DOC: use intersphinx mapping in pandas-compat ext (#15846)

~~If #15704 is merged~~

This PR changes the header in the admonition (pandas compat box) to be hyperlinked to the pandas docs instead of just text. See https://raybellwaves.github.io/compatsphinxext/compat.html which is the docs of a minimal repo where I have been testing

Authors:
  - Ray Bell (https://github.com/raybellwaves)
  - Bradley Dice (https://github.com/bdice)
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: #15846

docs/cudf/source/developer_guide/documentation.md

@@ -164,7 +164,7 @@ The directive should be used inside docstrings like so:
 Docstring body
 
 .. pandas-compat::
-    **$API_NAME**
+    :meth:`pandas.DataFrame.METHOD`
 
     Explanation of differences
 ```

python/cudf/cudf/core/column/lists.py

@@ -646,9 +646,17 @@ def sort_values(
         dtype: list
 
         .. pandas-compat::
-            **ListMethods.sort_values**
+            `pandas.Series.list.sort_values`
 
-            The ``inplace`` and ``kind`` arguments are currently not supported.
+            This method does not exist in pandas but it can be run
+            as:
+
+            >>> import pandas as pd
+            >>> s = pd.Series([[3, 2, 1], [2, 4, 3]])
+            >>> print(s.apply(sorted))
+            0    [1, 2, 3]
+            1    [2, 3, 4]
+            dtype: object
         """
         if inplace:
             raise NotImplementedError("`inplace` not currently implemented.")

python/cudf/cudf/core/column/string.py

@@ -612,7 +612,7 @@ def extract(
         dtype: object
 
         .. pandas-compat::
-            **StringMethods.extract**
+            :meth:`pandas.Series.str.extract`
 
             The `flags` parameter currently only supports re.DOTALL and
             re.MULTILINE.
@@ -738,7 +738,7 @@ def contains(
         dtype: bool
 
         .. pandas-compat::
-            **StringMethods.contains**
+            :meth:`pandas.Series.str.contains`
 
             The parameters `case` and `na` are not yet supported and will
             raise a NotImplementedError if anything other than the default
@@ -974,7 +974,7 @@ def replace(
         dtype: object
 
         .. pandas-compat::
-            **StringMethods.replace**
+            :meth:`pandas.Series.str.replace`
 
             The parameters `case` and `flags` are not yet supported and will
             raise a `NotImplementedError` if anything other than the default
@@ -2803,7 +2803,7 @@ def partition(self, sep: str = " ", expand: bool = True) -> SeriesOrIndex:
                    )
 
         .. pandas-compat::
-            **StringMethods.partition**
+            :meth:`pandas.Series.str.partition`
 
             The parameter `expand` is not yet supported and will raise a
             `NotImplementedError` if anything other than the default
@@ -3527,7 +3527,7 @@ def count(self, pat: str, flags: int = 0) -> SeriesOrIndex:
         Index([0, 0, 2, 1], dtype='int64')
 
         .. pandas-compat::
-            **StringMethods.count**
+            :meth:`pandas.Series.str.count`
 
             -   `flags` parameter currently only supports re.DOTALL
                 and re.MULTILINE.
@@ -3607,7 +3607,7 @@ def findall(self, pat: str, flags: int = 0) -> SeriesOrIndex:
         dtype: list
 
         .. pandas-compat::
-            **StringMethods.findall**
+            :meth:`pandas.Series.str.findall`
 
             The `flags` parameter currently only supports re.DOTALL and
             re.MULTILINE.
@@ -3811,7 +3811,7 @@ def endswith(self, pat: str) -> SeriesOrIndex:
         dtype: bool
 
         .. pandas-compat::
-            **StringMethods.endswith**
+            :meth:`pandas.Series.str.endswith`
 
             `na` parameter is not yet supported, as cudf uses
             native strings instead of Python objects.
@@ -4264,7 +4264,7 @@ def match(
         dtype: bool
 
         .. pandas-compat::
-            **StringMethods.match**
+            :meth:`pandas.Series.str.match`
 
             Parameters `case` and `na` are currently not supported.
