TYP: annotations (pandas-dev#40955)

Author: jbrockmendel

pandas/_libs/lib.pyi

@@ -5,8 +5,6 @@ from typing import (
     Any,
     Callable,
     Generator,
-    Literal,
-    overload,
 )
 
 import numpy as np
@@ -53,65 +51,23 @@ def is_float_array(values: np.ndarray, skipna: bool = False): ...
 def is_integer_array(values: np.ndarray, skipna: bool = False): ...
 def is_bool_array(values: np.ndarray, skipna: bool = False): ...
 
-def fast_multiget(mapping: dict, keys: np.ndarray, default=np.nan) -> np.ndarray: ...
+def fast_multiget(mapping: dict, keys: np.ndarray, default=np.nan) -> ArrayLike: ...
 
 def fast_unique_multiple_list_gen(gen: Generator, sort: bool = True) -> list: ...
 def fast_unique_multiple_list(lists: list, sort: bool = True) -> list: ...
 def fast_unique_multiple(arrays: list, sort: bool = True) -> list: ...
 
 def map_infer(
     arr: np.ndarray, f: Callable[[Any], Any], convert: bool = True, ignore_na: bool = False
-) -> np.ndarray: ...
-
-
-@overload  # both convert_datetime and convert_to_nullable_integer False -> np.ndarray
-def maybe_convert_objects(
-    objects: np.ndarray,  # np.ndarray[object]
-    try_float: bool = ...,
-    safe: bool = ...,
-    convert_datetime: Literal[False] = ...,
-    convert_timedelta: bool = ...,
-    convert_to_nullable_integer: Literal[False] = ...,
-) -> np.ndarray: ...
-
-@overload
-def maybe_convert_objects(
-    objects: np.ndarray,  # np.ndarray[object]
-    try_float: bool = ...,
-    safe: bool = ...,
-    convert_datetime: Literal[False] = False,
-    convert_timedelta: bool = ...,
-    convert_to_nullable_integer: Literal[True] = ...,
 ) -> ArrayLike: ...
 
-@overload
 def maybe_convert_objects(
     objects: np.ndarray,  # np.ndarray[object]
-    try_float: bool = ...,
-    safe: bool = ...,
-    convert_datetime: Literal[True] = ...,
-    convert_timedelta: bool = ...,
-    convert_to_nullable_integer: Literal[False] = ...,
-) -> ArrayLike: ...
-
-@overload
-def maybe_convert_objects(
-    objects: np.ndarray,  # np.ndarray[object]
-    try_float: bool = ...,
-    safe: bool = ...,
-    convert_datetime: Literal[True] = ...,
-    convert_timedelta: bool = ...,
-    convert_to_nullable_integer: Literal[True] = ...,
-) -> ArrayLike: ...
-
-@overload
-def maybe_convert_objects(
-    objects: np.ndarray,  # np.ndarray[object]
-    try_float: bool = ...,
-    safe: bool = ...,
-    convert_datetime: bool = ...,
-    convert_timedelta: bool = ...,
-    convert_to_nullable_integer: bool = ...,
+    try_float: bool = False,
+    safe: bool = False,
+    convert_datetime: bool = False,
+    convert_timedelta: bool = False,
+    convert_to_nullable_integer: bool = False,
 ) -> ArrayLike: ...
 
 def maybe_convert_numeric(
@@ -184,7 +140,7 @@ def map_infer_mask(
     convert: bool = ...,
     na_value: Any = ...,
     dtype: np.dtype = ...,
-) -> np.ndarray: ...
+) -> ArrayLike: ...
 
 def indices_fast(
     index: np.ndarray,   # ndarray[intp_t]

pandas/_libs/lib.pyx

@@ -68,9 +68,6 @@ cdef extern from "numpy/arrayobject.h":
             object fields
             tuple names
 
-cdef extern from "numpy/ndarrayobject.h":
-    bint PyArray_CheckScalar(obj) nogil
-
 
 cdef extern from "src/parse_helper.h":
     int floatify(object, float64_t *result, int *maybe_int) except -1
@@ -212,24 +209,6 @@ def is_scalar(val: object) -> bool:
             or is_offset_object(val))
 
 
-cdef inline int64_t get_itemsize(object val):
-    """
-    Get the itemsize of a NumPy scalar, -1 if not a NumPy scalar.
-
-    Parameters
-    ----------
-    val : object
-
-    Returns
-    -------
-    is_ndarray : bool
-    """
-    if PyArray_CheckScalar(val):
-        return cnp.PyArray_DescrFromScalar(val).itemsize
-    else:
-        return -1
-
-
 def is_iterator(obj: object) -> bool:
     """
     Check if the object is an iterator.
@@ -2209,7 +2188,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=False,
 
