only interchange necessary columns

CHANGELOG.md

@@ -23,7 +23,7 @@ This project adheres to [Semantic Versioning](http://semver.org/).
    this feature was anonymously sponsored: thank you to our sponsor!
     - Add `legend.xref` and `legend.yref` to enable container-referenced positioning of legends [[#6589](https://github.com/plotly/plotly.js/pull/6589)], with thanks to [Gamma Technologies](https://www.gtisoft.com/) for sponsoring the related development.
     - Add `colorbar.xref` and `colorbar.yref` to enable container-referenced positioning of colorbars [[#6593](https://github.com/plotly/plotly.js/pull/6593)], with thanks to [Gamma Technologies](https://www.gtisoft.com/) for sponsoring the related development.
-  - `px` methods now accept data-frame-like objects that support a `to_pandas()` method, such as polars, cudf, vaex etc
+  - `px` methods now accept data-frame-like objects that support a `to_pandas()` method, such as polars, cudf, vaex etc [[#4244](https://github.com/plotly/plotly.py/pull/4244)], [[#4286](https://github.com/plotly/plotly.py/pull/4286)]
 
 ### Fixed
   - Fixed another compatibility issue with Pandas 2.0, just affecting `px.*(line_close=True)` [[#4190](https://github.com/plotly/plotly.py/pull/4190)]

packages/python/plotly/plotly/express/_core.py

@@ -1018,7 +1018,7 @@ def _get_reserved_col_names(args):
     return reserved_names
 
 
-def _is_col_list(df_input, arg):
+def _is_col_list(columns, arg):
     """Returns True if arg looks like it's a list of columns or references to columns
     in df_input, and False otherwise (in which case it's assumed to be a single column
     or reference to a column).
@@ -1033,7 +1033,7 @@ def _is_col_list(df_input, arg):
         return False  # not iterable
     for c in arg:
         if isinstance(c, str) or isinstance(c, int):
-            if df_input is None or c not in df_input.columns:
+            if columns is None or c not in columns:
                 return False
         else:
             try:
@@ -1059,8 +1059,8 @@ def _isinstance_listlike(x):
         return True
 
 
-def _escape_col_name(df_input, col_name, extra):
-    while df_input is not None and (col_name in df_input.columns or col_name in extra):
+def _escape_col_name(columns, col_name, extra):
+    while columns is not None and (col_name in columns or col_name in extra):
         col_name = "_" + col_name
     return col_name
 
@@ -1307,37 +1307,36 @@ def build_dataframe(args, constructor):
 
     # Cast data_frame argument to DataFrame (it could be a numpy array, dict etc.)
     df_provided = args["data_frame"] is not None
+    needs_interchanging = False
     if df_provided and not isinstance(args["data_frame"], pd.DataFrame):
         if hasattr(args["data_frame"], "__dataframe__") and version.parse(
             pd.__version__
         ) >= version.parse("2.0.2"):
             import pandas.api.interchange
 
             df_not_pandas = args["data_frame"]
-            try:
-                df_pandas = pandas.api.interchange.from_dataframe(df_not_pandas)
-            except (ImportError, NotImplementedError) as exc:
-                # temporary workaround; developers of third-party libraries themselves
-                # should try a different implementation, if available. For example:
-                # def __dataframe__(self, ...):
-                #   if not some_condition:
-                #     self.to_pandas(...)
-                if not hasattr(df_not_pandas, "to_pandas"):
-                    raise exc
-                df_pandas = df_not_pandas.to_pandas()
-            args["data_frame"] = df_pandas
+            args["data_frame"] = df_not_pandas.__dataframe__()
+            columns = args["data_frame"].column_names()
+            needs_interchanging = True
         elif hasattr(args["data_frame"], "to_pandas"):
             args["data_frame"] = args["data_frame"].to_pandas()
+            columns = args["data_frame"].columns
         else:
             args["data_frame"] = pd.DataFrame(args["data_frame"])
+            columns = args["data_frame"].columns
+    elif df_provided:
+        columns = args["data_frame"].columns
+    else:
+        columns = None
+
     df_input = args["data_frame"]
 
     # now we handle special cases like wide-mode or x-xor-y specification
     # by rearranging args to tee things up for process_args_into_dataframe to work
     no_x = args.get("x") is None
     no_y = args.get("y") is None
-    wide_x = False if no_x else _is_col_list(df_input, args["x"])
-    wide_y = False if no_y else _is_col_list(df_input, args["y"])
+    wide_x = False if no_x else _is_col_list(columns, args["x"])
+    wide_y = False if no_y else _is_col_list(columns, args["y"])
 
