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Permit covariance of key type in read_csv converters argument #450

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Merged
Dr-Irv merged 2 commits into from
Nov 26, 2022
Merged

Permit covariance of key type in read_csv converters argument #450

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3f86e05
Permit covariance of key type in read_csv converters argument
gandhis1 Nov 26, 2022
02ca9f6
Allow Mapping inputs to all dict inputs to read_csv to permit value c…
gandhis1 Nov 26, 2022
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30 changes: 24 additions & 6 deletions pandas-stubs/io/parsers/readers.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,10 @@ def read_csv(
| None = ...,
dtype: DtypeArg | defaultdict | None = ...,
engine: CSVEngine | None = ...,
converters: dict[int | str, Callable[[str], Any]] | None = ...,
converters: dict[int | str, Callable[[str], Any]]
| dict[int, Callable[[str], Any]]
| dict[str, Callable[[str], Any]]
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@Dr-Irv Dr-Irv Nov 26, 2022

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Can you try Mapping[int | str, Callable[[str], Any]] instead of the union?

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@gandhis1 gandhis1 Nov 26, 2022

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I tried that first. But it seems Mapping isn't covariant over the key, only the value: python/typing#445

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@gandhis1 gandhis1 Nov 26, 2022

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But mapping may work for that the Type[str] issue since that is the value...will try that

I also wonder if creating a covariant TypeVar and then a Mapping[T_co, ...] will work...I will also try that.

| None = ...,
true_values: list[str] = ...,
false_values: list[str] = ...,
skipinitialspace: bool = ...,
Expand Down Expand Up @@ -111,7 +114,10 @@ def read_csv(
| None = ...,
dtype: DtypeArg | defaultdict | None = ...,
engine: CSVEngine | None = ...,
converters: dict[int | str, Callable[[str], Any]] | None = ...,
converters: dict[int | str, Callable[[str], Any]]
| dict[int, Callable[[str], Any]]
| dict[str, Callable[[str], Any]]
| None = ...,
true_values: list[str] = ...,
false_values: list[str] = ...,
skipinitialspace: bool = ...,
Expand Down Expand Up @@ -174,7 +180,10 @@ def read_csv(
| None = ...,
dtype: DtypeArg | defaultdict | None = ...,
engine: CSVEngine | None = ...,
converters: dict[int | str, Callable[[str], Any]] | None = ...,
converters: dict[int | str, Callable[[str], Any]]
| dict[int, Callable[[str], Any]]
| dict[str, Callable[[str], Any]]
| None = ...,
true_values: list[str] = ...,
false_values: list[str] = ...,
skipinitialspace: bool = ...,
Expand Down Expand Up @@ -237,7 +246,10 @@ def read_table(
| None = ...,
dtype: DtypeArg | defaultdict | None = ...,
engine: CSVEngine | None = ...,
converters: dict[int | str, Callable[[str], Any]] | None = ...,
converters: dict[int | str, Callable[[str], Any]]
| dict[int, Callable[[str], Any]]
| dict[str, Callable[[str], Any]]
| None = ...,
true_values: list[str] = ...,
false_values: list[str] = ...,
skipinitialspace: bool = ...,
Expand Down Expand Up @@ -300,7 +312,10 @@ def read_table(
| None = ...,
dtype: DtypeArg | defaultdict | None = ...,
engine: CSVEngine | None = ...,
converters: dict[int | str, Callable[[str], Any]] | None = ...,
converters: dict[int | str, Callable[[str], Any]]
| dict[int, Callable[[str], Any]]
| dict[str, Callable[[str], Any]]
| None = ...,
true_values: list[str] = ...,
false_values: list[str] = ...,
skipinitialspace: bool = ...,
Expand Down Expand Up @@ -363,7 +378,10 @@ def read_table(
| None = ...,
dtype: DtypeArg | defaultdict | None = ...,
engine: CSVEngine | None = ...,
converters: dict[int | str, Callable[[str], Any]] | None = ...,
converters: dict[int | str, Callable[[str], Any]]
| dict[int, Callable[[str], Any]]
| dict[str, Callable[[str], Any]]
| None = ...,
true_values: list[str] = ...,
false_values: list[str] = ...,
skipinitialspace: bool = ...,
Expand Down
41 changes: 41 additions & 0 deletions tests/test_frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
List,
Mapping,
Tuple,
TypedDict,
TypeVar,
Union,
)
Expand Down Expand Up @@ -1278,6 +1279,46 @@ def test_read_csv() -> None:
pd.DataFrame,
)

# Allow a variety of dict types for the converters parameter
converters1 = {"A": lambda x: str, "B": lambda x: str}
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@gandhis1 gandhis1 Nov 26, 2022

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Originally I wanted this dictionary to be {"A": str, "B": str}. But the type of str is Type[str], which apparently isn't compatible with Callable[[str], Any]. Any idea if this is fixable?

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@Dr-Irv Dr-Irv Nov 26, 2022

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Might be an issue with using a lambda function, which is untyped. So if you did

def convert_to_str(a: object) -> str:
    return str(a)

and then

converters1 = {"A":convert_to_str, "B": convert_to_str}

that might work

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@gandhis1 gandhis1 Nov 26, 2022

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Oh, the current version works fine because it's actually a Callable. What isn't working is passing Type[str] directly. That makes the dict a Dict[str, Type[str]]. That's what I was wondering about.

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@Dr-Irv Dr-Irv Nov 26, 2022

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I guess the type checkers know that str could be either a type or a callable.

check(
assert_type(pd.read_csv(path, converters=converters1), pd.DataFrame),
pd.DataFrame,
)
converters2 = {"A": lambda x: str, "B": lambda x: float}
check(
assert_type(pd.read_csv(path, converters=converters2), pd.DataFrame),
pd.DataFrame,
)
converters3 = {0: lambda x: str, 1: lambda x: str}
check(
assert_type(pd.read_csv(path, converters=converters3), pd.DataFrame),
pd.DataFrame,
)
converters4 = {0: lambda x: str, 1: lambda x: float}
check(
assert_type(pd.read_csv(path, converters=converters4), pd.DataFrame),
pd.DataFrame,
)
converters5: dict[int | str, Callable[[str], Any]] = {
0: lambda x: str,
"A": lambda x: float,
}
check(
assert_type(pd.read_csv(path, converters=converters5), pd.DataFrame),
pd.DataFrame,
)

class ReadCsvKwargs(TypedDict):
converters: dict[int, Callable[[str], Any]]

read_csv_kwargs: ReadCsvKwargs = {"converters": {0: int}}

check(
assert_type(pd.read_csv(path, **read_csv_kwargs), pd.DataFrame),
pd.DataFrame,
)


def test_groupby_series_methods() -> None:
df = pd.DataFrame({"x": [1, 2, 2, 3, 3], "y": [10, 20, 30, 40, 50]})
Expand Down