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Posted to reviews@spark.apache.org by "LuciferYang (via GitHub)" <gi...@apache.org> on 2023/08/22 02:52:49 UTC
[GitHub] [spark] LuciferYang opened a new pull request, #42596: [SPARK-44831][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
LuciferYang opened a new pull request, #42596:
URL: https://github.com/apache/spark/pull/42596
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1302445423
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
Review Comment:
change to `spark.createDataFrame([("apple"), ("orange"), ("apple"), ("banana")], ['fruit'])`, Is that so?
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300926056
##########
python/pyspark/sql/functions.py:
##########
@@ -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:
done
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300942444
##########
python/pyspark/sql/functions.py:
##########
@@ -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:
The issue with `from pyspark.sql import functions as F` is that `F` itself actually isn't very Pythonic naming (but we use this for the legacy/historical reason). If we can go back, I would have named it something like `from pyspark.sql import functions as sf`.
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1302445423
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
Review Comment:
like `spark.createDataFrame([("apple"), ("orange"), ("apple"), ("banana")], ['fruit'])`?
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300949439
##########
python/pyspark/sql/functions.py:
##########
@@ -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 import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
- >>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+ >>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
Review Comment:
I’m not professional in Python, but I personally prefer to add `from … import …`.：）
As for which one is better between `from pyspark.sql.functions import approx_count_distinct` and `from pyspark.sql import functions as sfunc`, I can make further changes
##########
python/pyspark/sql/functions.py:
##########
@@ -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 import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
- >>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+ >>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
Review Comment:
I’m not professional in Python, but I personally prefer to add `from … import …`.：）
As for which one is better between `from pyspark.sql.functions import approx_count_distinct` and `from pyspark.sql import functions as sfunc`, I can make further changes
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1302455703
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
+ >>> df.agg(approx_count_distinct("fruit").alias('distinct_fruits')).show()
+ +---------------+
+ |distinct_fruits|
+ +---------------+
+ | 3|
+ +---------------+
+
+ Example 3: Counting distinct values in a DataFrame with multiple columns
+
+ >>> from pyspark.sql.functions import approx_count_distinct, struct
+ >>> df = spark.createDataFrame([("Alice", 1),
+ ... ("Alice", 2),
+ ... ("Bob", 3),
+ ... ("Bob", 3)], ["name", "value"])
+ >>> df = df.withColumn("combined", struct("name", "value"))
+ >>> df.agg(approx_count_distinct(df["combined"]).alias('distinct_pairs')).show()
+ +--------------+
+ |distinct_pairs|
+ +--------------+
+ | 3|
+ +--------------+
+
+ Example 4: Counting distinct values with a specified relative standard deviation
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.range(100000)
+ >>> df.agg(approx_count_distinct("id", 0.1).alias('distinct_values')).show()
Review Comment:
Change to
```
>>> df.agg(approx_count_distinct("id").alias('with_default_rsd'),
... approx_count_distinct("id", 0.1).alias('with_rsd_0.1')).show()
+----------------+------------+
|with_default_rsd|with_rsd_0.1|
+----------------+------------+
| 95546| 102065|
+----------------+------------+
```
when I am not sure if this is the `compare ` you mentioned.
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1302446464
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
+ >>> df.agg(approx_count_distinct("fruit").alias('distinct_fruits')).show()
+ +---------------+
+ |distinct_fruits|
+ +---------------+
+ | 3|
+ +---------------+
+
+ Example 3: Counting distinct values in a DataFrame with multiple columns
+
+ >>> from pyspark.sql.functions import approx_count_distinct, struct
+ >>> df = spark.createDataFrame([("Alice", 1),
+ ... ("Alice", 2),
+ ... ("Bob", 3),
+ ... ("Bob", 3)], ["name", "value"])
+ >>> df = df.withColumn("combined", struct("name", "value"))
+ >>> df.agg(approx_count_distinct(df["combined"]).alias('distinct_pairs')).show()
Review Comment:
done
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1302445841
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
Review Comment:
done
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[GitHub] [spark] HyukjinKwon commented on pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on PR #42596:
URL: https://github.com/apache/spark/pull/42596#issuecomment-1687346854
cc @allisonwang-db @zhengruifeng FYI
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[GitHub] [spark] zhengruifeng commented on pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #42596:
URL: https://github.com/apache/spark/pull/42596#issuecomment-1690836699
thanks, merged to master
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300976769
##########
python/pyspark/sql/functions.py:
##########
@@ -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:
done
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[GitHub] [spark] allisonwang-db commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1301927648
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
Review Comment:
Nit: instead of using `toDF`, can we specify the schema of the dataframe when creating it?
