skip doctests

src/snowflake/snowpark/modin/plugin/docstrings/dataframe.py

@@ -713,6 +713,7 @@ def aggregate():
     agg = aggregate
 
     def apply():
+        # TODO SNOW-1739034 unskip UDF tests when pandas 2.2.3 is available in anaconda
         """
         Apply a function along an axis of the DataFrame.
 
@@ -821,15 +822,15 @@ def apply():
 
         Using a reducing function on ``axis=1``:
 
-        >>> df.apply(np.sum, axis=1)
+        >>> df.apply(np.sum, axis=1)  # doctest: +SKIP
         0     2
         1    10
         2    13
         dtype: int64
 
         Returning a list-like object will result in a Series:
 
-        >>> df.apply(lambda x: [1, 2], axis=1)
+        >>> df.apply(lambda x: [1, 2], axis=1)  # doctest: +SKIP
         0    [1, 2]
         1    [1, 2]
         2    [1, 2]
@@ -1022,6 +1023,7 @@ def keys():
         """
 
     def transform():
+        # TODO SNOW-1739034 unskip UDF tests when pandas 2.2.3 is available in anaconda
         """
         Call ``func`` on self producing a Snowpark pandas DataFrame with the same axis shape as self.
 
@@ -1055,15 +1057,15 @@ def transform():
         0     1     3
         1     2     4
         2     3     5
-        >>> df.transform(lambda x: x + 1, axis=1)
+        >>> df.transform(lambda x: x + 1, axis=1)  # doctest: +SKIP
            col1  col2
         0     2     4
         1     3     5
         2     4     6
 
         Apply a numpy ufunc to every value in the DataFrame.
 
-        >>> df.transform(np.square, axis=1)
+        >>> df.transform(np.square, axis=1)  # doctest: +SKIP
            col1  col2
         0     1     9
         1     4    16

src/snowflake/snowpark/modin/plugin/docstrings/groupby.py

@@ -989,6 +989,7 @@ def cummax():
         """
 
     def apply():
+        # TODO SNOW-1739034 unskip UDF tests when pandas 2.2.3 is available in anaconda
         """
         Apply function ``func`` group-wise and combine the results together.
 
@@ -1050,7 +1051,7 @@ def apply():
         its argument and returns a DataFrame. `apply` combines the result for
         each group together into a new DataFrame:
 
-        >>> g1[['B', 'C']].apply(lambda x: x.select_dtypes('number') / x.select_dtypes('number').sum()) # doctest: +NORMALIZE_WHITESPACE
+        >>> g1[['B', 'C']].apply(lambda x: x.select_dtypes('number') / x.select_dtypes('number').sum()) # doctest: +SKIP
                     B    C
         0.0  0.333333  0.4
         1.0  0.666667  0.6
@@ -1059,7 +1060,7 @@ def apply():
         In the above, the groups are not part of the index. We can have them included
         by using ``g2`` where ``group_keys=True``:
 
-        >>> g2[['B', 'C']].apply(lambda x: x.select_dtypes('number') / x.select_dtypes('number').sum()) # doctest: +NORMALIZE_WHITESPACE
+        >>> g2[['B', 'C']].apply(lambda x: x.select_dtypes('number') / x.select_dtypes('number').sum()) # doctest: +SKIP
                     B    C
         A
         a 0.0  0.333333  0.4
@@ -1942,6 +1943,7 @@ def cov():
         pass
 
     def transform():
+        # TODO SNOW-1739034 unskip UDF tests when pandas 2.2.3 is available in anaconda
         """
         Call function producing a same-indexed DataFrame on each group.
 
@@ -2011,7 +2013,7 @@ def transform():
         i     X     9    90    -9
         j     Y    10    10   -10
 
-        >>> df.groupby("col1", dropna=True).transform(lambda df, n: df.head(n), n=2)
+        >>> df.groupby("col1", dropna=True).transform(lambda df, n: df.head(n), n=2)  # doctest: +SKIP
            col2  col3  col4
         a   1.0  40.0  -1.0
         b   NaN   NaN   NaN
@@ -2024,7 +2026,7 @@ def transform():
         i   NaN   NaN   NaN
         j  10.0  10.0 -10.0
 
-        >>> df.groupby("col1", dropna=False).transform("mean")
+        >>> df.groupby("col1", dropna=False).transform("mean")  # doctest: +SKIP
            col2  col3  col4
         a  2.50  25.0 -2.50
         b  5.00  65.0 -5.00