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PySpark DataFrame | toDF method

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PySpark DataFrame
schedule Jul 1, 2022
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PySpark DataFrame's toDF(~) method returns a new DataFrame with the columns arranged in the order that you specify.

WARNING

This method only allows you to change the ordering of the columns - the new DataFrame must contain the same columns as before.

Parameters

1. *cols | str

The columns to include.

Return Value

A PySpark DataFrame.

Examples

Consider the following PySpark DataFrame:

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

Arranging columns in specific order in PySpark

To arrange the columns from age first and name second:

df.toDF("age", "name").show()
+----+----+
| age|name|
+----+----+
|Alex| 20|
| Bob| 30|
+----+----+

Note that if the columns of the new DataFrame do not match the original DataFrame, then an error will be thrown:

df.toDF("age").show()
IllegalArgumentException: requirement failed: The number of columns doesn't match.
Old column names (2): name, age
New column names (1): age

Arrange columns in alphabetical order in PySpark

To arrange the columns in alphabetical order:

df.toDF(*sorted(df.columns)).show()
+----+----+
| age|name|
+----+----+
|Alex| 20|
| Bob| 30|
+----+----+

Here:

  • sorted(~) returns the column labels in alphabetical order.

  • the * is used to convert the list into positional arguments.

robocat
Published by Isshin Inada
Edited by 0 others
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