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new 760cd665f6cd [SPARK-46557][PYTHON][DOCS] Refine docstring for
DataFrame.schema/explain/printSchema
760cd665f6cd is described below
commit 760cd665f6cdd41e352477b1e2d235dd3e8de335
Author: Hyukjin Kwon <[email protected]>
AuthorDate: Tue Jan 2 16:51:12 2024 +0900
[SPARK-46557][PYTHON][DOCS] Refine docstring for
DataFrame.schema/explain/printSchema
### What changes were proposed in this pull request?
This PR proposes to improve the docstring of `DataFrame.schema`,
`DataFrame.explain` and `DataFrame.printSchema`.
### Why are the changes needed?
For better usability.
### Does this PR introduce _any_ user-facing change?
Yes, it improves user-facing documentation.
### How was this patch tested?
Manually ran the tests via:
```bash
python/run-tests --python-executable=python3 --testnames
'pyspark.sql.dataframe'
```
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #44553 from HyukjinKwon/SPARK-46557.
Authored-by: Hyukjin Kwon <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>
---
python/pyspark/sql/dataframe.py | 53 ++++++++++++++++++++++++++++++++---------
1 file changed, 42 insertions(+), 11 deletions(-)
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index f138b817bd22..7933c62a3503 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -609,14 +609,32 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
Examples
--------
+ Example 1: Retrieve the inferred schema of the current DataFrame.
+
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
+ >>> df.schema
+ StructType([StructField('age', LongType(), True),
+ StructField('name', StringType(), True)])
- Retrieve the schema of the current DataFrame.
+ Example 2: Retrieve the schema of the current DataFrame (DDL-formatted
schema).
+ >>> df = spark.createDataFrame(
+ ... [(14, "Tom"), (23, "Alice"), (16, "Bob")],
+ ... "age INT, name STRING")
>>> df.schema
- StructType([StructField('age', LongType(), True),
+ StructType([StructField('age', IntegerType(), True),
StructField('name', StringType(), True)])
+
+ Example 3: Retrieve the specified schema of the current DataFrame.
+
+ >>> from pyspark.sql.types import StructType, StructField, StringType
+ >>> df = spark.createDataFrame(
+ ... [("a",), ("b",), ("c",)],
+ ... StructType([StructField("value", StringType(), False)]))
+ >>> df.schema
+ StructType([StructField('value', StringType(), False)])
+
"""
if self._schema is None:
try:
@@ -648,6 +666,8 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
Examples
--------
+ Example 1: Printing the schema of a DataFrame with basic columns
+
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
>>> df.printSchema()
@@ -655,18 +675,30 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
|-- age: long (nullable = true)
|-- name: string (nullable = true)
- >>> df = spark.createDataFrame([(1, (2,2))], ["a", "b"])
+ Example 2: Printing the schema with a specified level for nested
columns
+
+ >>> df = spark.createDataFrame([(1, (2, 2))], ["a", "b"])
>>> df.printSchema(1)
root
|-- a: long (nullable = true)
|-- b: struct (nullable = true)
+ Example 3: Printing the schema with deeper nesting level
+
>>> df.printSchema(2)
root
|-- a: long (nullable = true)
|-- b: struct (nullable = true)
| |-- _1: long (nullable = true)
| |-- _2: long (nullable = true)
+
+ Example 4: Printing the schema of a DataFrame with nullable and
non-nullable columns
+
+ >>> df = spark.range(1).selectExpr("id AS nonnullable", "NULL AS
nullable")
+ >>> df.printSchema()
+ root
+ |-- nonnullable: long (nullable = false)
+ |-- nullable: void (nullable = true)
"""
if level:
print(self._jdf.schema().treeString(level))
@@ -704,18 +736,17 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
Examples
--------
+ Example 1: Print out the physical plan only (default).
+
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
-
- Print out the physical plan only (default).
-
>>> df.explain() # doctest: +SKIP
== Physical Plan ==
*(1) Scan ExistingRDD[age...,name...]
- Print out all of the parsed, analyzed, optimized and physical plans.
+ Example 2: Print out all parsed, analyzed, optimized, and physical
plans.
- >>> df.explain(True)
+ >>> df.explain(extended=True)
== Parsed Logical Plan ==
...
== Analyzed Logical Plan ==
@@ -725,7 +756,7 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
== Physical Plan ==
...
- Print out the plans with two sections: a physical plan outline and
node details
+ Example 3: Print out the plans with two sections: a physical plan
outline and node details.
>>> df.explain(mode="formatted") # doctest: +SKIP
== Physical Plan ==
@@ -734,9 +765,9 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
Output [2]: [age..., name...]
...
- Print a logical plan and statistics if they are available.
+ Example 4: Print a logical plan and statistics if they are available.
- >>> df.explain("cost")
+ >>> df.explain(mode="cost")
== Optimized Logical Plan ==
...Statistics...
...
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