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The following commit(s) were added to refs/heads/branch-3.5 by this push:
new a9db96e0d51 [SPARK-44928][PYTHON][DOCS][3.5] Replace the module alias
'sf' instead of 'F' in pyspark.sql import functions
a9db96e0d51 is described below
commit a9db96e0d51a4f746a802c148c70ddb2bfe06ee0
Author: Hyukjin Kwon <gurwls...@apache.org>
AuthorDate: Thu Aug 24 14:57:55 2023 +0800
[SPARK-44928][PYTHON][DOCS][3.5] Replace the module alias 'sf' instead of
'F' in pyspark.sql import functions
### What changes were proposed in this pull request?
cherry-pick https://github.com/apache/spark/pull/42628 for 3.5
### Why are the changes needed?
for better doc
### 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 #42640 from zhengruifeng/replace_F_35.
Authored-by: Hyukjin Kwon <gurwls...@apache.org>
Signed-off-by: Ruifeng Zheng <ruife...@apache.org>
---
docs/quick-start.md | 8 +++---
docs/structured-streaming-programming-guide.md | 4 +--
python/docs/source/development/debugging.rst | 4 +--
.../source/user_guide/pandas_on_spark/options.rst | 10 ++++----
python/pyspark/pandas/namespace.py | 5 ++--
python/pyspark/pandas/utils.py | 7 ++---
python/pyspark/sql/column.py | 8 +++---
python/pyspark/sql/dataframe.py | 10 ++++----
python/pyspark/sql/functions.py | 30 +++++++++++-----------
python/pyspark/sql/group.py | 4 +--
10 files changed, 46 insertions(+), 44 deletions(-)
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 91b23851f72..cab541a0351 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -130,8 +130,8 @@ Dataset actions and transformations can be used for more
complex computations. L
<div data-lang="python" markdown="1">
{% highlight python %}
->>> from pyspark.sql.functions import *
->>> textFile.select(size(split(textFile.value,
"\s+")).name("numWords")).agg(max(col("numWords"))).collect()
+>>> from pyspark.sql import functions as sf
+>>> textFile.select(sf.size(sf.split(textFile.value,
"\s+")).name("numWords")).agg(sf.max(sf.col("numWords"))).collect()
[Row(max(numWords)=15)]
{% endhighlight %}
@@ -140,7 +140,7 @@ This first maps a line to an integer value and aliases it
as "numWords", creatin
One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can
implement MapReduce flows easily:
{% highlight python %}
->>> wordCounts = textFile.select(explode(split(textFile.value,
"\s+")).alias("word")).groupBy("word").count()
+>>> wordCounts = textFile.select(sf.explode(sf.split(textFile.value,
"\s+")).alias("word")).groupBy("word").count()
{% endhighlight %}
Here, we use the `explode` function in `select`, to transform a Dataset of
lines to a Dataset of words, and then combine `groupBy` and `count` to compute
the per-word counts in the file as a DataFrame of 2 columns: "word" and
"count". To collect the word counts in our shell, we can call `collect`:
@@ -313,7 +313,7 @@ named `SimpleApp.scala`:
import org.apache.spark.sql.SparkSession
object SimpleApp {
- def main(args: Array[String]) {
+ def main(args: Array[String]): Unit = {
val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your
system
val spark = SparkSession.builder.appName("Simple
Application").getOrCreate()
val logData = spark.read.textFile(logFile).cache()
diff --git a/docs/structured-streaming-programming-guide.md
b/docs/structured-streaming-programming-guide.md
index dc25adbdfd3..76a22621a0e 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -1215,12 +1215,12 @@ event start time and evaluated gap duration during the
query execution.
