CLN: remove is_datetimelike

pandas/core/algorithms.py

@@ -29,7 +29,6 @@
     is_complex_dtype,
     is_datetime64_any_dtype,
     is_datetime64_ns_dtype,
-    is_datetimelike,
     is_extension_array_dtype,
     is_float_dtype,
     is_integer,
@@ -834,7 +833,7 @@ def mode(values, dropna: bool = True) -> ABCSeries:
             return Series(values.values.mode(dropna=dropna), name=values.name)
         return values.mode(dropna=dropna)
 
-    if dropna and is_datetimelike(values):
+    if dropna and needs_i8_conversion(values.dtype):
         mask = values.isnull()
         values = values[~mask]
 

pandas/core/arrays/categorical.py

@@ -25,7 +25,6 @@
     ensure_platform_int,
     is_categorical_dtype,
     is_datetime64_dtype,
-    is_datetimelike,
     is_dict_like,
     is_dtype_equal,
     is_extension_array_dtype,
@@ -37,6 +36,7 @@
     is_scalar,
     is_sequence,
     is_timedelta64_dtype,
+    needs_i8_conversion,
 )
 from pandas.core.dtypes.dtypes import CategoricalDtype
 from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
@@ -1533,7 +1533,7 @@ def get_values(self):
 
     def _internal_get_values(self):
         # if we are a datetime and period index, return Index to keep metadata
-        if is_datetimelike(self.categories):
+        if needs_i8_conversion(self.categories):
             return self.categories.take(self._codes, fill_value=np.nan)
         elif is_integer_dtype(self.categories) and -1 in self._codes:
             return self.categories.astype("object").take(self._codes, fill_value=np.nan)

pandas/core/base.py

@@ -21,7 +21,6 @@
     is_categorical_dtype,
     is_datetime64_ns_dtype,
     is_datetime64tz_dtype,
-    is_datetimelike,
     is_extension_array_dtype,
     is_extension_type,
     is_list_like,
@@ -1172,7 +1171,7 @@ def tolist(self):
         --------
         numpy.ndarray.tolist
         """
-        if is_datetimelike(self._values):
+        if self.dtype.kind in ["m", "M"]:
             return [com.maybe_box_datetimelike(x) for x in self._values]
         elif is_extension_array_dtype(self._values):
             return list(self._values)
@@ -1194,7 +1193,7 @@ def __iter__(self):
         iterator
         """
         # We are explicitly making element iterators.
-        if is_datetimelike(self._values):
+        if self.dtype.kind in ["m", "M"]:
             return map(com.maybe_box_datetimelike, self._values)
         elif is_extension_array_dtype(self._values):
             return iter(self._values)

pandas/core/dtypes/cast.py

@@ -27,7 +27,6 @@
     is_datetime64_ns_dtype,
     is_datetime64tz_dtype,
     is_datetime_or_timedelta_dtype,
-    is_datetimelike,
     is_dtype_equal,
     is_extension_array_dtype,
     is_extension_type,
@@ -274,7 +273,7 @@ def maybe_upcast_putmask(result: np.ndarray, mask: np.ndarray, other):
         # in np.place:
         #   NaN -> NaT
         #   integer or integer array -> date-like array
-        if is_datetimelike(result.dtype):
+        if result.dtype.kind in ["m", "M"]:
             if is_scalar(other):
                 if isna(other):
                     other = result.dtype.type("nat")

pandas/core/dtypes/common.py

@@ -799,54 +799,6 @@ def is_datetime_arraylike(arr):
     return getattr(arr, "inferred_type", None) == "datetime"
 
 
-def is_datetimelike(arr):
-    """
-    Check whether an array-like is a datetime-like array-like.
-
-    Acceptable datetime-like objects are (but not limited to) datetime
-    indices, periodic indices, and timedelta indices.
-
-    Parameters
-    ----------
-    arr : array-like
-        The array-like to check.
-
-    Returns
-    -------
-    boolean
-        Whether or not the array-like is a datetime-like array-like.
-
-    Examples
-    --------
-    >>> is_datetimelike([1, 2, 3])
-    False
-    >>> is_datetimelike(pd.Index([1, 2, 3]))
-    False
-    >>> is_datetimelike(pd.DatetimeIndex([1, 2, 3]))
-    True
-    >>> is_datetimelike(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
-    True
-    >>> is_datetimelike(pd.PeriodIndex([], freq="A"))
-    True
-    >>> is_datetimelike(np.array([], dtype=np.datetime64))
-    True
-    >>> is_datetimelike(pd.Series([], dtype="timedelta64[ns]"))
-    True
-    >>>
-    >>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
-    >>> s = pd.Series([], dtype=dtype)
-    >>> is_datetimelike(s)
-    True
-    """
-
-    return (
-        is_datetime64_dtype(arr)
-        or is_datetime64tz_dtype(arr)
-        or is_timedelta64_dtype(arr)
-        or isinstance(arr, ABCPeriodIndex)
-    )
-
-
 def is_dtype_equal(source, target):
     """
     Check if two dtypes are equal.
@@ -1446,9 +1398,8 @@ def is_numeric(x):
         """
         return is_integer_dtype(x) or is_float_dtype(x)
 
