ENH: consistency of input args for boundaries for resample

doc/source/user_guide/timeseries.rst

@@ -1634,14 +1634,14 @@ a method of the returned object, including ``sum``, ``mean``, ``std``, ``sem``,
    ts.resample("5Min").max()
 
 
-For downsampling, ``closed`` can be set to 'left' or 'right' to specify which
+For downsampling, ``inclusive`` can be set to 'left' or 'right' to specify which
 end of the interval is closed:
 
 .. ipython:: python
 
-   ts.resample("5Min", closed="right").mean()
+   ts.resample("5Min", inclusive="right").mean()
 
-   ts.resample("5Min", closed="left").mean()
+   ts.resample("5Min", inclusive="left").mean()
 
 Parameters like ``label`` are used to manipulate the resulting labels.
 ``label`` specifies whether the result is labeled with the beginning or
@@ -1655,7 +1655,7 @@ the end of the interval.
 
 .. warning::
 
-    The default values for ``label`` and ``closed`` is '**left**' for all
+    The default values for ``label`` and ``inclusive`` is '**left**' for all
     frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W'
     which all have a default of 'right'.
 
@@ -1669,7 +1669,7 @@ the end of the interval.
         s.iloc[2] = pd.NaT
         s.dt.day_name()
 
-        # default: label='left', closed='left'
+        # default: label='left', inclusive='left'
         s.resample("B").last().dt.day_name()
 
     Notice how the value for Sunday got pulled back to the previous Friday.
@@ -1678,7 +1678,7 @@ the end of the interval.
 
     .. ipython:: python
 
-        s.resample("B", label="right", closed="right").last().dt.day_name()
+        s.resample("B", label="right", inclusive="right").last().dt.day_name()
 
 The ``axis`` parameter can be set to 0 or 1 and allows you to resample the
 specified axis for a ``DataFrame``.

doc/source/user_guide/window.rst

@@ -196,7 +196,7 @@ This can also be applied to datetime-like indices.
 Rolling window endpoints
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-The inclusion of the interval endpoints in rolling window calculations can be specified with the ``closed``
+The inclusion of the interval endpoints in rolling window calculations can be specified with the ``inclusive``
 parameter:
 
 =============  ====================
@@ -225,10 +225,10 @@ from present information back to past information. This allows the rolling windo
        ],
    )
 
-   df["right"] = df.rolling("2s", closed="right").x.sum()  # default
-   df["both"] = df.rolling("2s", closed="both").x.sum()
-   df["left"] = df.rolling("2s", closed="left").x.sum()
-   df["neither"] = df.rolling("2s", closed="neither").x.sum()
+   df["right"] = df.rolling("2s", inclusive="right").x.sum()  # default
+   df["both"] = df.rolling("2s", inclusive="both").x.sum()
+   df["left"] = df.rolling("2s", inclusive="left").x.sum()
+   df["neither"] = df.rolling("2s", inclusive="neither").x.sum()
 
    df
 
@@ -264,7 +264,7 @@ and we want to use an expanding window where ``use_expanding`` is ``True`` other
    In [2]: from pandas.api.indexers import BaseIndexer
 
    In [3]: class CustomIndexer(BaseIndexer):
-      ...:     def get_window_bounds(self, num_values, min_periods, center, closed):
+      ...:     def get_window_bounds(self, num_values, min_periods, center, inclusive):
       ...:         start = np.empty(num_values, dtype=np.int64)
       ...:         end = np.empty(num_values, dtype=np.int64)
       ...:         for i in range(num_values):

pandas/core/frame.py

@@ -11398,8 +11398,8 @@ def asfreq(
     def resample(
         self,
         rule,
-        axis: Axis = 0,
-        closed: str | None = None,
+        axis=0,
+        inclusive: str | None = None,
         label: str | None = None,
         convention: str = "start",
         kind: str | None = None,
@@ -11414,7 +11414,7 @@ def resample(
         return super().resample(
             rule=rule,
             axis=axis,
-            closed=closed,
+            inclusive=inclusive,
             label=label,
             convention=convention,
             kind=kind,

pandas/core/generic.py

@@ -8406,8 +8406,8 @@ def between_time(
     def resample(
         self,
         rule,
-        axis: Axis = 0,
-        closed: str | None = None,
+        axis=0,
+        inclusive: str | None = None,
         label: str | None = None,
         convention: str = "start",
         kind: str | None = None,
@@ -8435,7 +8435,7 @@ def resample(
             Which axis to use for up- or down-sampling. For `Series` this parameter
             is unused and defaults to 0. Must be
             `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.
-        closed : {{'right', 'left'}}, default None
+        inclusive : {{'right', 'left'}}, default None
             Which side of bin interval is closed. The default is 'left'
             for all frequency offsets except for 'M', 'A', 'Q', 'BM',
             'BA', 'BQ', and 'W' which all have a default of 'right'.
@@ -8570,7 +8570,7 @@ def resample(
         Downsample the series into 3 minute bins as above, but close the right
         side of the bin interval.
 