<div data-lang="python" markdown="1">
{% highlight python %}
-from pyspark.sql import functions as F
+from pyspark.sql import functions as sf
events = ... # streaming DataFrame of schema { timestamp: Timestamp, userId:
String }
session_window = session_window(events.timestamp, \
- F.when(events.userId == "user1", "5 seconds") \
+ sf.when(events.userId == "user1", "5 seconds") \
.when(events.userId == "user2", "20 seconds").otherwise("5 minutes"))
# Group the data by session window and userId, and compute the count of each
group
diff --git a/python/docs/source/development/debugging.rst
b/python/docs/source/development/debugging.rst
index a188d3f3e78..ef848a6e961 100644
--- a/python/docs/source/development/debugging.rst
+++ b/python/docs/source/development/debugging.rst
@@ -475,10 +475,10 @@ Example:
.. code-block:: python
- >>> import pyspark.sql.functions as F
+ >>> import pyspark.sql.functions as sf
>>> from pyspark.sql.functions import udf
>>> def f(x):
- ... return F.abs(x)
+ ... return sf.abs(x)
...
>>> spark.range(-1, 1).withColumn("abs", udf(f)("id")).collect()
22/04/12 14:52:31 ERROR Executor: Exception in task 7.0 in stage 37.0 (TID
232)
diff --git a/python/docs/source/user_guide/pandas_on_spark/options.rst
b/python/docs/source/user_guide/pandas_on_spark/options.rst
index 3f99e059431..92b572b9d80 100644
--- a/python/docs/source/user_guide/pandas_on_spark/options.rst
+++ b/python/docs/source/user_guide/pandas_on_spark/options.rst
@@ -175,11 +175,11 @@ This is conceptually equivalent to the PySpark example as
below:
.. code-block:: python
- >>> from pyspark.sql import functions as F, Window
+ >>> from pyspark.sql import functions as sf, Window
>>> import pyspark.pandas as ps
>>> spark_df = ps.range(3).to_spark()
- >>> sequential_index = F.row_number().over(
- ... Window.orderBy(F.monotonically_increasing_id().asc())) - 1
+ >>> sequential_index = sf.row_number().over(
+ ... Window.orderBy(sf.monotonically_increasing_id().asc())) - 1
>>> spark_df.select(sequential_index).rdd.map(lambda r: r[0]).collect()
[0, 1, 2]
@@ -225,10 +225,10 @@ This is conceptually equivalent to the PySpark example as
below:
.. code-block:: python
- >>> from pyspark.sql import functions as F
+ >>> from pyspark.sql import functions as sf
>>> import pyspark.pandas as ps
>>> spark_df = ps.range(3).to_spark()
- >>> spark_df.select(F.monotonically_increasing_id()) \
+ >>> spark_df.select(sf.monotonically_increasing_id()) \
... .rdd.map(lambda r: r[0]).collect()
[25769803776, 60129542144, 94489280512]
diff --git a/python/pyspark/pandas/namespace.py
b/python/pyspark/pandas/namespace.py
index 3563a6d81b4..5689471eb2f 100644
--- a/python/pyspark/pandas/namespace.py
+++ b/python/pyspark/pandas/namespace.py
@@ -3278,7 +3278,7 @@ def merge_asof(
... quotes,
... on="time",
... by="ticker",
- ... tolerance=F.expr("INTERVAL 2 MILLISECONDS") # pd.Timedelta("2ms")
+ ... tolerance=sf.expr("INTERVAL 2 MILLISECONDS") # pd.Timedelta("2ms")
... ).sort_values(["time", "ticker", "price"]).reset_index(drop=True)
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
@@ -3296,7 +3296,7 @@ def merge_asof(
... quotes,
... on="time",
... by="ticker",
- ... tolerance=F.expr("INTERVAL 10 MILLISECONDS"), #
pd.Timedelta("10ms")
+ ... tolerance=sf.expr("INTERVAL 10 MILLISECONDS"), #
pd.Timedelta("10ms")
... allow_exact_matches=False
... ).sort_values(["time", "ticker", "price"]).reset_index(drop=True)
time ticker price quantity bid ask
@@ -3776,6 +3776,7 @@ def _test() -> None:
globs = pyspark.pandas.namespace.__dict__.copy()
globs["ps"] = pyspark.pandas
+ globs["sf"] = F
spark = (
SparkSession.builder.master("local[4]")
.appName("pyspark.pandas.namespace tests")
diff --git a/python/pyspark/pandas/utils.py b/python/pyspark/pandas/utils.py
index 55b9a57ef61..ebeb1d69d1b 100644
--- a/python/pyspark/pandas/utils.py
+++ b/python/pyspark/pandas/utils.py
@@ -928,11 +928,11 @@ def spark_column_equals(left: Column, right: Column) ->
bool:
"""
Check both `left` and `right` have the same expressions.
