DEPR: Deprecate pandas.datetime

doc/source/whatsnew/v1.0.0.rst

@@ -225,6 +225,9 @@ Other enhancements
 - :meth:`DataFrame.drop_duplicates` has gained ``ignore_index`` keyword to reset index (:issue:`30114`)
 - Added new writer for exporting Stata dta files in version 118, ``StataWriter118``.  This format supports exporting strings containing Unicode characters (:issue:`23573`)
 - :meth:`Series.map` now accepts ``collections.abc.Mapping`` subclasses as a mapper (:issue:`29733`)
+- The ``pandas.datetime`` class is now deprecated. Import from ``datetime`` instead (:issue:`30296`)
+
+
 
 Build Changes
 ^^^^^^^^^^^^^

pandas/__init__.py

@@ -39,8 +39,6 @@
         "the C extensions first."
     )
 
-from datetime import datetime
-
 from pandas._config import (
     get_option,
     set_option,
@@ -210,6 +208,19 @@ class Panel:
 
             return Panel
 
+        elif name == "datetime":
+            warnings.warn(
+                "The pandas.datetime class is deprecated "
+                "and will be removed from pandas in a future version. "
+                "Import from datetime module instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+
+            from datetime import datetime as dt
+
+            return dt
+
         elif name == "np":
 
             warnings.warn(
@@ -264,13 +275,39 @@ def __getattr__(self, item):
                 FutureWarning,
                 stacklevel=2,
             )
+
             try:
                 return getattr(self.np, item)
             except AttributeError:
                 raise AttributeError(f"module numpy has no attribute {item}")
 
     np = __numpy()
 
+    class __Datetime:
+        def __init__(self):
+            from datetime import datetime as dt
+
+            self.datetime = dt
+
+        def __getattr__(self, item):
+            import warnings
+
+            warnings.warn(
+                "The pandas.datetime class is deprecated "
+                "and will be removed from pandas in a future version. "
+                "Import from datetime instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+
+            try:
+                return getattr(self.datetime, item)
+            except AttributeError:
+                raise AttributeError(f"module datetime has no attribute {item}")
+
+    datetime = __Datetime().datetime
+
+
 # module level doc-string
 __doc__ = """
 pandas - a powerful data analysis and manipulation library for Python

pandas/tests/api/test_api.py

@@ -92,7 +92,7 @@ class TestPDApi(Base):
     ]
     if not compat.PY37:
         classes.extend(["Panel", "SparseSeries", "SparseDataFrame"])
-        deprecated_modules.append("np")
+        deprecated_modules.extend(["np", "datetime"])
 
     # these are already deprecated; awaiting removal
     deprecated_classes: List[str] = []
@@ -101,7 +101,7 @@ class TestPDApi(Base):
     deprecated_classes_in_future: List[str] = []
 
     # external modules exposed in pandas namespace
-    modules = ["datetime"]
+    modules: List[str] = []
 
     # top-level functions
     funcs = [
@@ -232,11 +232,23 @@ def test_depr(self):
             with tm.assert_produces_warning(FutureWarning):
                 if compat.PY37:
                     getattr(pd, depr)
+                elif depr == "datetime":
+                    deprecated = getattr(pd, "__Datetime")
+                    deprecated().__getattr__(dir(pd.datetime)[-1])
                 else:
                     deprecated = getattr(pd, depr)
                     deprecated.__getattr__(dir(deprecated)[-1])
 
 
+def test_datetime():
+    from datetime import datetime
+    import warnings
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", FutureWarning)
+        assert datetime(2015, 1, 2, 0, 0) == pd.datetime(2015, 1, 2, 0, 0)
+
+
 def test_np():
     import numpy as np
     import warnings

pandas/tests/dtypes/test_common.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 from typing import List
 
 import numpy as np
@@ -490,7 +491,7 @@ def test_is_numeric_v_string_like():
 
 
 def test_is_datetimelike_v_numeric():
-    dt = np.datetime64(pd.datetime(2017, 1, 1))
+    dt = np.datetime64(datetime(2017, 1, 1))
 
     assert not com.is_datetimelike_v_numeric(1, 1)
     assert not com.is_datetimelike_v_numeric(dt, dt)

