Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/5238#discussion_r27350987
--- Diff: python/pyspark/sql/context.py ---
@@ -173,31 +173,8 @@ def _inferSchema(self, rdd, samplingRatio=None):
return schema
def inferSchema(self, rdd, samplingRatio=None):
- """Infer and apply a schema to an RDD of L{Row}.
-
- ::note:
- Deprecated in 1.3, use :func:`createDataFrame` instead
-
- When samplingRatio is specified, the schema is inferred by looking
- at the types of each row in the sampled dataset. Otherwise, the
- first 100 rows of the RDD are inspected. Nested collections are
- supported, which can include array, dict, list, Row, tuple,
- namedtuple, or object.
-
- Each row could be L{pyspark.sql.Row} object or namedtuple or
objects.
- Using top level dicts is deprecated, as dict is used to represent
Maps.
-
- If a single column has multiple distinct inferred types, it may
cause
- runtime exceptions.
-
- >>> rdd = sc.parallelize(
- ... [Row(field1=1, field2="row1"),
- ... Row(field1=2, field2="row2"),
- ... Row(field1=3, field2="row3")])
- >>> df = sqlCtx.inferSchema(rdd)
- >>> df.collect()[0]
- Row(field1=1, field2=u'row1')
- """
+ """DEPRECATED: use :func:`createDataFrame` instead"""
+ warnings.warn("Use createDataFrame instead of inferSchema.",
DeprecationWarning)
--- End diff --
I updated it.
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