Hi,
I have multiple datasets in my data lake that feature valid_from and valid_to columns indicating validity of rows.
If a row is valid currently, this is indicated by valid_to=9999-12-31 00:00:00.
Example:
However, for analysis and visualization purpose, I would like to do further processing with Pandas instead of Spark. But when trying to convert the dataframe to Pandas an error occurs:
ArrowInvalid: Casting from timestamp[us, tz=Etc/UTC] to timestamp[ns] would result in out of bounds timestamp: 253379592300000000
Code for simulating the issue:
import datetime
import pandas as pd
df_spark_native = sc.parallelize([
[1, 'Alice', datetime.date(1985, 4, 13), datetime.datetime(1985, 4, 13, 4,5)],
[2, 'Bob', datetime.date(9999, 1, 20), datetime.datetime(9999, 4, 13, 4,5)],
[3, 'Eve', datetime.date(1500, 1, 20), datetime.datetime(1500, 4, 13, 4,5)],
[3, 'Dave', datetime.date( 1, 1, 20), datetime.datetime( 1, 4, 13, 4,5)]
]).toDF(('ID', 'Some_Text', 'Some_Date', 'Some_Timestamp'))
display( df_spark_native )
df_spark_native.printSchema()
df_spark_to_pandas = df_spark_native.toPandas()
display( df_spark_to_pandas )To me, it appears, that under the hood, spark uses pyarrow to convert the dataframe to pandas.
Pyarrow already has some functionality for handling dates and timestamps that would otherwise cause out of range issue: parameter "timestamp_as_object" and "date_as_object" of pyarrow.Table.to_pandas(). However, Spark.toPandas() currently does not allow passing down parameters to pyarrow.
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