- >>> spark_column_equals(F.lit(0), F.lit(0))
+ >>> spark_column_equals(sf.lit(0), sf.lit(0))
True
- >>> spark_column_equals(F.lit(0) + 1, F.lit(0) + 1)
+ >>> spark_column_equals(sf.lit(0) + 1, sf.lit(0) + 1)
True
- >>> spark_column_equals(F.lit(0) + 1, F.lit(0) + 2)
+ >>> spark_column_equals(sf.lit(0) + 1, sf.lit(0) + 2)
False
>>> sdf1 = ps.DataFrame({"x": ['a', 'b', 'c']}).to_spark()
>>> spark_column_equals(sdf1["x"] + 1, sdf1["x"] + 1)
@@ -1045,6 +1045,7 @@ def _test() -> None:
globs = pyspark.pandas.utils.__dict__.copy()
globs["ps"] = pyspark.pandas
+ globs["sf"] = F
spark = (
SparkSession.builder.master("local[4]").appName("pyspark.pandas.utils
tests").getOrCreate()
)
diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 087cfaaa20b..a559cf2bab9 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -1281,10 +1281,10 @@ class Column:
Examples
--------
- >>> from pyspark.sql import functions as F
+ >>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(2, "Alice"), (5, "Bob")], ["age", "name"])
- >>> df.select(df.name, F.when(df.age > 4, 1).when(df.age < 3,
-1).otherwise(0)).show()
+ >>> df.select(df.name, sf.when(df.age > 4, 1).when(df.age < 3,
-1).otherwise(0)).show()
+-----+------------------------------------------------------------+
| name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0 END|
+-----+------------------------------------------------------------+
@@ -1327,10 +1327,10 @@ class Column:
Examples
--------
- >>> from pyspark.sql import functions as F
+ >>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(2, "Alice"), (5, "Bob")], ["age", "name"])
- >>> df.select(df.name, F.when(df.age > 3, 1).otherwise(0)).show()
+ >>> df.select(df.name, sf.when(df.age > 3, 1).otherwise(0)).show()
+-----+-------------------------------------+
| name|CASE WHEN (age > 3) THEN 1 ELSE 0 END|
+-----+-------------------------------------+
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 35f2c70f8c9..30ed73d3c47 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -2543,14 +2543,14 @@ class DataFrame(PandasMapOpsMixin,
PandasConversionMixin):
Row(a=5, left_val='b', right_val=3),
Row(a=10, left_val='c', right_val=7)]
- >>> from pyspark.sql import functions as F
+ >>> from pyspark.sql import functions as sf
>>> left._joinAsOf(
- ... right, leftAsOfColumn="a", rightAsOfColumn="a",
tolerance=F.lit(1)
+ ... right, leftAsOfColumn="a", rightAsOfColumn="a",
tolerance=sf.lit(1)
... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
[Row(a=1, left_val='a', right_val=1)]
>>> left._joinAsOf(
- ... right, leftAsOfColumn="a", rightAsOfColumn="a", how="left",
tolerance=F.lit(1)
+ ... right, leftAsOfColumn="a", rightAsOfColumn="a", how="left",
tolerance=sf.lit(1)
... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
[Row(a=1, left_val='a', right_val=1),
Row(a=5, left_val='b', right_val=None),
@@ -3679,7 +3679,7 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
Examples
--------
- >>> from pyspark.sql import functions as F
+ >>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")],
schema=["age", "name"])
>>> df.agg({"age": "max"}).show()
+--------+
@@ -3687,7 +3687,7 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
+--------+
| 5|
+--------+
- >>> df.agg(F.min(df.age)).show()
+ >>> df.agg(sf.min(df.age)).show()
+--------+
|min(age)|
+--------+
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index c5c84518eb9..398ad15676c 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -4311,8 +4311,8 @@ def monotonically_increasing_id() -> Column:
Examples
--------
- >>> from pyspark.sql import functions as F
- >>> spark.range(0, 10, 1, 2).select(F.monotonically_increasing_id()).show()
+ >>> from pyspark.sql import functions as sf
