def _createFromRDD(self, rdd, schema, samplingRatio):
        """
        Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
        """
        if schema is None or isinstance(schema, (list, tuple)):
            struct = self._inferSchema(rdd, samplingRatio)
            converter = _create_converter(struct)
            rdd = rdd.map(converter)
            if isinstance(schema, (list, tuple)):
                for i, name in enumerate(schema):
                    struct.fields[i].name = name
                    struct.names[i] = name
            schema = struct

        elif isinstance(schema, StructType):
            # take the first few rows to verify schema
            rows = rdd.take(10)
            for row in rows:
                _verify_type(row, schema)

        else:
            raise TypeError("schema should be StructType or list or None, but got: %s" % schema)

        # convert python objects to sql data
        rdd = rdd.map(schema.toInternal)
        return rdd, schema

    def _createFromLocal(self, data, schema):
        """
        Create an RDD for DataFrame from a list or pandas.DataFrame, returns
        the RDD and schema.
        """
        # make sure data could consumed multiple times
        if not isinstance(data, list):
            data = list(data)

        if schema is None or isinstance(schema, (list, tuple)):
            struct = self._inferSchemaFromList(data, names=schema)
            converter = _create_converter(struct)
            data = map(converter, data)
            if isinstance(schema, (list, tuple)):
                for i, name in enumerate(schema):
                    struct.fields[i].name = name
                    struct.names[i] = name
            schema = struct

        elif not isinstance(schema, StructType):
            raise TypeError("schema should be StructType or list or None, but got: %s" % schema)

        # convert python objects to sql data
        data = [schema.toInternal(row) for row in data]
        return self._sc.parallelize(data), 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')
        """

        if isinstance(rdd, DataFrame):
            raise TypeError("Cannot apply schema to DataFrame")

        schema = self._inferSchema(rdd, samplingRatio)
        converter = _create_converter(schema)
        rdd = rdd.map(converter)
        return self.applySchema(rdd, schema)

    def createDataFrame(self, data, schema=None, samplingRatio=None):
        """
        Creates a :class:`DataFrame` from an :class:`RDD` of :class:`tuple`/:class:`list`,
        list or :class:`pandas.DataFrame`.

        When ``schema`` is a list of column names, the type of each column
        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`.

        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 :class:`Row`/:class:`tuple`/:class:`list`/:class:`dict`,
            :class:`list`, or :class:`pandas.DataFrame`.
        :param schema: a :class:`StructType` or list of column names. default None.
        :param samplingRatio: the sample ratio of rows used for inferring

        >>> l = [('Alice', 1)]
        >>> sqlContext.createDataFrame(l).collect()
        [Row(_1=u'Alice', _2=1)]
        >>> sqlContext.createDataFrame(l, ['name', 'age']).collect()
        [Row(name=u'Alice', age=1)]

        >>> d = [{'name': 'Alice', 'age': 1}]
        >>> sqlContext.createDataFrame(d).collect()
        [Row(age=1, name=u'Alice')]

        >>> rdd = sc.parallelize(l)
        >>> sqlContext.createDataFrame(rdd).collect()
        [Row(_1=u'Alice', _2=1)]
        >>> df = sqlContext.createDataFrame(rdd, ['name', 'age'])
        >>> df.collect()
        [Row(name=u'Alice', age=1)]

        >>> from pyspark.sql import Row
        >>> Person = Row('name', 'age')
        >>> person = rdd.map(lambda r: Person(*r))
        >>> df2 = sqlContext.createDataFrame(person)
        >>> df2.collect()
        [Row(name=u'Alice', age=1)]

        >>> from pyspark.sql.types import *
        >>> schema = StructType([
        ...    StructField("name", StringType(), True),
        ...    StructField("age", IntegerType(), True)])
        >>> df3 = sqlContext.createDataFrame(rdd, schema)
        >>> df3.collect()
        [Row(name=u'Alice', age=1)]

