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PySpark SQL Functions | month method

Machine Learning
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PySpark
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PySpark SQL Functions
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark SQL Functions' month(~) method extracts the month component of each column value, which can be of type string or date.

Parameters

1. col | string or Column

The date column from which to extract the month.

Return Value

A Column object of integers.

Examples

Consider the following PySpark DataFrame with some datetime values:

import datetime
df = spark.createDataFrame([["Alex", datetime.date(1995,12,16)], ["Bob", datetime.date(1995,5,9)]], ["name", "birthday"])
df.show()
+----+----------+
|name| birthday|
+----+----------+
|Alex|1995-12-16|
| Bob|1995-05-09|
+----+----------+

Extracting the month component of datetime values in PySpark DataFrame

To get the month component of datetime values:

from pyspark.sql import functions as F
df.select(F.month("birthday").alias("month")).show()
+-----+
|month|
+-----+
| 12|
| 5|
+-----+

Here, we are assigning the name "month" to the Column object returned by month(~).

Extracting the month component of date strings in PySpark DataFrame

To get the month component of date strings:

df = spark.createDataFrame([["Alex", "1995-12-16"], ["Bob", "1990-05-06"]], ["name", "birthday"])
df.select(F.month("birthday").alias("day")).show()
+-----+
|month|
+-----+
| 12|
| 5|
+-----+
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Published by Isshin Inada
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