How to Efficiently Convert a Pyspark String Column to a Date Column?

Converting Pyspark String Column to Date Format

When working with Pyspark, it can be necessary to convert a string column representing dates into a DateType column. This is commonly encountered when the data is ingested as strings.

To illustrate this, consider the following DataFrame with a string column named STRING_COLUMN in the format MM-dd-yyyy:

df = spark.createDataFrame([
    ("01-01-2023",),
    ("01-02-2023",),
    ("01-03-2023",),
], ["STRING_COLUMN"])
df.show()

To convert the STRING_COLUMN into a DateType column, one method is to use the to_date() function, as suggested in the original attempt:

df.select(to_date(df.STRING_COLUMN).alias("new_date")).show()

However, this approach may result in a column filled with nulls. To address this, consider using one of the following methods:

1. to_timestamp Function (Spark 2.2 ):

Starting with Spark 2.2, the to_timestamp() function provides a more efficient and straightforward way to convert strings to timestamps. It supports specifying the input format using the format argument:

df.select(to_timestamp(df.STRING_COLUMN, "MM-dd-yyyy").alias("new_date")).show()

2. unix_timestamp and from_unixtime Functions (Spark < 2.2):

For Spark versions prior to 2.2, a combination of unix_timestamp and from_unixtime functions can be used:

from pyspark.sql.functions import unix_timestamp, from_unixtime

df.select(
    from_unixtime(unix_timestamp(df.STRING_COLUMN, "MM-dd-yyyy")).alias("new_date")
).show()

In both cases, the show() method can be used to display the converted DateType column.

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