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Posted to reviews@spark.apache.org by "michaelzhan-db (via GitHub)" <gi...@apache.org> on 2023/08/23 22:40:18 UTC
[GitHub] [spark] michaelzhan-db opened a new pull request, #42637: Add examples to approxQuantile docstring
michaelzhan-db opened a new pull request, #42637:
URL: https://github.com/apache/spark/pull/42637
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[GitHub] [spark] zhengruifeng commented on pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #42637:
URL: https://github.com/apache/spark/pull/42637#issuecomment-1698410976
the ticket should be SPARK-44949
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42637:
URL: https://github.com/apache/spark/pull/42637#discussion_r1308109375
##########
python/pyspark/sql/dataframe.py:
##########
@@ -4850,6 +4850,40 @@ def approxQuantile(
-----
Null values will be ignored in numerical columns before calculation.
For columns only containing null values, an empty list is returned.
+
+ Examples
+ --------
+ Example 1: Calculating quantiles for a single column
+
+ >>> data = [(1,), (2,), (3,), (4,), (5,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 5.0]
+
+ Example 2: Calculating quantiles for multiple columns
+
+ >>> data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
+ >>> df = spark.createDataFrame(data, ["col1", "col2"])
+ >>> quantiles = df.approxQuantile(["col1", "col2"], [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [[1.0, 10.0], [3.0, 30.0], [5.0, 50.0]]
+
+ Example 3: Handling null values
+
+ >>> data = [(1,), (None,), (3,), (4,), (None,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 4.0]
+
+ Example 4: Calculating quantiles with different target precision
+
+ >>> data = [(1,), (2,), (3,), (4,), (5,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.2)
+ >>> quantiles
+ [1.0, 3.0, 5.0] # (with a larger error bound)
Review Comment:
```suggestion
>>> quantiles # (with a large error bound)
[1.0, 2.0, 5.0]
```
the median should be 3.0 but get 2.0 due to the low precision
##########
python/pyspark/sql/dataframe.py:
##########
@@ -4850,6 +4850,40 @@ def approxQuantile(
-----
Null values will be ignored in numerical columns before calculation.
For columns only containing null values, an empty list is returned.
+
+ Examples
+ --------
+ Example 1: Calculating quantiles for a single column
+
+ >>> data = [(1,), (2,), (3,), (4,), (5,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 5.0]
+
+ Example 2: Calculating quantiles for multiple columns
+
+ >>> data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
+ >>> df = spark.createDataFrame(data, ["col1", "col2"])
+ >>> quantiles = df.approxQuantile(["col1", "col2"], [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [[1.0, 10.0], [3.0, 30.0], [5.0, 50.0]]
Review Comment:
```suggestion
[[1.0, 3.0, 5.0], [10.0, 30.0, 50.0]]
```
##########
python/pyspark/sql/dataframe.py:
##########
@@ -4850,6 +4850,40 @@ def approxQuantile(
-----
Null values will be ignored in numerical columns before calculation.
For columns only containing null values, an empty list is returned.
+
+ Examples
+ --------
+ Example 1: Calculating quantiles for a single column
+
+ >>> data = [(1,), (2,), (3,), (4,), (5,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 5.0]
+
+ Example 2: Calculating quantiles for multiple columns
+
+ >>> data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
+ >>> df = spark.createDataFrame(data, ["col1", "col2"])
+ >>> quantiles = df.approxQuantile(["col1", "col2"], [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [[1.0, 10.0], [3.0, 30.0], [5.0, 50.0]]
+
+ Example 3: Handling null values
+
+ >>> data = [(1,), (None,), (3,), (4,), (None,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 4.0]
+
+ Example 4: Calculating quantiles with different target precision
Review Comment:
```suggestion
Example 4: Calculating quantiles with low precision
```
This example is to illustrate that when the given `relativeError` is high (0.2), the output may have large error.
may need to explain this in the example
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[GitHub] [spark] zhengruifeng commented on pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #42637:
URL: https://github.com/apache/spark/pull/42637#issuecomment-1698410278
oops, @michaelzhan-db let's don't use the umbrella ticket SPARK-42728, you could create a subtask under it next time :)
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[GitHub] [spark] michaelzhan-db commented on a diff in pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
Posted by "michaelzhan-db (via GitHub)" <gi...@apache.org>.
michaelzhan-db commented on code in PR #42637:
URL: https://github.com/apache/spark/pull/42637#discussion_r1309069098
##########
python/pyspark/sql/dataframe.py:
##########
@@ -4850,6 +4850,40 @@ def approxQuantile(
-----
Null values will be ignored in numerical columns before calculation.
For columns only containing null values, an empty list is returned.
+
+ Examples
+ --------
+ Example 1: Calculating quantiles for a single column
+
+ >>> data = [(1,), (2,), (3,), (4,), (5,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 5.0]
+
+ Example 2: Calculating quantiles for multiple columns
+
+ >>> data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
+ >>> df = spark.createDataFrame(data, ["col1", "col2"])
+ >>> quantiles = df.approxQuantile(["col1", "col2"], [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [[1.0, 10.0], [3.0, 30.0], [5.0, 50.0]]
+
+ Example 3: Handling null values
+
+ >>> data = [(1,), (None,), (3,), (4,), (None,)]
+ >>> df = spark.createDataFrame(data, ["values"])
+ >>> quantiles = df.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)
+ >>> quantiles
+ [1.0, 3.0, 4.0]
+
+ Example 4: Calculating quantiles with different target precision
Review Comment:
Good idea! I'll update the example description
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[GitHub] [spark] zhengruifeng commented on pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #42637:
URL: https://github.com/apache/spark/pull/42637#issuecomment-1698407872
thanks, merged to master
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[GitHub] [spark] zhengruifeng commented on pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #42637:
URL: https://github.com/apache/spark/pull/42637#issuecomment-1696650461
To test this docstring, you can run
`python/run-tests --python-executables python3 --testnames 'pyspark.sql.dataframe'`
in your local env
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[GitHub] [spark] zhengruifeng closed pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng closed pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
URL: https://github.com/apache/spark/pull/42637
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