PySpark : Adding and Subtracting Months to a Date or Timestamp while Preserving End-of-Month Information

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This article will explain how to add or subtract a specific number of months from a date or timestamp while preserving end-of-month information. This is especially useful when dealing with financial, retail, or similar data, where preserving the end-of-month status of a date is critical.

Setting up the Environment

Before we begin, we must set up our PySpark environment. Assuming you’ve installed Spark and PySpark properly, you should be able to initialize a SparkSession as follows:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName(" Learning Adding and Subtracting Months ").getOrCreate()

Understanding add_months and date_add Functions

We will utilize PySpark’s built-in functions add_months and date_add or date_sub for our operations. The add_months function adds a specified number of months to a date, and if the original date was the last day of the month, the resulting date will also be the last day of the new month.

The date_add or date_sub function, on the other hand, adds or subtracts a certain number of days from a date, which is not ideal for preserving end-of-month information.

Using add_months Function

To demonstrate, let’s create a DataFrame with some sample dates:

from pyspark.sql import functions as F
from pyspark.sql.types import DateType
data = [("2023-01-31",), ("2023-02-28",), ("2023-07-15",)]
df = spark.createDataFrame(data, ["Date"])
df = df.withColumn("Date", F.col("Date").cast(DateType()))
|      Date|

Now, we will add two months to each date using add_months:

df.withColumn("New_Date", F.add_months(F.col("Date"), 2)).show()
|      Date|  New_Date|

Note how the dates originally at the end of a month are still at the end of the month in the New_Date column.

Subtracting Months

Subtracting months is as simple as adding months. We simply use a negative number as the second parameter to the add_months function:

df.withColumn("New_Date", F.add_months(F.col("Date"), -2)).show()
|      Date|  New_Date|

Adding or Subtracting Months to a Timestamp

To work with timestamps instead of dates, we need to cast our column to a TimestampType. Let’s create a new DataFrame to demonstrate:

from pyspark.sql.types import TimestampType
data = [("2023-01-31 13:45:30",), ("2023-02-28 08:20:00",), ("2023-07-15 22:15:00",)]
df = spark.createDataFrame(data, ["Timestamp"])
df = df.withColumn("Timestamp", F.col("Timestamp").cast(TimestampType()))
|Timestamp          |
|2023-01-31 13:45:30|
|2023-02-28 08:20:00|
|2023-07-15 22:15:00|

Then, we can add or subtract months as before:

df.withColumn("New_Timestamp", F.add_months(F.col("Timestamp"), 2)).show(truncate=False)
df.withColumn("New_Timestamp", F.add_months(F.col("Timestamp"), -2)).show(truncate=False)
|Timestamp          |New_Timestamp|
|2023-01-31 13:45:30|2023-03-31   |
|2023-02-28 08:20:00|2023-04-28   |
|2023-07-15 22:15:00|2023-09-15   |
|Timestamp          |New_Timestamp|
|2023-01-31 13:45:30|2022-11-30   |
|2023-02-28 08:20:00|2022-12-28   |
|2023-07-15 22:15:00|2023-05-15   |
PySpark’s built-in add_months function provides a straightforward way to add or subtract a specified number of months from dates and timestamps, preserving end-of-month information.
Author: user

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