How to change a dataframe column from String type to Double type in PySpark?
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()
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>'
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"))
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()))