SNOW-1859608: Add missing docs for DataFrame.map (#2766)

<!---
Please answer these questions before creating your pull request. Thanks!
--->

1. Which Jira issue is this PR addressing? Make sure that there is an
accompanying issue to your PR.

   <!---
   In this section, please add a Snowflake Jira issue number.

Note that if a corresponding GitHub issue exists, you should still
include
   the Snowflake Jira issue number. For example, for GitHub issue
#1400, you should
   add "SNOW-1335071" here.
    --->

   Fixes SNOW-1859608

2. Fill out the following pre-review checklist:

- [ ] I am adding a new automated test(s) to verify correctness of my
new code
- [ ] If this test skips Local Testing mode, I'm requesting review from
@snowflakedb/local-testing
   - [ ] I am adding new logging messages
   - [ ] I am adding a new telemetry message
   - [ ] I am adding new credentials
   - [ ] I am adding a new dependency
- [ ] If this is a new feature/behavior, I'm adding the Local Testing
parity changes.
- [ ] I acknowledge that I have ensured my changes to be thread-safe.
Follow the link for more information: [Thread-safe Developer
Guidelines](https://github.com/snowflakedb/snowpark-python/blob/main/CONTRIBUTING.md#thread-safe-development)

3. Please describe how your code solves the related issue.

   Add missing docs for DataFrame.map.

CHANGELOG.md

@@ -26,6 +26,7 @@
 #### Improvements
 - Improve performance of `DataFrame.map`, `Series.apply` and `Series.map` methods by mapping numpy functions to snowpark functions if possible.
 - Updated integration testing for `session.lineage.trace` to exclude deleted objects
+- 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')  # doctest: +SKIP
+             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():