HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300942561
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python/pyspark/sql/functions.py:
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@@ -3669,38 +3669,83 @@ def approxCountDistinct(col: "ColumnOrName", rsd:
Optional[float] = None) -> Col
@try_remote_functions
def approx_count_distinct(col: "ColumnOrName", rsd: Optional[float] = None) ->
Column:
- """Aggregate function: returns a new :class:`~pyspark.sql.Column` for
approximate distinct count
- of column `col`.
+ """
+ Applies an aggregate function to return an approximate distinct count of
the specified column.
- .. versionadded:: 2.1.0
+ This function returns a new :class:`~pyspark.sql.Column` that estimates
the number of distinct
+ elements in a column or a group of columns.
- .. versionchanged:: 3.4.0
- Supports Spark Connect.
+ .. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
+ The label of the column to count distinct values in.
rsd : float, optional
- maximum relative standard deviation allowed (default = 0.05).
- For rsd < 0.01, it is more efficient to use :func:`count_distinct`
+ The maximum allowed relative standard deviation (default = 0.05).
+ If rsd < 0.01, it would be more efficient to use
:func:`count_distinct`.
Returns
-------
:class:`~pyspark.sql.Column`
- the column of computed results.
+ A new Column object representing the approximate unique count.
+
+ See Also
+ ----------
+ :meth:`pyspark.sql.functions.count_distinct`
Examples
--------
+ Example 1: Counting distinct values in a single column DataFrame
representing integers
+
+ >>> from pyspark.sql.functions import approx_count_distinct
Review Comment:
and second reason is that `from pyspark.sql.functions import
approx_count_distinct` is perfectly fine.
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