FIX: Ignore typing and numba warnings in 2.0.x

doc/source/user_guide/window.rst

@@ -89,6 +89,7 @@ For example, a `weighted mean <https://en.wikipedia.org/wiki/Weighted_arithmetic
 be calculated with :meth:`~Rolling.apply` by specifying a separate column of weights.
 
 .. ipython:: python
+   :okwarning:
 
    def weighted_mean(x):
        arr = np.ones((1, x.shape[1]))
@@ -114,6 +115,7 @@ the ``update`` argument to continue the windowing calculation.
    df.ewm(0.5).mean()
 
 .. ipython:: python
+   :okwarning:
 
    online_ewm = df.head(2).ewm(0.5).online()
    online_ewm.mean()

doc/source/whatsnew/v2.0.2.rst

@@ -38,6 +38,7 @@ Bug fixes
 - Bug in :meth:`pd.array` raising for ``NumPy`` array and ``pa.large_string`` or ``pa.large_binary`` (:issue:`52590`)
 - Bug in :meth:`DataFrame.__getitem__` not preserving dtypes for :class:`MultiIndex` partial keys (:issue:`51895`)
 -
+
 .. ---------------------------------------------------------------------------
 .. _whatsnew_202.other:
 

pandas/core/_numba/kernels/mean_.py

@@ -100,7 +100,7 @@ def sliding_mean(
                     neg_ct,
                     compensation_add,
                     num_consecutive_same_value,
-                    prev_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
                 )
         else:
             for j in range(start[i - 1], s):
@@ -125,7 +125,7 @@ def sliding_mean(
                     neg_ct,
                     compensation_add,
                     num_consecutive_same_value,
-                    prev_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
                 )
 
         if nobs >= min_periods and nobs > 0:

pandas/core/_numba/kernels/sum_.py

@@ -92,7 +92,7 @@ def sliding_sum(
                     sum_x,
                     compensation_add,
                     num_consecutive_same_value,
-                    prev_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
                 )
         else:
             for j in range(start[i - 1], s):
@@ -115,7 +115,7 @@ def sliding_sum(
                     sum_x,
                     compensation_add,
                     num_consecutive_same_value,
-                    prev_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
                 )
 
         if nobs == 0 == min_periods:

pandas/core/_numba/kernels/var_.py

@@ -110,7 +110,7 @@ def sliding_var(
                     ssqdm_x,
                     compensation_add,
                     num_consecutive_same_value,
-                    prev_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
                 )
         else:
             for j in range(start[i - 1], s):
@@ -135,7 +135,7 @@ def sliding_var(
                     ssqdm_x,
                     compensation_add,
                     num_consecutive_same_value,
-                    prev_value,
+                    prev_value,  # pyright: ignore[reportGeneralTypeIssues]
                 )
 
         if nobs >= min_periods and nobs > ddof:

pandas/core/groupby/groupby.py

@@ -3074,12 +3074,12 @@ def _nth(
                 # error: No overload variant of "where" matches argument types
                 #        "Any", "NAType", "Any"
                 values = np.where(nulls, NA, grouper)  # type: ignore[call-overload]
-                grouper = Index(values, dtype="Int64")
+                grouper = Index(values, dtype="Int64")  # type: ignore[assignment]
 
         else:
             # create a grouper with the original parameters, but on dropped
             # object
-            grouper, _, _ = get_grouper(
+            grouper, _, _ = get_grouper(  # type: ignore[assignment]
                 dropped,
                 key=self.keys,
                 axis=self.axis,