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BUG: pd.Series.replace does not preserve the original dtype #33622

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BUG: pd.Series.replace does not preserve the original dtype #33622

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9ef9f9e
BUG: pd.Series.replace does not preserve the original dtype
kotamatsuoka Apr 18, 2020
8be0127
Changes according to comments
kotamatsuoka Apr 18, 2020
078aca1
Remove hard-code
kotamatsuoka Apr 22, 2020
571ab8a
Update infer_dtype_from
kotamatsuoka Apr 23, 2020
6ae7a09
Merge branch 'master' into original-dtype-with-replace
kotamatsuoka Apr 25, 2020
b227024
Move conditional branch　out of infer_dtype_from()
kotamatsuoka May 1, 2020
a62c896
Remove specifically handling
kotamatsuoka May 3, 2020
1a9fa40
add test cases
kotamatsuoka May 30, 2020
b777345
move is_categorical_dtype()
kotamatsuoka May 30, 2020
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11 changes: 9 additions & 2 deletions pandas/core/internals/blocks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1005,8 +1005,15 @@ def coerce_to_target_dtype(self, other):
we can also safely try to coerce to the same dtype
and will receive the same block
"""
# if we cannot then coerce to object
dtype, _ = infer_dtype_from(other, pandas_dtype=True)
if isinstance(other, str):
dtype = "string"
if is_dtype_equal(self.dtype, dtype):
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please don't hard-code things like this, instead you can try to teach infer_dtype_from to do this

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Sorry...I removed hard-code.

return self
else:
dtype = np.object_
else:
# if we cannot then coerce to object
dtype, _ = infer_dtype_from(other, pandas_dtype=True)
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The suggestion from @jreback here was to make infer_dtype_from return StringDtype when appropriate, so that L1090-L1013 here wouldnt be necessary

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I tried to move into infer_dtype_from(), but a lot of tests fail. So, I didn't move.


if is_dtype_equal(self.dtype, dtype):
return self
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19 changes: 18 additions & 1 deletion pandas/tests/series/methods/test_replace.py
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Expand Up @@ -254,7 +254,7 @@ def test_replace2(self):
def test_replace_with_dictlike_and_string_dtype(self):
# GH 32621
s = pd.Series(["one", "two", np.nan], dtype="string")
expected = pd.Series(["1", "2", np.nan])
expected = pd.Series(["1", "2", np.nan], dtype="string")
result = s.replace({"one": "1", "two": "2"})
tm.assert_series_equal(expected, result)

Expand Down Expand Up @@ -406,3 +406,20 @@ def test_replace_only_one_dictlike_arg(self):
msg = "Series.replace cannot use dict-value and non-None to_replace"
with pytest.raises(ValueError, match=msg):
ser.replace(to_replace, value)

@pytest.mark.parametrize(
"series, to_replace, expected",
[
(
pd.Series(["one", "two"], dtype="string"),
{"one": "1", "two": "2"},
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can you add a bunch more tests which can hit the other EA types, e.g. Intervval, Period, Datetime w/tz

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I added test cases.

"string",
),
(pd.Series([1, 2], dtype="int64"), {1: 10, 2: 20}, "int64"),
(pd.Series([True, False], dtype="bool"), {True: False}, "bool"),
],
)
def test_replace_dtype(self, series, to_replace, expected):
# GH 33484
result = str(series.replace(to_replace).dtype)
assert expected == result