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BUG: melt with extension dtype column #54297

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Merged
merged 2 commits into from
Jul 31, 2023
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BUG: melt with extension dtype column #54297

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BUG: melt with extension dtype column
mroeschke Jul 28, 2023
4f96ca9
add whatsnew num
mroeschke Jul 28, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
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Expand Up @@ -636,6 +636,7 @@ Reshaping
^^^^^^^^^
- Bug in :func:`concat` coercing to ``object`` dtype when one column has ``pa.null()`` dtype (:issue:`53702`)
- Bug in :func:`crosstab` when ``dropna=False`` would not keep ``np.nan`` in the result (:issue:`10772`)
- Bug in :func:`melt` where the ``variable`` column would lose extension dtypes (:issue:`54297`)
- Bug in :func:`merge_asof` raising ``KeyError`` for extension dtypes (:issue:`52904`)
- Bug in :func:`merge_asof` raising ``ValueError`` for data backed by read-only ndarrays (:issue:`53513`)
- Bug in :func:`merge_asof` with ``left_index=True`` or ``right_index=True`` with mismatched index dtypes giving incorrect results in some cases instead of raising ``MergeError`` (:issue:`53870`)
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3 changes: 1 addition & 2 deletions pandas/core/reshape/melt.py
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Expand Up @@ -141,8 +141,7 @@ def melt(
else:
mdata[value_name] = frame._values.ravel("F")
for i, col in enumerate(var_name):
# asanyarray will keep the columns as an Index
mdata[col] = np.asanyarray(frame.columns._get_level_values(i)).repeat(N)
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excellent! I've got a comment in my "notes to self branch" on this line: # TODO: potential problem with losing EA dtypes?

mdata[col] = frame.columns._get_level_values(i).repeat(N)

result = frame._constructor(mdata, columns=mcolumns)

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20 changes: 20 additions & 0 deletions pandas/tests/reshape/test_melt.py
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Expand Up @@ -437,6 +437,26 @@ def test_melt_ea_dtype(self, dtype):
)
tm.assert_frame_equal(result, expected)

def test_melt_ea_columns(self):
# GH 54297
df = DataFrame(
{
"A": {0: "a", 1: "b", 2: "c"},
"B": {0: 1, 1: 3, 2: 5},
"C": {0: 2, 1: 4, 2: 6},
}
)
df.columns = df.columns.astype("string[python]")
result = df.melt(id_vars=["A"], value_vars=["B"])
expected = DataFrame(
{
"A": list("abc"),
"variable": pd.Series(["B"] * 3, dtype="string[python]"),
"value": [1, 3, 5],
}
)
tm.assert_frame_equal(result, expected)


class TestLreshape:
def test_pairs(self):
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