pandas/tests/frame/indexing/test_indexing.py

@@ -1146,18 +1146,18 @@ def test_setitem_mixed_datetime(self):
             {
                 "a": [0, 0, 0, 0, 13, 14],
                 "b": [
-                    pd.datetime(2012, 1, 1),
+                    datetime(2012, 1, 1),
                     1,
                     "x",
                     "y",
-                    pd.datetime(2013, 1, 1),
-                    pd.datetime(2014, 1, 1),
+                    datetime(2013, 1, 1),
+                    datetime(2014, 1, 1),
                 ],
             }
         )
         df = pd.DataFrame(0, columns=list("ab"), index=range(6))
         df["b"] = pd.NaT
-        df.loc[0, "b"] = pd.datetime(2012, 1, 1)
+        df.loc[0, "b"] = datetime(2012, 1, 1)
         df.loc[1, "b"] = 1
         df.loc[[2, 3], "b"] = "x", "y"
         A = np.array(

pandas/tests/groupby/test_groupby.py

@@ -1729,9 +1729,7 @@ def test_pivot_table_values_key_error():
     # This test is designed to replicate the error in issue #14938
     df = pd.DataFrame(
         {
-            "eventDate": pd.date_range(
-                pd.datetime.today(), periods=20, freq="M"
-            ).tolist(),
+            "eventDate": pd.date_range(datetime.today(), periods=20, freq="M").tolist(),
             "thename": range(0, 20),
         }
     )

pandas/tests/indexes/datetimes/test_constructors.py

@@ -1,4 +1,4 @@
-from datetime import timedelta
+from datetime import datetime, timedelta
 from functools import partial
 from operator import attrgetter
 
@@ -10,15 +10,7 @@
 from pandas._libs.tslibs import OutOfBoundsDatetime, conversion
 
 import pandas as pd
-from pandas import (
-    DatetimeIndex,
-    Index,
-    Timestamp,
-    date_range,
-    datetime,
-    offsets,
-    to_datetime,
-)
+from pandas import DatetimeIndex, Index, Timestamp, date_range, offsets, to_datetime
 from pandas.core.arrays import DatetimeArray, period_array
 import pandas.util.testing as tm
 

pandas/tests/indexes/datetimes/test_misc.py

@@ -1,12 +1,13 @@
 import calendar
+from datetime import datetime
 import locale
 import unicodedata
 
 import numpy as np
 import pytest
 
 import pandas as pd
-from pandas import DatetimeIndex, Index, Timestamp, date_range, datetime, offsets
+from pandas import DatetimeIndex, Index, Timestamp, date_range, offsets
 import pandas.util.testing as tm
 
 

pandas/tests/indexing/test_iloc.py

@@ -1,5 +1,6 @@
 """ test positional based indexing with iloc """
 
+from datetime import datetime
 from warnings import catch_warnings, simplefilter
 
 import numpy as np
@@ -122,7 +123,7 @@ def check(result, expected):
         [
             ([slice(None), ["A", "D"]]),
             (["1", "2"], slice(None)),
-            ([pd.datetime(2019, 1, 1)], slice(None)),
+            ([datetime(2019, 1, 1)], slice(None)),
         ],
     )
     def test_iloc_non_integer_raises(self, index, columns, index_vals, column_vals):

pandas/tests/io/excel/test_writers.py

@@ -252,7 +252,7 @@ def test_read_excel_parse_dates(self, ext):
             res = pd.read_excel(pth, parse_dates=["date_strings"], index_col=0)
             tm.assert_frame_equal(df, res)
 
-            date_parser = lambda x: pd.datetime.strptime(x, "%m/%d/%Y")
+            date_parser = lambda x: datetime.strptime(x, "%m/%d/%Y")
             res = pd.read_excel(
                 pth, parse_dates=["date_strings"], date_parser=date_parser, index_col=0
             )

pandas/tests/io/sas/test_sas7bdat.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 import io
 import os
 from pathlib import Path
@@ -23,7 +24,7 @@ def setup_method(self, datapath):
         for j in 1, 2:
             fname = os.path.join(self.dirpath, f"test_sas7bdat_{j}.csv")
             df = pd.read_csv(fname)
-            epoch = pd.datetime(1960, 1, 1)
+            epoch = datetime(1960, 1, 1)
             t1 = pd.to_timedelta(df["Column4"], unit="d")
             df["Column4"] = epoch + t1
             t2 = pd.to_timedelta(df["Column12"], unit="d")