     wide_mode = False
     var_name = None  # will likely be "variable" in wide_mode
@@ -1352,15 +1351,18 @@ def build_dataframe(args, constructor):
             )
         if df_provided and no_x and no_y:
             wide_mode = True
-            if isinstance(df_input.columns, pd.MultiIndex):
+            if isinstance(columns, pd.MultiIndex):
                 raise TypeError(
                     "Data frame columns is a pandas MultiIndex. "
                     "pandas MultiIndex is not supported by plotly express "
                     "at the moment."
                 )
-            args["wide_variable"] = list(df_input.columns)
-            var_name = df_input.columns.name
-            if var_name in [None, "value", "index"] or var_name in df_input:
+            args["wide_variable"] = list(columns)
+            if isinstance(columns, pd.Index):
+                var_name = columns.name
+            else:
+                var_name = None
+            if var_name in [None, "value", "index"] or var_name in columns:
                 var_name = "variable"
             if constructor == go.Funnel:
                 wide_orientation = args.get("orientation") or "h"
@@ -1371,12 +1373,12 @@ def build_dataframe(args, constructor):
         elif wide_x != wide_y:
             wide_mode = True
             args["wide_variable"] = args["y"] if wide_y else args["x"]
-            if df_provided and args["wide_variable"] is df_input.columns:
-                var_name = df_input.columns.name
+            if df_provided and args["wide_variable"] is columns:
+                var_name = columns.name
             if isinstance(args["wide_variable"], pd.Index):
                 args["wide_variable"] = list(args["wide_variable"])
             if var_name in [None, "value", "index"] or (
-                df_provided and var_name in df_input
+                df_provided and var_name in columns
             ):
                 var_name = "variable"
             if hist1d_orientation:
@@ -1389,8 +1391,35 @@ def build_dataframe(args, constructor):
                 wide_cross_name = "__x__" if wide_y else "__y__"
 
     if wide_mode:
-        value_name = _escape_col_name(df_input, "value", [])
-        var_name = _escape_col_name(df_input, var_name, [])
+        value_name = _escape_col_name(columns, "value", [])
+        var_name = _escape_col_name(columns, var_name, [])
+
+    if needs_interchanging:
+        try:
+            if wide_mode or not hasattr(args["data_frame"], "select_columns_by_name"):
+                args["data_frame"] = pd.api.interchange.from_dataframe(
+                    args["data_frame"]
+                )
+            else:
+                # Save precious resources by only interchanging columns that are
+                # actually going to be plotted.
+                columns = [
+                    i for i in args.values() if isinstance(i, str) and i in columns
+                ]
+                args["data_frame"] = pd.api.interchange.from_dataframe(
+                    args["data_frame"].select_columns_by_name(columns)
+                )
+        except (ImportError, NotImplementedError) as exc:
+            # temporary workaround; developers of third-party libraries themselves
+            # should try a different implementation, if available. For example:
+            # def __dataframe__(self, ...):
+            #   if not some_condition:
+            #     self.to_pandas(...)
+            if not hasattr(df_not_pandas, "to_pandas"):
+                raise exc
+            args["data_frame"] = df_not_pandas.to_pandas()
+
+    df_input = args["data_frame"]
 
     missing_bar_dim = None
     if (

packages/python/plotly/plotly/tests/test_optional/test_px/test_px_input.py

@@ -252,21 +252,58 @@ def test_build_df_with_index():
 def test_build_df_using_interchange_protocol_mock(
     add_interchange_module_for_old_pandas,
 ):
+    class InterchangeDataFrame:
+        def __init__(self, columns):
+            self._columns = columns
+
+        def column_names(self):
+            return self._columns
+
+    interchange_dataframe = InterchangeDataFrame(
+        ["petal_width", "sepal_length", "sepal_width"]
+    )
+    interchange_dataframe_reduced = InterchangeDataFrame(
+        ["petal_width", "sepal_length"]
+    )
+    interchange_dataframe.select_columns_by_name = mock.MagicMock(
+        return_value=interchange_dataframe_reduced
+    )
+    interchange_dataframe_reduced.select_columns_by_name = mock.MagicMock(
+        return_value=interchange_dataframe_reduced
+    )
+
     class CustomDataFrame:
         def __dataframe__(self):
-            pass
+            return interchange_dataframe
+
+    class CustomDataFrameReduced:
+        def __dataframe__(self):
+            return interchange_dataframe_reduced
 
     input_dataframe = CustomDataFrame()
-    args = dict(data_frame=input_dataframe, x="petal_width", y="sepal_length")
+    input_dataframe_reduced = CustomDataFrameReduced()
 
     iris_pandas = px.data.iris()
 
     with mock.patch("pandas.__version__", "2.0.2"):
+        args = dict(data_frame=input_dataframe, x="petal_width", y="sepal_length")
         with mock.patch(
             "pandas.api.interchange.from_dataframe", return_value=iris_pandas
         ) as mock_from_dataframe:
             build_dataframe(args, go.Scatter)
-        mock_from_dataframe.assert_called_once_with(input_dataframe)
+        mock_from_dataframe.assert_called_once_with(interchange_dataframe_reduced)
+        interchange_dataframe.select_columns_by_name.assert_called_with(
+            ["petal_width", "sepal_length"]
+        )
+
+        args = dict(data_frame=input_dataframe_reduced, color=None)
+        with mock.patch(
+            "pandas.api.interchange.from_dataframe",
+            return_value=iris_pandas[["petal_width", "sepal_length"]],
+        ) as mock_from_dataframe:
+            build_dataframe(args, go.Scatter)
+        mock_from_dataframe.assert_called_once_with(interchange_dataframe_reduced)
+        interchange_dataframe_reduced.select_columns_by_name.assert_not_called()
 
 
 @pytest.mark.skipif(