-        >>> series.resample('3T', label='right', closed='right').sum()
+        >>> series.resample('3T', label='right', inclusive='right').sum()
         2000-01-01 00:00:00     0
         2000-01-01 00:03:00     6
         2000-01-01 00:06:00    15
@@ -8832,7 +8832,7 @@ def resample(
             self,
             freq=rule,
             label=label,
-            closed=closed,
+            inclusive=inclusive,
             axis=axis,
             kind=kind,
             loffset=loffset,
@@ -11950,7 +11950,7 @@ def rolling(
         win_type: str | None = None,
         on: str | None = None,
         axis: Axis = 0,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
         method: str = "single",
     ) -> Window | Rolling:
@@ -11965,7 +11965,7 @@ def rolling(
                 win_type=win_type,
                 on=on,
                 axis=axis,
-                closed=closed,
+                inclusive=inclusive,
                 step=step,
                 method=method,
             )
@@ -11978,7 +11978,7 @@ def rolling(
             win_type=win_type,
             on=on,
             axis=axis,
-            closed=closed,
+            inclusive=inclusive,
             step=step,
             method=method,
         )

pandas/core/groupby/groupby.py

@@ -2762,7 +2762,7 @@ def resample(self, rule, *args, **kwargs):
         Downsample the series into 3 minute bins as above, but close the right
         side of the bin interval.
 
-        >>> df.groupby('a').resample('3T', closed='right').sum()
+        >>> df.groupby('a').resample('3T', inclusive='right').sum()
                                  a  b
         a
         0   1999-12-31 23:57:00  0  1
@@ -2773,7 +2773,7 @@ def resample(self, rule, *args, **kwargs):
         the bin interval, but label each bin using the right edge instead of
         the left.
 
-        >>> df.groupby('a').resample('3T', closed='right', label='right').sum()
+        >>> df.groupby('a').resample('3T', inclusive='right', label='right').sum()
                                  a  b
         a
         0   2000-01-01 00:00:00  0  1

pandas/core/indexers/objects.py

@@ -25,8 +25,8 @@
     min_periods passed from the top level rolling API
 center : bool, default None
     center passed from the top level rolling API
-closed : str, default None
-    closed passed from the top level rolling API
+inclusive : str, default None
+    inclusive passed from the top level rolling API
 step : int, default None
     step passed from the top level rolling API
     .. versionadded:: 1.5
@@ -64,7 +64,7 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
 
@@ -80,7 +80,7 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
 
@@ -91,9 +91,9 @@ def get_window_bounds(
 
         end = np.arange(1 + offset, num_values + 1 + offset, step, dtype="int64")
         start = end - self.window_size
-        if closed in ["left", "both"]:
+        if inclusive in ["left", "both"]:
             start -= 1
-        if closed in ["left", "neither"]:
+        if inclusive in ["left", "neither"]:
             end -= 1
 
         end = np.clip(end, 0, num_values)
@@ -111,7 +111,7 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
 
@@ -124,7 +124,7 @@ def get_window_bounds(
             self.window_size,
             min_periods,
             center,  # type: ignore[arg-type]
-            closed,
+            inclusive,
             self.index_array,  # type: ignore[arg-type]
         )
 
@@ -150,7 +150,7 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
 
@@ -160,11 +160,11 @@ def get_window_bounds(
             return np.empty(0, dtype="int64"), np.empty(0, dtype="int64")
 
         # if windows is variable, default is 'right', otherwise default is 'both'
-        if closed is None:
-            closed = "right" if self.index is not None else "both"
+        if inclusive is None:
+            inclusive = "right" if self.index is not None else "both"
 
-        right_closed = closed in ["right", "both"]
-        left_closed = closed in ["left", "both"]
+        right_closed = inclusive in ["right", "both"]
+        left_closed = inclusive in ["left", "both"]
 
         if self.index[num_values - 1] < self.index[0]:
             index_growth_sign = -1
@@ -226,7 +226,7 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
 
@@ -267,15 +267,15 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
 
         if center:
             raise ValueError("Forward-looking windows can't have center=True")
-        if closed is not None:
+        if inclusive is not None:
             raise ValueError(
-                "Forward-looking windows don't support setting the closed argument"
+                "Forward-looking windows don't support setting the inclusive argument"
             )
         if step is None:
             step = 1
@@ -333,7 +333,7 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
 
@@ -356,7 +356,7 @@ def get_window_bounds(
                 **self.indexer_kwargs,
             )
             start, end = indexer.get_window_bounds(
-                len(indices), min_periods, center, closed, step
+                len(indices), min_periods, center, inclusive, step
             )
             start = start.astype(np.int64)
             end = end.astype(np.int64)
@@ -391,7 +391,7 @@ def get_window_bounds(
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
-        closed: str | None = None,
+        inclusive: str | None = None,
         step: int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]: