DOC: Fix assorted doc warnings (pandas-dev#47080)

Author: rhshadrach

doc/source/conf.py

@@ -9,13 +9,13 @@
 #
 # All configuration values have a default; values that are commented out
 # serve to show the default.
-
 from datetime import datetime
 import importlib
 import inspect
 import logging
 import os
 import sys
+import warnings
 
 import jinja2
 from numpydoc.docscrape import NumpyDocString
@@ -640,7 +640,10 @@ def linkcode_resolve(domain, info):
     obj = submod
     for part in fullname.split("."):
         try:
-            obj = getattr(obj, part)
+            with warnings.catch_warnings():
+                # Accessing deprecated objects will generate noisy warnings
+                warnings.simplefilter("ignore", FutureWarning)
+                obj = getattr(obj, part)
         except AttributeError:
             return None
 

pandas/core/arrays/sparse/array.py

@@ -1253,7 +1253,7 @@ def astype(self, dtype: AstypeArg | None = None, copy: bool = True):
         IntIndex
         Indices: array([2, 3], dtype=int32)
 
-        >>> arr.astype(np.dtype('int32'))
+        >>> arr.astype(SparseDtype(np.dtype('int32')))
         [0, 0, 1, 2]
         Fill: 0
         IntIndex
@@ -1262,19 +1262,19 @@ def astype(self, dtype: AstypeArg | None = None, copy: bool = True):
         Using a NumPy dtype with a different kind (e.g. float) will coerce
         just ``self.sp_values``.
 
-        >>> arr.astype(np.dtype('float64'))
+        >>> arr.astype(SparseDtype(np.dtype('float64')))
         ... # doctest: +NORMALIZE_WHITESPACE
-        [0.0, 0.0, 1.0, 2.0]
-        Fill: 0.0
+        [nan, nan, 1.0, 2.0]
+        Fill: nan
         IntIndex
         Indices: array([2, 3], dtype=int32)
 
-        Use a SparseDtype if you wish to be change the fill value as well.
+        Using a SparseDtype, you can also change the fill value as well.
 
-        >>> arr.astype(SparseDtype("float64", fill_value=np.nan))
+        >>> arr.astype(SparseDtype("float64", fill_value=0.0))
         ... # doctest: +NORMALIZE_WHITESPACE
-        [nan, nan, 1.0, 2.0]
-        Fill: nan
+        [0.0, 0.0, 1.0, 2.0]
+        Fill: 0.0
         IntIndex
         Indices: array([2, 3], dtype=int32)
         """

pandas/core/generic.py

@@ -8109,9 +8109,9 @@ def resample(
         Freq: 30S, dtype: float64
 
         Upsample the series into 30 second bins and fill the ``NaN``
-        values using the ``pad`` method.
+        values using the ``ffill`` method.
 
-        >>> series.resample('30S').pad()[0:5]
+        >>> series.resample('30S').ffill()[0:5]
         2000-01-01 00:00:00    0
         2000-01-01 00:00:30    0
         2000-01-01 00:01:00    1

pandas/core/groupby/groupby.py

@@ -415,7 +415,7 @@ class providing the base-class of operations.
 ...                           'two', 'two'],
 ...                    'C' : [1, 5, 5, 2, 5, 5],
 ...                    'D' : [2.0, 5., 8., 1., 2., 9.]})
->>> grouped = df.groupby('A')
+>>> grouped = df.groupby('A')[['C', 'D']]
 >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
           C         D
 0 -1.154701 -0.577350
@@ -428,20 +428,20 @@ class providing the base-class of operations.
 Broadcast result of the transformation
 
 >>> grouped.transform(lambda x: x.max() - x.min())
-   C    D
-0  4  6.0
-1  3  8.0
-2  4  6.0
-3  3  8.0
-4  4  6.0
-5  3  8.0
+     C    D
+0  4.0  6.0
+1  3.0  8.0
+2  4.0  6.0
+3  3.0  8.0
+4  4.0  6.0
+5  3.0  8.0
 
 .. versionchanged:: 1.3.0
 
     The resulting dtype will reflect the return value of the passed ``func``,
     for example:
 
->>> grouped[['C', 'D']].transform(lambda x: x.astype(int).max())
+>>> grouped.transform(lambda x: x.astype(int).max())
    C  D
 0  5  8
 1  5  9

pandas/core/indexes/base.py

@@ -5514,10 +5514,10 @@ def equals(self, other: Any) -> bool:
 
         The dtype is *not* compared
 
-        >>> int64_idx = pd.Int64Index([1, 2, 3])
+        >>> int64_idx = pd.Index([1, 2, 3], dtype='int64')
         >>> int64_idx
         Int64Index([1, 2, 3], dtype='int64')
-        >>> uint64_idx = pd.UInt64Index([1, 2, 3])
+        >>> uint64_idx = pd.Index([1, 2, 3], dtype='uint64')
         >>> uint64_idx
         UInt64Index([1, 2, 3], dtype='uint64')
         >>> int64_idx.equals(uint64_idx)