PySpark: Add Months to a Date Column

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You can use the following syntax to add a specific number of months to a date column in a PySpark DataFrame:

from pyspark.sql import functions as F

df.withColumn('add5months', F.add_months(df['date'], 5)).show()

This particular example creates a new column called add5months that adds 5 months to each date in the date column.

The following example shows how to use this syntax in practice.

Example: How to Add Months to a Date Column in PySpark

Suppose we have the following PySpark DataFrame that contains information about sales made on various dates at some company:

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()

#define data
data = [['2023-01-15', 225],
        ['2023-02-24', 260],
        ['2023-07-14', 413],
        ['2023-10-30', 368],
        ['2023-11-03', 322],
        ['2023-11-26', 278]] 
  
#define column names
columns = ['date', 'sales'] 
  
#create dataframe using data and column names
df = spark.createDataFrame(data, columns) 
  
#view dataframe
df.show()

+----------+-----+
|      date|sales|
+----------+-----+
|2023-01-15|  225|
|2023-02-24|  260|
|2023-07-14|  413|
|2023-10-30|  368|
|2023-11-03|  322|
|2023-11-26|  278|
+----------+-----+

Suppose we would like to add a new column that adds 5 months to each date in the date column.

We can use the following syntax to do so:

from pyspark.sql import functions as F

#add 5 months to each date in 'date' column
df.withColumn('add5months', F.add_months(df['date'], 5)).show()

+----------+-----+----------+
|      date|sales|add5months|
+----------+-----+----------+
|2023-01-15|  225|2023-06-15|
|2023-02-24|  260|2023-07-24|
|2023-07-14|  413|2023-12-14|
|2023-10-30|  368|2024-03-30|
|2023-11-03|  322|2024-04-03|
|2023-11-26|  278|2024-04-26|
+----------+-----+----------+

Notice that the new add5months column contains each of the dates from the date column with five months added.

Note that if you would instead like to subtract 5 months, you could use a value of -5 in the add_months() function instead:

from pyspark.sql import functions as F

#subtract 5 months from each date in 'date' column
df.withColumn('sub5months', F.add_months(df['date'], -5)).show()

+----------+-----+----------+
|      date|sales|sub5months|
+----------+-----+----------+
|2023-01-15|  225|2022-08-15|
|2023-02-24|  260|2022-09-24|
|2023-07-14|  413|2023-02-14|
|2023-10-30|  368|2023-05-30|
|2023-11-03|  322|2023-06-03|
|2023-11-26|  278|2023-06-26|
+----------+-----+----------+

Notice that the new sub5months column contains each of the dates from the date column with five months subtracted.

Note that we used the withColumn function to return a new DataFrame with the sub5months column added and all other columns left the same.

You can find the complete documentation for the PySpark withColumn function .