DEPR: resample/groupby.pad/backfill in favor of ffill/bfill

doc/source/whatsnew/v1.4.0.rst

@@ -547,7 +547,10 @@ Other Deprecations
 - Deprecated :meth:`Categorical.replace`, use :meth:`Series.replace` instead (:issue:`44929`)
 - Deprecated :meth:`Index.__getitem__` with a bool key; use ``index.values[key]`` to get the old behavior (:issue:`44051`)
 - Deprecated downcasting column-by-column in :meth:`DataFrame.where` with integer-dtypes (:issue:`44597`)
--
+- Deprecated :meth:`.Groupby.pad` in favor of :meth:`.Groupby.ffill` (:issue:`33396`)
+- Deprecated :meth:`.Groupby.backfill` in favor of :meth:`.Groupby.bfill` (:issue:`33396`)
+- Deprecated :meth:`.Resample.pad` in favor of :meth:`.Resample.ffill` (:issue:`33396`)
+- Deprecated :meth:`.Resample.backfill` in favor of :meth:`.Resample.bfill` (:issue:`33396`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/groupby/groupby.py

@@ -2547,7 +2547,7 @@ def blk_func(values: ArrayLike) -> ArrayLike:
 
     @final
     @Substitution(name="groupby")
-    def pad(self, limit=None):
+    def ffill(self, limit=None):
         """
         Forward fill the values.
 
@@ -2563,18 +2563,27 @@ def pad(self, limit=None):
 
         See Also
         --------
-        Series.pad: Returns Series with minimum number of char in object.
-        DataFrame.pad: Object with missing values filled or None if inplace=True.
+        Series.ffill: Returns Series with minimum number of char in object.
+        DataFrame.ffill: Object with missing values filled or None if inplace=True.
         Series.fillna: Fill NaN values of a Series.
         DataFrame.fillna: Fill NaN values of a DataFrame.
         """
         return self._fill("ffill", limit=limit)
 
-    ffill = pad
+    def pad(self, limit=None):
+        warnings.warn(
+            "pad is deprecated and will be removed in a future version. "
+            "Use ffill instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.ffill(limit=limit)
+
+    pad.__doc__ = ffill.__doc__
 
     @final
     @Substitution(name="groupby")
-    def backfill(self, limit=None):
+    def bfill(self, limit=None):
         """
         Backward fill the values.
 
@@ -2590,14 +2599,23 @@ def backfill(self, limit=None):
 
         See Also
         --------
-        Series.backfill :  Backward fill the missing values in the dataset.
-        DataFrame.backfill:  Backward fill the missing values in the dataset.
+        Series.bfill :  Backward fill the missing values in the dataset.
+        DataFrame.bfill:  Backward fill the missing values in the dataset.
         Series.fillna: Fill NaN values of a Series.
         DataFrame.fillna: Fill NaN values of a DataFrame.
         """
         return self._fill("bfill", limit=limit)
 
-    bfill = backfill
+    def backfill(self, limit=None):
+        warnings.warn(
+            "backfill is deprecated and will be removed in a future version. "
+            "Use bfill instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.bfill(limit=limit)
+
+    backfill.__doc__ = bfill.__doc__
 
     @final
     @Substitution(name="groupby")
@@ -3435,7 +3453,7 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
     @final
     @Substitution(name="groupby")
     @Appender(_common_see_also)
-    def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None, axis=0):
+    def pct_change(self, periods=1, fill_method="ffill", limit=None, freq=None, axis=0):
         """
         Calculate pct_change of each value to previous entry in group.
 
@@ -3457,7 +3475,7 @@ def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None, axis=0
                 )
             )
         if fill_method is None:  # GH30463
-            fill_method = "pad"
+            fill_method = "ffill"
             limit = 0
         filled = getattr(self, fill_method)(limit=limit)
         fill_grp = filled.groupby(self.grouper.codes, axis=self.axis)

pandas/core/resample.py

@@ -11,6 +11,7 @@
     final,
     no_type_check,
 )
+import warnings
 
 import numpy as np
 
@@ -40,6 +41,7 @@
     deprecate_nonkeyword_arguments,
     doc,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -509,7 +511,7 @@ def _wrap_result(self, result):
 
         return result
 
-    def pad(self, limit=None):
+    def ffill(self, limit=None):
         """
         Forward fill the values.
 
@@ -527,9 +529,18 @@ def pad(self, limit=None):
         Series.fillna: Fill NA/NaN values using the specified method.
         DataFrame.fillna: Fill NA/NaN values using the specified method.
         """
-        return self._upsample("pad", limit=limit)
+        return self._upsample("ffill", limit=limit)
+
+    def pad(self, limit=None):
+        warnings.warn(
+            "pad is deprecated and will be removed in a future version. "
+            "Use ffill instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.ffill(limit=limit)
 
-    ffill = pad
+    pad.__doc__ = ffill.__doc__
 
     def nearest(self, limit=None):
         """
@@ -591,7 +602,7 @@ def nearest(self, limit=None):
         """
         return self._upsample("nearest", limit=limit)
 
-    def backfill(self, limit=None):
+    def bfill(self, limit=None):
         """
         Backward fill the new missing values in the resampled data.
 
