


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:
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To convert the STRING_COLUMN into a DateType column, one method is to use the to_date() function, as suggested in the original attempt:
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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:
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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:
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In both cases, the show() method can be used to display the converted DateType column.
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