        >>> sqlContext.createDataFrame(df.toPandas()).collect()  # doctest: +SKIP
        [Row(name=u'Alice', age=1)]
        >>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]]).collect())  # doctest: +SKIP
        [Row(0=1, 1=2)]
        """
        if isinstance(data, DataFrame):
            raise TypeError("data is already a DataFrame")

        if has_pandas and isinstance(data, pandas.DataFrame):
            if schema is None:
                schema = [str(x) for x in data.columns]
            data = [r.tolist() for r in data.to_records(index=False)]

        if not isinstance(data, RDD):
            try:
                # data could be list, tuple, generator ...
                rdd = self._sc.parallelize(data)
            except Exception:
                raise ValueError("cannot create an RDD from type: %s" % type(data))
        else:
            rdd = data

        if schema is None:
            schema = self._inferSchema(rdd, samplingRatio)
            converter = _create_converter(schema)
            rdd = rdd.map(converter)

        if isinstance(schema, (list, tuple)):
            first = rdd.first()
            if not isinstance(first, (list, tuple)):
                raise ValueError("each row in `rdd` should be list or tuple, "
                                 "but got %r" % type(first))
            row_cls = Row(*schema)
            schema = self._inferSchema(rdd.map(lambda r: row_cls(*r)), samplingRatio)

        # take the first few rows to verify schema
        rows = rdd.take(10)
        # Row() cannot been deserialized by Pyrolite
        if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row':
            rdd = rdd.map(tuple)
            rows = rdd.take(10)

        for row in rows:
            _verify_type(row, schema)

        # convert python objects to sql data
        converter = _python_to_sql_converter(schema)
        rdd = rdd.map(converter)
#.........这里部分代码省略.........

    def inferSchema(self, rdd, samplingRatio=None):
        """Infer and apply a schema to an RDD of L{Row}.

        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')

        >>> NestedRow = Row("f1", "f2")
        >>> nestedRdd1 = sc.parallelize([
        ...     NestedRow(array('i', [1, 2]), {"row1": 1.0}),
        ...     NestedRow(array('i', [2, 3]), {"row2": 2.0})])
        >>> df = sqlCtx.inferSchema(nestedRdd1)
        >>> df.collect()
        [Row(f1=[1, 2], f2={u'row1': 1.0}), ..., f2={u'row2': 2.0})]

        >>> nestedRdd2 = sc.parallelize([
        ...     NestedRow([[1, 2], [2, 3]], [1, 2]),
        ...     NestedRow([[2, 3], [3, 4]], [2, 3])])
        >>> df = sqlCtx.inferSchema(nestedRdd2)
        >>> df.collect()
        [Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), ..., f2=[2, 3])]

        >>> from collections import namedtuple
        >>> CustomRow = namedtuple('CustomRow', 'field1 field2')
        >>> rdd = sc.parallelize(
        ...     [CustomRow(field1=1, field2="row1"),
        ...      CustomRow(field1=2, field2="row2"),
        ...      CustomRow(field1=3, field2="row3")])
        >>> df = sqlCtx.inferSchema(rdd)
        >>> df.collect()[0]
        Row(field1=1, field2=u'row1')
        """

        if isinstance(rdd, DataFrame):
            raise TypeError("Cannot apply schema to DataFrame")

        first = rdd.first()
        if not first:
            raise ValueError("The first row in RDD is empty, "
                             "can not infer schema")
        if type(first) is dict:
            warnings.warn("Using RDD of dict to inferSchema is deprecated,"
                          "please use pyspark.sql.Row instead")

        if samplingRatio is None:
            schema = _infer_schema(first)
            if _has_nulltype(schema):
                for row in rdd.take(100)[1:]:
                    schema = _merge_type(schema, _infer_schema(row))
                    if not _has_nulltype(schema):
                        break
                else:
                    warnings.warn("Some of types cannot be determined by the "
                                  "first 100 rows, please try again with sampling")
        else:
            if samplingRatio > 0.99:
                rdd = rdd.sample(False, float(samplingRatio))
            schema = rdd.map(_infer_schema).reduce(_merge_type)

        converter = _create_converter(schema)
        rdd = rdd.map(converter)
        return self.applySchema(rdd, schema)