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BUG: Fix style.background_gradient() for nullable dtype (ex: Int64) series with pd.NA values #50713

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Merged
merged 10 commits into from
Jan 16, 2023
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BUG: Fix style.background_gradient() for nullable dtype (ex: Int64) series with pd.NA values #50713

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708f39e
Fix style.background_gradient() for Int64 series with null values
dhendry Jan 12, 2023
3ea6d21
Update whatsnew
dhendry Jan 12, 2023
5ddd725
Use the correct issue number
dhendry Jan 12, 2023
3628022
Merge branch 'main' into main
dhendry Jan 12, 2023
9d67462
Merge branch 'main' into main
dhendry Jan 13, 2023
264b179
Response to comments: move entry in whatsnew, include github issue in…
dhendry Jan 13, 2023
adce6e0
Merge branch 'pandas-dev:main' into main
dhendry Jan 13, 2023
da8e229
Merge branch 'main' into main
dhendry Jan 13, 2023
e24e052
Update doc/source/whatsnew/v2.0.0.rst
dhendry Jan 16, 2023
b464f42
Merge branch 'main' into main
dhendry Jan 16, 2023
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v2.0.0.rst
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Expand Up @@ -1079,7 +1079,7 @@ ExtensionArray

Styler
^^^^^^
-
- Fix :meth:`~pandas.io.formats.style.Styler.background_gradient` for nullable dtype :class:`Series` with ``NA`` values (:issue:`50712`)
-

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2 changes: 1 addition & 1 deletion pandas/io/formats/style.py
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Expand Up @@ -3619,7 +3619,7 @@ def _background_gradient(
Color background in a range according to the data or a gradient map
"""
if gmap is None: # the data is used the gmap
gmap = data.to_numpy(dtype=float)
gmap = data.to_numpy(dtype=float, na_value=np.nan)
else: # else validate gmap against the underlying data
gmap = _validate_apply_axis_arg(gmap, "gmap", float, data)

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10 changes: 10 additions & 0 deletions pandas/tests/io/formats/style/test_matplotlib.py
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Expand Up @@ -260,6 +260,16 @@ def test_background_gradient_gmap_wrong_series(styler_blank):
styler_blank.background_gradient(gmap=gmap, axis=None)._compute()


def test_background_gradient_nullable_dtypes():
# GH 50712
df1 = DataFrame([[1], [0], [np.nan]], dtype=float)
df2 = DataFrame([[1], [0], [None]], dtype="Int64")

ctx1 = df1.style.background_gradient()._compute().ctx
ctx2 = df2.style.background_gradient()._compute().ctx
assert ctx1 == ctx2


@pytest.mark.parametrize(
"cmap",
["PuBu", mpl.colormaps["PuBu"]],
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