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Posted to reviews@spark.apache.org by "HyukjinKwon (via GitHub)" <gi...@apache.org> on 2023/11/06 20:28:25 UTC
[PR] [SPARK-45260][PYTHON][DOCS] Refine docstring of `count_distinct` [spark]
HyukjinKwon opened a new pull request, #43686:
URL: https://github.com/apache/spark/pull/43686
### What changes were proposed in this pull request?
This PR proposes to improve the docstring of `count_distinct`.
### Why are the changes needed?
For end users, and better usability of PySpark.
### Does this PR introduce _any_ user-facing change?
Yes, it fixes the user facing documentation.
### How was this patch tested?
Manually tested.
### Was this patch authored or co-authored using generative AI tooling?
No.
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Re: [PR] [SPARK-45260][PYTHON][DOCS] Refine docstring of `count_distinct` [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon closed pull request #43686: [SPARK-45260][PYTHON][DOCS] Refine docstring of `count_distinct`
URL: https://github.com/apache/spark/pull/43686
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Re: [PR] [SPARK-45260][PYTHON][DOCS] Refine docstring of `count_distinct` [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on PR #43686:
URL: https://github.com/apache/spark/pull/43686#issuecomment-1799343621
Merged to master.
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Re: [PR] [SPARK-45260][PYTHON][DOCS] Refine docstring of `count_distinct` [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43686:
URL: https://github.com/apache/spark/pull/43686#discussion_r1384260727
##########
python/pyspark/sql/functions.py:
##########
@@ -4626,26 +4626,38 @@ def count_distinct(col: "ColumnOrName", *cols: "ColumnOrName") -> Column:
Examples
--------
- >>> from pyspark.sql import types
- >>> df1 = spark.createDataFrame([1, 1, 3], types.IntegerType())
- >>> df2 = spark.createDataFrame([1, 2], types.IntegerType())
- >>> df1.join(df2).show()
- +-----+-----+
- |value|value|
- +-----+-----+
- | 1| 1|
- | 1| 2|
- | 1| 1|
- | 1| 2|
- | 3| 1|
- | 3| 2|
- +-----+-----+
- >>> df1.join(df2).select(count_distinct(df1.value, df2.value)).show()
- +----------------------------+
- |count(DISTINCT value, value)|
- +----------------------------+
- | 4|
- +----------------------------+
+ Example 1: Counting distinct values of a single column
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([(1,), (1,), (3,)], ["value"])
+ >>> df.select(sf.count_distinct(df.value)).show()
+ +---------------------+
+ |count(DISTINCT value)|
+ +---------------------+
+ | 2|
+ +---------------------+
+
+ Example 2: Counting distinct values of multiple columns
+
+ >>> from pyspark.sql import functions as sf
+ >>> df1 = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
+ >>> df1.select(sf.count_distinct(df1.value1, df1.value2)).show()
Review Comment:
```suggestion
>>> df = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
>>> df.select(sf.count_distinct(df.value1, df.value2)).show()
```
##########
python/pyspark/sql/functions.py:
##########
@@ -4626,26 +4626,38 @@ def count_distinct(col: "ColumnOrName", *cols: "ColumnOrName") -> Column:
Examples
--------
- >>> from pyspark.sql import types
- >>> df1 = spark.createDataFrame([1, 1, 3], types.IntegerType())
- >>> df2 = spark.createDataFrame([1, 2], types.IntegerType())
- >>> df1.join(df2).show()
- +-----+-----+
- |value|value|
- +-----+-----+
- | 1| 1|
- | 1| 2|
- | 1| 1|
- | 1| 2|
- | 3| 1|
- | 3| 2|
- +-----+-----+
- >>> df1.join(df2).select(count_distinct(df1.value, df2.value)).show()
- +----------------------------+
- |count(DISTINCT value, value)|
- +----------------------------+
- | 4|
- +----------------------------+
+ Example 1: Counting distinct values of a single column
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([(1,), (1,), (3,)], ["value"])
+ >>> df.select(sf.count_distinct(df.value)).show()
+ +---------------------+
+ |count(DISTINCT value)|
+ +---------------------+
+ | 2|
+ +---------------------+
+
+ Example 2: Counting distinct values of multiple columns
+
+ >>> from pyspark.sql import functions as sf
+ >>> df1 = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
+ >>> df1.select(sf.count_distinct(df1.value1, df1.value2)).show()
+ +------------------------------+
+ |count(DISTINCT value1, value2)|
+ +------------------------------+
+ | 2|
+ +------------------------------+
+
+ Example 3: Counting distinct values with column names as strings
+
+ >>> from pyspark.sql import functions as sf
+ >>> df3 = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
+ >>> df3.select(sf.count_distinct("value1", "value2")).show()
Review Comment:
```suggestion
>>> df = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
>>> df.select(sf.count_distinct("value1", "value2")).show()
```
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