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Fix: DataFrame.replace incompatible with type "dict[str, dict[int, int]]" #1164

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Merged
merged 3 commits into from
Mar 12, 2025
Merged

Fix: DataFrame.replace incompatible with type "dict[str, dict[int, int]]" #1164

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378983c
add failing test
MarcoGorelli Mar 12, 2025
694d40d
fix
MarcoGorelli Mar 12, 2025
ed1acee
use HashableT3 for value/regex
MarcoGorelli Mar 12, 2025
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2 changes: 1 addition & 1 deletion pandas-stubs/_typing.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -740,7 +740,7 @@ ReplaceValue: TypeAlias = (
| Pattern
| NAType
| Sequence[Scalar | Pattern]
| Mapping[Hashable, Scalar]
| Mapping[HashableT, ScalarT]
| Series[Any]
| None
)
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12 changes: 6 additions & 6 deletions pandas-stubs/core/frame.pyi
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Expand Up @@ -797,20 +797,20 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
@overload
def replace(
self,
to_replace: ReplaceValue | Mapping[Hashable, ReplaceValue] = ...,
value: ReplaceValue | Mapping[Hashable, ReplaceValue] = ...,
to_replace: ReplaceValue | Mapping[HashableT2, ReplaceValue] = ...,
value: ReplaceValue | Mapping[HashableT2, ReplaceValue] = ...,
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should be HashableT3 since it might not be the same type as HashableT2.

Same here and throughout

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I've made the change, but I think if someone writes df.replace({'a': 1}, {0: 9}) then that's likely a user error. Though there's likely some valid usage I can't think, so perhaps best to err on the side of not letting false positives through

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I've made the change, but I think if someone writes df.replace({'a': 1}, {0: 9}) then that's likely a user error. Though there's likely some valid usage I can't think, so perhaps best to err on the side of not letting false positives through

Yes, I agree. While this is valid:

>>> import pandas as pd
>>> df = pd.DataFrame([["a", 1], ["b", 2]], columns=["ab", "num"])
>>> df
  ab  num
0  a    1
1  b    2
>>> df.replace({"a":"foo", 2: 20})
    ab  num
0  foo    1
1    b   20

it's not a great idea to be mixing types in one statement. With the stubs, I try to support "normal" usage of pandas. Otherwise, the stubs get too wide and don't catch common errors.

*,
inplace: Literal[True],
regex: ReplaceValue | Mapping[Hashable, ReplaceValue] = ...,
regex: ReplaceValue | Mapping[HashableT2, ReplaceValue] = ...,
) -> None: ...
@overload
def replace(
self,
to_replace: ReplaceValue | Mapping[Hashable, ReplaceValue] = ...,
value: ReplaceValue | Mapping[Hashable, ReplaceValue] = ...,
to_replace: ReplaceValue | Mapping[HashableT2, ReplaceValue] = ...,
value: ReplaceValue | Mapping[HashableT2, ReplaceValue] = ...,
*,
inplace: Literal[False] = ...,
regex: ReplaceValue | Mapping[Hashable, ReplaceValue] = ...,
regex: ReplaceValue | Mapping[HashableT2, ReplaceValue] = ...,
) -> Self: ...
def shift(
self,
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6 changes: 5 additions & 1 deletion tests/test_frame.py
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Expand Up @@ -2574,8 +2574,10 @@ def test_types_replace() -> None:


def test_dataframe_replace() -> None:
df = pd.DataFrame({"col1": ["a", "ab", "ba"]})
df = pd.DataFrame({"col1": ["a", "ab", "ba"], "col2": [0, 1, 2]})
pattern = re.compile(r"^a.*")
replace_dict_scalar = {0: 1}
replace_dict_per_column = {"col2": {0: 1}}
check(assert_type(df.replace("a", "x"), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace(pattern, "x"), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace("a", "x", regex=True), pd.DataFrame), pd.DataFrame)
Expand All @@ -2592,6 +2594,7 @@ def test_dataframe_replace() -> None:
)

check(assert_type(df.replace({"a": "x"}), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace(replace_dict_scalar), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace({pattern: "x"}), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace(pd.Series({"a": "x"})), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace(regex={"a": "x"}), pd.DataFrame), pd.DataFrame)
Expand Down Expand Up @@ -2670,6 +2673,7 @@ def test_dataframe_replace() -> None:
)

check(assert_type(df.replace({"col1": {"a": "x"}}), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace(replace_dict_per_column), pd.DataFrame), pd.DataFrame)
check(assert_type(df.replace({"col1": {pattern: "x"}}), pd.DataFrame), pd.DataFrame)
check(
assert_type(df.replace({"col1": pd.Series({"a": "x"})}), pd.DataFrame),
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5 changes: 5 additions & 0 deletions tests/test_series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1448,12 +1448,17 @@ def test_types_replace() -> None:
def test_series_replace() -> None:
s: pd.Series[str] = pd.DataFrame({"col1": ["a", "ab", "ba"]})["col1"]
pattern = re.compile(r"^a.*")
replace_dict = {"a": "b"}
check(assert_type(s.replace("a", "x"), "pd.Series[str]"), pd.Series)
check(assert_type(s.replace(pattern, "x"), "pd.Series[str]"), pd.Series)
check(
assert_type(s.replace({"a": "z"}), "pd.Series[str]"),
pd.Series,
)
check(
assert_type(s.replace(replace_dict), "pd.Series[str]"),
pd.Series,
)
check(
assert_type(s.replace(pd.Series({"a": "z"})), "pd.Series[str]"),
pd.Series,
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