TYP: type all arguments with str default values

.pre-commit-config.yaml

@@ -268,7 +268,7 @@ repos:
             |/_testing/
     -   id: autotyping
         name: autotyping
-        entry: python -m libcst.tool codemod autotyping.AutotypeCommand --none-return --scalar-return --annotate-magics --annotate-imprecise-magics --bool-param
+        entry: python -m libcst.tool codemod autotyping.AutotypeCommand --none-return --scalar-return --annotate-magics --annotate-imprecise-magics --bool-param --bytes-param --str-param --float-param
         types_or: [python, pyi]
         files: ^pandas
         exclude: ^(pandas/tests|pandas/_version.py|pandas/io/clipboard)

pandas/_testing/__init__.py

@@ -25,7 +25,10 @@
     set_locale,
 )
 
-from pandas._typing import Dtype
+from pandas._typing import (
+    Dtype,
+    Frequency,
+)
 from pandas.compat import pa_version_under1p01
 
 from pandas.core.dtypes.common import (
@@ -401,13 +404,17 @@ def makeFloatIndex(k=10, name=None) -> Float64Index:
     return Float64Index(base_idx)
 
 
-def makeDateIndex(k: int = 10, freq="B", name=None, **kwargs) -> DatetimeIndex:
+def makeDateIndex(
+    k: int = 10, freq: Frequency = "B", name=None, **kwargs
+) -> DatetimeIndex:
     dt = datetime(2000, 1, 1)
     dr = bdate_range(dt, periods=k, freq=freq, name=name)
     return DatetimeIndex(dr, name=name, **kwargs)
 
 
-def makeTimedeltaIndex(k: int = 10, freq="D", name=None, **kwargs) -> TimedeltaIndex:
+def makeTimedeltaIndex(
+    k: int = 10, freq: Frequency = "D", name=None, **kwargs
+) -> TimedeltaIndex:
     return pd.timedelta_range(start="1 day", periods=k, freq=freq, name=name, **kwargs)
 
 
@@ -484,7 +491,7 @@ def getSeriesData() -> dict[str, Series]:
     return {c: Series(np.random.randn(_N), index=index) for c in getCols(_K)}
 
 
-def makeTimeSeries(nper=None, freq="B", name=None) -> Series:
+def makeTimeSeries(nper=None, freq: Frequency = "B", name=None) -> Series:
     if nper is None:
         nper = _N
     return Series(
@@ -498,7 +505,7 @@ def makePeriodSeries(nper=None, name=None) -> Series:
     return Series(np.random.randn(nper), index=makePeriodIndex(nper), name=name)
 
 
-def getTimeSeriesData(nper=None, freq="B") -> dict[str, Series]:
+def getTimeSeriesData(nper=None, freq: Frequency = "B") -> dict[str, Series]:
     return {c: makeTimeSeries(nper, freq) for c in getCols(_K)}
 
 
@@ -507,7 +514,7 @@ def getPeriodData(nper=None) -> dict[str, Series]:
 
 
 # make frame
-def makeTimeDataFrame(nper=None, freq="B") -> DataFrame:
+def makeTimeDataFrame(nper=None, freq: Frequency = "B") -> DataFrame:
     data = getTimeSeriesData(nper, freq)
     return DataFrame(data)
 
@@ -542,7 +549,7 @@ def makePeriodFrame(nper=None) -> DataFrame:
 def makeCustomIndex(
     nentries,
     nlevels,
-    prefix="#",
+    prefix: str = "#",
     names: bool | str | list[str] | None = False,
     ndupe_l=None,
     idx_type=None,
@@ -760,7 +767,7 @@ def makeCustomDataframe(
     return DataFrame(data, index, columns, dtype=dtype)
 
 
-def _create_missing_idx(nrows, ncols, density, random_state=None):
+def _create_missing_idx(nrows, ncols, density: float, random_state=None):
     if random_state is None:
         random_state = np.random
     else:
@@ -787,7 +794,7 @@ def _gen_unique_rand(rng, _extra_size):
     return i.tolist(), j.tolist()
 
 
-def makeMissingDataframe(density=0.9, random_state=None) -> DataFrame:
+def makeMissingDataframe(density: float = 0.9, random_state=None) -> DataFrame:
     df = makeDataFrame()
     i, j = _create_missing_idx(*df.shape, density=density, random_state=random_state)
     df.values[i, j] = np.nan

pandas/_testing/_io.py

@@ -117,7 +117,7 @@ def dec(f):
 @optional_args  # type: ignore[misc]
 def network(
     t,
-    url="https://www.google.com",
+    url: str = "https://www.google.com",
     raise_on_error: bool = False,
     check_before_test: bool = False,
     error_classes=None,
@@ -369,7 +369,7 @@ def round_trip_localpath(writer, reader, path: str | None = None):
     return obj
 
 
-def write_to_compressed(compression, path, data, dest="test"):
+def write_to_compressed(compression, path, data, dest: str = "test"):
     """
     Write data to a compressed file.
 