     Parameters
     ----------
-    objects : ndarray[object]
+    values : ndarray[object]
         Array of object elements to convert.
     try_float : bool, default False
         If an array-like object contains only float or NaN values is
@@ -2233,7 +2212,7 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=False,
         Array of converted object values to more specific dtypes if applicable.
     """
     cdef:
-        Py_ssize_t i, n, itemsize_max = 0
+        Py_ssize_t i, n
         ndarray[float64_t] floats
         ndarray[complex128_t] complexes
         ndarray[int64_t] ints
@@ -2266,10 +2245,6 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=False,
 
     for i in range(n):
         val = objects[i]
-        if itemsize_max != -1:
-            itemsize = get_itemsize(val)
-            if itemsize > itemsize_max or itemsize == -1:
-                itemsize_max = itemsize
 
         if val is None:
             seen.null_ = True
@@ -2371,101 +2346,92 @@ def maybe_convert_objects(ndarray[object] objects, bint try_float=False,
         seen.object_ = True
 
     if not seen.object_:
-        result = None
         if not safe:
             if seen.null_ or seen.nan_:
                 if seen.is_float_or_complex:
                     if seen.complex_:
-                        result = complexes
+                        return complexes
                     elif seen.float_:
-                        result = floats
+                        return floats
                     elif seen.int_:
                         if convert_to_nullable_integer:
                             from pandas.core.arrays import IntegerArray
-                            result = IntegerArray(ints, mask)
+                            return IntegerArray(ints, mask)
                         else:
-                            result = floats
+                            return floats
                     elif seen.nan_:
-                        result = floats
+                        return floats
             else:
                 if not seen.bool_:
                     if seen.datetime_:
                         if not seen.numeric_ and not seen.timedelta_:
-                            result = datetimes
+                            return datetimes
                     elif seen.timedelta_:
                         if not seen.numeric_:
-                            result = timedeltas
+                            return timedeltas
                     elif seen.nat_:
                         if not seen.numeric_:
                             if convert_datetime and convert_timedelta:
                                 # TODO: array full of NaT ambiguity resolve here needed
                                 pass
                             elif convert_datetime:
-                                result = datetimes
+                                return datetimes
                             elif convert_timedelta:
-                                result = timedeltas
+                                return timedeltas
                     else:
                         if seen.complex_:
-                            result = complexes
+                            return complexes
                         elif seen.float_:
-                            result = floats
+                            return floats
                         elif seen.int_:
                             if seen.uint_:
-                                result = uints
+                                return uints
                             else:
-                                result = ints
+                                return ints
                 elif seen.is_bool:
-                    result = bools.view(np.bool_)
+                    return bools.view(np.bool_)
 
         else:
             # don't cast int to float, etc.
             if seen.null_:
                 if seen.is_float_or_complex:
                     if seen.complex_:
                         if not seen.int_:
-                            result = complexes
+                            return complexes
                     elif seen.float_ or seen.nan_:
                         if not seen.int_:
-                            result = floats
+                            return floats
             else:
                 if not seen.bool_:
                     if seen.datetime_:
                         if not seen.numeric_ and not seen.timedelta_:
-                            result = datetimes
+                            return datetimes
                     elif seen.timedelta_:
                         if not seen.numeric_:
-                            result = timedeltas
+                            return timedeltas
                     elif seen.nat_:
                         if not seen.numeric_:
                             if convert_datetime and convert_timedelta:
                                 # TODO: array full of NaT ambiguity resolve here needed
                                 pass
                             elif convert_datetime:
-                                result = datetimes
+                                return datetimes
                             elif convert_timedelta:
-                                result = timedeltas
+                                return timedeltas
                     else:
                         if seen.complex_:
                             if not seen.int_:
-                                result = complexes
+                                return complexes
                         elif seen.float_ or seen.nan_:
                             if not seen.int_:
-                                result = floats
+                                return floats
                         elif seen.int_:
                             if seen.uint_:
-                                result = uints
+                                return uints
                             else:
-                                result = ints
+                                return ints
                 elif seen.is_bool and not seen.nan_:
-                    result = bools.view(np.bool_)
-
-        if result is uints or result is ints or result is floats or result is complexes:
-            # cast to the largest itemsize when all values are NumPy scalars
-            if itemsize_max > 0 and itemsize_max != result.dtype.itemsize:
-                result = result.astype(result.dtype.kind + str(itemsize_max))
-            return result
-        elif result is not None:
-            return result
+                    return bools.view(np.bool_)
 
     return objects
 
@@ -2488,7 +2454,7 @@ no_default = NoDefault.no_default  # Sentinel indicating the default value.
 @cython.wraparound(False)
 def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=True,
                    object na_value=no_default, cnp.dtype dtype=np.dtype(object)
-                   ) -> np.ndarray:
+                   ) -> "ArrayLike":
     """
     Substitute for np.vectorize with pandas-friendly dtype inference.
 
@@ -2508,7 +2474,7 @@ def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=Tr
 
     Returns
     -------
-    np.ndarray
+    np.ndarray or ExtensionArray
     """
     cdef:
         Py_ssize_t i, n
@@ -2545,7 +2511,7 @@ def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=Tr
 @cython.wraparound(False)
 def map_infer(
     ndarray arr, object f, bint convert=True, bint ignore_na=False
-) -> np.ndarray:
+) -> "ArrayLike":
     """
     Substitute for np.vectorize with pandas-friendly dtype inference.
 
@@ -2559,7 +2525,7 @@ def map_infer(
 
     Returns
     -------
-    np.ndarray
+    np.ndarray or ExtensionArray
     """
     cdef:
         Py_ssize_t i, n
@@ -2697,7 +2663,7 @@ def to_object_array_tuples(rows: object) -> np.ndarray:
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def fast_multiget(dict mapping, ndarray keys, default=np.nan) -> np.ndarray:
+def fast_multiget(dict mapping, ndarray keys, default=np.nan) -> "ArrayLike":
     cdef:
         Py_ssize_t i, n = len(keys)
         object val