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
Review Comment:
Can we use lower case for `INT` here?
##########
python/pyspark/sql/functions.py:
##########
@@ -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.
Review Comment:
I think this is a bit redundant. Can we combine it with the previous sentence?
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
+ >>> df.agg(approx_count_distinct("fruit").alias('distinct_fruits')).show()
+ +---------------+
+ |distinct_fruits|
+ +---------------+
+ | 3|
+ +---------------+
+
+ Example 3: Counting distinct values in a DataFrame with multiple columns
+
+ >>> from pyspark.sql.functions import approx_count_distinct, struct
+ >>> df = spark.createDataFrame([("Alice", 1),
+ ... ("Alice", 2),
+ ... ("Bob", 3),
+ ... ("Bob", 3)], ["name", "value"])
+ >>> df = df.withColumn("combined", struct("name", "value"))
+ >>> df.agg(approx_count_distinct(df["combined"]).alias('distinct_pairs')).show()
Review Comment:
can we make this example consistent with the others by using `approx_count_distinct("combined")`?
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
+ >>> df.agg(approx_count_distinct("fruit").alias('distinct_fruits')).show()
+ +---------------+
+ |distinct_fruits|
+ +---------------+
+ | 3|
+ +---------------+
+
+ Example 3: Counting distinct values in a DataFrame with multiple columns
+
+ >>> from pyspark.sql.functions import approx_count_distinct, struct
+ >>> df = spark.createDataFrame([("Alice", 1),
+ ... ("Alice", 2),
+ ... ("Bob", 3),
+ ... ("Bob", 3)], ["name", "value"])
+ >>> df = df.withColumn("combined", struct("name", "value"))
+ >>> df.agg(approx_count_distinct(df["combined"]).alias('distinct_pairs')).show()
+ +--------------+
+ |distinct_pairs|
+ +--------------+
+ | 3|
+ +--------------+
+
+ Example 4: Counting distinct values with a specified relative standard deviation
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.range(100000)
+ >>> df.agg(approx_count_distinct("id", 0.1).alias('distinct_values')).show()
Review Comment:
can we compare the results of approx_count_distinct when 1) using the default rsd and 2) using 0.1 in this example?
##########
python/pyspark/sql/functions.py:
##########
@@ -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:
yea I would also prefer `from pyspark.sql.functions import approx_count_distinct`
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300946158
##########
python/pyspark/sql/functions.py:
##########
@@ -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 import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
- >>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+ >>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
Review Comment:
Can we set the small set of rules and policies in the unbrella JIRA at SPARK-44728 before we go further? Would be great if we can have moment, take a look at other docs, and set the template or guides down to the JIRA.
cc @allisonwang-db and @zhengruifeng .
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300855101
##########
python/pyspark/sql/functions.py:
##########
@@ -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.
+
+ References
+ ----------
+ .. seealso:: :func:`count_distinct`
Review Comment:
it seems the pyspark docstrings normally use the `See Also` section
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1302445423
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
Review Comment:
change to `df = spark.createDataFrame([("apple",), ("orange",), ("apple",), ("banana",)], ['fruit'])`, Is that so?
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300948300
##########
python/pyspark/sql/functions.py:
##########
@@ -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 import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
- >>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+ >>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
Review Comment:
At least, [these three reasons](https://github.com/apache/spark/pull/42596#discussion_r1300942444) were my initial intention when I fixed all the docs. If we want sth different from it, we should probably set it down before fixing all the docs.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300967798
##########
python/pyspark/sql/functions.py:
##########
@@ -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:
@HyukjinKwon sound good. thanks for the explaination.