pandas/_testing/_random.py

@@ -2,6 +2,8 @@
 
 import numpy as np
 
+from pandas._typing import NpDtype
+
 
 def randbool(size=(), p: float = 0.5):
     return np.random.rand(*size) <= p
@@ -14,7 +16,7 @@ def randbool(size=(), p: float = 0.5):
 )
 
 
-def rands_array(nchars, size, dtype="O", replace: bool = True) -> np.ndarray:
+def rands_array(nchars, size, dtype: NpDtype = "O", replace: bool = True) -> np.ndarray:
     """
     Generate an array of byte strings.
     """

pandas/_testing/asserters.py

@@ -311,7 +311,7 @@ def assert_index_equal(
     """
     __tracebackhide__ = True
 
-    def _check_types(left, right, obj="Index") -> None:
+    def _check_types(left, right, obj: str = "Index") -> None:
         if not exact:
             return
 
@@ -429,7 +429,9 @@ def _get_ilevel_values(index, level):
             assert_categorical_equal(left._values, right._values, obj=f"{obj} category")
 
 
-def assert_class_equal(left, right, exact: bool | str = True, obj="Input") -> None:
+def assert_class_equal(
+    left, right, exact: bool | str = True, obj: str = "Input"
+) -> None:
     """
     Checks classes are equal.
     """
@@ -527,7 +529,7 @@ def assert_categorical_equal(
     right,
     check_dtype: bool = True,
     check_category_order: bool = True,
-    obj="Categorical",
+    obj: str = "Categorical",
 ) -> None:
     """
     Test that Categoricals are equivalent.
@@ -584,7 +586,7 @@ def assert_categorical_equal(
 
 
 def assert_interval_array_equal(
-    left, right, exact="equiv", obj="IntervalArray"
+    left, right, exact: bool | Literal["equiv"] = "equiv", obj: str = "IntervalArray"
 ) -> None:
     """
     Test that two IntervalArrays are equivalent.
@@ -614,15 +616,15 @@ def assert_interval_array_equal(
     assert_attr_equal("closed", left, right, obj=obj)
 
 
-def assert_period_array_equal(left, right, obj="PeriodArray") -> None:
+def assert_period_array_equal(left, right, obj: str = "PeriodArray") -> None:
     _check_isinstance(left, right, PeriodArray)
 
     assert_numpy_array_equal(left._data, right._data, obj=f"{obj}._data")
     assert_attr_equal("freq", left, right, obj=obj)
 
 
 def assert_datetime_array_equal(
-    left, right, obj="DatetimeArray", check_freq: bool = True
+    left, right, obj: str = "DatetimeArray", check_freq: bool = True
 ) -> None:
     __tracebackhide__ = True
     _check_isinstance(left, right, DatetimeArray)
@@ -634,7 +636,7 @@ def assert_datetime_array_equal(
 
 
 def assert_timedelta_array_equal(
-    left, right, obj="TimedeltaArray", check_freq: bool = True
+    left, right, obj: str = "TimedeltaArray", check_freq: bool = True
 ) -> None:
     __tracebackhide__ = True
     _check_isinstance(left, right, TimedeltaArray)
@@ -693,7 +695,7 @@ def assert_numpy_array_equal(
     check_dtype: bool | Literal["equiv"] = True,
     err_msg=None,
     check_same=None,
-    obj="numpy array",
+    obj: str = "numpy array",
     index_values=None,
 ) -> None:
     """
@@ -887,7 +889,7 @@ def assert_series_equal(
     check_flags: bool = True,
     rtol: float = 1.0e-5,
     atol: float = 1.0e-8,
-    obj="Series",
+    obj: str = "Series",
     *,
     check_index: bool = True,
     check_like: bool = False,
@@ -1157,7 +1159,7 @@ def assert_frame_equal(
     check_flags: bool = True,
     rtol: float = 1.0e-5,
     atol: float = 1.0e-8,
-    obj="DataFrame",
+    obj: str = "DataFrame",
 ) -> None:
     """
     Check that left and right DataFrame are equal.

pandas/_typing.py

@@ -332,3 +332,17 @@ def closed(self) -> bool:
 
