如何在pyspark中将数据框列从字符串类型更改为双精度类型 [英] how to change a Dataframe column from String type to Double type in pyspark

问题描述

我有一个数据列，其列为String. 我想在PySpark中将列类型更改为Double类型.

I have a dataframe with column as String. I wanted to change the column type to Double type in PySpark.

以下是我的方法:

toDoublefunc = UserDefinedFunction(lambda x: x,DoubleType())
changedTypedf = joindf.withColumn("label",toDoublefunc(joindf['show']))

只是想知道，这是跑步时正确的方法吗? 通过Logistic回归，我遇到了一些错误，所以我想知道， 这就是麻烦的原因.

Just wanted to know, is this the right way to do it as while running through Logistic Regression, I am getting some error, so I wonder, is this the reason for the trouble.

推荐答案

此处无需UDF. Column已经提供了 方法与 DataType 实例:

There is no need for an UDF here. Column already provides cast method with DataType instance :

from pyspark.sql.types import DoubleType

changedTypedf = joindf.withColumn("label", joindf["show"].cast(DoubleType()))

或短字符串:

changedTypedf = joindf.withColumn("label", joindf["show"].cast("double"))

其中规范字符串名称(也可以支持其他变体)对应于simpleString值.因此对于原子类型:

where canonical string names (other variations can be supported as well) correspond to simpleString value. So for atomic types:

from pyspark.sql import types 

for t in ['BinaryType', 'BooleanType', 'ByteType', 'DateType', 
          'DecimalType', 'DoubleType', 'FloatType', 'IntegerType', 
           'LongType', 'ShortType', 'StringType', 'TimestampType']:
    print(f"{t}: {getattr(types, t)().simpleString()}")

BinaryType: binary
BooleanType: boolean
ByteType: tinyint
DateType: date
DecimalType: decimal(10,0)
DoubleType: double
FloatType: float
IntegerType: int
LongType: bigint
ShortType: smallint
StringType: string
TimestampType: timestamp

例如复杂类型

types.ArrayType(types.IntegerType()).simpleString()   

'array<int>'

types.MapType(types.StringType(), types.IntegerType()).simpleString()

'map<string,int>'

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