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PySpark SQL Functions | array method

Machine Learning
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PySpark
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PySpark SQL Functions
schedule Jul 6, 2022
Last updated
local_offer PySpark
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PySpark SQL Functions' array(~) method combines multiples columns into a single column of arrays.

NOTE

If you want to combine multiple columns of array-type, then use the concat(~) instead.

Parameters

1. *cols | string or Column

The columns to combine.

Return Value

A new PySpark Column.

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([['A', 'a', '1'], ['B', 'b', '2'], ['C', 'c', '3']], ['col1', 'col2', 'col3'])
df.show()
+----+----+----+
|col1|col2|col3|
+----+----+----+
| A| a| 1|
| B| b| 2|
| C| c| 3|
+----+----+----+

To combine the columns col1, col2 and col3 into a single column of arrays, use the array(~) method:

from pyspark.sql import functions as F
# Assign label to PySpark column returned by array(~) using alias(~)
df.select(F.array('col1','col2','col3').alias('combined_col')).show()
+------------+
|combined_col|
+------------+
| [A, a, 1]|
| [B, b, 2]|
| [C, c, 3]|
+------------+

Instead of passing a column labels, we could also supply Column objects instead:

df.select(F.array(F.col('col1'),df['col2'],'col3').alias('combined_col')).show()
+------------+
|combined_col|
+------------+
| [A, a, 1]|
| [B, b, 2]|
| [C, c, 3]|
+------------+
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Published by Isshin Inada
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