Make transformed_data public and add initial docs

altair/vegalite/v5/api.py

@@ -2658,7 +2658,7 @@ def to_dict(
             validate=validate, format=format, ignore=ignore, context=context
         )
 
-    def _transformed_data(
+    def transformed_data(
         self,
         row_limit: Optional[int] = None,
         exclude: Optional[Iterable[str]] = None,
@@ -2859,7 +2859,7 @@ def __init__(
             **kwds,
         )
 
-    def _transformed_data(
+    def transformed_data(
         self,
         row_limit: Optional[int] = None,
         exclude: Optional[Iterable[str]] = None,
@@ -2970,7 +2970,7 @@ def __or__(self, other):
         copy |= other
         return copy
 
-    def _transformed_data(
+    def transformed_data(
         self,
         row_limit: Optional[int] = None,
         exclude: Optional[Iterable[str]] = None,
@@ -3067,7 +3067,7 @@ def __or__(self, other):
         copy |= other
         return copy
 
-    def _transformed_data(
+    def transformed_data(
         self,
         row_limit: Optional[int] = None,
         exclude: Optional[Iterable[str]] = None,
@@ -3164,7 +3164,7 @@ def __and__(self, other):
         copy &= other
         return copy
 
-    def _transformed_data(
+    def transformed_data(
         self,
         row_limit: Optional[int] = None,
         exclude: Optional[Iterable[str]] = None,
@@ -3260,7 +3260,7 @@ def __init__(self, data=Undefined, layer=(), **kwargs):
         for prop in combined_dict:
             self[prop] = combined_dict[prop]
 
-    def _transformed_data(
+    def transformed_data(
         self,
         row_limit: Optional[int] = None,
         exclude: Optional[Iterable[str]] = None,
@@ -3375,7 +3375,7 @@ def __init__(
             data=data, spec=spec, facet=facet, params=params, **kwargs
         )
 
-    def _transformed_data(
+    def transformed_data(
         self,
         row_limit: Optional[int] = None,
         exclude: Optional[Iterable[str]] = None,

doc/user_guide/transform/index.rst

@@ -47,6 +47,70 @@ Transform                                  Method
 :ref:`user-guide-window-transform`         :meth:`~Chart.transform_window`            Compute a windowed aggregation
 =========================================  =========================================  ================================================================================
 
+Accessing Transformed Data
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+When charts are displayed, data transformations are performed in the browser by
+the Vega JavaScript library. It's often helpful to inspect transformed data
+results in the process of building a chart. One approach is to display the
+transformed data results in a table composed of :ref:`Text<user-guide-text-marks>`
+marks as in the :ref:`gallery_scatter_linked_table` gallery example.
+
+While this approach works, it's somewhat cumbersome, and still does not make it
+possible to access the transformed data from Python. To make transformed data
+results available in Python, Altair provides the :meth:`~Chart.transformed_data`
+Chart method which integrates with `VegaFusion <https://vegafusion.io/>`_
+to evaluate data transformations in the Python kernel.
+
+First, install VegaFusion with the embed extras enabled.
+
+.. code-block:: none
+
+   pip install "vegafusion[embed]"
+
+Then create an Altair chart and call the :meth:`~Chart.transformed_data` method
+to extract a pandas DataFrame containing the transformed data.
+
+.. altair-plot::
+    :output: repr
+
+    import altair as alt
+    from vega_datasets import data
+
+    cars = data.cars.url
+    chart = alt.Chart(cars).mark_bar().encode(
+        y='Cylinders:O',
+        x='mean_acc:Q'
+    ).transform_aggregate(
+        mean_acc='mean(Acceleration)',
+        groupby=["Cylinders"]
+    )
+    chart.transformed_data()
+
+The :meth:`~Chart.transformed_data` method currently supports most, but not all,
+of Altair's transforms. See the table below.
+
+=========================================  =========
+Transform                                  Supported
+=========================================  =========
+:ref:`user-guide-aggregate-transform`        ✔
+:ref:`user-guide-bin-transform`              ✔
+:ref:`user-guide-calculate-transform`        ✔
+:ref:`user-guide-density-transform`
+:ref:`user-guide-filter-transform`           ✔
+:ref:`user-guide-flatten-transform`
+:ref:`user-guide-fold-transform`             ✔
+:ref:`user-guide-impute-transform`           ✔
+:ref:`user-guide-joinaggregate-transform`    ✔
+:ref:`user-guide-loess-transform`
+:ref:`user-guide-lookup-transform`
+:ref:`user-guide-pivot-transform`            ✔
+:ref:`user-guide-quantile-transform`
+:ref:`user-guide-regression-transform`
+:ref:`user-guide-sample-transform`
+:ref:`user-guide-stack-transform`            ✔
+:ref:`user-guide-timeunit-transform`         ✔
+:ref:`user-guide-window-transform`           ✔
+=========================================  =========
 
 .. toctree::
    :hidden:

tests/test_transformed_data.py

@@ -60,7 +60,7 @@
 def test_primitive_chart_examples(filename, rows, cols):
     source = pkgutil.get_data(examples_methods_syntax.__name__, filename)
     chart = eval_block(source)
-    df = chart._transformed_data()
+    df = chart.transformed_data()
     assert len(df) == rows
     assert set(cols).issubset(set(df.columns))
 
@@ -101,7 +101,7 @@ def test_compound_chart_examples(filename, all_rows, all_cols):
     chart = eval_block(source)
     print(chart)
 
-    dfs = chart._transformed_data()
+    dfs = chart.transformed_data()
     assert len(dfs) == len(all_rows)
     for df, rows, cols in zip(dfs, all_rows, all_cols):
         assert len(df) == rows
@@ -119,7 +119,7 @@ def test_transformed_data_exclude():
     )
 
     chart = (bar + rule + some_annotation).properties(width=600)
-    datasets = chart._transformed_data(exclude=["some_annotation"])
+    datasets = chart.transformed_data(exclude=["some_annotation"])
 
     assert len(datasets) == 2
     assert len(datasets[0]) == 52