-    is_datetimelike = needs_i8_conversion
-    return (is_datetimelike(a) and is_numeric(b)) or (
-        is_datetimelike(b) and is_numeric(a)
+    return (needs_i8_conversion(a) and is_numeric(b)) or (
+        needs_i8_conversion(b) and is_numeric(a)
     )
 
 

pandas/core/dtypes/missing.py

@@ -17,7 +17,6 @@
     is_complex_dtype,
     is_datetime64_dtype,
     is_datetime64tz_dtype,
-    is_datetimelike,
     is_datetimelike_v_numeric,
     is_dtype_equal,
     is_extension_array_dtype,
@@ -494,7 +493,7 @@ def _infer_fill_value(val):
     if not is_list_like(val):
         val = [val]
     val = np.array(val, copy=False)
-    if is_datetimelike(val):
+    if needs_i8_conversion(val):
         return np.array("NaT", dtype=val.dtype)
     elif is_object_dtype(val.dtype):
         dtype = lib.infer_dtype(ensure_object(val), skipna=False)

pandas/core/groupby/generic.py

@@ -40,14 +40,14 @@
     ensure_int64,
     ensure_platform_int,
     is_bool,
-    is_datetimelike,
     is_dict_like,
     is_integer_dtype,
     is_interval_dtype,
     is_list_like,
     is_numeric_dtype,
     is_object_dtype,
     is_scalar,
+    needs_i8_conversion,
 )
 from pandas.core.dtypes.missing import _isna_ndarraylike, isna, notna
 
@@ -1287,7 +1287,7 @@ def first_not_none(values):
                 # if we have date/time like in the original, then coerce dates
                 # as we are stacking can easily have object dtypes here
                 so = self._selected_obj
-                if so.ndim == 2 and so.dtypes.apply(is_datetimelike).any():
+                if so.ndim == 2 and so.dtypes.apply(needs_i8_conversion).any():
                     result = _recast_datetimelike_result(result)
                 else:
                     result = result._convert(datetime=True)

pandas/core/reshape/merge.py

@@ -24,7 +24,6 @@
     is_bool_dtype,
     is_categorical_dtype,
     is_datetime64tz_dtype,
-    is_datetimelike,
     is_dtype_equal,
     is_extension_array_dtype,
     is_float_dtype,
@@ -1120,9 +1119,9 @@ def _maybe_coerce_merge_keys(self):
                     raise ValueError(msg)
 
             # datetimelikes must match exactly
-            elif is_datetimelike(lk) and not is_datetimelike(rk):
+            elif needs_i8_conversion(lk) and not needs_i8_conversion(rk):
                 raise ValueError(msg)
-            elif not is_datetimelike(lk) and is_datetimelike(rk):
+            elif not needs_i8_conversion(lk) and needs_i8_conversion(rk):
                 raise ValueError(msg)
             elif is_datetime64tz_dtype(lk) and not is_datetime64tz_dtype(rk):
                 raise ValueError(msg)
@@ -1637,7 +1636,7 @@ def _get_merge_keys(self):
                 )
             )
 
-            if is_datetimelike(lt):
+            if needs_i8_conversion(lt):
                 if not isinstance(self.tolerance, datetime.timedelta):
                     raise MergeError(msg)
                 if self.tolerance < Timedelta(0):

pandas/core/series.py

@@ -25,7 +25,6 @@
     is_categorical,
     is_categorical_dtype,
     is_datetime64_dtype,
-    is_datetimelike,
     is_dict_like,
     is_extension_array_dtype,
     is_extension_type,
@@ -2886,7 +2885,7 @@ def combine_first(self, other):
         new_index = self.index.union(other.index)
         this = self.reindex(new_index, copy=False)
         other = other.reindex(new_index, copy=False)
-        if is_datetimelike(this) and not is_datetimelike(other):
+        if this.dtype.kind == "M" and other.dtype.kind != "M":
             other = to_datetime(other)
 
         return this.where(notna(this), other)

pandas/tests/dtypes/test_common.py

@@ -309,21 +309,6 @@ def test_is_datetime_arraylike():
     assert com.is_datetime_arraylike(pd.DatetimeIndex([1, 2, 3]))
 
 
-def test_is_datetimelike():
-    assert not com.is_datetimelike([1, 2, 3])
-    assert not com.is_datetimelike(pd.Index([1, 2, 3]))
-
-    assert com.is_datetimelike(pd.DatetimeIndex([1, 2, 3]))
-    assert com.is_datetimelike(pd.PeriodIndex([], freq="A"))
-    assert com.is_datetimelike(np.array([], dtype=np.datetime64))
-    assert com.is_datetimelike(pd.Series([], dtype="timedelta64[ns]"))
-    assert com.is_datetimelike(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
-
-    dtype = DatetimeTZDtype("ns", tz="US/Eastern")
-    s = pd.Series([], dtype=dtype)
-    assert com.is_datetimelike(s)
-
-
 integer_dtypes = []  # type: List