@@ -618,7 +629,7 @@ def backfill(self, limit=None):
         fillna : Fill NaN values using the specified method, which can be
             'backfill'.
         nearest : Fill NaN values with nearest neighbor starting from center.
-        pad : Forward fill NaN values.
+        ffill : Forward fill NaN values.
         Series.fillna : Fill NaN values in the Series using the
             specified method, which can be 'backfill'.
         DataFrame.fillna : Fill NaN values in the DataFrame using the
@@ -640,15 +651,15 @@ def backfill(self, limit=None):
         2018-01-01 02:00:00    3
         Freq: H, dtype: int64
 
-        >>> s.resample('30min').backfill()
+        >>> s.resample('30min').bfill()
         2018-01-01 00:00:00    1
         2018-01-01 00:30:00    2
         2018-01-01 01:00:00    2
         2018-01-01 01:30:00    3
         2018-01-01 02:00:00    3
         Freq: 30T, dtype: int64
 
-        >>> s.resample('15min').backfill(limit=2)
+        >>> s.resample('15min').bfill(limit=2)
         2018-01-01 00:00:00    1.0
         2018-01-01 00:15:00    NaN
         2018-01-01 00:30:00    2.0
@@ -671,15 +682,15 @@ def backfill(self, limit=None):
         2018-01-01 01:00:00  NaN  3
         2018-01-01 02:00:00  6.0  5
 
-        >>> df.resample('30min').backfill()
+        >>> df.resample('30min').bfill()
                                a  b
         2018-01-01 00:00:00  2.0  1
         2018-01-01 00:30:00  NaN  3
         2018-01-01 01:00:00  NaN  3
         2018-01-01 01:30:00  6.0  5
         2018-01-01 02:00:00  6.0  5
 
-        >>> df.resample('15min').backfill(limit=2)
+        >>> df.resample('15min').bfill(limit=2)
                                a    b
         2018-01-01 00:00:00  2.0  1.0
         2018-01-01 00:15:00  NaN  NaN
@@ -691,9 +702,18 @@ def backfill(self, limit=None):
         2018-01-01 01:45:00  6.0  5.0
         2018-01-01 02:00:00  6.0  5.0
         """
-        return self._upsample("backfill", limit=limit)
+        return self._upsample("bfill", limit=limit)
+
+    def backfill(self, limit=None):
+        warnings.warn(
+            "backfill is deprecated and will be removed in a future version. "
+            "Use bfill instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.bfill(limit=limit)
 
-    bfill = backfill
+    backfill.__doc__ = bfill.__doc__
 
     def fillna(self, method, limit=None):
         """
@@ -727,8 +747,8 @@ def fillna(self, method, limit=None):
 
         See Also
         --------
-        backfill : Backward fill NaN values in the resampled data.
-        pad : Forward fill NaN values in the resampled data.
+        bfill : Backward fill NaN values in the resampled data.
+        ffill : Forward fill NaN values in the resampled data.
         nearest : Fill NaN values in the resampled data
             with nearest neighbor starting from center.
         interpolate : Fill NaN values using interpolation.

pandas/tests/apply/test_str.py

@@ -245,7 +245,11 @@ def test_agg_cython_table_transform_frame(df, func, expected, axis):
 @pytest.mark.parametrize("op", series_transform_kernels)
 def test_transform_groupby_kernel_series(string_series, op):
     # GH 35964
-
+    # TODO(2.0) Remove after pad/backfill deprecation enforced
+    if op == "backfill":
+        op = "bfill"
+    elif op == "pad":
+        op = "ffill"
     args = [0.0] if op == "fillna" else []
     ones = np.ones(string_series.shape[0])
     expected = string_series.groupby(ones).transform(op, *args)
@@ -257,6 +261,11 @@ def test_transform_groupby_kernel_series(string_series, op):
 def test_transform_groupby_kernel_frame(
     axis, float_frame, op, using_array_manager, request
 ):
+    # TODO(2.0) Remove after pad/backfill deprecation enforced
+    if op == "backfill":
+        op = "bfill"
+    elif op == "pad":
+        op = "ffill"
     # GH 35964
     if using_array_manager and op == "pct_change" and axis in (1, "columns"):
         # TODO(ArrayManager) shift with axis=1

pandas/tests/groupby/test_groupby.py

@@ -2269,7 +2269,11 @@ def test_groupby_duplicate_index():
 def test_dup_labels_output_shape(groupby_func, idx):
     if groupby_func in {"size", "ngroup", "cumcount"}:
         pytest.skip("Not applicable")
-
+    # TODO(2.0) Remove after pad/backfill deprecation enforced
+    if groupby_func == "backfill":
+        groupby_func = "bfill"
+    elif groupby_func == "pad":
+        groupby_func = "ffill"
     df = DataFrame([[1, 1]], columns=idx)
     grp_by = df.groupby([0])
 
