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new e9e9320b878d [SPARK-45234][PYTHON][DOCS] Refine DocString of `regr_*`
functions
e9e9320b878d is described below
commit e9e9320b878d4ad40e242379484008058fce08d3
Author: Ruifeng Zheng <[email protected]>
AuthorDate: Thu Sep 21 07:53:53 2023 +0800
[SPARK-45234][PYTHON][DOCS] Refine DocString of `regr_*` functions
### What changes were proposed in this pull request?
Refine DocString of `regr_*` functions
### Why are the changes needed?
fix the wildcard import
### Does this PR introduce _any_ user-facing change?
yes
### How was this patch tested?
CI
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #43012 from zhengruifeng/doc_refine_reg.
Lead-authored-by: Ruifeng Zheng <[email protected]>
Co-authored-by: Ruifeng Zheng <[email protected]>
Signed-off-by: Ruifeng Zheng <[email protected]>
---
python/pyspark/sql/functions.py | 144 +++++++++++++++++++++++++++-------------
1 file changed, 99 insertions(+), 45 deletions(-)
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 7b049328ec54..6819bdd73194 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -3053,11 +3053,17 @@ def regr_avgx(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_avgx("y", "x")).first()
- Row(regr_avgx(y, x)=0.999)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_avgx("y", "x"), sf.avg("x")
+ ... ).show()
+ +---------------+------+
+ |regr_avgx(y, x)|avg(x)|
+ +---------------+------+
+ | 0.999| 0.999|
+ +---------------+------+
"""
return _invoke_function_over_columns("regr_avgx", y, x)
@@ -3084,11 +3090,17 @@ def regr_avgy(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_avgy("y", "x")).first()
- Row(regr_avgy(y, x)=9.980732994136464)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_avgy("y", "x"), sf.avg("y")
+ ... ).show()
+ +-----------------+-----------------+
+ | regr_avgy(y, x)| avg(y)|
+ +-----------------+-----------------+
+ |9.980732994136...|9.980732994136...|
+ +-----------------+-----------------+
"""
return _invoke_function_over_columns("regr_avgy", y, x)
@@ -3115,11 +3127,17 @@ def regr_count(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_count("y", "x")).first()
- Row(regr_count(y, x)=1000)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_count("y", "x"), sf.count(sf.lit(0))
+ ... ).show()
+ +----------------+--------+
+ |regr_count(y, x)|count(0)|
+ +----------------+--------+
+ | 1000| 1000|
+ +----------------+--------+
"""
return _invoke_function_over_columns("regr_count", y, x)
@@ -3147,11 +3165,17 @@ def regr_intercept(y: "ColumnOrName", x:
"ColumnOrName") -> Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_intercept("y", "x")).first()
- Row(regr_intercept(y, x)=-0.04961745990969568)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_intercept("y", "x")
+ ... ).show()
+ +--------------------+
+ |regr_intercept(y, x)|
+ +--------------------+
+ |-0.04961745990969568|
+ +--------------------+
"""
return _invoke_function_over_columns("regr_intercept", y, x)
@@ -3178,11 +3202,17 @@ def regr_r2(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_r2("y", "x")).first()
- Row(regr_r2(y, x)=0.9851908293645436)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_r2("y", "x")
+ ... ).show()
+ +------------------+
+ | regr_r2(y, x)|
+ +------------------+
+ |0.9851908293645...|
+ +------------------+
"""
return _invoke_function_over_columns("regr_r2", y, x)
@@ -3209,11 +3239,17 @@ def regr_slope(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_slope("y", "x")).first()
- Row(regr_slope(y, x)=10.040390844891048)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_slope("y", "x")
+ ... ).show()
+ +------------------+
+ | regr_slope(y, x)|
+ +------------------+
+ |10.040390844891...|
+ +------------------+
"""
return _invoke_function_over_columns("regr_slope", y, x)
@@ -3240,11 +3276,17 @@ def regr_sxx(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_sxx("y", "x")).first()
- Row(regr_sxx(y, x)=666.9989999999996)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_sxx("y", "x")
+ ... ).show()
+ +-----------------+
+ | regr_sxx(y, x)|
+ +-----------------+
+ |666.9989999999...|
+ +-----------------+
"""
return _invoke_function_over_columns("regr_sxx", y, x)
@@ -3271,11 +3313,17 @@ def regr_sxy(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_sxy("y", "x")).first()
- Row(regr_sxy(y, x)=6696.93065315148)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_sxy("y", "x")
+ ... ).show()
+ +----------------+
+ | regr_sxy(y, x)|
+ +----------------+
+ |6696.93065315...|
+ +----------------+
"""
return _invoke_function_over_columns("regr_sxy", y, x)
@@ -3302,11 +3350,17 @@ def regr_syy(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> x = (col("id") % 3).alias("x")
- >>> y = (randn(42) + x * 10).alias("y")
- >>> df = spark.range(0, 1000, 1, 1).select(x, y)
- >>> df.select(regr_syy("y", "x")).first()
- Row(regr_syy(y, x)=68250.53503811295)
+ >>> from pyspark.sql import functions as sf
+ >>> x = (sf.col("id") % 3).alias("x")
+ >>> y = (sf.randn(42) + x * 10).alias("y")
+ >>> spark.range(0, 1000, 1, 1).select(x, y).select(
+ ... sf.regr_syy("y", "x")
+ ... ).show()
+ +-----------------+
+ | regr_syy(y, x)|
+ +-----------------+
+ |68250.53503811...|
+ +-----------------+
"""
return _invoke_function_over_columns("regr_syy", y, x)
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