I have a date pyspark dataframe with a string column in the format of MM-dd-yyyy
and I am attempting to convert this into a date column.
I tried:
df.select(to_date(df.STRING_COLUMN).alias('new_date')).show()
and I get a string of nulls. Can anyone help?
Update (1/10/2018):
For Spark 2.2+ the best way to do this is probably using the to_date
or to_timestamp
functions, which both support the format
argument. From the docs:
>>> from pyspark.sql.functions import to_timestamp
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(to_timestamp(df.t, 'yyyy-MM-dd HH:mm:ss').alias('dt')).collect()
[Row(dt=datetime.datetime(1997, 2, 28, 10, 30))]
Original Answer (for Spark < 2.2)
It is possible (preferrable?) to do this without a udf:
from pyspark.sql.functions import unix_timestamp, from_unixtime
df = spark.createDataFrame(
[("11/25/1991",), ("11/24/1991",), ("11/30/1991",)],
['date_str']
)
df2 = df.select(
'date_str',
from_unixtime(unix_timestamp('date_str', 'MM/dd/yyy')).alias('date')
)
print(df2)
#DataFrame[date_str: string, date: timestamp]
df2.show(truncate=False)
#+----------+-------------------+
#|date_str |date |
#+----------+-------------------+
#|11/25/1991|1991-11-25 00:00:00|
#|11/24/1991|1991-11-24 00:00:00|
#|11/30/1991|1991-11-30 00:00:00|
#+----------+-------------------+