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Consider necessary columns from complex arguments when interchanging dataframes

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packages/python/plotly/plotly
- express
  - _core.py
- tests/test_optional/test_px
  - test_px_input.py

+35

-1

lines changed

Lines changed: 9 additions & 1 deletion

Original file line number	Diff line number	Diff line change
`@@ -1419,9 +1419,17 @@ def build_dataframe(args, constructor):`
`1419`	`1419`	`else:`
`1420`	`1420`	`# Save precious resources by only interchanging columns that are`
`1421`	`1421`	`# actually going to be plotted.`
`1422`		`- columns = [`
	`1422`	`+ necessary_columns = [`
`1423`	`1423`	`i for i in args.values() if isinstance(i, str) and i in columns`
`1424`	`1424`	`]`
	`1425`	`+ for field in args:`
	`1426`	`+ if field in array_attrables and isinstance(`
	`1427`	`+ args[field], (list, dict)`
	`1428`	`+ ):`
	`1429`	`+ necessary_columns.extend(`
	`1430`	`+ [i for i in args[field] if i in columns]`
	`1431`	`+ )`
	`1432`	`+ columns = list(dict.fromkeys(necessary_columns))`
`1425`	`1433`	`args["data_frame"] = pd.api.interchange.from_dataframe(`
`1426`	`1434`	`args["data_frame"].select_columns_by_name(columns)`
`1427`	`1435`	`)`

Lines changed: 26 additions & 0 deletions

Original file line number	Diff line number	Diff line change
`@@ -327,6 +327,32 @@ def test_build_df_from_vaex_and_polars(test_lib):`
`327`	`327`	`)`
`328`	`328`
`329`	`329`
	`330`	`+@pytest.mark.skipif(`
	`331`	`+ version.parse(pd.__version__) < version.parse("2.0.2"),`
	`332`	`+ reason="plotly doesn't use a dataframe interchange protocol for pandas < 2.0.2",`
	`333`	`+)`
	`334`	`+@pytest.mark.parametrize("test_lib", ["vaex", "polars"])`
	`335`	`+def test_build_df_with_hover_data_from_vaex_and_polars(test_lib):`
	`336`	`+ if test_lib == "vaex":`
	`337`	`+ import vaex as lib`
	`338`	`+ else:`
	`339`	`+ import polars as lib`
	`340`	`+`
	`341`	`+ # take out the 'species' columns since the vaex implementation does not cover strings yet`
	`342`	`+ iris_pandas = px.data.iris()[["petal_width", "sepal_length", "sepal_width"]]`
	`343`	`+ iris_vaex = lib.from_pandas(iris_pandas)`
	`344`	`+ args = dict(`
	`345`	`+ data_frame=iris_vaex,`
	`346`	`+ x="petal_width",`
	`347`	`+ y="sepal_length",`
	`348`	`+ hover_data=["sepal_width"],`
	`349`	`+ )`
	`350`	`+ out = build_dataframe(args, go.Scatter)`
	`351`	`+ assert_frame_equal(`
	`352`	`+ iris_pandas.reset_index()[out["data_frame"].columns], out["data_frame"]`
	`353`	`+ )`
	`354`	`+`
	`355`	`+`
`330`	`356`	`def test_timezones():`
`331`	`357`	`df = pd.DataFrame({"date": ["2015年04月04日 19:31:30+1:00"], "value": [3]})`
`332`	`358`	`df["date"] = pd.to_datetime(df["date"])`

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