PySpark: How to Add Days to a Date Column

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

from pyspark.sql import functions as F

df.withColumn('date_plus_5', F.date_add(df['date'], 5)).show()

This particular example creates a new column called date_plus_5 that adds 5 days to each date in the date column.

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

Example: How to Add Days 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 days 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 days to each date in 'date' column
df.withColumn('date_plus_5', F.date_add(df['date'], 5)).show()

+----------+-----+-----------+
|      date|sales|date_plus_5|
+----------+-----+-----------+
|2023-01-15|  225| 2023-01-20|
|2023-02-24|  260| 2023-03-01|
|2023-07-14|  413| 2023-07-19|
|2023-10-30|  368| 2023-11-04|
|2023-11-03|  322| 2023-11-08|
|2023-11-26|  278| 2023-12-01|
+----------+-----+-----------+

Notice that the new date_plus_5 column contains each of the dates from the date column with five days added.

Note that if you would instead like to subtract 5 days, you could use the date_sub() function instead:

from pyspark.sql import functions as F

#subtract 5 days from each date in 'date' column
df.withColumn('date_sub_5', F.date_sub(df['date'], 5)).show()

+----------+-----+----------+
|      date|sales|date_sub_5|
+----------+-----+----------+
|2023-01-15|  225|2023-01-10|
|2023-02-24|  260|2023-02-19|
|2023-07-14|  413|2023-07-09|
|2023-10-30|  368|2023-10-25|
|2023-11-03|  322|2023-10-29|
|2023-11-26|  278|2023-11-21|
+----------+-----+----------+

Notice that the new date_sub_5 column contains each of the dates from the date column with five days subtracted.

Note that we used the withColumn function to return a new DataFrame with the date_sub_5 column added and all other columns left the same.

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