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PySpark Column | alias 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 alias(~) method assigns a column label to a PySpark Column.

Parameters

1. *alias | string

The column label.

2. metadata | dict | optional

A dictionary holding additional meta-information to store in the StructField of the returned Column.

Return Value

A new PySpark Column.

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|
+-----+---+

Most methods in the PySpark SQL Functions library return Column objects whose label is governed by the method that we use. For instance, consider the lower(~) method:

df.select(F.lower("name")).show()
+-----------+
|lower(name)|
+-----------+
| alex|
| bob|
| cathy|
+-----------+

Here, the PySpark Column returned by lower(~) has the label lower(name) by default.

Assigning new label to PySpark Column using the alias method

We can assign a new label to a column by using the alias(~) method:

df.select(F.lower("name").alias("lower_name")).show()
+----------+
|lower_name|
+----------+
| alex|
| bob|
| cathy|
+----------+

Here, we have assigned the label "lower_name" to the column returned by lower(~).

Storing meta-data in PySpark Column's alias method

To store some meta-data in a PySpark Column, we can add the metadata option in alias(~):

df_new = df.select(F.lower("name").alias("lower_name", metadata={"some_data": 10}))
df_new.show()
+----------+
|lower_name|
+----------+
| alex|
| bob|
| cathy|
+----------+

The metadata is a dictionary that will be stored in the Column object.

To access the metadata, we can use the PySpark DataFrame's schema property:

df_new.schema["lower_name"].metadata["some_data"]
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Published by Isshin Inada
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