mstill3 commented on a change in pull request #26299: Fix typo in example
documentation
URL: https://github.com/apache/spark/pull/26299#discussion_r340739262
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File path: python/pyspark/sql/session.py
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@@ -624,20 +624,20 @@ def createDataFrame(self, data, schema=None,
samplingRatio=None, verifySchema=Tr
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column
names and types)
- from ``data``, which should be an RDD of :class:`Row`,
- or :class:`namedtuple`, or :class:`dict`.
+ from ``data``, which should be an RDD of either :class:`Row`,
+ :class:`namedtuple`, or :class:`dict`.
When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype
string, it must match
the real data, or an exception will be thrown at runtime. If the given
schema is not
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
- :class:`pyspark.sql.types.StructType` as its only field, and the field
name will be "value",
- each record will also be wrapped into a tuple, which can be converted
to row later.
+ :class:`pyspark.sql.types.StructType` as its only field, and the field
name will be "value".
+ Each record will also be wrapped into a tuple, which can be converted
to row later.
If schema inference is needed, ``samplingRatio`` is used to determined
the ratio of
rows used for schema inference. The first row will be used if
``samplingRatio`` is ``None``.
:param data: an RDD of any kind of SQL data representation(e.g. row,
tuple, int, boolean,
Review comment:
okay can do!
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