StructType¶

class pyspark.sql.types.StructType(fields: Optional[List[pyspark.sql.types.StructField]] = None)¶

Struct type, consisting of a list of StructField.

This is the data type representing a Row.

Iterating a StructType will iterate over its StructFields. A contained StructField can be accessed by its name or position.

Examples

>>> struct1 = StructType([StructField("f1", StringType(), True)])
>>> struct1["f1"]
StructField('f1', StringType(), True)
>>> struct1[0]
StructField('f1', StringType(), True)

>>> struct1 = StructType([StructField("f1", StringType(), True)])
>>> struct2 = StructType([StructField("f1", StringType(), True)])
>>> struct1 == struct2
True
>>> struct1 = StructType([StructField("f1", CharType(10), True)])
>>> struct2 = StructType([StructField("f1", CharType(10), True)])
>>> struct1 == struct2
True
>>> struct1 = StructType([StructField("f1", VarcharType(10), True)])
>>> struct2 = StructType([StructField("f1", VarcharType(10), True)])
>>> struct1 == struct2
True
>>> struct1 = StructType([StructField("f1", StringType(), True)])
>>> struct2 = StructType([StructField("f1", StringType(), True),
...     StructField("f2", IntegerType(), False)])
>>> struct1 == struct2
False

Methods

`add`(field[, data_type, nullable, metadata])	Construct a `StructType` by adding new elements to it, to define the schema.
`fieldNames`()	Returns all field names in a list.
`fromInternal`(obj)	Converts an internal SQL object into a native Python object.
`fromJson`(json)
`json`()
`jsonValue`()
`needConversion`()	Does this type needs conversion between Python object and internal SQL object.
`simpleString`()
`toInternal`(obj)	Converts a Python object into an internal SQL object.
`typeName`()

Methods Documentation

add(field: Union[str, pyspark.sql.types.StructField], data_type: Union[str, pyspark.sql.types.DataType, None] = None, nullable: bool = True, metadata: Optional[Dict[str, Any]] = None) → pyspark.sql.types.StructType ¶

Construct a StructType by adding new elements to it, to define the schema. The method accepts either:

A single parameter which is a StructField object.

Between 2 and 4 parameters as (name, data_type, nullable (optional), metadata(optional). The data_type parameter may be either a String or a DataType object.

Parameters

fieldstr or StructField: Either the name of the field or a StructField object
data_typeDataType, optional: If present, the DataType of the StructField to create
nullablebool, optional: Whether the field to add should be nullable (default True)
metadatadict, optional: Any additional metadata (default None)

Returns

StructType

Examples

>>> struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None)
>>> struct2 = StructType([StructField("f1", StringType(), True), \
...     StructField("f2", StringType(), True, None)])
>>> struct1 == struct2
True
>>> struct1 = StructType().add(StructField("f1", StringType(), True))
>>> struct2 = StructType([StructField("f1", StringType(), True)])
>>> struct1 == struct2
True
>>> struct1 = StructType().add("f1", "string", True)
>>> struct2 = StructType([StructField("f1", StringType(), True)])
>>> struct1 == struct2
True

fieldNames() → List[str]¶

Returns all field names in a list.

Examples

>>> struct = StructType([StructField("f1", StringType(), True)])
>>> struct.fieldNames()
['f1']

fromInternal(obj: Tuple) → pyspark.sql.types.Row¶: Converts an internal SQL object into a native Python object.

classmethod fromJson(json: Dict[str, Any]) → pyspark.sql.types.StructType ¶

json() → str¶

jsonValue() → Dict[str, Any]¶

needConversion() → bool¶

Does this type needs conversion between Python object and internal SQL object.

This is used to avoid the unnecessary conversion for ArrayType/MapType/StructType.

simpleString() → str¶

toInternal(obj: Tuple) → Tuple¶: Converts a Python object into an internal SQL object.

classmethod typeName() → str¶

StructField

TimestampType