HyukjinKwon commented on code in PR #43572:
URL: https://github.com/apache/spark/pull/43572#discussion_r1375409233
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python/pyspark/sql/types.py:
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@@ -1216,6 +1216,45 @@ def fieldNames(self) -> List[str]:
"""
return list(self.names)
+ def treeString(self, maxDepth: Optional[int] = None) -> str:
+ """Return tree representation of the schema
+
+ .. versionadded:: 4.0.0
+
+ Parameters
+ ----------
+ maxDepth : int, optional, default None
+ Depth of the schema for nested schemas.
+
+ Examples
+ --------
+ >>> from pyspark.sql.types import *
+ >>> s = StructType(
+ ... [StructField("level1", StructType([StructField("f1", StringType(),
True)]), True)]
+ ... )
+
+ >>> s.treeString()
+ 'root\\n |-- level1: struct (nullable = true)\\n | |-- f1: string
(nullable = true)\\n'
+ >>> print(s.treeString())
+ root
+ |-- level1: struct (nullable = true)
+ | |-- f1: string (nullable = true)
+
+ >>> print(s.treeString(1))
+ root
+ |-- level1: struct (nullable = true)
+ """
+ from pyspark.sql import SparkSession
+
+ # Intentionally uses SparkSession so one implementation can be shared
with/without
+ # Spark Connect.
Review Comment:
connect DataFrame doesn't have `_tree_string` and access to JVM so it would
fail. We should add a test at `test_dataframe` as an example.
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