             The `flags` parameter currently only supports re.DOTALL and

python/cudf/cudf/core/dataframe.py

@@ -2750,7 +2750,7 @@ def reindex(
         Chrome                200          0.02
 
         .. pandas-compat::
-            **DataFrame.reindex**
+            :meth:`pandas.DataFrame.reindex`
 
             Note: One difference from Pandas is that ``NA`` is used for rows
             that do not match, rather than ``NaN``. One side effect of this is
@@ -3350,7 +3350,7 @@ def diff(self, periods=1, axis=0):
         5     2     5    20
 
         .. pandas-compat::
-            **DataFrame.diff**
+            :meth:`pandas.DataFrame.diff`
 
             Diff currently only supports numeric dtype columns.
         """
@@ -3555,7 +3555,7 @@ def rename(
         30  3  6
 
         .. pandas-compat::
-            **DataFrame.rename**
+            :meth:`pandas.DataFrame.rename`
 
             * Not Supporting: level
 
@@ -3670,7 +3670,7 @@ def agg(self, aggs, axis=None):
             ``DataFrame`` is returned.
 
         .. pandas-compat::
-            **DataFrame.agg**
+            :meth:`pandas.DataFrame.agg`
 
             * Not supporting: ``axis``, ``*args``, ``**kwargs``
 
@@ -3843,7 +3843,7 @@ def nlargest(self, n, columns, keep="first"):
         Brunei      434000    12128      BN
 
         .. pandas-compat::
-            **DataFrame.nlargest**
+            :meth:`pandas.DataFrame.nlargest`
 
             - Only a single column is supported in *columns*
         """
@@ -3915,7 +3915,7 @@ def nsmallest(self, n, columns, keep="first"):
         Nauru         337000  182      NR
 
         .. pandas-compat::
-            **DataFrame.nsmallest**
+            :meth:`pandas.DataFrame.nsmallest`
 
             - Only a single column is supported in *columns*
         """
@@ -3997,7 +3997,7 @@ def transpose(self):
         a new (ncol x nrow) dataframe. self is (nrow x ncol)
 
         .. pandas-compat::
-            **DataFrame.transpose, DataFrame.T**
+            :meth:`pandas.DataFrame.transpose`, :attr:`pandas.DataFrame.T`
 
             Not supporting *copy* because default and only behavior is
             copy=True
@@ -4188,7 +4188,7 @@ def merge(
         from both sides.
 
         .. pandas-compat::
-            **DataFrame.merge**
+            :meth:`pandas.DataFrame.merge`
 
             DataFrames merges in cuDF result in non-deterministic row
             ordering.
@@ -4263,7 +4263,7 @@ def join(
         joined : DataFrame
 
         .. pandas-compat::
-            **DataFrame.join**
+            :meth:`pandas.DataFrame.join`
 
             - *other* must be a single DataFrame for now.
             - *on* is not supported yet due to lack of multi-index support.
@@ -4385,7 +4385,7 @@ def query(self, expr, local_dict=None):
         1 2018-10-08
 
         .. pandas-compat::
-            **DataFrame.query**
+            :meth:`pandas.DataFrame.query`
 
             One difference from pandas is that ``query`` currently only
             supports numeric, datetime, timedelta, or bool dtypes.
@@ -5447,10 +5447,11 @@ def from_arrow(cls, table):
         2  3  6
 
         .. pandas-compat::
-            **DataFrame.from_arrow**
+            `pandas.DataFrame.from_arrow`
 
-            -   Does not support automatically setting index column(s) similar
-                to how ``to_pandas`` works for PyArrow Tables.
+            This method does not exist in pandas but it is similar to
+            how :meth:`pyarrow.Table.to_pandas` works for PyArrow Tables i.e.
+            it does not support automatically setting index column(s).
         """
         index_col = None
         col_index_names = None
@@ -5884,7 +5885,7 @@ def quantile(
         0.5  2.5  55.0
 
         .. pandas-compat::
-            **DataFrame.quantile**
+            :meth:`pandas.DataFrame.quantile`
 
             One notable difference from Pandas is when DataFrame is of
             non-numeric types and result is expected to be a Series in case of
