Fix Dates in Pyspark DataFrame - set to minimum value

Question 1

I have a data frame with a timestamp field - RECEIPTDATEREQUESTED:timestamp For some reason, there are dates that are less than 1900-01-01. I don't want these, what I want to do, is for every value in the column of the dataframe where the RECEIPTDATEREQUESTED<'1900-01-01 00:00:00' then set the timestamp to either 1900-01-01 or null. I've tried a few ways to do this, but it seems some more simple must exist. I thought something like this might work, but

import datetime
def testdate(date_value):
    oldest = datetime.datetime.strptime('1900-01-01 00:00:00', '%Y-%m-%d')
    try:
        if (date_value < oldest):
            return oldest
        else:
            return date_value
    except ValueError:
        return oldest
udf_testdate = udf(lambda x:testdate(x),TimestampType())
bdf = olddf.withColumn("RECEIPTDATEREQUESTED",udf_testdate(col("RECEIPTDATEREQUESTED")))

Question 2

You can use conditional evaluation using when and otherwise to set RECEIPTDATEREQUESTED to either null or 1900-01-01 00:00:00 whenerver the value is < '1900-01-01 00:00:00'.


from pyspark.sql import functions as F

data = [("1000-01-01 00:00:00",), 
        ("1899-12-31 23:59:59",),
        ("1900-01-01 00:00:00",), 
        ("1901-01-01 00:00:00",)]

df = spark.createDataFrame(data, ("RECEIPTDATEREQUESTED",))\
          .withColumn("RECEIPTDATEREQUESTED", F.to_timestamp(F.col("RECEIPTDATEREQUESTED")))


# Fill null

df.withColumn("RECEIPTDATEREQUESTED", 
              F.when(F.col("RECEIPTDATEREQUESTED") < "1900-01-01 00:00:00", F.lit(None))
               .otherwise(F.col("RECEIPTDATEREQUESTED")))\
  .show(200, False)

# Fill default value

df.withColumn("RECEIPTDATEREQUESTED", 
              F.when(F.col("RECEIPTDATEREQUESTED") < "1900-01-01 00:00:00", F.lit("1900-01-01 00:00:00").cast("timestamp"))
               .otherwise(F.col("RECEIPTDATEREQUESTED")))\
  .show(200, False)

Output

Fill null

+--------------------+
|RECEIPTDATEREQUESTED|
+--------------------+
|null                |
|null                |
|1900-01-01 00:00:00 |
|1901-01-01 00:00:00 |
+--------------------+

Fill 1900-01-01 00:00:00

+--------------------+
|RECEIPTDATEREQUESTED|
+--------------------+
|1900-01-01 00:00:00 |
|1900-01-01 00:00:00 |
|1900-01-01 00:00:00 |
|1901-01-01 00:00:00 |
+--------------------+

Nithish · Answer 1 · 2021-11-23T20:53:37

You can use conditional evaluation using when and otherwise to set RECEIPTDATEREQUESTED to either null or 1900-01-01 00:00:00 whenerver the value is < '1900-01-01 00:00:00'.


from pyspark.sql import functions as F

data = [("1000-01-01 00:00:00",), 
        ("1899-12-31 23:59:59",),
        ("1900-01-01 00:00:00",), 
        ("1901-01-01 00:00:00",)]

df = spark.createDataFrame(data, ("RECEIPTDATEREQUESTED",))\
          .withColumn("RECEIPTDATEREQUESTED", F.to_timestamp(F.col("RECEIPTDATEREQUESTED")))


# Fill null

df.withColumn("RECEIPTDATEREQUESTED", 
              F.when(F.col("RECEIPTDATEREQUESTED") < "1900-01-01 00:00:00", F.lit(None))
               .otherwise(F.col("RECEIPTDATEREQUESTED")))\
  .show(200, False)

# Fill default value

df.withColumn("RECEIPTDATEREQUESTED", 
              F.when(F.col("RECEIPTDATEREQUESTED") < "1900-01-01 00:00:00", F.lit("1900-01-01 00:00:00").cast("timestamp"))
               .otherwise(F.col("RECEIPTDATEREQUESTED")))\
  .show(200, False)

Output

Fill null

+--------------------+
|RECEIPTDATEREQUESTED|
+--------------------+
|null                |
|null                |
|1900-01-01 00:00:00 |
|1901-01-01 00:00:00 |
+--------------------+

Fill 1900-01-01 00:00:00

+--------------------+
|RECEIPTDATEREQUESTED|
+--------------------+
|1900-01-01 00:00:00 |
|1900-01-01 00:00:00 |
|1900-01-01 00:00:00 |
|1901-01-01 00:00:00 |
+--------------------+

yeah. thank you. I'm not sure how or why this works, so I google it some more but thank you.

Fix Dates in Pyspark DataFrame - set to minimum value

Output

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