@LuciferYang sorry, let go back to `from pyspark.sql.functions import approx_count_distinct`
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300942906
##########
python/pyspark/sql/functions.py:
##########
@@ -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 import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
- >>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+ >>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
Review Comment:
You're right, but adding `from pyspark.sql import functions as F` makes it easier for users to copy and run test cases.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300942444
##########
python/pyspark/sql/functions.py:
##########
@@ -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:
The issue with `from pyspark.sql import functions as F` is that `F` itself actually isn't very Pythonic naming (but we use this for the legacy/historical reason). If we can go back, I would have named it something like `from pyspark.sql import functions as sfunc`.
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1302455703
##########
python/pyspark/sql/functions.py:
##########
@@ -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
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+---------------+
|distinct_values|
+---------------+
| 3|
+---------------+
+
+ Example 2: Counting distinct values in a single column DataFrame representing strings
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.createDataFrame(["apple", "orange", "apple", "banana"], "string").toDF("fruit")
+ >>> df.agg(approx_count_distinct("fruit").alias('distinct_fruits')).show()
+ +---------------+
+ |distinct_fruits|
+ +---------------+
+ | 3|
+ +---------------+
+
+ Example 3: Counting distinct values in a DataFrame with multiple columns
+
+ >>> from pyspark.sql.functions import approx_count_distinct, struct
+ >>> df = spark.createDataFrame([("Alice", 1),
+ ... ("Alice", 2),
+ ... ("Bob", 3),
+ ... ("Bob", 3)], ["name", "value"])
+ >>> df = df.withColumn("combined", struct("name", "value"))
+ >>> df.agg(approx_count_distinct(df["combined"]).alias('distinct_pairs')).show()
+ +--------------+
+ |distinct_pairs|
+ +--------------+
+ | 3|
+ +--------------+
+
+ Example 4: Counting distinct values with a specified relative standard deviation
+
+ >>> from pyspark.sql.functions import approx_count_distinct
+ >>> df = spark.range(100000)
+ >>> df.agg(approx_count_distinct("id", 0.1).alias('distinct_values')).show()
Review Comment:
Change to xx
```
>>> df.agg(approx_count_distinct("id").alias('with_default_rsd'),
... approx_count_distinct("id", 0.1).alias('with_rsd_0.1')).show()
+----------------+------------+
|with_default_rsd|with_rsd_0.1|
+----------------+------------+
| 95546| 102065|
+----------------+------------+
```
when I am not sure if this is the `compare ` you mentioned.
##########
python/pyspark/sql/functions.py:
##########
@@ -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.
Review Comment:
done
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[GitHub] [spark] zhengruifeng closed pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng closed pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
URL: https://github.com/apache/spark/pull/42596
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300942736
##########
python/pyspark/sql/functions.py:
##########
@@ -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 .. the last reason is that:
> We can just assume that users are reading pyspark.sql.functions doc, and assume that they are already imported.
##########
python/pyspark/sql/functions.py:
##########
@@ -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 .. the last reason is that:
> We can just assume that users are reading pyspark.sql.functions doc, and assume that they are already imported.
This is what pandas does too.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300941322
##########
python/pyspark/sql/functions.py:
##########
@@ -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 import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
- >>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+ >>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
Review Comment:
Actually I think we don't need to add `from pyspark.sql import functions as F` here. We can just assume that users are reading `pyspark.sql.functions` doc, and assume that they are already imported.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300941773
##########
python/pyspark/sql/functions.py:
##########
@@ -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 import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
- >>> df.agg(approx_count_distinct("value").alias('distinct_values')).show()
+ >>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
Review Comment:
cc @allisonwang-db and @zhengruifeng .
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300942561
##########
python/pyspark/sql/functions.py:
##########
@@ -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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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42596: [SPARK-44903][PYTHON][DOCS] Refine docstring of `approx_count_distinct`
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42596:
URL: https://github.com/apache/spark/pull/42596#discussion_r1300878510
##########
python/pyspark/sql/functions.py:
##########
@@ -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:
what about using this style of import in the new examples?
```
>>> from pyspark.sql import functions as F
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(F.approx_count_distinct("value").alias('distinct_values')).show()
```
I want to make all examples and docstrings follow this way.
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