SNOW-1859608: Add missing docs for DataFrame.map

CHANGELOG.md

@@ -24,7 +24,8 @@
 - Added support for mixed case field names in struct type columns.
 
 #### Improvements
-- Improve performance of `DataFrame.map`, `Series.apply` and `Series.map` methods by mapping numpy functions to snowpark functions if possible.
+- Improved performance of `DataFrame.map`, `Series.apply` and `Series.map` methods by mapping numpy functions to snowpark functions if possible.
+- Added documentation for `DataFrame.map`.
 
 ## 1.26.0 (2024-12-05)
 

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

@@ -4818,7 +4818,77 @@ def value_counts():
 
     def map():
         """
-        Apply a function to the `DataFrame` elementwise.
+        Apply a function to a Dataframe elementwise.
+
+        Added in version 2.1.0: DataFrame.applymap was deprecated and renamed to DataFrame.map.
+
+        This method applies a function that accepts and returns a scalar to every element of a DataFrame.
+
+        Parameters
+        ----------
+        func : callable
+            Python function, returns a single value from a single value.
+        na_action : {None, ‘ignore’}, default None
+            If ‘ignore’, propagate NaN values, without passing them to func.
+        **kwargs
+            Additional keyword arguments to pass as keywords arguments to func.
+
+        Returns
+        -------
+        DataFrame
+            Transformed DataFrame.
+
+        See also
+        --------
+        DataFrame.apply
+            Apply a function along input axis of DataFrame.
+        DataFrame.replace
+            Replace values given in to_replace with value.
+        Series.map
+            Apply a function elementwise on a Series.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
+        >>> df
+               0      1
+        0  1.000  2.120
+        1  3.356  4.567
+
+        >>> df.map(lambda x: len(str(x)))
+           0  1
+        0  3  4
+        1  5  5
+
+        Like Series.map, NA values can be ignored:
+
+        >>> df_copy = df.copy()
+        >>> df_copy.iloc[0, 0] = pd.NA
+        >>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
+             0  1
+        0  NaN  4
+        1  5.0  5
+
+        It is also possible to use map with functions that are not lambda functions:
+
+        >>> df.map(round, ndigits=1)
+             0    1
+        0  1.0  2.1
+        1  3.4  4.6
+
+        Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.
+
+        >>> df.map(lambda x: x**2)
+                   0          1
+        0   1.000000   4.494400
+        1  11.262736  20.857489
+
+        But it’s better to avoid map in that case.
+
+        >>> df ** 2
+                   0          1
+        0   1.000000   4.494400
+        1  11.262736  20.857489
         """
 
     def mask():