How to change a dataframe column from String type to Double type in PySpark?

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

Following is the way, I did:

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

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.

5 Answers

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())) 

or short string:

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

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 

and for example complex types

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

'array<int>' 

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

'map<string,int>' 

Preserve the name of the column and avoid extra column addition by using the same name as input column:

from pyspark.sql.types import DoubleType changedTypedf = joindf.withColumn("show", joindf["show"].cast(DoubleType())) 

Given answers are enough to deal with the problem but I want to share another way which may be introduced the new version of Spark (I am not sure about it) so given answer didn't catch it.

We can reach the column in spark statement with col("colum_name") keyword:

from pyspark.sql.functions import col changedTypedf = joindf.withColumn("show", col("show").cast("double")) 

PySpark version:

df = <source data> df.printSchema() from pyspark.sql.types import * # Change column type df_new = df.withColumn("myColumn", df["myColumn"].cast(IntegerType())) df_new.printSchema() df_new.select("myColumn").show() 

the solution was simple -

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