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PySpark Column | cast method

Machine Learning
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PySpark
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PySpark Column
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark Column's cast(~) method returns a new Column of the specified type.

Parameters

1. dataType | Type or string

The type to convert the column to.

Return Value

A new Column object.

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([("Alex", 20), ("Bob", 30), ("Cathy", 40)], ["name", "age"])
df.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Bob| 30|
|Cathy| 40|
+-----+---+

Converting PySpark column type to string

To convert the type of the DataFrame's age column from numeric to string:

df_new = df.withColumn("age", df["age"].cast("string"))
df_new.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Bob| 30|
|Cathy| 40|
+-----+---+

Equivalently, we can pass in the StringType() method like so:

from pyspark.sql.types import StringType
df_new = df.withColumn("age", df["age"].cast(StringType()))
df_new.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Bob| 30|
|Cathy| 40|
+-----+---+

I recommend passing in "string" instead of StringType() for simplicity.

To confirm that the column type has been converted to string, use the printSchema() method:

df_new.printSchema()
root
|-- name: string (nullable = true)
|-- age: string (nullable = true)

Converting PySpark column type to integer

To convert the column type to integer, use cast("int"):

df_new = df.withColumn("age", df["age"].cast("int"))
df_new.printSchema()
root
|-- name: string (nullable = true)
|-- age: integer (nullable = true)

Converting PySpark column type to float

To convert the column type to float, use cast("float"):

df_new = df.withColumn("age", df["age"].cast("float"))
df_new.printSchema()
root
|-- name: string (nullable = true)
|-- age: float (nullable = true)

Converting PySpark column type to date

To convert the PySpark column type to date, use the to_date(~) method instead of cast(~).

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Published by Isshin Inada
Edited by 0 others
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