@@ -2614,3 +2618,12 @@ def test_rolling_wrong_param_min_period():
     result_error_msg = r"__init__\(\) got an unexpected keyword argument 'min_period'"
     with pytest.raises(TypeError, match=result_error_msg):
         test_df.groupby("name")["val"].rolling(window=2, min_period=1).sum()
+
+
+def test_pad_backfill_deprecation():
+    # GH 33396
+    s = Series([1, 2, 3])
+    with tm.assert_produces_warning(FutureWarning, match="backfill"):
+        s.groupby(level=0).backfill()
+    with tm.assert_produces_warning(FutureWarning, match="pad"):
+        s.groupby(level=0).pad()

pandas/tests/groupby/test_groupby_subclass.py

@@ -23,7 +23,11 @@ def test_groupby_preserves_subclass(obj, groupby_func):
 
     if isinstance(obj, Series) and groupby_func in {"corrwith"}:
         pytest.skip("Not applicable")
-
+    # TODO(2.0) Remove after pad/backfill deprecation enforced
+    if groupby_func == "backfill":
+        groupby_func = "bfill"
+    elif groupby_func == "pad":
+        groupby_func = "ffill"
     grouped = obj.groupby(np.arange(0, 10))
 
     # Groups should preserve subclass type

pandas/tests/groupby/transform/test_transform.py

@@ -171,6 +171,11 @@ def test_transform_axis_1(request, transformation_func, using_array_manager):
         request.node.add_marker(
             pytest.mark.xfail(reason="ArrayManager: shift axis=1 not yet implemented")
         )
+    # TODO(2.0) Remove after pad/backfill deprecation enforced
+    if transformation_func == "backfill":
+        transformation_func = "bfill"
+    elif transformation_func == "pad":
+        transformation_func = "ffill"
     warn = None
     if transformation_func == "tshift":
         warn = FutureWarning
@@ -357,7 +362,11 @@ def test_transform_transformation_func(request, transformation_func):
         },
         index=date_range("2020-01-01", "2020-01-07"),
     )
-
+    # TODO(2.0) Remove after pad/backfill deprecation enforced
+    if transformation_func == "backfill":
+        transformation_func = "bfill"
+    elif transformation_func == "pad":
+        transformation_func = "ffill"
     if transformation_func == "cumcount":
         test_op = lambda x: x.transform("cumcount")
         mock_op = lambda x: Series(range(len(x)), x.index)

pandas/tests/resample/test_datetime_index.py

@@ -438,7 +438,7 @@ def test_resample_upsample():
     s = Series(np.random.rand(len(dti)), dti)
 
     # to minutely, by padding
-    result = s.resample("Min").pad()
+    result = s.resample("Min").ffill()
     assert len(result) == 12961
     assert result[0] == s[0]
     assert result[-1] == s[-1]
@@ -1810,7 +1810,7 @@ def test_resample_calendar_day_with_dst(
 ):
     # GH 35219
     ts = Series(1.0, date_range(first, last, freq=freq_in, tz="Europe/Amsterdam"))
-    result = ts.resample(freq_out).pad()
+    result = ts.resample(freq_out).ffill()
     expected = Series(
         1.0, date_range(first, exp_last, freq=freq_out, tz="Europe/Amsterdam")
     )

pandas/tests/resample/test_deprecated.py

@@ -305,3 +305,12 @@ def test_interpolate_posargs_deprecation():
 
     expected.index._data.freq = "3s"
     tm.assert_series_equal(result, expected)
+
+
+def test_pad_backfill_deprecation():
+    # GH 33396
+    s = Series([1, 2, 3], index=date_range("20180101", periods=3, freq="h"))
+    with tm.assert_produces_warning(FutureWarning, match="backfill"):
+        s.resample("30min").backfill()
+    with tm.assert_produces_warning(FutureWarning, match="pad"):
+        s.resample("30min").pad()

pandas/tests/resample/test_resampler_grouper.py

@@ -227,7 +227,7 @@ def test_methods():
         expected = g.B.apply(lambda x: getattr(x.resample("2s"), f)())
         tm.assert_series_equal(result, expected)
 
-    for f in ["nearest", "backfill", "ffill", "asfreq"]:
+    for f in ["nearest", "bfill", "ffill", "asfreq"]:
         result = getattr(r, f)()
         expected = g.apply(lambda x: getattr(x.resample("2s"), f)())
         tm.assert_frame_equal(result, expected)