.zoneinfo; python_version < '3.9'", "tzdata; platform_system == 'Windows'"]
 xlsx2csv = ["xlsx2csv >= 0.8.0"]
 xlsxwriter = ["xlsxwriter"]
 all = [
-  "polars[adbc,async,cloudpickle,connectorx,deltalake,fastexcel,fsspec,gevent,numpy,pandas,plot,pyarrow,pydantic,iceberg,sqlalchemy,timezone,xlsx2csv,xlsxwriter]",
+  "polars[adbc,async,cloudpickle,connectorx,deltalake,fastexcel,fsspec,gevent,style,numpy,pandas,plot,pyarrow,pydantic,iceberg,sqlalchemy,timezone,xlsx2csv,xlsxwriter]",
 ]
 
 [tool.maturin]
@@ -89,6 +90,7 @@ module = [
   "deltalake.*",
   "fsspec.*",
   "gevent",
+  "great_tables",
   "hvplot.*",
   "jax.*",
   "kuzu",

py-polars/requirements-dev.txt

@@ -47,6 +47,8 @@ zstandard
 # Plotting
 hvplot>=0.9.1
 matplotlib
+# Styling
+great-tables>=0.8.0; python_version >= '3.9'
 # Other
 gevent
 nest_asyncio