 # dropna
 AnyAll = Literal["any", "all"]
+
+MatplotlibColor = Union[str, Sequence[float]]
+TimeGrouperOrigin = Union[
+    "Timestamp", Literal["epoch", "start", "start_day", "end", "end_day"]
+]
+TimeAmbiguous = Union[Literal["infer", "NaT", "raise"], "npt.NDArray[np.bool_]"]
+TimeNonexistent = Union[
+    Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta
+]
+DropKeep = Literal["first", "last", False]
+CorrelationMethod = Union[
+    Literal["pearson", "kendall", "spearman"], Callable[[np.ndarray, np.ndarray], float]
+]
+AlignJoin = Literal["outer", "inner", "left", "right"]

pandas/core/arrays/arrow/array.py

@@ -12,6 +12,7 @@
 from pandas._typing import (
     Dtype,
     PositionalIndexer,
+    SortKind,
     TakeIndexer,
     npt,
 )
@@ -472,7 +473,7 @@ def isna(self) -> npt.NDArray[np.bool_]:
     def argsort(
         self,
         ascending: bool = True,
-        kind: str = "quicksort",
+        kind: SortKind = "quicksort",
         na_position: str = "last",
         *args,
         **kwargs,

pandas/core/arrays/base.py

@@ -37,6 +37,7 @@
     ScalarIndexer,
     SequenceIndexer,
     Shape,
+    SortKind,
     TakeIndexer,
     npt,
 )
@@ -671,7 +672,7 @@ def _values_for_argsort(self) -> np.ndarray:
     def argsort(
         self,
         ascending: bool = True,
-        kind: str = "quicksort",
+        kind: SortKind = "quicksort",
         na_position: str = "last",
         *args,
         **kwargs,

pandas/core/arrays/categorical.py

@@ -44,6 +44,7 @@
     NpDtype,
     Ordered,
     Shape,
+    SortKind,
     npt,
     type_t,
 )
@@ -1827,7 +1828,7 @@ def check_for_ordered(self, op) -> None:
     # error: Signature of "argsort" incompatible with supertype "ExtensionArray"
     @deprecate_nonkeyword_arguments(version=None, allowed_args=["self"])
     def argsort(  # type: ignore[override]
-        self, ascending: bool = True, kind="quicksort", **kwargs
+        self, ascending: bool = True, kind: SortKind = "quicksort", **kwargs
     ):
         """
         Return the indices that would sort the Categorical.
@@ -2200,7 +2201,9 @@ def _repr_footer(self) -> str:
         info = self._repr_categories_info()
         return f"Length: {len(self)}\n{info}"
 
-    def _get_repr(self, length: bool = True, na_rep="NaN", footer: bool = True) -> str:
+    def _get_repr(
+        self, length: bool = True, na_rep: str = "NaN", footer: bool = True
+    ) -> str:
         from pandas.io.formats import format as fmt
 
         formatter = fmt.CategoricalFormatter(
@@ -2716,7 +2719,7 @@ def _str_map(
         result = PandasArray(categories.to_numpy())._str_map(f, na_value, dtype)
         return take_nd(result, codes, fill_value=na_value)
 
-    def _str_get_dummies(self, sep="|"):
+    def _str_get_dummies(self, sep: str = "|"):
         # sep may not be in categories. Just bail on this.
         from pandas.core.arrays import PandasArray
 

pandas/core/arrays/datetimelike.py

@@ -63,6 +63,8 @@
     PositionalIndexerTuple,
     ScalarIndexer,
     SequenceIndexer,
+    TimeAmbiguous,
+    TimeNonexistent,
     npt,
 )
 from pandas.compat.numpy import function as nv
@@ -308,7 +310,7 @@ def asi8(self) -> npt.NDArray[np.int64]:
     # Rendering Methods
 
     def _format_native_types(
-        self, *, na_rep="NaT", date_format=None
+        self, *, na_rep: str | float = "NaT", date_format=None
     ) -> npt.NDArray[np.object_]:
         """
         Helper method for astype when converting to strings.
@@ -556,7 +558,7 @@ def _concat_same_type(
         new_obj._freq = new_freq
         return new_obj
 
-    def copy(self: DatetimeLikeArrayT, order="C") -> DatetimeLikeArrayT:
+    def copy(self: DatetimeLikeArrayT, order: str = "C") -> DatetimeLikeArrayT:
         # error: Unexpected keyword argument "order" for "copy"
         new_obj = super().copy(order=order)  # type: ignore[call-arg]
         new_obj._freq = self.freq
@@ -2085,15 +2087,30 @@ def _round(self, freq, mode, ambiguous, nonexistent):
         return self._simple_new(result, dtype=self.dtype)
 
     @Appender((_round_doc + _round_example).format(op="round"))
-    def round(self, freq, ambiguous="raise", nonexistent="raise"):
+    def round(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
         return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous, nonexistent)
 
     @Appender((_round_doc + _floor_example).format(op="floor"))
-    def floor(self, freq, ambiguous="raise", nonexistent="raise"):
+    def floor(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
         return self._round(freq, RoundTo.MINUS_INFTY, ambiguous, nonexistent)
 
     @Appender((_round_doc + _ceil_example).format(op="ceil"))
-    def ceil(self, freq, ambiguous="raise", nonexistent="raise"):
+    def ceil(
+        self,
+        freq,
+        ambiguous: TimeAmbiguous = "raise",
+        nonexistent: TimeNonexistent = "raise",
+    ):
         return self._round(freq, RoundTo.PLUS_INFTY, ambiguous, nonexistent)
 
     # --------------------------------------------------------------