diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9ba4bde693d..bdb01caf1a1 100644
--- a/CHANGELOG.md
+++ b/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)]
diff --git a/packages/python/plotly/plotly/express/_core.py b/packages/python/plotly/plotly/express/_core.py
index 452c0a7ff79..937ef9b5da7 100644
--- a/packages/python/plotly/plotly/express/_core.py
+++ b/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
@@ -1307,6 +1307,7 @@ 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__
@@ -1314,30 +1315,28 @@ def build_dataframe(args, constructor):
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 (
diff --git a/packages/python/plotly/plotly/tests/test_optional/test_px/test_px_input.py b/packages/python/plotly/plotly/tests/test_optional/test_px/test_px_input.py
index 1acbf3f1e64..fa0f1298fdc 100644
--- a/packages/python/plotly/plotly/tests/test_optional/test_px/test_px_input.py
+++ b/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(