@@ -6174,7 +6175,7 @@ def count(self, axis=0, numeric_only=False):
         dtype: int64
 
         .. pandas-compat::
-            **DataFrame.count**
+            :meth:`pandas.DataFrame.count`
 
             Parameters currently not supported are `axis` and `numeric_only`.
         """
@@ -6412,7 +6413,7 @@ def mode(self, axis=0, numeric_only=False, dropna=True):
         1  <NA>    2.0
 
         .. pandas-compat::
-            **DataFrame.mode**
+            :meth:`pandas.DataFrame.transpose`
 
             ``axis`` parameter is currently not supported.
         """
@@ -7594,7 +7595,7 @@ def interleave_columns(self):
         The interleaved columns as a single column
 
         .. pandas-compat::
-            **DataFrame.interleave_columns**
+            `pandas.DataFrame.interleave_columns`
 
             This method does not exist in pandas but it can be run
             as ``pd.Series(np.vstack(df.to_numpy()).reshape((-1,)))``.
@@ -7696,7 +7697,7 @@ def eval(self, expr: str, inplace: bool = False, **kwargs):
         4  5   2   7  3
 
         .. pandas-compat::
-            **DataFrame.eval**
+            :meth:`pandas.DataFrame.eval`
 
             * Additional kwargs are not supported.
             * Bitwise and logical operators are not dtype-dependent.

python/cudf/cudf/core/frame.py

@@ -591,7 +591,7 @@ def where(self, cond, other=None, inplace: bool = False) -> Self | None:
         dtype: int64
 
         .. pandas-compat::
-            **DataFrame.where, Series.where**
+            :meth:`pandas.DataFrame.where`, :meth:`pandas.Series.where`
 
             Note that ``where`` treats missing values as falsy,
             in parallel with pandas treatment of nullable data:
@@ -1641,7 +1641,7 @@ def min(
         1
 
         .. pandas-compat::
-            **DataFrame.min, Series.min**
+            :meth:`pandas.DataFrame.min`, :meth:`pandas.Series.min`
 
             Parameters currently not supported are `level`, `numeric_only`.
         """
@@ -1689,7 +1689,7 @@ def max(
         dtype: int64
 
         .. pandas-compat::
-            **DataFrame.max, Series.max**
+            :meth:`pandas.DataFrame.max`, :meth:`pandas.Series.max`
 
             Parameters currently not supported are `level`, `numeric_only`.
         """
@@ -1742,7 +1742,7 @@ def all(self, axis=0, skipna=True, **kwargs):
         dtype: bool
 
         .. pandas-compat::
-            **DataFrame.all, Series.all**
+            :meth:`pandas.DataFrame.all`, :meth:`pandas.Series.all`
 
             Parameters currently not supported are `axis`, `bool_only`,
             `level`.
@@ -1795,7 +1795,7 @@ def any(self, axis=0, skipna=True, **kwargs):
         dtype: bool
 
         .. pandas-compat::
-            **DataFrame.any, Series.any**
+            :meth:`pandas.DataFrame.any`, :meth:`pandas.Series.any`
 
             Parameters currently not supported are `axis`, `bool_only`,
             `level`.

python/cudf/cudf/core/groupby/groupby.py

@@ -744,7 +744,8 @@ def _reduce(
             Computed {op} of values within each group.
 
         .. pandas-compat::
-            **{cls}.{op}**
+            :meth:`pandas.core.groupby.DataFrameGroupBy.{op}`,
+             :meth:`pandas.core.groupby.SeriesGroupBy.{op}`
 
             The numeric_only, min_count
         """
@@ -1482,7 +1483,8 @@ def mult(df):
           6    2    6   12
 
         .. pandas-compat::
-            **GroupBy.apply**
+            :meth:`pandas.core.groupby.DataFrameGroupBy.apply`,
+             :meth:`pandas.core.groupby.SeriesGroupBy.apply`
 
             cuDF's ``groupby.apply`` is limited compared to pandas.
             In some situations, Pandas returns the grouped keys as part of
@@ -2358,7 +2360,8 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
             Object shifted within each group.
 
         .. pandas-compat::
-            **GroupBy.shift**
+            :meth:`pandas.core.groupby.DataFrameGroupBy.shift`,
+             :meth:`pandas.core.groupby.SeriesGroupBy.shift`
 
             Parameter ``freq`` is unsupported.
         """