Skip to content

type_conversions.rst

Latest commit

 

History

History
254 lines (216 loc) · 9.02 KB

type_conversions.rst

File metadata and controls

254 lines (216 loc) · 9.02 KB

Python to Spark Type Conversions

.. currentmodule:: pyspark.sql.types

When working with PySpark, you will often need to consider the conversions between Python-native objects to their Spark equivalents. For instance, when working with user-defined functions, the function return type will be cast by Spark to an appropriate Spark SQL type. Or, when creating a DataFrame, you may supply numpy or pandas objects as the inputted data. This guide will cover the various conversions between Python and Spark SQL types.

Browsing Type Conversions

Though this document provides a comprehensive list of type conversions, you may find it easier to interactively check the conversion behavior of Spark. To do so, you can test small examples of user-defined functions, and use the spark.createDataFrame interface.

All data types of Spark SQL are located in the package of pyspark.sql.types. You can access them by doing:

from pyspark.sql.types import *

Configuration

There are several configurations that affect the behavior of type conversions. These configurations are listed below:

Configuration Description Default
spark.sql.execution.pythonUDF.arrow.enabled Enable PyArrow in PySpark. See more here. True
spark.sql.pyspark.inferNestedDictAsStruct.enabled When enabled, nested dictionaries are inferred as StructType. Otherwise, they are inferred as MapType. False
spark.sql.timestampType If set to TIMESTAMP_NTZ, the default timestamp type is TimestampNTZType. Otherwise, the default timestamp type is TimestampType. ""
spark.sql.execution.pandas.inferPandasDictAsMap When enabled, Pandas dictionaries are inferred as MapType. Otherwise, they are inferred as StructType. False
spark.sql.execution.pyspark.binaryAsBytes Introduced in Spark 4.1.0. When enabled, BinaryType is mapped consistently to Python bytes; when disabled, matches the PySpark default behavior before 4.1.0. True

All Conversions

Data type Value type in Python API to access or create a data type
ByteType
int

Note

Numbers will be converted to 1-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -128 to 127.

 ByteType()
ShortType
int

Note

Numbers will be converted to 2-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -32768 to 32767.

 ShortType()
IntegerType int IntegerType()
LongType
int

Note

Numbers will be converted to 8-byte signed integer numbers at runtime. Please make sure that numbers are within the range of -9223372036854775808 to 9223372036854775807. Otherwise, please convert data to decimal.Decimal and use DecimalType.

 LongType()
FloatType
float

Note

Numbers will be converted to 4-byte single-precision floating point numbers at runtime.

 FloatType()
DoubleType float DoubleType()
DecimalType decimal.Decimal DecimalType()|
StringType string StringType()
BinaryType bytes BinaryType()
BooleanType bool BooleanType()
TimestampType datetime.datetime TimestampType()
TimestampNTZType datetime.datetime TimestampNTZType()
DateType datetime.date DateType()
DayTimeIntervalType datetime.timedelta DayTimeIntervalType()
ArrayType list, tuple, or array
ArrayType(elementType, [containsNull])

Note

The default value of containsNull is True.
MapType dict
MapType(keyType, valueType, [valueContainsNull])

Note

The default value of valueContainsNull is True.
StructType list or tuple
StructType(fields)

Note

fields is a Seq of StructFields. Also, two fields with the same name are not allowed.
StructField The value type in Python of the data type of this field. For example, Int for a StructField with the data type IntegerType.
StructField(name, dataType, [nullable])

Note

The default value of nullable is True.

Conversions in Practice - UDFs

A common conversion case is returning a Python value from a UDF. In this case, the return type of the UDF must match the provided return type.

Note

If the actual return type of your function does not match the provided return type, Spark will implicitly cast the value to null.

from pyspark.sql.types import (
    StructType,
    StructField,
    IntegerType,
    StringType,
    FloatType,
)
from pyspark.sql.functions import udf, col

df = spark.createDataFrame(
    [[1]], schema=StructType([StructField("int", IntegerType())])
)

@udf(returnType=StringType())
def to_string(value):
    return str(value)

@udf(returnType=FloatType())
def to_float(value):
    return float(value)

df.withColumn("cast_int", to_float(col("int"))).withColumn(
    "cast_str", to_string(col("int"))
).printSchema()
# root
# |-- int: integer (nullable = true)
# |-- cast_int: float (nullable = true)
# |-- cast_str: string (nullable = true)

Conversions in Practice - Creating DataFrames

Another common conversion case is when creating a DataFrame from values in Python. In this case, you can supply a schema, or allow Spark to infer the schema from the provided data.

data = [
    ["Wei", "Math", 93.0, 1],
    ["Jerry", "Physics", 85.0, 4],
    ["Katrina", "Geology", 90.0, 2],
]
cols = ["Name", "Subject", "Score", "Period"]

spark.createDataFrame(data, cols).printSchema()
# root
# |-- Name: string (nullable = true)
# |-- Subject: string (nullable = true)
# |-- Score: double (nullable = true)
# |-- Period: long (nullable = true)

import pandas as pd

df = pd.DataFrame(data, columns=cols)
spark.createDataFrame(df).printSchema()
# root
# |-- Name: string (nullable = true)
# |-- Subject: string (nullable = true)
# |-- Score: double (nullable = true)
# |-- Period: long (nullable = true)

import numpy as np

spark.createDataFrame(np.zeros([3, 2], "int8")).printSchema()
# root
# |-- _1: byte (nullable = true)
# |-- _2: byte (nullable = true)

Conversions in Practice - Nested Data Types

Nested data types will convert to StructType, MapType, and ArrayType, depending on the passed data.

data = [
    ["Wei", [[1, 2]], {"RecordType": "Scores", "Math": { "H1": 93.0, "H2": 85.0}}],
]
cols = ["Name", "ActiveHalfs", "Record"]

spark.createDataFrame(data, cols).printSchema()
# root
#  |-- Name: string (nullable = true)
#  |-- ActiveHalfs: array (nullable = true)
#  |    |-- element: array (containsNull = true)
#  |    |    |-- element: long (containsNull = true)
#  |-- Record: map (nullable = true)
#  |    |-- key: string
#  |    |-- value: string (valueContainsNull = true)

spark.conf.set('spark.sql.pyspark.inferNestedDictAsStruct.enabled', True)

spark.createDataFrame(data, cols).printSchema()
# root
#  |-- Name: string (nullable = true)
#  |-- ActiveHalfs: array (nullable = true)
#  |    |-- element: array (containsNull = true)
#  |    |    |-- element: long (containsNull = true)
#  |-- Record: struct (nullable = true)
#  |    |-- RecordType: string (nullable = true)
#  |    |-- Math: struct (nullable = true)
#  |    |    |-- H1: double (nullable = true)
#  |    |    |-- H2: double (nullable = true)