+ >>> spark.range(0, 10, 1,
2).select(sf.monotonically_increasing_id()).show()
+-----------------------------+
|monotonically_increasing_id()|
+-----------------------------+
@@ -4611,8 +4611,8 @@ def rand(seed: Optional[int] = None) -> Column:
Examples
--------
- >>> from pyspark.sql import functions as F
- >>> spark.range(0, 2, 1, 1).withColumn('rand', F.rand(seed=42) * 3).show()
+ >>> from pyspark.sql import functions as sf
+ >>> spark.range(0, 2, 1, 1).withColumn('rand', sf.rand(seed=42) * 3).show()
+---+------------------+
| id| rand|
+---+------------------+
@@ -4652,8 +4652,8 @@ def randn(seed: Optional[int] = None) -> Column:
Examples
--------
- >>> from pyspark.sql import functions as F
- >>> spark.range(0, 2, 1, 1).withColumn('randn', F.randn(seed=42)).show()
+ >>> from pyspark.sql import functions as sf
+ >>> spark.range(0, 2, 1, 1).withColumn('randn', sf.randn(seed=42)).show()
+---+------------------+
| id| randn|
+---+------------------+
@@ -5154,9 +5154,9 @@ def log(arg1: Union["ColumnOrName", float], arg2:
Optional["ColumnOrName"] = Non
Examples
--------
- >>> from pyspark.sql import functions as F
+ >>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1), (2), (4) AS t(value)")
- >>> df.select(F.log(2.0, df.value).alias('log2_value')).show()
+ >>> df.select(sf.log(2.0, df.value).alias('log2_value')).show()
+----------+
|log2_value|
+----------+
@@ -5167,7 +5167,7 @@ def log(arg1: Union["ColumnOrName", float], arg2:
Optional["ColumnOrName"] = Non
And Natural logarithm
- >>> df.select(F.log(df.value).alias('ln_value')).show()
+ >>> df.select(sf.log(df.value).alias('ln_value')).show()
+------------------+
| ln_value|
+------------------+
@@ -11571,11 +11571,11 @@ def explode(col: "ColumnOrName") -> Column:
Examples
--------
>>> from pyspark.sql import Row
- >>> eDF = spark.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a":
"b"})])
- >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
+ >>> df = spark.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a":
"b"})])
+ >>> df.select(explode(df.intlist).alias("anInt")).collect()
[Row(anInt=1), Row(anInt=2), Row(anInt=3)]
- >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
+ >>> df.select(explode(df.mapfield).alias("key", "value")).show()
+---+-----+
|key|value|
+---+-----+
@@ -11610,11 +11610,11 @@ def posexplode(col: "ColumnOrName") -> Column:
Examples
--------
>>> from pyspark.sql import Row
- >>> eDF = spark.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a":
"b"})])
- >>> eDF.select(posexplode(eDF.intlist)).collect()
+ >>> df = spark.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a":
"b"})])
+ >>> df.select(posexplode(df.intlist)).collect()
[Row(pos=0, col=1), Row(pos=1, col=2), Row(pos=2, col=3)]
- >>> eDF.select(posexplode(eDF.mapfield)).show()
+ >>> df.select(posexplode(df.mapfield)).show()
+---+---+-----+
|pos|key|value|
+---+---+-----+
diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py
index 1b64e7666fd..730f4736909 100644
--- a/python/pyspark/sql/group.py
+++ b/python/pyspark/sql/group.py
@@ -125,7 +125,7 @@ class GroupedData(PandasGroupedOpsMixin):
Examples
--------
- >>> from pyspark.sql import functions as F
+ >>> from pyspark.sql import functions as sf
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> df = spark.createDataFrame(
... [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")],
["age", "name"])
@@ -154,7 +154,7 @@ class GroupedData(PandasGroupedOpsMixin):
Group-by name, and calculate the minimum age.
- >>> df.groupBy(df.name).agg(F.min(df.age)).sort("name").show()
+ >>> df.groupBy(df.name).agg(sf.min(df.age)).sort("name").show()
+-----+--------+
| name|min(age)|
+-----+--------+
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