You are viewing a plain text version of this content. The canonical link for it is here.
Posted to reviews@spark.apache.org by "zhengruifeng (via GitHub)" <gi...@apache.org> on 2023/08/29 01:45:48 UTC
[GitHub] [spark] zhengruifeng commented on a diff in pull request #42637: [SPARK-44728][PYTHON][DOCS]Add examples to approxQuantile docstring
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
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org